needs analysis research meaning

How to Conduct a Needs Analysis: Definition, Importance, and Templates

A needs analysis is a critical process that helps organizations identify performance gaps and determine solutions to bridge those gaps. This comprehensive guide will explain what a needs analysis is, why it's important, the steps for conducting an effective needs analysis, and provide templates to help you perform your own organizational needs analysis.

A needs analysis, also called a needs assessment or training needs analysis, is a systematic process of evaluating the current state and desired or required state of knowledge, skills, attitudes and processes within an organization. It helps identify performance gaps that can be addressed by potential training and development programs.

Conducting a thorough needs analysis is a vital component of designing any effective training program. It ensures that the training you design and deliver will be relevant, useful and targeted to address actual organizational needs and performance gaps. This helps maximize the value of training initiatives and the ROI of your training budget.

In this comprehensive guide, you'll learn:

What is a needs analysis and why is it important?

The goals and benefits of performing a needs analysis

The main types of needs analysis

Steps for conducting an effective needs analysis

Tips for developing needs analysis questions

Needs analysis templates and examples

Let's get started!

What is a Needs Analysis?

A needs analysis, also called a “training needs analysis”, "needs assessment" or "needs evaluation", is a systematic process for determining and addressing needs, or "gaps" between current conditions and desired conditions.

The goal of a needs analysis is to identify the gap between the knowledge, skills and abilities of people in an organization and the knowledge, skills and abilities required to meet organizational goals. It is a tool for defining problems and opportunities related to learning needs in the workplace.

A needs analysis helps you determine:

Where performance improvements are needed in an organization

The cause of performance gaps

Possible solutions to address those gaps

Whether training or non-training interventions are required

The needs analysis process aims to identify the training, resources and support employees need to improve individual and organizational performance. It provides direction for developing effective training programs, learning objectives and instructional strategies that are aligned to specific business needs.

Why Perform a Needs Analysis?

Conducting a needs analysis provides many benefits for your organization and training function, including:

Ensuring training programs align to organizational goals and priorities

Identifying skills gaps that may be hindering performance

Determining the most effective solutions, including training and non-training interventions

Providing data to gain support from stakeholders for proposed initiatives

Selecting the most appropriate training approaches, tools and content

Diagnosing weaknesses and strengths in processes, procedures, tools or job roles

Identifying areas for efficiency improvements or cost reductions

Ensuring training resources are utilized effectively by focusing on key needs

Demonstrating the potential ROI and impact of proposed training programs

Organizations that fail to carry out a needs analysis risk developing training that lacks focus, is irrelevant or misses critical needs. This wastes resources and limits the potential for training to improve performance. A thorough needs analysis enables you to design targeted, high-impact training programs that provide true value.

Types of Needs Analyses

There are three main types of needs analyses:

Organizational Needs Analysis

An organizational analysis focuses on the needs of the organization as a whole. It aligns training initiatives to broader organizational objectives and identifies skills gaps that may be hindering goals. Data is gathered through methods like surveys, interviews, focus groups, and analysis of strategy documents.

Task and Process Needs Analysis

A task analysis evaluates the duties, steps, knowledge and skills required for employees to effectively perform critical job tasks and processes. It pinpoints training needs gaps between current and desired job performance. Data collection methods include observation, surveys, interviews and job/task analysis.

Individual Needs Analysis

An individual analysis identifies the training needs of current employees through reviews of performance evaluations, coaching sessions, surveys, assessments, and interviews with employees and their managers. It uncovers individual-level gaps in skills, knowledge and behaviors.

Most needs analyses utilize a combination of these approaches to form a comprehensive understanding of organizational and job-specific needs.

4 Steps for Conducting a Needs Analysis

The needs analysis process typically involves four key phases:

1. Planning and Preparation

Define the scope and goals of the needs analysis

Determine data collection methods - surveys, interviews, focus groups, assessments, performance reviews etc.

Identify key stakeholders to provide input - employees, managers, executives, clients etc.

Develop resources like questionnaires, templates, schedules and communication plans

2. Information Gathering

Gather data through selected methods and tools

Conduct interviews, focus groups, observations, surveys etc.

Review existing documentation like performance evaluations, training records, productivity reports etc.

3. Data Analysis

Analyze results to identify themes, trends and patterns in training needs

Prioritize needs based on urgency, impact, number of staff affected etc.

Identify whether needs are best addressed by training or non-training solutions

Consider costs, resources required, and impact on performance

4. Reporting

Document findings and recommendations in a formal report

Outline priorities, costs, timelines, resources required, potential learning solutions

Present report to stakeholders and decision makers

Gather feedback to refine solutions before development

Developing Needs Analysis Questions

The questions you ask in surveys, interviews and focus groups are key to uncovering the right information during a needs analysis.

Here are some examples of effective needs analysis questions for key stakeholder groups:

For Senior Leaders/Executives:

What are the organization's top 3 performance priorities right now?

What skills gaps do you see that may hinder achieving strategic goals?

What concerns do you have about employee capabilities or performance?

What processes, policies or tools need improvement?

For Managers:

What are your department’s top 3 business objectives?

What obstacles prevent your team from achieving goals?

What capabilities are lacking in your team?

What impacts staff productivity or performance?

Where do you see knowledge or skills gaps in your team?

For Frontline Employees:

What main tasks do you perform in your role?

What duties are most challenging for you? Why?

What knowledge or skills do you need to improve to perform your role better?

What prevents you from being more productive?

What training have you already received? What training do you need?

For Customers:

What can we do to improve our products/services?

What new offerings would benefit your organization?

What capabilities could our employees improve to serve you better?

Tailor your questions to dig into the potential root causes behind performance gaps and identify opportunities for training interventions.

Needs Analysis Template

A needs analysis template can help guide you through the process and ensure you gather all required information.

Here is an example template you can use or customize for your own needs analysis:

Needs Analysis Report Template

Overview: High level summary of the purpose, goals and methodology of the needs analysis.

Key Findings: Summary of major themes, trends and performance gaps identified.

Recommendations: Proposed solutions and interventions. Specify training and non-training solutions.

Organizational Needs & Goals: Describe key organizational needs and strategic goals relevant to training needs.

Participant Analysis: Breakdown of stakeholders included in needs analysis - roles, departments etc.

Methodology: Data collection methods used - surveys, interviews, focus groups, assessments etc.

Present State Analysis: Details on current state performance, capabilities, processes and pain points.

Desired State: Description of desired performance outputs, capabilities and changes needed.

Gap Analysis: Difference between present and desired state. Skills, knowledge, process and performance gaps uncovered.

Prioritized Needs: List of training needs ranked by priority level - high, medium, low.

Causes of Needs: Summary of potential root causes and factors contributing to each performance gap.

Proposed Solutions: Training interventions and non-training solutions proposed to address each need.

Timeline: Proposed timeline for implementing solutions.

Costs: Budgets for proposed solutions.

Metrics: KPIs to measure impact of solutions.

Support & Resources Required: People, funds, facilities and equipment needed. 

Potential Obstacles: Anticipated barriers to implementing solutions and how to address them.

Stakeholder Sign Off: Final approval from stakeholders on recommended solutions.

Next Steps: Detailed action plan and responsibilities for initiating proposed solutions.

Tips for an Effective Needs Analysis Process

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Follow these best practices for ensuring your needs analysis provides maximum value:

Get support from leaders and executives early on

Identify key stakeholders and subject matter experts to provide input

Use a combination of data collection methods for a comprehensive view

Ask probing, open-ended questions to uncover root causes

Identify needs at organizational, process and individual job levels

Consider costs, resources required and impact on performance for solutions

Distinguish between training and non-training interventions

Prioritize needs, focusing on quick wins and high-impact areas first

Determine measurable outcomes and metrics to gauge impact

Secure stakeholder sign-off before designing solutions

Maintain open communication and feedback loops during implementation

Continuously re-evaluate needs and adjust approaches as required

Needs Analysis Drives Training Effectiveness

Conducting a thorough needs analysis is a foundational step in developing impactful training programs. Without understanding true organizational needs, training is unlikely to achieve the desired performance improvements.

Needs analysis templates provide a framework to uncover needs, identify solutions and gain stakeholder buy-in. This process ultimately enables you to design targeted, high-value training that bridges skills gaps and drives organizational success.

To recap, a strong needs analysis:

Aligns training to business goals and priorities

Uncovers the root causes behind performance gaps 

Helps identify the most effective solutions to address needs

Ensures training resources are optimized to provide maximum value

Provides data to gain support from stakeholders

Enables design of customized, relevant training content

Sets the foundation for training success and positive ROI

Take the time upfront to perform a detailed needs analysis - it's an investment that delivers significant payoff when it comes to enhancing workforce performance.

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Home » What Is Needs Analysis? Definition, Steps and Tools

What Is Needs Analysis? Definition, Steps and Tools

June 16, 2023 max 5min read.

This article covers:

What Is a Needs Analysis?

Types of needs analysis, steps in a needs analysis, benefits of needs analysis, tools and techniques for needs analysis.

In our dynamic and ever-evolving world, understanding the needs of individuals, organizations, and communities has become essential for success. Enter the fascinating realm of needs analysis, a tricky process that uncovers the intricate tapestry of desires, requirements, and aspirations that drive us forward.

Needs analysis is a systematic approach akin to peering through a magnifying glass. It allows us to delve deep into what people truly crave. It goes beyond surface-level assumptions, piercing through the noise. After which, it reveals the core necessities that underpin personal growth, organizational effectiveness, and societal progress.

Needs Analysis Definition:

A needs analysis is a systematic process of determining and evaluating the requirements, gaps, and objectives of individuals, organizations, or communities. It involves gathering and analyzing information. This information will then help identify the current and desired future states and the steps required to bridge the gap between them.

Needs analysis is sometimes referred to as the gap analysis and needs assessment. 

A needs analysis aims to understand the specific needs, challenges, and opportunities. These then help to develop effective strategies, interventions, or solutions to address them.

Needs analysis is crucial for evaluating employees and identifying their training requirements. It plays a significant role in bridging performance gaps. This ensures that training initiatives are effective and targeted. 

User Needs Analysis

User needs analysis focuses on understanding the requirements, preferences, and expectations of the end users or customers. It is about gathering and analyzing data to identify user needs, desires, and pain points . This analysis helps design products, services, or systems that meet user expectations and provide a satisfactory user experience. User needs analysis often involves surveys, interviews, user testing, and observation. All these help to gather insights directly from the users.

System Needs Analysis

The system needs analysis concentrates on determining the functional and technical requirements of a system or software application. It involves examining the existing system or analyzing the business processes. This helps identify improvement areas, define system functionalities, and specify technical specifications. 

The system needs analysis to help align the system with the organization’s goals and objectives. It also enhances system performance and ensures compatibility with other systems. Techniques used in system needs analysis include:

• Documentation review
• Stakeholder interviews
• Process mapping
• Feasibility studies

Organizational Needs Analysis

Organizational needs analysis involves assessing and understanding the needs and objectives of an organization as a whole. It aims to identify gaps between the current state and the organization’s desired state. It considers factors such as structure, resources, processes, and culture to do all that. 

Organizational needs analysis helps determine:

• Strategic initiatives
• Develop training programs
• Optimize workflows
• Implement organizational changes

It often involves analyzing financial data, conducting interviews with key stakeholders, and utilizing tools like SWOT (Strengths, Weaknesses, Opportunities, and Threats) analysis.

Understand Long and Short-Term Business Goals

In this step, you must clearly understand the organization’s long-term and short-term business objectives. This includes identifying the strategic direction, key priorities, and goals the organization wants to achieve. Understanding these goals can align the needs analysis process with the overall business objectives.

Identify the Desired Performance Results

Once you grasp the business goals, the next step is determining the specific performance outcomes or results necessary to achieve those goals. This involves identifying the knowledge, skills, competencies, or behaviors that individuals or teams need to exhibit. These contribute effectively to the organization’s success.

Examine the Current Performance

In this step, you evaluate the existing performance of individuals or teams against the desired performance results identified in the previous step. This may involve gathering data, conducting surveys, observations, interviews, or performance reviews to assess the current state. You can pinpoint areas where improvements are needed by identifying the performance gaps.

Establish Solutions

Based on the performance gaps identified, you can now determine appropriate solutions or interventions. These can help you to address those gaps and bridge the difference between the current and desired performance levels. This may involve the following:

• Designing training programs
• Developing resources
• Implementing new processes
• Modifying existing systems
• Any other actions required to support performance improvement

Needs analysis is an iterative process. As you implement solutions, you may need to revisit the analysis. This can be to refine and adjust your approach based on feedback and new information.

• Needs analysis helps organizations and individuals identify areas for improvement and development. Assessing the current state of knowledge, skills, and competencies is vital. It becomes easier to identify gaps and determine the specific areas where growth and learning are needed. 
• It helps align training and development efforts with individual and organizational goals. 
• Needs analysis allows organizations to prioritize training initiatives based on the identified needs and their importance. 
• It identifies individuals or groups within an organization who require additional training or development. This targeted approach ensures that training resources are utilized efficiently. It also ensures that individuals receive the support they need to enhance their performance.
• Needs analysis finds the specific type of training required to address the identified needs. It provides insights into the knowledge, skills, and competencies that must be developed or improved. 

Surveys and Questionnaires

Surveys and questionnaires are commonly used for conducting needs analysis. They allow researchers to gather data from many individuals. It also helps them to obtain quantitative information about their needs, preferences, and opinions.

Interviews and Focus Groups

Interviews and focus groups involve direct interaction with individuals or groups to gather qualitative data. They allow one to delve deeper into participants’ experiences, perspectives, and specific needs. This results in a more detailed analysis.

Observation and Shadowing

Observation involves observing individuals or groups in their natural environment. This helps them to understand their needs and behaviors. Shadowing goes further by following individuals closely and observing their activities firsthand. 

Document Analysis

Document analysis involves reviewing existing reports, policies, or records. It assists them in extracting relevant information about needs, gaps, and requirements. It helps identify patterns, trends, and areas that require improvement.

SWOT Analysis

SWOT (Strengths, Weaknesses, Opportunities, and Threats) analysis is a technique that assesses an organization’s internal strengths and weaknesses. It also looks at the external opportunities and threats. 

Gap Analysis

Gap analysis involves comparing current affairs with the desired state or established benchmarks.

These tools and techniques are often combined to gather comprehensive data and insights during a needs analysis process. The selection of specific tools depends on the following:

• The nature of the analysis
• The target audience
• The available resources
• The research objectives

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Examples of needs analysis can include:

• Training Needs Analysis: Assessing the skills and knowledge gaps within an organization to determine the training requirements of employees.
• Customer Needs Analysis: Gathering information from customers to understand their preferences, expectations, and requirements for a product or service.
• Performance Needs Analysis: Evaluating the performance of individuals or teams to identify areas where improvement is needed and determine the necessary resources or support.

The three major components of a needs analysis are:

Identification of Needs: Understand and pinpoint the current needs or problems.

Analysis of Needs: Assess the identified needs’ causes, patterns, and significance.

Development of Solutions: Create appropriate strategies and interventions to address the needs.

The most important part of needs analysis is accurately identifying and understanding the specific needs and requirements of the individual or group for whom the analysis is being conducted. This involves gathering comprehensive information about their current situation, desired outcomes, constraints, and challenges.

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Project Management

The Beginners Guide to Needs Analysis: Methods and Examples

Max 10 min read

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Imagine you’re preparing for the launch of a new restaurant venture.

The location is prime, the interior is stylish, and there’s palpable excitement in the air amongst the staff. But on opening day, only a handful of patrons stroll in.

You’ve stocked up on the finest sushi ingredients in a neighborhood that’s yearning for authentic Italian. This mismatch underscores the essence of why a needs analysis is vital.

In much the same way as understanding your potential clientele’s culinary desires, a needs analysis dives deep into what is genuinely required in a given situation.

Needs analysis is about bridging the gap between the current state and the desired state. It’s the art and science of asking, “What’s missing?” and “How can we address it?”.

Whether you’re launching a product, starting a community initiative, or even deciding on a career move, recognizing and understanding these ‘needs’ is the foundation upon which successful strategies are built.

In this article, we’ll delve into what needs analysis is, why it’s an important function early on in a project, and how to conduct one effectively. Let’s get started!

What is Needs Analysis in Project Management?

Needs analysis in project management is about understanding and specifying precisely what a project requires to succeed.

It’s an exploration of the disparities between the existing conditions and the desired state in a particular project. Identifying these gaps means project managers can draft a clear roadmap on how best to achieve their goals.

The needs analysis process was created by Roger Kaufmen – also known as the needs assessment.

Historically, the concept of needs analysis was initially more prevalent in fields like education. Instructors and curriculum developers would evaluate the requirements of learners to ensure their educational experiences were tailored to their unique needs.

Over time, as industries and their associated projects grew in complexity, the necessity of identifying specific needs before embarking on projects became crystal clear.

Project managers adopted the methodology, refining it further to fit the unique contours of intricate business endeavors.

Needs analysis has adapted to the increasing complexity of contemporary projects. What began as a relatively straightforward process of identifying basic requirements has expanded to consider a vast array of factors, from technological needs and stakeholder expectations to regulatory compliance and potential future changes.

The Methods Behind Needs Analysis

Just as a project’s requirements can vary immensely based on its nature and context, the methods employed to decipher those needs are equally diverse.

Let’s explore some of the common methods and understand their individual merits.

Surveys and Questionnaires

Surveys and questionnaires are conversations, albeit in a structured format. And just like any good conversation, they shine a light on perspectives, concerns, and aspirations.

The beauty of surveys lies in their quantitative nature. Think of it as taking the temperature of a large group.

With structured questions, often scaled or multiple-choice in nature, project managers can easily aggregate and analyze the data. This gives a clear pulse on prevalent needs or preferences, helping in making informed decisions.

Needs Analysis Questions for Surveys:

• What are the top three needs or requirements not being met currently?
• Are there any tools or resources you feel the project lacks which would be beneficial?
• How often do you feel the need for more training or upskilling? (Never / Rarely / Monthly / Weekly)

However, the effectiveness of surveys is greatly amplified when used with clarity of purpose. It’s essential to have a precise understanding of what you’re seeking. Are you after general feedback or exploring a specific issue?

Your survey’s structure and questions will pivot based on this.

While surveys might give you numbers and general trends, interviews grant project managers a peek into the unique experiences, feelings, and perceptions of stakeholders. It’s this qualitative richness that makes interviews invaluable.

Interviews allow for dynamic and responsive interactions.

A skilled interviewer can read between the lines, notice non-verbal cues, and pivot questions based on the interviewee’s responses. This flexibility can unearth concerns, suggestions, or viewpoints that a respondent might not think to express in a survey.

Needs Analysis Questions for Interviews:

• Describe a situation where a tool or resource would have significantly changed the outcome of a project phase.
• Tell me about a time when a lack of training or understanding posed a major challenge.
• How do you see our project or product evolving in the next year, based on your current needs?

Post-interview, collate and analyze the data. Patterns will emerge, highlighting common needs, concerns, or suggestions. This information is gold for project managers, offering actionable insights that can directly shape the direction of a project.

Focus Groups

Focus groups bring together a diverse set of individuals together in a single room. You get a melting pot of ideas, experiences, and concerns, all bubbling to the surface in real-time.

Participants not only share their thoughts but also respond to others. This interactivity can spark new insights, leading to a richer understanding of needs.

Focus groups need to following to be a success:

• Selection of Participants: Diversity is king. Including representatives from different departments, roles, or user groups ensures a comprehensive discussion.
• Moderation: A skilled moderator keeps the conversation on track, ensures everyone has a voice, and can probe deeper into comments to extract maximum value.
• Clear Objectives: Before the session, be clear about what you hope to achieve. Is it brainstorming solutions? Understanding user feedback? Pinpointing project challenges? A defined goal guides the discussion.

Once your focus group wraps up, the real work begins. Transcribe, categorize, and analyze the insights. Look for patterns and recurring themes. These collective insights, when combined with other needs analysis methods, offer a robust foundation for project decisions and direction.

Observational Analysis

Observational analysis thrives on the principle that seeing is truly understanding.

Unlike methods such as surveys or interviews that rely on an individual’s recollections or perceptions, observational analysis captures the intricacies of actions, reactions, and interactions firsthand.

Observational analysis is particularly potent when examining operational processes. By watching how teams manage their tasks, interact with tools, or collaborate with each other, a project manager can identify bottlenecks, redundancies, or even areas ripe for automation.

Several techniques stand out:

• Shadowing: Involves following an individual or team closely, absorbing and documenting their activities throughout their workday. It’s immersive, granting a detailed look into daily routines.
• Workplace tours: Allow managers to walk through physical or even virtual workspaces. Such tours give a panoramic view of the environment, the tools in use, any diagrams, or systems that guide workflow.
• Video analysis: Beneficial for digital interfaces, permits session recordings. This allows for multiple reviews, capturing nuances that might be missed in real-time observations.

A neutral observer is best placed to refrain from judgments or interference during the observational process. Concurrently, note-taking becomes the backbone of this method.

Documenting patterns, anomalies, or areas that spark questions ensures that observations translate into actionable insights.

Each of these methods carries its own set of strengths. While surveys might be excellent for gauging general sentiments, observational analysis could be the key when refining user-centric designs.

How To Implement the Needs Analysis Steps

Each phase of needs analysis serves a distinct purpose and, collectively, they form the backbone of informed decision-making in project management.

Let’s break down the process of needs analysis so you’re best placed to implement the necessary steps:

• Preparation: The foundation of an effective needs analysis process begins with a clear understanding of the objectives. What do you hope to achieve? What’s the scope? Defining these upfront aligns the entire process towards a clear direction.
• Data Collection: Whether it’s through interviews, surveys, focus groups, or observational analysis, gathering data is vital. This raw data provides insights into the current state of affairs, revealing gaps, inefficiencies, and areas of improvement.
• Data Analysis: After collection, the raw data is evaluated and analyzed. Patterns emerge, specific needs are identified, and potential solutions begin to take shape. Using statistical tools or qualitative methods, this step transforms data into actionable insights.
• Prioritization: Not all needs have the same urgency or importance. This step involves ranking the identified needs based on factors like urgency, impact, and feasibility. It ensures that critical needs are addressed first, optimizing resource allocation.
• Recommendation & Report: Based on the analysis and prioritization, recommendations are made. These might be changes to processes, the introduction of new tools, or even team realignments. Typically, a comprehensive report is drafted to summarize findings and proposed actions.
• Feedback Loop: After recommendations are implemented, it’s essential to revisit and evaluate their effectiveness. Did the proposed changes address the needs as intended? If not, what adjustments are necessary? Establishing a feedback loop ensures continuous improvement and refinement.

Although the steps seem linear, they are deeply interconnected. Data collection might lead back to a refinement in objectives, or the feedback loop might necessitate a reevaluation of the data.

Skipping or overlooking any of these steps can lead to incomplete insights or misaligned solutions. Thus, for those aiming for success in their projects, understanding and committing to each phase of the needs analysis process is paramount.

Needs Analysis Examples

The beauty of needs analysis lies in its adaptability. While the underlying principle remains consistent, the ways it’s employed can vary widely depending on the context.

Let’s explore a few examples to see how needs analysis can play out in different scenarios.

Educational Institution

A popular university notices a drop in its student retention rate. To address this, the university conducts a needs analysis. They send out surveys to students asking about their experiences, challenges, and reasons for considering a change.

Additionally, focus groups are organized with teaching staff and a few dropout students to gain deeper insights. Through this analysis, it’s discovered that the primary need is a more engaging curriculum and better mental health support for students.

As a result, the university revises its syllabus and sets up a dedicated counseling center.

Retail Business

A chain of clothing stores realizes that, despite steady footfall, sales have been declining. They decide to perform observational analysis, discreetly watching how customers move within the store, what they touch, where they linger, and what seems to put them off.

They find that many customers struggle to locate sizes and styles they want. They then conduct short in-person interviews with shoppers to understand this better. The main need identified is clearer in-store categorization and better-trained floor staff to guide shoppers.

Tech Start-Up

A newly-founded tech company, after launching its beta product, wants feedback for improvements. They set up focus group sessions with select users. These discussions highlight some non-intuitive features in the software.

Surveys are also sent to a broader user base reiterating these findings, pointing to a need for a more user-friendly interface. Acting on this, the start-up revamps its design for greater ease of use.

Healthcare Facility

A clinic sees a steady decline in patient appointments. To identify the root cause, they employ a combination of methods: surveys sent to patients, interviews with staff, and even observations of patient-staff interactions.

The analysis shows that extended waiting times and limited appointment slots are primary concerns. The clinic then restructures its scheduling system, addressing the identified needs.

Each of these examples demonstrates the versatility of needs analysis. By combining methods, asking the right questions, and critically evaluating responses, organizations and institutions can hone in on their primary needs and act strategically to address them.

Benefits of a Comprehensive Needs Analysis

Rooted in its systematic approach, a thorough needs analysis can be the guiding star of a project, ensuring that resources, time, and effort are directed appropriately.

For businesses or projects looking to carve a niche for themselves in a hyper-competitive market, the competitive advantage a needs analysis can offer is undeniable.

When a business truly understands the needs of its stakeholders, be they customers, employees, or partners, it can tailor its offerings or solutions with pinpoint accuracy. Such precision can be the difference between a project that struggles and one that thrives.

Yet, some bypass this crucial step. Perhaps it’s the allure of quick results, the temptation to rely on assumptions, or simply the oversight due to myriad other pressing concerns.

Overlooking needs analysis is a misstep. While the initial process might seem time-consuming, the dividends it pays in terms of project clarity, stakeholder satisfaction, and overall efficiency are well worth the investment.

Can Needs Analysis Solve Every Problem?

Needs analysis is undoubtedly a powerful tool in the project management toolkit. By thoroughly assessing the gap between the current state and the desired state, organizations can map out a clear path to bridge that divide.

However, does that mean it’s a silver bullet for every conceivable challenge?

Well, here are the five factors that affect the answer.

• Depth of Understanding: The efficacy of needs analysis is largely dependent on how comprehensively and thoughtfully it’s conducted. A shallow or rushed needs analysis might gloss over deeper, systemic issues, leading to misdiagnosed problems and misguided solutions.
• Fluidity of Needs: Needs aren’t always static. Especially in rapidly evolving industries or contexts, what’s deemed a pressing need today might become obsolete or evolve tomorrow. Relying solely on a needs analysis from a year ago without reassessing the current landscape can lead to outdated strategies.
• Human Element: Needs analysis, while systematic, is inherently tied to human perceptions, beliefs, and biases. There might be instances where stakeholders, despite their best intentions, are unaware of their true needs or can’t articulate them effectively.
• External Factors: Not all challenges faced by a project or organization can be traced back to internal needs. External factors, such as market dynamics, regulatory changes, or global events, can exert pressures that a needs analysis might not be equipped to identify or address.
• Solution Creation: Identifying needs is just one side of the coin. The real challenge often lies in crafting effective solutions to address these needs. While needs analysis can illuminate what’s missing or desired, the creative and strategic process of addressing these gaps is a different beast altogether.

A holistic approach, one that combines needs analysis with ongoing market research, feedback mechanisms, and adaptive strategy formulation, is key for multifaceted challenges of today’s projects and industries.

Never underestimate the importance of asking the right questions. Before you dive into execution, take a moment to listen, observe, and understand. Your projects will be richer, more impactful, and infinitely more successful when built on the solid foundation of a meticulously conducted needs analysis.

The real value lies not just in identifying what’s missing or what can be improved but in crafting solutions that truly resonate with stakeholders, be it customers, students, or users.

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Customer needs analysis: definition & research methods.

10 min read Customer needs analysis is the process of identifying a customer’s requirements for a product or service. It’s used in all kinds of product and brand management contexts, including concept development, product development, value analysis, and more.

The goal of a customer needs analysis survey is to understand the customers’ needs and their position in the overall market.

What do we mean by customer needs?

Customer needs are the attributes of a product, brand or service that motivates someone to buy. The term covers basic must-haves, such as good-enough quality and affordable price , and also extends to more abstract and complex purchase drivers such as an aspirational brand image or a sense of alignment between a customer’s personal opinions and a brand’s ethics .

Customer needs vary a lot – between individual customers across your target audience , and from product to product and brand to brand. To identify customer needs effectively, you need an ongoing program of analysis that captures and analyzes customer feedback . Surveys can be an important part of that process.

Because customer needs can be complex and deep-seated, you may need to go beyond what customers explicitly tell you in order to uncover the full picture. That’s where customer needs analysis methods come in.

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How does understanding customer needs help?

A good understanding of customer needs helps your business in a few ways.

Firstly, it helps with product development and product packaging decisions. If you know your customers want a range of color and size options in a given product, you can make sure you provide them. If they want a range of colors and sizes but it matters more to them to get your product at the right price, then you know how to prioritize your resources to balance those needs correctly. You can also use customer needs assessments around existing products and services to enhance and develop your product offering in the future.

Secondly, it helps you to market the products you already have in the most effective way possible. You can make sure that your marketing messages reflect a customer’s desires and objectives and highlight the features and benefits that matter the most. For example, if you’re selling outdoor gear, as well as mentioning that it’s durable and waterproof, you could highlight the fact that your sustainable manufacturing methods result in a zero-carbon outcome for every garment. Thanks to your customer needs analysis, you’ll know if that’s something your nature-loving customers really value.

Types of customer needs

Here are just a few examples of customer needs that your analysis might turn up.

• Price The item is affordable and appropriately priced for the quality
• Convenience Saves time and effort
• Image and status (as in an item of clothing or technology) Looks good, impresses others, makes the customer feel good about themselves
• Durability and lifespan Built to last, dependable, and won’t break
• Packaging type Resealable, refillable, recyclable, or all of the above
• Support and aftercare Customer knows they can get questions answered and problems solved
• Effectiveness Gets the job done
• Formulation Free from unwanted ingredients or materials, containing desirable elements (for example gluten-free, or containing active friendly bacteria)

A means-end approach to customer needs analysis

Customer needs analysis is a means-end approach, meaning that customers make purchase decisions based on product features that get them to a value-based goal or state. For example, one consumer might buy a watch because he likes to be timely, and another might buy it because it looks cool. They’re both buying the same feature (time-tracking), but using it for different means (timeliness vs. status).

This principle is the basis of a powerful research technique which has been used to place U.S. presidents into office, successfully re-image industries, achieve competitive advantage over the competition through target advertising messages, and design innovative and successful new products.

Means-end analysis identifies linkages between three areas of product and customer interaction.

• Product features and attributes
• Benefits (real and perceived) a customer gains from the use of the product
• The unique values or traits of a customer that enable them to experience the product benefits, such as a person’s functional, physical, financial, social, and psychological characteristics.

With the right tools, it’s possible to quantify all these elements with respect to a specific product and audience. A Qualtrics study for the development of a new bank credit card found that nine attributes were critical to consumers considering a new card: no annual fee, status, low-interest rate, added value features, acceptance, credit limit, ability to carry a balance, location of the sponsoring bank, and availability.

These attributes were found to be linked to 12 benefits (consequences) that were perceived as part of card usage: not feeling cheated, independence, convenience, dependability, and saving money.

Brand attitudes – and how to discover them

Brand attitude tells us what consumers think of a brand or product and if it solves a particular need. When developing customer analysis surveys, it’s important to determine the consumer’s brand attitude. Here are a few of the elements a good customer needs analysis survey should cover:

Top-of-mind imaging

Positive and negative associations for the brand or product category are elicited, along with reasons why the characteristic is viewed that way. Top-of-mind studies are used to uncover the attributes and consequences that distinguish the characteristic.

Brand category analysis

Identifies similar and dissimilar brand groupings within a product category and the reasons for this perceived similarity or dissimilarity. The primary reasons, most important attributes, and most representative brands are identified, and attributes and consequences are laddered.

Contextual environment scan

The usage context for a brand or product is critical in marketing. Physical occasions (place, time, people), or need state occasions (relaxing, rejuvenating, building relationships, feeling powerful, reducing stress, and getting organized) may exist. A brand or product is associated with a usage context that is critical in effective positioning and advertising.

Preference-usage/similarity-dissimilarity analysis

Comparing brands based on personal preference or usage is a common distinguishing point for brands. Groupings by similarity and dissimilarity also provide a direct method of distinguishing between brands. Success critical attributes and consequences are identified that lead to higher market performance.

Purchase and consumption timing

Issues are often related to product or brand choice and usage. For example, a respondent might be asked to identify products used for relief of a stuffy nose across several stages like onset, full-blown, and on-the-mend, or daytime and nighttime. Brand preference is identified for each time-related stage.

Usage trends

Past and expected future usage of a brand is instrumental in identifying attributes and consequences that lead to different usage patterns. For example, respondents may be asked, “Will this brand be used more often, less often, or about the same as you have used it in the past?” Then, reasons for increased, decreased, or unchanged usage are determined. The follow-up analysis of reasons for trends produces a vivid insight into market drivers and potential areas of market growth.

Product or brand substitution analysis

Product and brand substitution methods elicit the degree of similarity of perceived attributes and consequences associated with usage. When questions are asked about the degree of substitutability, attributes and consequences are discovered that inhibit or promote substitution (attributes or consequences that need to be added or removed for substitution or trial to occur). For an unfamiliar brand, the respondent first can sample or be given a description of the brand, followed by a question like, “How likely would you be to substitute (name of the new brand) for your current brand for this occasion—why is that?”

Alternative usage occasions

Alternative uses are presented to the respondent to determine if and why the brand is present or absent from the choice set. Questions might be phrased to ask, “Why would you consider using Brand A for this occasion?”, or “What is keeping you from using Brand A for this occasion now?” Both positive reasons why a brand fits a new occasion and negative reasons why it does not fit can be elicited. Alternative usage occasion analysis identifies market segments and details how to approach them.

How to better meet your customers’ needs

Customer needs assessment needn’t be a one-time event or even a per-product one. You can help make sure you’re continually meeting customer needs by maintaining an overall high standard of knowledge about how your customers think and feel. This will pay dividends not only in designing and marketing your products more effectively, but also in making customers feel known, understood, and valued when they interact with you.

Becoming more customer-centric is a choice that more and more businesses are making, focusing less on operational data and more on experience-based insights that reflect how customers think and feel about the experiences you provide .

With the Qualtrics experience management platform , you can build surveys and dive deep into what matters most to your customers and your business.

Related resources

Market intelligence 10 min read, marketing insights 11 min read, ethnographic research 11 min read, qualitative vs quantitative research 13 min read, qualitative research questions 11 min read, qualitative research design 12 min read, primary vs secondary research 14 min read, request demo.

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Perform a needs analysis that creates insights – and a willingness to buy!

Table of contents.

What exactly is a needs analysis, and how do you do it? These are questions we will answer in this article. Continue reading to learn about our needs analysis model and become a pro at how to perform a needs analysis that creates insights and a willingness to buy.

What is a needs analysis?

In B2B sales , a Needs Analysis is often carried out during the first customer meeting. In larger transactions, more than one meeting may be required to sort out what the customer’s needs are. The purpose of the needs analysis is to identify the gap between where a customer wants to be and where they are today.

The purpose of a needs analysis is to understand the customer’s needs, by asking the customer a variety of questions. This way you can see if there is something your product/service can solve.

Also, read our article about customer value and how you can create value for the customer here .

Your template for a world-class needs analysis

When doing a needs analysis, there are four areas you need to be an expert in identifying. Below is our template for needs analysis that summarizes the areas and what each means.

1.     The first area of needs analysis: The desired result

You need to find out what the customer wants to achieve, both in the short and long term. And you need to become good at really understanding what different goals the customer has.

Once you’ve found out what your customer’s different goals are. Do you want to become good at understanding why specific goals are important? Above all, you need to understand what is the most important goal right now.

To get to know the customer, it is often good to schedule a meeting, or even make a visit to the customer. You can read more about booking meetings and appointments booking here.

2.    The second area of needs analysis is: Purchase criteria

For you to have a chance to sell to the customer, you need to ensure that you know the customer’s purchase criteria. Different purchase criteria can be:

• Experience from the customer’s industry
• A fixed delivery time from the time the order is placed
• Product specifications, configurations, or clean features
• Specific payment terms and guarantees

Once you have gained an understanding of what the customer’s purchase criteria are. In the next step, you should find out what the requirements are for the customer. Requirements are things that are important enough to the customer that they are willing not to make a deal if they cannot be met. When you start asking the customer questions, you’ll probably help the customer understand that most of the requirements they’ve lined up are wishes.

3.    The third area of needs analysis is: Personal purchase motive

Once you have understood what the customer wants to achieve and what purchase criteria they have, your next step will be to find out the customer’s motives. And the personal motive is something many salespeople tend to miss.

Remember that you are doing business with people. And as human beings, we are driven by emotions. Therefore, you need to become good at understanding why it is important for the customer to make a decision.

• Does the person want to be seen as innovative?
• Maybe they want to take the next step in their career.
• Is it about proving yourself in a new workplace?
• Maybe the person wants to make more money.
• Is it a problem that keeps the person up at night?

Regardless of why the customer needs to make a decision, you as a seller need to become good at understanding what the personal motive is. If you succeed, not only will the customer want to buy, but they will want to buy from you.

4.    The fourth area of needs analysis: Profit & Loss

The fourth and final area of the needs analysis is based on profit and loss. An important method is to delve into the loss. What does the customer risk losing by not acting, buying from you, or making a decision? You need to become good at asking those kinds of tough questions. At the same time, you also use the information you received earlier in the needs analysis. What does happen if they do not achieve their desired result? What would that mean for their personal goals?

After you talk about the loss, the energy in the meeting has probably gone down. Just when the customer is at their lowest energy level and thinks that they would miss out. Is the perfect time for you to shift focus. Instead of talking about the loss, you get the customer to think about what they can gain by acting and buying from you.

The reason why you need to ask about both profit and loss? Research from Daniel Kahneman shows that people are more afraid of losing something. Then they are motivated by winning or achieving something new. Hence, we ask about losses. But to awaken a motivation, will, and joyful collaboration. Does the customer also need to understand the benefits of the collaboration?

Sharpen your needs analysis with our insight-driven question method

You have been given a template with 4 areas to make a world-class needs analysis. But how are you supposed to find out the required information during a meeting? The answer is by asking questions. And not just any questions.

To help you ask the right questions and arouse an interest in the customer to want to buy. You will get an insight-driven question approach. The question method will help you control the needs analysis and find out what you need about the customer. So what parts does the insight-driven query approach have?

• The first step is to ask the customer about their wishes. If you call the customer in 1, 3, or 5 years. What does the customer want to tell you that they have achieved then?
• The second step is to ask the customer questions about their current situation. Where are they today? What challenges do they have? What works well?
• The third step includes change issues. What does the customer need to do more/less/differently to succeed in achieving their goals?
• The fourth and final step contains questions about value. What would it mean if they achieved their goals? What value does it create for the company’s employees, customers, and for the person you are talking to?

Create your question battery and improve quality

To make sure that all salespeople ask the right questions. It may be a good idea to create a battery of queries. The battery of questions guides the seller through the customer meeting and ensures that the necessary questions are asked.

The advantage of a battery of questions is that the quality of the needs analysis meetings is increased. This means that salespeople, regardless of experience level, can make good meetings together with potential customers. A further effect is that the entire sales force increases the quality of their needs analyses.

A disadvantage of question batteries can be that the meeting becomes too structured and “robotic”-like. To counteract this, you need to make sure to practice the implementation of the needs analysis with your question battery. The advantage of this is that you get better at finding “bridges” between different types of questions. The effect will be that the meeting feels more natural for the customer and the seller will be able to create a more personal meeting.

Examples of the  most common mistakes in a needs analysis

Forgetting to ask questions about the customer’s long-term goals.

It’s common for salespeople to forget to ask the customer about their long-term goals. Which makes it difficult for them to understand when it may be relevant for the customer to purchase their solution. The risk with this is that the customer turns to another supplier. When they feel ready to invest in the area you can contribute.

Gets tunnel vision on the first need the customer mentions

Salespeople are sometimes so focused on finding a need that they stab at the first thing mentioned. The disadvantage is that the first need does not have to be the most important for the customer. And that makes you easily end up in the “supplier tray.”

Fails to handle the customer’s perception

Many salespeople fail to ask about and manage the customer’s perception. The risk with this is that the customer tries to adapt their answers. After what they think you’re interested in. This means that you do not find out needs that you can solve.

Assume that they understand the customer’ s needs

  It is common for sellers to assume that they understand what the customer is looking for. Often it is a consequence of forgetting to summarize the customer’s needs. So that the customer gets an opportunity to clarify and add things to the seller’s picture of the need.

Does not ask questions about the customer’s past experiences

Salespeople may perceive the question of the customer’s past experiences as a loaded topic. What if the customer has had a bad experience with a similar solution/supplier? The risk if you don’t ask the question is that you don’t know why the customer is skeptical and cautious.

Does not capture the personal motives of the decision maker

You work in B2B sales. Then you don’t have to ask about the decision-makers personal motives, do you? Yes, you do! One of the most common mistakes salespeople make is that they think that way. Your customer may be a company, but the decision-maker is a human being and people have feelings. The risk is that the customer does not have sufficient motivation to act if you miss the motives of the decision-maker.

Does not coach the customer through the questions they ask

Instead of coaching the customer on what they need to do. Asking sellers a lot of questions. To then go ahead and persuade the customer. No one likes to be persuaded. Your customer wants to own their ideas and initiatives.

Frequently asked questions about needs analysis

How do i get better at doing needs analyses.

There is a lot you can do to get better at needs analysis, but the most important piece of advice we want to send you is the following: Practice doing needs analyses. You need to train together with colleagues and in real customer meetings. Then make sure to constantly develop what questions you ask, how you use the answers you get, and how to create a stronger relationship in the meeting. Here you must involve a manager or colleague who can identify things you may miss.

If you or your sales reps need to develop their ability to conduct needs analysis, you can let them join our Sales Acceleration Program . Through the sales training program, they will learn more important techniques and structures in the sales work and they will receive direct coaching while they practice applying what they have learned.

Why should I do a needs analysis?

The most important reason why you need to do a needs analysis is to be able to recommend the smartest solution to the customer. While you’re doing your needs analysis, you may find that the customer is only looking to cure one of their “symptoms” instead of addressing the real root cause. Only when you understand what the customer’s goals are, where they are today, what changes are required, and their motives, can you recommend the smartest solution to the customer. Then you lift yourself from being a salesperson to becoming an advisor for real.

What is good to think about?

Something good to keep in mind is to ask the customer many follow-up questions. Instead of settling for the answer “We want to grow”, you need to understand how much they want to grow. Is the customer talking about revenue growth or number of employees? Only when you have dug deep enough that you understand what the customer means can you move on to the next point in the needs analysis.

What questions should I ask?

To ask good questions, you first need to ensure that you have similar definitions of the topic being discussed. Maybe your customer thinks that salespeople are just those who have the title of salesperson while you think that all the people with customer contact who are trying to solve their customers’ problems are salespeople. Then you will most likely encounter problems in the conversation and it is important to sort it out first. Then you can go in and talk about what they want to achieve, where they are now, and of course, ask questions about why things are important. We can’t give you an exact script for what questions you need to ask to make a good needs analysis. However, we can inspire you by giving some examples of questions to ask to find out what you need about the customer:

• What is your opinion of us at Salesonomics?
• What are your expectations for this meeting?
• If I call you on New Year’s Eve next year, what do you want to tell me you’ve achieved by then?
• On a scale of 1-10, how satisfied are you with your current solution for this area?
• What do you see that you need to do differently?
• What obstacles do you see linked to these changes?
• What would it mean to you if you failed in your goal?
• If you succeed, what will it mean for you and the company?

Simon Blanche

• Tags: needs analysis , Sales

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• October 30, 2023

Needs Analysis: Common Types and How to Do One Step-by-Step

Learning and development have become paramount in the contemporary workplace. Organizations recognize that a skilled and knowledgeable workforce is a key driver of success.

Couple this understanding with the understanding that today’s marketplaces’ move at high speed and the pace at which technologies and practices become outdated is accelerating. This leaves organizations with the glaring reality that skills development and continual learning are essential components of staying relevant and competitive.

As can often happen, once change or realization gives rise to others, in this case, how can organizations ensure that employees are equipped with the right skills and knowledge? How can they become aware of skill gaps and address them effectively? Incorporating needs analysis system as part of learning and development, becomes an essential tool for organizations seeking to uncover skill-gaps, develop talent, and apply foresight to their learning and development initiatives.

A needs analysis is a common part of any design framework – product and service designers regularly use them as part of their processes, however a needs analysis can just as easily (and should be) be applied when planning learning development and training programs as well.

A needs analysis in this framework is, in essence, a systematic examination of the knowledge, skills, and competencies required by employees and how they align with the organization’s goals.

It’s not just a formality; it’s a strategic approach that uncovers valuable insights, enabling organizations to bridge the gap between current and desired employee performance. 

READ: The “Teach 1 Thing” Approach to Organizational Learning and Development

Types of Needs Analysis: Choosing the Right Approach  

When it comes to understanding learning and training needs within an organization, one size does not fit all. Various types of needs analysis methodologies exist, each tailored to specific situations and objectives.

Choosing the right approach is pivotal to successful outcomes.

The following are a few common types of needs analysis and how to determine which one suits your needs. If you have the time and resources, combining 2 or more of the different approaches may provide a more wholistic picture of the learning and training needs. 

  Organizational Needs Analysis:  

• When to Use: Consider this approach when you need a bird’s-eye view of the organization’s training needs. It aligns the training programs with the overall strategic goals and objectives of the company. 
• How to Determine: Start by identifying the organization’s strategic priorities and goals. Then assess the skills and knowledge required to achieve these objectives. This method provides a holistic view of the organization’s training needs. 

  Task Analysis:  

• When to Use: Task analysis is ideal when you want to break down specific job roles and identify the skills and knowledge needed to perform those roles effectively. 
• How to Determine: Analyze each job role within the organization. What are the specific tasks and responsibilities? What skills and knowledge are necessary to excel in each role? Task analysis offers a granular understanding of role-specific needs. 

  Person Analysis:  

• When to Use: This approach is suitable for situations where individual employees’ performance gaps need to be addressed, whether due to skill deficiencies or performance issues. 
• How to Determine: Assess individual employee performance through performance appraisals, feedback, or self-assessments. Identify areas where employees are falling short and require additional training or support. 

  Competency Gaps Analysis:  

• When to Use: When you want to compare the competencies, employees possess with those required for their roles. 
• How to Determine: Define the competencies needed for each role or job category. Then assess employees’ current competencies. The gaps reveal which training is necessary to meet role requirements. 

Feedback and Survey Analysis:  

• When to Use: When you want to gather insights directly from employees about their training needs and preferences. Regardless of the training being done its always a good idea to conclude it with a feedback collection method – this will help you analyse the impact and effectiveness of your programs. 
• How to Determine: Conduct surveys, focus groups, or feedback sessions to collect employee input. Analyze the data to identify common themes and areas where additional training or learning support is desired. 

How to Choose the Right Need Analysis: Determining the Best Fit For You

• Assess the specific situation: Consider the scope and objectives of your analysis. Is it focused on individual employees, specific job roles, or the organization as a whole? 
• Understand the purpose: What do you aim to achieve with the analysis? Is it to align training with strategic goals, enhance job performance, or bridge individual competency gaps? 
• Collect relevant data: Ensure you have access to the necessary data, whether it’s related to organizational goals, job roles, individual performance, or employee feedback. 
• Evaluate resources: Consider the resources available for the analysis. Some methods may be more resource-intensive than others. 
• Be flexible: In some cases, a combination of approaches might be most effective, allowing for a comprehensive understanding of learning and training needs.   

By choosing the right needs analysis approach tailored to your objectives, you can ensure that your learning and training initiatives are well-aligned with your organization’s goals and the needs of your workforce. 

Once you’ve determined your needs and the methods you will use it’s time to conduct your analysis while there is always room for variation and customization as needed, a common structure can be followed. Such a structure might look something like the following: 

How to Conduct a Needs Analysis: A Step-by-Step Guide

  step 1: define the purpose  .

• Why: Begin by clarifying the specific purpose of your needs analysis. What are you trying to achieve? Is it to improve overall job performance, align training with organizational goals, or address individual competency gaps? 

  Step 2: Assemble Your Team  

• Why: Collaborative efforts yield richer insights. Involve key stakeholders, including HR, managers, and subject matter experts. 
• Example: If you’re conducting a task analysis for a specific job role, include the employees performing that role and their supervisors. 

  Step 3: Identify Data Sources  

• Why: Data is the heart of needs analysis. Determine which data sources you need, such as performance records, employee feedback, or strategic objectives. 
• Example: For organizational needs analysis, you’ll need access to strategic planning documents and reports. 

  Step 4: Choose the Right Data Collection Methods  

• Why: Different types of data require various collection methods. Surveys, interviews, observations, and document reviews are common methods. 
• Example: For feedback and survey analysis, create questionnaires or hold focus group sessions to gather employee input. 

  Step 5: Collect Data  

• Why: Execute the chosen data collection methods, ensuring accuracy and consistency. 
• Example: If you’re conducting a person analysis, you may use performance appraisals and self-assessments to gather individual employee data. 

  Step 6: Analyze Data  

• Why: Examine the collected data for trends, gaps, and insights. Identify common themes or areas where training is needed. 
• Example: In an organizational needs analysis, you might identify gaps between strategic goals and current employee skills. 

  Step 7: Prioritize Needs  

• Why: Not all identified needs are of equal importance. Prioritize them based on their impact on organizational goals or employee performance. 
• Example: In a task analysis for a sales role, prioritize needs that directly affect sales targets. 

  Step 8: Develop an Action Plan  

• Why: Create a clear plan outlining how you will address the identified needs. This may involve developing new training programs, revising existing ones, or individual coaching. 
• Example: If the needs analysis reveals a gap in technology skills, plan to roll out a technology training program. 

  Step 9: Implement and Monitor  

• Why: Put your action plan into motion and closely monitor its progress. Adjust as necessary to ensure effectiveness. 
• Example: For an individual competency gap, implement targeted training and monitor the employee’s performance improvements. 

  Step 10: Evaluate Results  

• Why: After implementing training or learning interventions, evaluate the impact. Did it address the identified needs and yield desired outcomes? 
• Example: In an organizational needs analysis, measure the impact of training on strategic goal achievement. 

  Step 11: Repeat Periodically  

• Why: Needs evolve. Regularly conduct needs analyses to stay aligned with changing organizational objectives and workforce requirements. 
• Example: Perform an annual needs analysis to ensure that your training efforts remain current and effective. 

By following this step-by-step guide, you can conduct a needs analysis that is practical, straightforward, and effective in identifying and addressing the learning and training needs within your organization.

This process ensures that your efforts remain closely aligned with your organization’s goals and your employees’ development requirements. 

Learning and development at organizations should be an ongoing, diverse practice. There are always skills to be gained and new information available. While there might not always be time in the day for study and learning, incorporating the practice into your professional environment and practice is a vital component of a dynamic organization, and having a defined, regular approach to monitoring and assessing needs is an essential component of effective learning and development.  

Chris Hutchinson

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Needs Analysis

• First Online: 13 January 2022

Cite this chapter

• Li-Shih Huang 3  

Part of the book series: Springer Texts in Education ((SPTE))

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2 Citations

Defining the term “needs” entails a complex inquiry involving a variety of viewpoints and labels. The term “language-learning needs” further encompasses various perspectives and factors in determining what, how, and why learners need to learn (e.g., Hyland, 2006; Long, 2005; Munby, 1981).

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Li-Shih Huang

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Hassan Mohebbi

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Christine Coombe

The Research Questions

Needs analysis factors: Consider the different factors offered by Dudley-Evans and St. John ( 1998 ) and the ESP or EAP courses you have taught or envisage teaching. Which factors or types of needs-related information in your context would be feasible to collect to help you understand your students’ needs?

Needs analysis methods: What methods would be suitable for collecting the data for examining the factors identified in Question 1? What are the strengths and limitations of the methods identified?

Needs for occupational purposes: If you were involved in an initiative to settle Syrian refugee learners into the workforce by providing them with the language training needed for occupational purposes, how would you go about identifying their language-learning needs for purposes of developing a program?

Needs for professional purposes: You have been assigned to develop an ESP course for pharmacists or physicians who are recent immigrants from Syria and who need to pass the certification examination in order to continue practicing their profession. What steps would you take to understand their English language needs in order to design this course?

Needs for workplace communication: When examining the needs associated with specialized discourse, one option is to gather samples of language use. Select as your aim the development of a workplace-focused ESP class. When collecting data from participants is not possible, look into other potential data sources (e.g., corpora, manuals, official documents, websites, and research articles). What existing data sources can you locate to begin your discourse/genre analysis?

Needs for academic purposes: Select an academic genre or a specific topic in the genre of pedagogical interests. What are the disciplinary similarities and variations in discourse across two or more disciplines? What methods could you use to compare and contrast differences in order to inform pedagogical practices?

Task-based needs analysis: As pointed out by Basturkmen ( 2010 ), task-based needs analyses have increasingly been used in ESP. Is the task-based approach to needs analysis applicable across English for professional purposes, English for academic purposes, and English for occupational purposes?

Task-based needs analysis: Using tasks as the focus of inquiry, identify some real-world tasks in English for general purposes, English for academic purposes, or English for occupational purposes that a target group of learners will encounter. As a researcher, what can you do in your analysis of language features and communication skills needed for those identified real-world, language-based tasks to inform instructors’ practices?

English for academic purposes: EAP is often described as “needs driven” in that it aims to address the needs of students within various target contexts, which might risk promoting conformity to conventions or institutional requirements and lead to what Hyland (2018), citing Huckin (2003), called “a teacher-centred prescriptivism and an overly rigid focus on certain genres, forms, and tasks at the expense of others” (p. 387). In conducting needs analysis in the EAP context, what deviations from institutional conventions can be explored that could enrich our understanding of language features and discourse practices and also promote more dynamic pedagogy?

Research replication: As argued by Huang ( 2010 ), “efforts to seek findings’ generalizability may be fruitless because needs analysis is, by definition, context-dependent and context-specific, taking into account the very different linguistic cultures and the variety of institutional environments” (p. 535). Conduct a literature review to locate a needs analysis research report that, within the context of the research, is similar to your own teaching context and that would be possible to replicate. Implement your replication study in your own context to answer this question: Do your results converge with the original study or do they support the argument?

Suggested Resources

Basturkmen, H. (2010). Developing courses in English for specific purposes. Palgrave Macmillan .

This volume focuses on developing courses for English for specific purposes at both an institutional and a classroom level in a variety of contexts. It contains an informative needs analysis chapter that provides an overview of the development of the defining key terms and their complexity as well as the processes involved in examining learner needs, in addition to an overview of the aims, methods, and stages involved in needs analysis. The chapter also highlights the dynamics of needs analysis as an ongoing and evolving process during a course’s implementation, and includes hypothetical scenarios with follow-up questions. The importance of needs assessment permeates the volume, and its practical coverage provides both prospective and practicing teachers and researchers an easy start on getting up to speed in grasping the fundamentals of needs analysis.

Blaj-Ward, L. (2014). Researching contexts, practices and pedagogies in English for academic purposes. Palgrave Macmillan .

Of particular relevance to readers interested in needs analysis, Chap. 3 of this volume builds on the premise that needs analysis is fundamental to EAP provision. The chapter delves into topics on teaching materials and course design, with a central focus on needs analysis dealing with target situations using, for example, discourse and corpus analysis that examine the discourse norms in academia that students are expected to maintain. Through presenting and analyzing hypothetical scenarios, the author engages readers in needs analysis in practice. Chapter 4 shifts to needs analysis in relation to students’ perceived needs rather than to features of the target situations. It also discusses needs analysis studies and the need to shift the critical stance from terminology to the broader issues related to the non-neutral nature of needs analysis in evaluating perceptions, practices, and outcomes within the macro context of higher education.

Paltridge, B. & Starfield, S. (Eds.). (2014). The handbook of English for specific purposes. Wiley-Blackwell .

This 28-chapter handbook contains a wealth of resources for teaching and research. Of relevance is the chapter by Johns (pp. 5–30), who critically reviews the historical development of ESP research, within which she traces the period when ESP research and pedagogic practice broadened to include a focus on needs assessment. In dealing with the pedagogical issues of ESP, most notably, Flowerdew’s chapter (pp. 325–346) on needs analysis and curriculum development presents evolving definitions, sources, and methods of needs analysis in various ESP course types (e.g., EAP and EOP [English for occupational purposes]). This coverage is followed by a discussion of the future directions of needs analyses and curriculum development in ESP.

Long, M. (2005). Methodology issues in learner needs analysis. In M. H. Long (Ed.), Second language needs analysis (pp. 19–76). Cambridge University Press .

For researchers interested in the field of needs analysis, this seminal publication on second language needs analysis presents a collection of studies with overviews of the field, design, implementation, and outcomes in a wide variety of contexts (e.g., the hotel industry, the US military, and academia) and geographical areas (the United States, Europe, and the Pacific Rim). The volume also informs readers about various data collection methods along with insights about their strengths and limitations. Underscored are Long’s overview and his discussion of the importance of data triangulation, which provide readers a sense of the complexity and challenges involved in conducting needs analysis and of the issues and approaches one must be aware of.

West, R. (1994). Needs analysis in language teaching . Language Teaching, 27 , 1 – 19 . https://doi.org/10.1017/S0261444800007527 .

For researchers interested in needs assessment, West’s state-of-the-art article is a must read. Surveying the field broadly, it provides a comprehensive review of needs analysis in English-language teaching by first describing its origins and theoretical basis. The article also covers the questions one must ask in any needs analysis procedure, namely, what (such as necessities, lacks, wants, learning strategies, and constraints), when (i.e., when needs analysis should be carried out), who (e.g., teacher-perceived, learner-perceived, or institution-perceived needs), for whom (i.e., whom will the needs analysis benefit), how (methods and procedures), and how long (i.e., the length of time required to conduct a needs analysis). It reviews different forms of needs analysis (e.g., target-situation analysis, for instance, the language requirements in ESP or EAP contexts that learners are being prepared for), deficiency analysis (e.g., learners’ present needs and the requirements of the target situation analysis), strategy analysis (i.e., learners’ preferred learning approaches or methods), and means analysis (e.g., logistics, constraints in implementation, and classroom cultural factors). The article concludes with a critical discussion of the limitations of needs analysis.

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Huang, LS. (2021). Needs Analysis. In: Mohebbi, H., Coombe, C. (eds) Research Questions in Language Education and Applied Linguistics. Springer Texts in Education. Springer, Cham. https://doi.org/10.1007/978-3-030-79143-8_64

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Needs Analysis: What is it and why is it performed?

This document explains at a high level what a Needs Analysis is, when it is used, why it used and how it is peformed. This resource is used by Berkeley Lab Training to communicate why we start with this process.

Background: In the mid 1960’s the US Airforce developed a detailed handbook on what the military branded as the instructional systems design (ISD) approach. The first step in this process is to perform a Needs Analysis which was born by the fact that training departments couldn’t resolve some of the problems they were being asked to fix. This led to taking a more organizational approach in order to identify training and non-training factors that affect performance. Because it was so succesful, the Department of Energy (and many others) used the Airforce’s work as the foundation for its Systematic Approach to Training (SAT) which Berkeley Lab Training follows ( DOE-HDBK-1074-95 ). In fact, the DOE states that an effective Training Needs Analysis avoids developing expensive training that does not address true needs. The DOE understands that needs analysis can determine solutions to performance problems other than training.

What is a Needs Analysis? A needs analysis defines deficiencies or problems and identifies causes and solutions. It can be thought of as the process of identifying gaps between what should be happening and what is happening, and accounting for the causes of these gaps. In this way, it is a systematic search for identifying deficiencies between actual and desired job performance and the factors that prevent desired job performance as presented in the following steps:

• What are the performance expectations (desired state)?
• What is the current state of performance?
• What are the gaps to peformance (Needs) and causes?
• What are the solutions to bridge the gap?

When is a Needs Analysis used? Berkeley Lab Training performs a needs analysis in the following situations:

• When there is a request for a new training (or change to current training)
• Whenever new requirements are issued
• When it has been identified that job performance is below expectations (identified through work observation, self-assessments, audits, etc).

How is a Needs Analysis performed?

Outcome of process A Needs Analysis report that includes the following:

• Description of the needs analysis process
• Major outcomes identified
• Prioritized needs (and criteria used)
• Action plan recommendations

A U.S. Department of Energy National Laboratory Operated by the University of California

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• Needs Analysis

What is Needs Analysis?

Learn everything about needs analysis, its value, and how to capitalize on this to improve the organization’s workflows and revenues.

Needs analysis is the process organizations use to identify deficiencies or problems of an organization and pinpoint the root cause to provide the most appropriate solution. This practice helps in numerous ways, improving employee performance, finding opportunities, yielding better revenues, and serving the target audience better.

Analyzing needs was first used specifically to improve methods of teaching English in schools. Today, companies across industries regularly use and benefit from a needs analysis.

Why Perform Needs Analysis?

All companies experience hitches in their processes that prevent them from approaching their goals. What sets thriving businesses apart from unproductive ones is how they get to the bottom of issues to develop strategies for success. Here are specific reasons why conducting a needs assessment is a must.

• Improves decision-making – The process requires laying out all the factors affecting the deficiency or failure in the organization. With the help of insightful analytics, you can make definite and logical conclusions that help inform better decisions.
• Targets priorities – Companies have numerous problems. Finding remedies for everything all at once is both labor-intensive and expensive. Business needs analysis allows managers to rank these issues and deal with the most pressing ones first.
• Drives better performance – More often than not, organizational difficulties are brought about by a lack of knowledge and skills. This activity points out who in the team struggles and what specific training or additional studies are needed to be more effective at work.
• Enhances workflows – Organizational needs analysis can determine deficient operational elements or deteriorating equipment. Work efficiency is restored or increased through targeted interventions.
• Creates a proactive culture – If done regularly, this practice fosters forward-thinking. Instead of being reactive to issues, employees are empowered to take initiative to benefit the whole organization.
• Increases customer satisfaction and revenues – Gaining insights into your targeted audience helps you serve them better.

Whether you run a small start-up or a huge conglomerate, identifying and evaluating the needs of the organization and its stakeholders is a must. Understanding requirements will help businesses reap more benefits. Listed below are the kinds of needs analysis companies need to consider:

• Training or Learning – Possibly the most common kind of needs analysis, this is focused on the employees. By comparing job descriptions and performance data, managers identify skills and knowledge their workers require so they can do their job efficiently.
• Performance – This determines the gap between current performance and expected performance in providing services, creating products, or accomplishing tasks in the operation.
• Compliance – Often used in highly-regulated industries like healthcare and finance, this reviews the organization’s compliance with industry standards and government regulations. This involves going over policies and standard operating procedures and conducting audits.
• Organizational – This type of needs analysis evaluates the organization as a whole and looks at ways to improve resource management and daily operation. It takes into consideration the structure, culture, assets, and current workflows.
• Financial – Here, businesses figure out how to reduce costs, increase revenues, and locate investments. Managers review financial statements, analyze market trends , and conduct customer surveys to complete their needs analysis.

Upholding Best Practices in the Process of Needs Analysis

Different companies have specific methodologies for identifying and evaluating their needs. Listed below are needs analysis questions that should be asked throughout the activity. The best practices are also described per step to ensure success in this endeavor.

What is your desired performance?

This step identifies the purpose and the scope of the evaluation. This is also the part where company managers set goals for improvement.

Best Practice: The devil is in the details, so company leaders must specify the purpose and delineate the scope of the activity. Objectives should also be clearly defined and achievable so stakeholders can feel valued when they fulfill them.

What is the current performance?

The second phase of the process is information gathering . This involves collecting data from various sources like employee interviews, past performance evaluations, customer surveys, financial reports, and market research.

Best Practice: Managers can acquire accurate information through reliable collection methods. Digitizing and automation help in this matter because results are more precise. These also cost little time and effort.

What are the gaps and corresponding causes?

The third step involves detailed data analysis to characterize the gaps identified and detect their causes. Use quantitative and qualitative methods, such as statistics, content analysis, and in-depth reviews of case studies.

Best Practice: Stakeholders must be involved in this phase of the needs analysis process to voice their concerns about the identified deficiencies and state reasons for the possible causes. Engaging relevant team members prevents finger-pointing and presents a more comprehensive view of the problem.

What are the solutions offered?

This phase involves presenting the findings to relevant stakeholders and developing solutions to benefit them and the organization.

Best Practice: Managers should ensure actionable results. Stakeholder participation is also essential. Employees should be able to suggest solutions since they are involved in this task.

How effective are the solutions?

Many companies stop at the fourth step, thinking that providing solutions would end their problems. That shouldn’t be the case because monitoring employee training or new operating systems is crucial in ascertaining real progress.

Best Practice: Digitization and automation are critical in this process. Managers would not have a sweeping view of the whole operations and will surely miss important details without the help of these digital solutions.

Analyze Customer and Operational Needs with SafetyCulture

Why use safetyculture.

Conducting needs analysis is essential as it helps organizations be more proactive in resolving pressing issues before they become full-blown tragedies. It is a tedious undertaking that will demand so much time and effort from the company, and you will need all the support you can get. SafetyCulture (formerly iAuditor) is the partner you can rely on.

• Digitize data and automate workflows to identify gaps in training or processes.
• Gather salient data to find gaps and deficiencies during meetings and inspections
• Identify and characterize issues , use analytics to get insights for decision-making, and assign preventive or corrective actions .
• Provide training or courses to be studied if needed.
• Document the whole process complete with media attachments and generate relevant reports.
• Track the progress of employees and continuously monitor the operations to ensure that the solutions provided are working
• Create your own needs analysis template from the Public Library or download any of the following checklists for your next needs analysis:
• Site Survey & Needs Analysis Form – SafetyCulture
• AVI-SPL, UK – Needs Analysis
• 5.1.2 Saftey Department Training Needs Analysis – SafetyCulture
• Client Needs Analysis 
• HR Policy Needs Analysis 

FAQs about Needs Analysis

When is it best to conduct a needs analysis.

Companies call for needs analysis when these are observed:

• Suboptimal employee performance
• Stagnating operations
• Declining outputs
• Diminishing resources
• Bad service reviews

It’s better to do needs analysis periodically to ensure the optimum performance of the employees and the organization as a whole.

Who conducts needs analysis?

Usually, managers perform this function. But there are some cases wherein multiple raters are required, as in the case of needs analysis for training.

What is the most important element of needs analysis?

Companies often encounter numerous problems or gaps, but it is hard and expensive to deal with all of them at once. That is why deciding which need should be prioritized is the most important element of this activity.

Are there disadvantages to conducting a needs analysis?

Admittedly, this can be time-consuming and costly. Assigning skilled personnel, using the right tools, and getting priorities straight will ensure a smooth-sailing process that won’t require too much of your resources.

Eunice Arcilla Caburao

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Qualitative Research: Needs Assessment

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What is needs assessment?

Definitions of needs assessment may vary by purpose.

Curriculum and Training Development

A needs assessment is the process of collecting information about an expressed or implied organizational need that could be met by conducting training. ( Source )

Program Planning

Needs assessment has been defined as the process of measuring the extent and nature of the needs of a particular target population so that services can respond to them. ( Source )

An analysis of requirements. It determines what people, functions or systems are currently lacking in order to achieve the goals of an organization. A needs assessment also prioritizes the requirements so that the most glaring deficiency can be fulfilled first. ( Source )

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• What is a needs assessment? 3 types and ...

What is a needs assessment? 3 types and examples

A needs assessment is a process for determining the needs, otherwise known as "gaps," between current and desired outcomes. When used properly, this assessment provides valuable insight into your team’s processes and highlights areas for efficiency improvements.

When you’re balancing multiple growth initiatives and new projects, it’s hard to know which team improvements to prioritize. Where do you even begin?

When in doubt, try a needs assessment. A needs assessment helps you determine the most important process gaps so you can achieve your desired outcome in the shortest amount of time. Not only will assessing your current processes give you insight into how your team works, but it can also help identify areas of potential efficiency improvements.

What is a needs assessment?

A needs assessment is a process for determining the needs, or "gaps," between a current and desired outcome. It’s a part of strategic planning—essentially, a needs assessment helps you pinpoint how you’ll accomplish your strategic goals. 

A need is an opportunity for improvement within a particular process or system. When you identify—and resolve—needs, you can act on potential new opportunities, like making processes more efficient, streamlining resource allocation , and identifying resource gaps in your current workflow .  

For example, say your team is working on a process to organize customer data. A needs assessment would be a great way to understand where gaps exist in the data collection process—such as missing or inaccurate information—and where internal resources could be better utilized.

What is the purpose of a needs assessment?

A needs assessment identifies areas within your organization that need improvement. Use a needs assessment on existing processes to analyze data and inform internal changes.

Examples of processes you might use a needs assessment to accomplish include:

A process to automate duplicative manual work

A customer journey process that is underperforming

It can be challenging to pinpoint exactly where enhancements are needed. When you’re faced with multiple areas of opportunity, a needs analysis can help you identify the best areas of improvement. 

Example of a needs assessment

A needs assessment is a great way to improve processes, but it’s not always easy to get started. Start by taking a look at some example questions to get a better understanding of the data you’re looking for.

Success rate questions

What activities must be done to accomplish our objectives? 

What is the probability our solution is a success? 

What tasks are required to successfully solve our needs?

Performance questions

Which KPIs are we using to measure performance?

What does excellent performance look like?

What does current performance look like?

Operational questions

Which stakeholders are involved?

Where does the need occur within the process?

How frequently do we observe the need?

Identifying needs requires team communication, problem solving skills, and out-of-the-box ideas. Use these questions as a jumping off point to get the ball rolling. Once you know which questions to ask, you can begin to gather data. 

How to conduct a needs assessment

A needs assessment is a great way to analyze and interpret relevant data. To do this, you need to understand your team’s baseline needs, as well as the process’s overall desired outcome. 

Success rate questions:

Performance questions:

Operational questions:

Identifying needs requires team communication, problem-solving skills, and out-of-the-box ideas. Use these questions as a jumping-off point to get the ball rolling. Once you know which questions to ask, you can begin to gather data.

6 steps for conducting a needs assessment

A needs assessment is a great way to analyze and interpret relevant data that will influence your decision-making. To do this, you need to understand your team’s baseline needs, as well as the process’s overall desired outcome. 

Enlist the help of key stakeholders, funders, and decision makers and collect feedback through meetings or brainstorming sessions. However you choose to start, here are the four steps to follow when conducting a needs assessment. 

1. Identify your team’s needs

To determine the gaps between existing and ideal processes, you first need to understand what the ideal process looks like. Clear objectives are the best way to ensure you’re creating a measurable, actionable, and results-oriented needs assessment. 

Before you can start collecting and analyzing information for your needs assessment, take some time to consider your desired outcomes. Set objectives and gather data on areas of opportunity to plan deadlines and understand the intended outcome. 

Your team members are probably closer to the process than you are, and they have valuable insight into potential process improvements. Gather feedback from your project team, or host a general brainstorming session to identify your team’s biggest gaps. 

Work with your team to answer the following questions: 

What needs are you trying to solve? 

How is this process currently implemented? 

Where are the biggest opportunity gaps? 

What are your desired outcomes? 

Are you looking to solve a specific problem or a more general process? 

Do you have clear, measurable data sources? 

How will you measure success?

2. Measure and allocate your resources

Before you start your assessment, decide exactly how much bandwidth your team has and how much you’re willing to spend on the project. Also, determine how much time you’re giving yourself to meet your goals. Do you want to fill the gaps in six months? A year? Knowing exactly how much bandwidth you have will allow you to take a systematic approach to your report. 

Your team’s availability and organizational resources will impact the comprehensiveness of your needs assessment. If you allot more time to your needs assessment, you’ll be able to spend more time on data collection. 

3. Collect internal information

Next, gather information and collect data on how to best solve the identified gaps. Remember that the goal of a needs assessment is to understand how to get from your current process to the desired outcome. 

Gather data from various departments and stakeholders who are closest to the process. At this point, you’ve already brainstormed with your close project team members, but it’s also critical to understand what your cross-functional partners need from this process improvement as well. 

In order to create a good needs assessment, you need detailed information, so encourage stakeholders to share in depth data about their specific needs. The more information you have, the more likely your needs assessment is to succeed.

Some questions to consider when gathering information include: 

Where are improvements needed?

Why are current methods underperforming?

Do we have enough resources to execute a more successful process?

These questions will help you gather the necessary details to move on to step four.

4. Gather external information

Once you’ve gathered information from your project team and from cross-functional stakeholders, all that’s left is to gather information from external sources. Getting information from external sources, in addition to your internal collaborators, gives you a bird’s-eye view of the process from start to finish. 

There are multiple ways to gather external information on your target group, including:

Customer questionnaires: Used to gather quick, high-level customer data from multiple geographical locations

Focus groups: Used to gather in-depth information from a specific geographical location

It’s also a good idea to enlist a fresh pair of eyes to follow the process from start to finish to catch additional inefficiencies. While the type of needs assessment technique you use will depend on your situation, you should opt for the one that gives you the best chance of correcting inefficiencies.

5. Get feedback

A needs assessment is all about corporate and community needs. Test your findings with diverse groups of people who might have varying perspectives (and biases ) on your data. Share it with stakeholders and community members alike to gauge how both your higher-ups and target audience are going to react to any process changes. 

A few people who may want to see your assessment include: 

Project partners

Community members

Stakeholders

With the feedback you receive, you can make any necessary adjustments to the report before you start making large-scale changes to your identified needs. 

6. Use your data

At this point, you’ve collected all of the information you can. The only thing left to do is to use your needs assessment results and insights to make a final report and an action plan.

Use the information you gathered in steps one through five to transform your needs assessment data into a cumulative report. In addition to the notes, details, and observations you’ve made during your brainstorming sessions, add a summary documenting the next steps—in particular, the phases, technical assistance, training programs, and other components that will help you implement the process changes. 

Implementing the results of your needs assessment will take time. Make sure your team has an effective process in place to guide the improvement, like:

Project management tools : Help to organize information and communicate with team members

Change management : Assists with documenting need and gap changes

Business process management (BPA) : Helps to analyze and improve processes

Process implementation planning : Outlines the steps needed to reach a shared goal

Needs assessment examples

There are many different data collection methods—from quantitative techniques like surveys to qualitative techniques such as focus groups. Your target demographic may influence your methodology, so take into account whose perspective you’re looking for before you decide. 

Needs assessments provide crucial data on existing processes and help teams create more effective systems. 

Here are three of the most popular methods of collecting needs assessment data:

Questionnaires

Questionnaires and interviews are the most popular methods for collecting data. A questionnaire is a surface-level form with general yes or no questions. This is a great way to get quick information from respondents.

Use for things like: Evaluating the effectiveness of your brand identity

Many teams use surveys to collect external information around customer experience. Surveys often include open-ended questions, so they provide more in-depth information than questionnaires. This is a great way to find accurate but quick information.

Use for things like: Evaluating the success of your post-purchase experience from the customer’s perspective

Focus groups

A focus group is an interview involving a small number of participants who share common traits or experiences. While they require considerably more time than the other two methods, focus groups provide extensive information around needs and customer experience. This is a great way to gather in-depth information.

Use for things like: Evaluating how your customers experience your brand and what they think could be improved

Identify your team’s needs with an analysis

Performing a needs assessment is a great way to understand how current processes are being handled and how you can streamline tasks and communication. Knowing which needs are most important isn’t always obvious. With a needs analysis, you can gather the data you need to make your team more efficient. 

If you’re looking to improve efficiency and productivity as a team, keep information and tasks streamlined with productivity software. From empowering collaboration to creating and sharing templates, Asana can help.

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• > Second Language Needs Analysis
• > Overview: A rationale for needs analysis and needs analysis research

Book contents

• Frontmatter
• List of contributors
• Acknowledgments
• Overview: A rationale for needs analysis and needs analysis research
• I Methodological issues
• II The public sector
• III The occupational sector
• IV The academic sector
• V Analyzing target discourse

Published online by Cambridge University Press:  25 January 2010

In an era of shrinking resources, there are growing demands for accountability in public life, including education. In foreign and second language teaching, one of several consequences is the increasing importance attached to careful studies of learner needs as a prerequisite for effective course design.

Successful language learning is vital for refugees, immigrants, international students, those receiving education or vocational training through the medium of a second language in their own country, and individuals in occupations requiring advanced foreign language proficiency, among others. The combination of target language varieties, skills, lexicons, genres, registers, etc., that each of these and other groups needs varies greatly, however, meaning that language teaching using generic programs and materials, not designed with particular groups in mind, will be inefficient, at the very least, and in all probability, grossly inadequate. Just as no medical intervention would be prescribed before a thorough diagnosis of what ails the patient, so no language teaching program should be designed without a thorough needs analysis. Every language course should be considered a course for specific purposes, varying only (and considerably, to be sure) in the precision with which learner needs can be specified – from little or none in the case of programs for most young children to minute detail in the case of occupationally-, academically-, or vocationally-oriented programs for most adults.

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• By Michael H. Long
• Edited by Michael H. Long
• Book: Second Language Needs Analysis
• Online publication: 25 January 2010
• Chapter DOI: https://doi.org/10.1017/CBO9780511667299.001

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A Guide to Conducting a Training Needs Analysis [Free Template]

Want to improve employee efficiency and performance? Conducting a comprehensive training needs analysis might be your answer.

What is a training needs analysis?

Knowledge, Skills, and Abilities KSA refer to the knowledge, skills, and abilities that an employee must have to perform their responsibilities within their roles. They’re listed in the job description and guide candidates and employers to assess the person’s chance to succeed. Knowledge Topics and subjects that can be used when performing work functions when the person is hired. Examples: Knowledge of accounting principles and practices  Knowledge of budget control policies and procedures  Skills Technical or manual proficiencies are usually gained or learned through training. They are observable and measurable. Examples: Skills in analysis and problem-solving  Skills in using Microsoft Excel and accounting software  Abilities Capacity to apply knowledge and skills to perform a task. It also includes personal and social traits which are innate or acquired without formal training. Examples: Ability to process large amounts of numerical data Ability to prioritize work and meet deadlines

Training needs analysis levels

• Organizational level TNA – It determines training needs related to performance metrics, new employee knowledge at the company-wide level, and continuous training to optimize company performance and productivity to achieve its goals. It’s designed to address problems and weaknesses of the organization as well as to further improve the company’s current competencies and strengths. More importantly, it takes into account other factors like trends and changes in the economy, politics, technology, and demographics. 
• Group/job role level TNA – This type of analysis identifies specific training needed to upskill a team, department, or business unit. Moreover, it determines which occupational groups experience  skills gaps or discrepancies and ways to eliminate them. 
• Individual level TNA – This training needs assessment is dedicated to an individual or individuals in a team. It is conducted in conjunction with a project or changes that could impact each team member. It is also used for an employee’s personal development for future career advancement.

What is the purpose of conducting a training needs analysis?

• Aligning training with business goals – Alignment ensures that you’re investing in training that will help your organization achieve its business goals. Identifying the short and long-term objectives for your organization and the skills needed to achieve them helps L&D professionals to focus on the scope of the training. 
• Uncovering skills and performance gaps early on  – Performance gaps occur, for instance, when a business is undergoing change or new technologies emerge. As such, employees need to constantly upskill to acclimate to these changes. TNA allows organizations to resolve these gaps before they become a major issue. However, a study by PWC pointed out that only 40% of employers are upskilling their workers to address skills and labor shortages.
• Prioritizing training  – A TNA will help you determine which training you need to prioritize with respect to time and budget . “Training needs analysis is critical if you want to ensure you don’t waste resources, time, and energy,” notes Emily Chipman, executive coach and principal consultant at Rushman Consulting Solutions . “When done correctly, people learn more quickly, there is a greater impact on job performance, and it reduces the frustration that comes for employees when taking on new roles and tasks, thereby impacting employee engagement.”
• Planning targeted training   – You can create training plans that target exactly the skills and knowledge you identified are missing, so resources are invested properly.
• Determining who gets trained  – With TNA, you can make sure that specific people get trained on what they need. Customizing your training program based on your employees’ needs allows you to maximize the benefits of your training programs.

Training needs analysis best practices

• Start with the desired outcome. Identify which activities lead to these organizational outcomes before identifying training activities. This outcome can be an organizational or departmental goal. Or it could be an individual that needs improving.
• Manage expectations. Training and training need analysis requires advanced stakeholder management. Stakeholders include employees, service users (or customers), educational providers who design and deliver the program, and internal sponsors who pay for the educational event. Ensuring that the training satisfies all groups is crucial for its success. In other words, when a manager thinks a communication training session will solve all their internal problems, you need to manage their expectations.
• Use an integrated approach. Research shows that training programs that place new skills in a broader job or organizational perspective and integrate them with other organizational processes and activities are more successful. This does not mean that you cannot focus your training on something specific, but you must place what people learn into an organizational perspective.

How to conduct a training needs analysis

Step 1. Defining organizational goals

• an organization losing its innovative lead
• a sales department struggling to increase market share for a fast-growing scale-up
• the board has come up with an organizational capability that every employee must develop.
• Introducing new technology or processes that employees need to be trained on
• Trying to improve compliance or safety within the workplace
• Wanting to develop the skills of the organization’s workforce to prepare for future business opportunities or to stay competitive in the job market
• Financial performance
• Return on Equity
• Return on Capital Employed
• Earning growth
• Share price

Step 2. Define relevant job behaviors

Step 3. Define the required knowledge & skills

Training needs analysis techniques You can apply different training needs analysis techniques to map the required and available skills. Some common techniques include: Observations: Directly watching employees perform their duties to identify skills they possess, as well as gaps and areas for improvement. Questionnaires: Distributing structured surveys to employees to gather insights about their skills, perceived training needs and areas of interest. Interviews: Conducting one-on-one or group discussions with employees to explore their training needs, challenges, and suggestions for development opportunities. Assessments: Utilizing tests or simulations to evaluate employees’ current skill levels and identify specific areas where training is needed. Skills audits and skills inventories : Conducting comprehensive reviews of the skills and qualifications currently available within the organization to identify strengths, gaps, and areas for development, and inventorizing the data. Employee development plans : Identifying groups of employees with similar KSA. HRIS data mining and text mining CVs: Applying data and text mining techniques to HRIS data, resumes and CVs to uncover patterns, trends, and gaps in the workforce’s skills and qualifications. Text mining of job descriptions or job vacancy texts: Determining required competency levels per function. Job analysis : Breaking down jobs into their component tasks and determining the necessary skills and knowledge for each task.

Step 4. Develop training

• Define instructional goals and their alignment with organizational goals
• Determine the target audience
• Recognize behavioral outcomes, and
• Identify learning constraints.

Training needs analysis examples

1. email marketing executive example.

2. Organizational level training needs analysis example

• Increase quarterly sales performance by 15% to reverse the recent decline and improve overall revenue.
• Effective negotiation with clients to secure sales
• Accurate and persuasive presentation of product features and benefits
• Advanced negotiation techniques, including how to overcome objections and close deals
• Effective communication skills for presenting product features in a compelling way
• Up-to-date information on the latest product features and how they compare to competitors
• Understanding of customer needs and how the company’s products meet those needs
• The sales team requires training in advanced negotiation techniques and an in-depth product knowledge update, including competitive analysis.
• Organize a series of workshops on advanced negotiation skills led by an external expert
• Conduct product training sessions to update the team on the latest features, benefits, and competitive positioning

3. Hiring Manager example

Training needs analysis template.

Training needs analysis questions

• What problems are occurring in the organization?
• What is the organization trying to achieve?
• Which organizational goals require the biggest change in employee behaviors?
• Which departmental goals are lagging?
• Which individual performance goals should be improved?
• Can these problems be solved through different behaviors? 
• Which job behaviors contribute to the goals defined in step 1?
• If the listed job behaviors are ‘fixed’, does that bring us closer to the goals defined in step 1?
• Do the listed job behaviors align with our organizational core values?
• Which cultural cues reinforce undesirable behavior?
• What other influences play a role in reinforcing undesirable behavior?
• Which skills are required to display the behaviors we defined in step 2?
• Which knowledge components are required to display the behaviors we defined in step 2?
• Once the listed skills and knowledge components are taught, will the relevant job behaviors always be displayed?
• What is hindering the display of relevant job behaviors once the listed skills and knowledge components are taught?
• Is all the information required to start the training design and development process present?
• Are there non-training alternatives that we can deploy that will have a similar effect?

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• Open access
• Published: 29 July 2024

Predicting hospital length of stay using machine learning on a large open health dataset

• Raunak Jain 1 ,
• Mrityunjai Singh 1 ,
• A. Ravishankar Rao 2 &
• Rahul Garg 1  

BMC Health Services Research volume  24 , Article number:  860 ( 2024 ) Cite this article

227 Accesses

Metrics details

Governments worldwide are facing growing pressure to increase transparency, as citizens demand greater insight into decision-making processes and public spending. An example is the release of open healthcare data to researchers, as healthcare is one of the top economic sectors. Significant information systems development and computational experimentation are required to extract meaning and value from these datasets. We use a large open health dataset provided by the New York State Statewide Planning and Research Cooperative System (SPARCS) containing 2.3 million de-identified patient records. One of the fields in these records is a patient’s length of stay (LoS) in a hospital, which is crucial in estimating healthcare costs and planning hospital capacity for future needs. Hence it would be very beneficial for hospitals to be able to predict the LoS early. The area of machine learning offers a potential solution, which is the focus of the current paper.

We investigated multiple machine learning techniques including feature engineering, regression, and classification trees to predict the length of stay (LoS) of all the hospital procedures currently available in the dataset. Whereas many researchers focus on LoS prediction for a specific disease, a unique feature of our model is its ability to simultaneously handle 285 diagnosis codes from the Clinical Classification System (CCS). We focused on the interpretability and explainability of input features and the resulting models. We developed separate models for newborns and non-newborns.

The study yields promising results, demonstrating the effectiveness of machine learning in predicting LoS. The best R 2 scores achieved are noteworthy: 0.82 for newborns using linear regression and 0.43 for non-newborns using catboost regression. Focusing on cardiovascular disease refines the predictive capability, achieving an improved R 2 score of 0.62. The models not only demonstrate high performance but also provide understandable insights. For instance, birth-weight is employed for predicting LoS in newborns, while diagnostic-related group classification proves valuable for non-newborns.

Our study showcases the practical utility of machine learning models in predicting LoS during patient admittance. The emphasis on interpretability ensures that the models can be easily comprehended and replicated by other researchers. Healthcare stakeholders, including providers, administrators, and patients, stand to benefit significantly. The findings offer valuable insights for cost estimation and capacity planning, contributing to the overall enhancement of healthcare management and delivery.

Peer Review reports

Introduction

Democratic governments worldwide are placing an increasing importance on transparency, as this leads to better governance, market efficiency, improvement, and acceptance of government policies. This is highlighted by reports from the Organization for Economic Co-operation and Development (OECD) an international organization whose mission it is to shape policies that foster prosperity, equality, opportunity and well-being for all [ 1 ]. Openness and transparency have been recognized as pillars for democracy, and also for fostering sustainable development goals [ 2 ], which is a major focus of the United Nations ( https://sustainabledevelopment.un.org/sdg16 ).

An important government function is to provide for the healthcare needs of its citizens. The U.S. spends about $3.6 trillion a year on healthcare, which represents 18% of its GDP [ 3 ]. Other developed nations spend around 10% of their GDP on healthcare. The percentage of GDP spent on healthcare is rising as populations age. Consequently, research on healthcare expenditure and patient outcomes is crucial to maintain viable national economies. It is advantageous for nations to combine investigations by the private sector, government sector, non-profit agencies, and universities to find the best solutions. A promising path is to make health data open, which allows investigators from all sectors to participate and contribute their expertise. Though there are obvious patient privacy concerns, open health data has been made available by organizations such as New York State Statewide Planning and Research Cooperative System (SPARCS) [ 4 ].

Once the data is made available, it needs to be suitably processed to extract meaning and insights that will help healthcare providers and patients. We favor the creation and use of an open-source analytics system so that the entire research community can benefit from the effort [ 5 , 6 , 7 ]. As a concrete demonstration of the utility of our system and approach, we revealed that there is a growing incidence of mental health issues amongst adolescents in specific counties in New York State [ 8 ]. This has resulted in targeted interventions to address these problems in these communities [ 8 ]. Knowing where the problems lie allows policymakers and funding agencies to direct resources where needed.

Healthcare in the U.S. is largely provided through private insurance companies and it is difficult for patients to reliably understand what their expected healthcare costs are [ 9 , 10 ]. It is ironic that consumers can readily find prices of electronics items, books, clothes etc. online, but cannot find information about healthcare as easily. The availability of healthcare information including costs, incidence of diseases, and the expected length of stay for different procedures will allow consumers and patients to make better and more informed choices. For instance, in the U.S., patients can budget pre-tax contributions to health savings accounts, or decide when to opt for an elective surgery based on the expected duration of that procedure.

To achieve this capability, it is essential to have the underlying data and models that interpret the data. Our goal in this paper is twofold: (a) to demonstrate how to design an analytics system that works with open health data and (b) to apply it to a problem of interest to both healthcare providers and patients. Significant advances have been made recently in the fields of data mining, machine-learning and artificial intelligence, with growing applications in healthcare [ 11 ]. To make our work concrete, we use our machine-learning system to predict the length of stay (LoS) in hospitals given the patient information in the open healthcare data released by New York State SPARCS [ 4 ].

The LoS is an important variable in determining healthcare costs, as costs directly increase for longer stays. The analysis by Jones [ 12 ] shows that the trends in LoS, hospital bed capacity and population growth have to be carefully analyzed for capacity planning and to ensure that adequate healthcare can be provided in the future. With certain health conditions such as cardiovascular disease, the hospital LoS is expected to increase due to the aging of the population in many countries worldwide [ 13 ]. During the COVID-19 pandemic, hospital bed capacity became a critical issue [ 14 ], and many regions in the world experienced a shortage of healthcare resources. Hence it is desirable to have models that can predict the LoS for a variety of diseases from available patient data.

The LoS is usually unknown at the time a patient is admitted. Hence, the objective of our research is to investigate whether we can predict the patient LoS from variables collected at the time of admission. By building a predictive model through machine learning techniques, we demonstrate that it is possible to predict the LoS from data that includes the Clinical Classifications Software (CCS) diagnosis code, severity of illness, and the need for surgery. We investigate several analytics techniques including feature selection, feature encoding, feature engineering, model selection, and model training in order to thoroughly explore the choices that affect eventual model performance. By using a linear regression model, we obtain an R 2 value of 0.42 when we predict the LoS from a set of 23 patient features. The success of our model will be beneficial to healthcare providers and policymakers for capacity planning purposes and to understand how to control healthcare costs. Patients and consumers can also use our model to estimate the LoS for procedures they are undergoing or for planning elective surgeries.

Stone et al. [ 15 ] present a survey of techniques used to predict the LoS, which include statistical and arithmetic methods, intelligent data mining approaches and operations-research based methods. Lequertier et al. [ 16 ] surveyed methods for LoS prediction.

The main gap in the literature is that most methods focus on analyzing trends in the LoS or predicting the LoS only for specific conditions or restrict their analysis to data from specific hospitals. For instance, Sridhar et al. [ 17 ] created a model to predict the LoS for joint replacements in rural hospitals in the state of Montana by using a training set with 127 patients and a test set with 31 patients. In contrast, we have developed our model to predict the LoS for 285 different CCS diagnosis codes, over a set of 2.3 million patients over all hospitals in New York state. The CCS diagnosis code refers to the code used by the Clinical Classifications Software system, which encompasses 285 possible diagnosis and procedure categories [ 18 ]. Since the CCS diagnosis codes are too numerous to list, we give a few examples that we analyzed, including but not limited to abdominal hernia, acute myocardial infarction, acute renal failure, behavioral disorders, bladder cancer, Hodgkins disease, multiple sclerosis, multiple myeloma, schizophrenia, septicemia, and varicose veins. To the best of our knowledge, we are not aware of models that predict the LoS on such a variety of diagnosis codes, with a patient sample greater than 2 million records, and with freely available open data. Hence, our investigation is unique from this point of view.

Sotodeh et al. [ 19 ] developed a Markov model to predict the LoS in intensive care unit patients. Ma et al. [ 20 ] used decision tree methods to predict LoS in 11,206 patients with respiratory disease.

Burn et. al. examined trends in the LoS for patients undergoing hip-replacement and knee-replacement in the U.K. [ 21 ]. Their study demonstrated a steady decline in the LoS from 1997–2012. The purpose of their study was to determine factors that contributed to this decline, and they identified improved surgical techniques such as fast-track arthroplasty. However, they did not develop any machine-learning models to predict the LoS.

Hachesu et al. examined the LoS for cardiac disease patients [ 22 ] and found that blood pressure is an important predictor of LoS. Garcia et al. determined factors influencing the LoS for undergoing treatment for hip fracture [ 23 ]. B. Vekaria et al. analyzed the variability of LoS for COVID-19 patients [ 24 ]. Arjannikov et al. [ 25 ] used positive-unlabeled learning to develop a predictive model for LoS.

Gupta et al. [ 26 ] conducted a meta-analysis of previously published papers on the role of nutrition on the LoS of cancer patients, and found that nutrition status is especially important in predicting LoS for gastronintestinal cancer. Similarly, Almashrafi et al. [ 27 ] performed a meta-analysis of existing literature on cardiac patients and reviewed factors affecting their LoS. However, they did not develop quantitative models in their work. Kalgotra et al. [ 28 ] use recurrent neural networks to build a prediction model for LoS.

Daghistani et al. [ 13 ] developed a machine learning model to predict length of stay for cardiac patients. They used a database of 16,414 patient records and predicted the length of stay into three classes, consisting of short LoS (< 3 days), intermediate LoS ( 3–5 days) and long LoS (> 5 days). They used detailed patient information, including blood test results, blood pressure, and patient history including smoking habits. Such detailed information is not available in the much larger SPARCS dataset that we utilized in our study.

Awad et al. [ 29 ] provide a comprehensive review of various techniques to predict the LoS. Though simple statistical methods have been used in the past, they make assumptions that the LoS is normally distributed, whereas the LoS has an exponential distribution [ 29 ]. Consequently, it is preferable to use techniques that do not make assumptions about the distribution of the data. Candidate techniques include regression, classification and regression trees, random forests, and neural networks. Rather than using statistical parametric techniques that fit parameters to specific statistical distributions, we favor data-driven techniques that apply machine-learning.

In 2020, during the height of the COVID-19 pandemic, the Lancet, a premier medical journal drew widespread rebuke [ 30 , 31 , 32 ] for publishing a paper based on questionable data. Many medical journals published expressions of concern [ 33 , 34 ]. The Lancet itself retracted the questionable paper [ 35 ], which is available at [ 36 ] with the stamp “retracted” placed on all pages. One possible solution to prevent such incidents from occurring is for top medical journals to require authors to make their data available for verification by the scientific community. Patient privacy concerns can be mitigated by de-identifying the records made available, as is already done by the New York State SPARCS effort [ 4 ]. Our methodology and analytics system design will become more relevant in the future, as there is a desire to prevent a repetition of the Lancet debacle. Even before the Lancet incident, there was declining trust amongst the public related to medicine and healthcare policy [ 37 ]. This situation continues today, with multiple factors at play, including biased news reporting in mainstream media [ 38 ]. A desirable solution is to make these fields more transparent, by releasing data to the public and explaining the various decisions in terms that the public can understand. The research in this paper demonstrates how such a solution can be developed.

Requirements

We describe the following three requirements of an ideal system for processing open healthcare data

Utilize open-source platforms to permit easy replicability and reproducibility.

Create interpretable and explainable models.

Demonstrate an understanding of how the input features determine the outcomes of interest.

The first requirement captures the need for research to be easily reproduced by peers in the field. There is growing concern that scientific results are becoming hard for researchers to reproduce [ 39 , 40 , 41 ]. This undermines the validity of the research and ultimately hurts the fields. Baker termed this the “reproducibility crisis”, and performed an analysis of the top factors that lead to irreproducibility of research [ 39 ]. Two of the top factors consist of the unavailability of raw data and code.

The second requirement addresses the need for the machine-learning models to produce explanations of their results. Though deep-learning models are popular today, they have been criticized for functioning as black-boxes, and the precise working of the model is hard to discern. In the field of healthcare, it is more desirable to have models that can be explained easily [ 42 ]. Unless healthcare providers understand how a model works, they will be reluctant to apply it in their practice. For instance, Reyes et al. determined that interpretable Artificial Intelligence systems can be better verified, trusted, and adopted in radiology practice [ 43 ].

The third requirement shows that it is important for relevant patient features to be captured that can be related to the outcomes of interest, such as LoS, total cost, mortality rate etc. Furthermore, healthcare providers should be able to understand the influence of these features on the performance of the model [ 44 ]. This is especially critical when feature engineering methods are used to combine existing features and create new features.

In the subsequent sections, we present our design for a healthcare analytics system that satisfies these requirements. We apply this methodology to the specific problem of predicting the LoS.

We have designed the overall system architecture as shown in Fig.  1 . This system is built to handle any open data source. We have shown the New York SPARCS as one of the data sources for the sake of specificity. Our framework can be applied to data from multiple sources such as the Center for Medicare and Medicaid Services (CMS in the U.S.) as shown in our previous work [ 6 ]. We chose a Python-based framework that utilizes Pandas [ 45 ] and Scikit learn [ 46 ]. Python is currently the most popular programming language for engineering and system design applications [ 47 ].

Shows the system architecture. We use Python-based open-source tools such as Pandas and Scikit-Learn to implement the system

In Fig.  2 , we provide a detailed overview of the necessary processing stages. The specific algorithms used in each stage are described in the following sections.

Shows the processing stages in our analytics pipeline

Recent research has shown that it is highly desirable for machine learning models used in the healthcare domain to be explainable to healthcare providers and professionals [ 48 ]. Hence, we focused on the interpretability and explainability of input features in our dataset and the models we chose to explore. We restricted our investigation to models that are explainable, including regression models, multinomial logistic regression, random forests, and decision trees. We also developed separate models for newborns and non-newborns.

Brief description of the dataset

During our investigation, we utilized open-health data provided by the New York State SPARCS system. The data we accessed was from the year 2016, which was the most recent year available at the time. This data was provided in the form of a CSV file, containing 2,343,429 rows and 34 columns. Each row contains de-identified in-patient discharge information. The dataset columns contained various types of information. They included geographic descriptors related to the hospital where care was provided, demographic descriptors such as patient race, ethnicity, and age, medical descriptors such as the CCS diagnosis code, APR DRG code, severity of illness, and length of stay. Additionally, payment descriptors were present, which included information about the type of insurance, total charges, and total cost of the procedure.

Detailed descriptions of all the elements in the data can be found in [ 49 ]. The CCS diagnosis code has been described earlier. The term “DRG” stands for Diagnostic Related Group [ 49 ], which is used by the Center for Medicare and Medicaid services in the U.S. for reimbursement purposes [ 50 ].

The data includes all patients who underwent inpatient procedures at all New York State Hospitals [ 51 ]. The payment for the care can come from multiple sources: Department of Corrections, Federal/State/Local/Veterans Administration, Managed Care, Medicare, Medicaid, Miscellaneous, Private Health Insurance, and Self-Pay. The dataset sourced from the New York State SPARCS system, encompassing a wider patient population beyond Medicare/Medicaid, holds greater value compared to datasets exclusively composed of Medicare/Medicaid patients. For instance, Gilmore et al. analyzed only Medicare patients [ 52 ].

We examine the distribution of the LoS in the dataset, as shown in Fig.  3 . We note that the providers of the data have truncated the length of stay to 120 days. This explains the peak we see at the tail of the distribution.

Distribution of the length of stay in the dataset

Data pre-processing and cleaning

We identified 36,280 samples, comprising 1.55% of the data where there were missing values. These were discarded for further analysis. We removed samples which have Type of Admission = ‘Unknown’ (0.02% samples). So, the final data set has 2,306,668 samples. ‘Payment Typology 2’, and ‘Payment Typology 3’, have missing values (> = 50% samples), which were replaced by a ‘None’ string.

We note that approximately 10% of the dataset consists of rows representing newborns. We treat this group as a separate category. We found that the ‘Birth Weight’ feature had a zero value for non-newborn samples. Accordingly, to better use the ‘Birth Weight’ feature, we partitioned the data into two classes: newborns and non-newborns. This results in two classes of models, one for newborns and the second for all other patients. We removed the ‘Birth Weight’ feature in the input for the non-newborn samples as its value was zero for those samples.

The column ‘Total Costs’ (and in a similar way, ‘Total Charges’) are usually proportional to the LoS, and it would not be fair to use these variables to predict the LoS. Hence, we removed this column. We found that the columns 'Discharge Year', 'Abortion Edit Indicator'' are redundant for LoS prediction models, and we removed them. We also removed the columns ‘CCS Diagnosis Description’, ‘CCS Procedure Description’, ‘APR DRG Description’, ‘APR MDC Description’, and ‘APR Severity of Illness Description’ as we were given their corresponding numerical codes as features.

Since the focus of this paper is on the prediction of the LoS, we analyzed the distribution of LoS values in the dataset.

We developed regression models using all the LoS values, from 1–120. We also developed classification models where we discretized the LoS into specific bins. Since the distribution of LoS values is not uniform, and is heavily clustered around smaller values, we discretized the LoS into a small number of bins, e.g. 6 to 8 bins.

We utilized 10% of the data as a holdout test-set, which was not seen during the training phase. For the remaining 90% of the data, we used tenfold cross-validation in order to train the model and determine the best parameters to use.

Feature encoding

Many variables in the dataset are categorical, e.g., the variable “APR Severity of Illness Description” has the values in the set [Major, Minor, Moderate, Extreme]. We used distribution-dependent target encoding techniques and one-hot techniques to improve the model performance [ 53 ]. We replaced categorical data with the product of mean LoS and median LoS for a category value. The categorical feature can then better capture the dependence distribution of LoS with the value of the categorical feature.

For the linear regression model [ 54 ], we sampled a set of 6 categorical features, [‘Type of Admission’, ‘Patient Disposition’, ‘APR Severity of Illness Code’, ‘APR Medical Surgical Description’, ‘APR MDC Code’] which we target encoded with the mean of the LoS and the median of the LoS. We then one-hot encoded every feature (all features are categorical) and for each such one-hot encoded feature, created a new feature for each of the features in the sampled set, by replacing the ones in the one-hot encoded feature with the value of the corresponding feature in the sampled set. For example, we one-hot encoded ‘Operating Certificate Number’, and for samples where ‘Operating Certificate Number’ was 3, we created 6 features, each where samples having the value 3 were assigned the target encoded values of the sampled set features, and the other samples were assigned zero. We used such techniques to exploit the linear relation between LoS and each feature.

According to the sklearn documentation [ 55 ], a random forest regressor is “a meta estimator that fits a number of decision tree regressors on various sub-samples of the dataset and uses averaging to improve the predictive accuracy and control over-fitting”. The random forest regressor leverages ensemble learning based on many randomized decision trees to make accurate and robust predictions for regression problems. The averaging of many trees protects against single trees overfitting the training data.

The random forest classifier is also an ensemble learning technique and uses many randomized decision trees to make predictions for classification problems. The 'wisdom of crowds' concept suggests that the decision made by a larger group of people is typically better than an individual. The random forest classifier uses this intuition, and allows each decision tree to make a prediction. Finally, the most popular predicted class is chosen as the overall classification.

For the Random Forest Regressor [ 56 , 57 ] and Random Forest Classifier [ 58 ], we only used a similar distribution dependent target encoding as a random forest classifier/ regressor is unsuitable for sparse one-hot encoded columns.

Multinomial logistic regression is a type of regression analysis that predicts the probabilities of the different possible outcomes of a categorically distributed dependent variable, given a set of independent variables. It allows for more than two discrete outcomes, extending binomial logistic regression for binary classification to models with multiple class membership. For the multinomial logistic regression model [ 59 ], we used only one-hot encoding, and not target encoding, as the target value was categorical.

Finally, we experimented with combinations of target encoding and one-hot encoding. We can either use target encoding, or one-hot encoding, or both. When both encodings are employed, the dimensionality of the data increases to accommodate the one-hot encoded features. For each combination of encodings, we also experimented with different regression models including linear regression and random forest regression.

Feature importance, selection, and feature engineering

We experimented with different feature selection methods. Since the focus of our work is on developing interpretable and explainable models, we used SHAP analysis to determine relevant features.

We examine the importance of different features in the dataset. We used the SHAP value (Shapley Additive Explanations), a popular measure for feature importance [ 60 ]. Intuitively, the SHAP value measures the difference in model predictions when a feature is used versus omitted. It is captured by the following formula.

where \({{\varnothing }}_{i}\) is the SHAP value of feature \(i\) , \(p\) is the prediction by the model, n is the number of features and S is any set of features that does not include the feature \(i\) . The specific model we used for the prediction was the random forest regressor where we target-encoded all features with the product of the mean and the median of the LoS, since most of the features were categorical.

Classification models

One approach to the problem is to bin the LoS into different classes, and train a classifier to predict which class an input sample falls in. We binned the LoS into roughly balanced classes as follows: 1 day, 2 days, 3 days, 4–6 days, > 6 days. This strategy is based on the distribution of the LoS as shown earlier in Figs.  3 and  4 .

A density plot of the distribution of the length of stay. The area under the curve is 1. We used a kernel density estimation with a Gaussian kernel [ 61 ] to generate the plot

We used three different classification models, comprising the following:

Multinomial Logistic Regression

Random Forest Classifier

CatBoost classifier [ 62 ].

We used a Multinomial Logistic Regression model [ 59 ] trained and tested using tenfold cross validation to classify the LoS into one of the bins. The multinomial logistic regression model is capable of providing explainable results, which is part of the requirements. We used the feature engineering techniques described in the previous section.

We used a Random Forest Classifier model trained and tested using tenfold cross validation to classify the LoS into one of the bins. We used a maximum depth of 10 so as to get explainable insights into the model.

Finally, we used a CatBoost Classifier model trained and tested using tenfold cross validation to classify the LoS into one of the bins.

Regression models

We used three different regression models with the feature engineering techniques mentioned above ( Feature encoding section). These comprise:

Linear regression

Catboost regression

Random forest regression

The linear regression was implemented using the nn.Linear() function in the open source library PyTorch [ 63 ]. We used the ‘Adam’ optimization algorithm [ 64 ] in mini-batch settings to train the model weights for linear regression.

We investigated CatBoost regression in order to create models with minimal feature sets, whereby models with a low number of input features would provide adequate results. Accordingly, we trained a CatBoost Regressor [ 65 ] in order to determine the relationship between combinations of features and the prediction accuracy as determined by the R 2 correlation score.

The random forest regression was implemented using the function RandomForestRegressor() in scikit learn [ 55 ].

Model performance measures

For the regression models, we used the following metrics to compare the model performance.

The R 2 score and the p -value. We use a significance level of α = 0.05 (5 %) for our statistical tests.  If the p -value is small, i.e. less than α = 0.05, then the R 2 score is statistically significant.

For classifier models, we used the following metrics to compare the model performance.

True positive rate, false negative rate, and F1 score [ 66 ].

We computed the Brier score using Brier’s original calculation in his paper [ 67 ]. In this formulation, for R classes the Brier score B can vary between 0 and R, with 0 being the best score possible.

where \({\widehat{y}}_{i,c}\) is the class probability as per the model and \({I}_{i,c}=1\) if the i th sample belongs to class c and \({I}_{i,c}=0\) if it does not belong to class c .

We used the Delong test [ 68 ] to compare the AUC for different classifiers.

These metrics will allow other researchers to replicate our study and provide benchmarks for future improvements.

In this section we present the results of applying the techniques in the Methods section.

Descriptive statistics

We provide descriptive statistics that help the reader understand the distributions of the variables of interest.

Table 1 summarizes basic statistical properties of the LoS variable.

Figure  5 shows the distribution of the LoS variable for newborns.

This figure depicts the distribution of the LoS variable for newborns

Table 2 shows the top 20 APR DRG descriptions based on their frequency of occurrence in the dataset.

Figure  6 shows the distribution of the LoS variable for the top 20 most frequently occurring APR DRG descriptions shown in Table  2 .

A 3-d plot showing the distribution of the LoS for the top-20 most frequently occuring APR DRG descriptions. The x-axis (horizontal) depicts the LoS, the y-axis shows the APR DRG codes and the z-axis shows the density or frequency of occurrence of the LoS

We experimented with different encoding schemes for the categorical variables and for each encoding we examined different regression techniques. Our results are shown in Table 3 . We experimented with the three encoding schemes shown in the first column. The last row in the table shows a combination of one-hot encoding and target encoding, where the number of columns in the dataset are increased to accommodate one-hot encoded feature values for categorical variables.

Feature importance, selection and feature engineering

We obtained the SHAP plots using a Random Forest Regressor trained with target-encoded features.

Figures  7  and 8 show the SHAP values plots obtained for the features in the newborn partition of the dataset. We find that the features, “APR DRG Code”, “APR Severity of Illness Code”, “Patient Disposition”, “CCS Procedure Code”, are very useful in predicting the LoS. For instance, high feature values for “APR Severity of Illness Code”, which are encoded by red dots have higher SHAP values than the blue dots, which correspond to low feature values.

SHAP Value plot for newborns

1-D SHAP plot, in order of decreasing feature importance: top to bottom (for non-newborns)

A similar interpretation can be applied to the features in the non-newborn partition of the dataset. We note that “Operating Certificate Number” is among the top-10 most important features in both the newborn and non-newborn partitions. This finding is discussed in the Discussion section.

From Fig.  9 , we observe that as the severity of illness code increases from 1–4, there is a corresponding increase in the SHAP values.

A 2-D plot showing the relationship between SHAP values for one feature, “APR Severity of Illness Code”, and the feature values themselves (non-newborns)

To further understand the relationship between the APR Severity of Illness code and the LoS, we created the plot in Fig.  10 . This shows that the most frequently occurring APR Severity of Illness code is 1 (Minor), and that the most frequently occurring LoS is 2 days. We provide this 2-D projection of the overall distribution of the multi-dimensional data as a way of understanding the relationship between the input features and the target variable, LoS.

A density plot showing the relationship between APR Severity of Illness Code and the LoS. The color scale on the right determines the interpretation of colors in the plot. We used a kernel density estimation with a Gaussian kernel [ 61 ] to generate the plot

Similarly, Fig.  11 shows the relationship between the birth weight and the length of stay. The most common length of stay is two days.

A density plot showing the distribution of the birth weight values (in grams) versus the LoS. The colorbar on the right shows the interpretation of color values shown in the plot. We used a kernel density estimation with a Gaussian kernel [ 61 ] to generate the plot

Classification

We obtained a classification accuracy of 46.98% using Multinomial Logistic Regression with tenfold cross-validation in the 5-class classification task for non-newborn cases. The confusion matrix in Fig.  12 shows that the highest density of correctly classified samples is in or close to the diagonal region. The regions where out model fails occurs between adjacent classes as can be inferred from the given confusion matrix.

Confusion matrix for classification of non-newborns. The number inside each square along the diagonal represents the number of correctly classified samples. The color is coded so lighter colors represent lower numbers

For the newborn cases, we obtained a classification accuracy of 60.08% using Random Forest Classification model with tenfold cross-validation in the 5-class classification task. The confusion matrix in Fig.  13 shows that the majority of data samples lie in or close to the diagonal region. The regions where our model does not do well occurs between adjacent classes as can be inferred from the given confusion matrix,

Confusion matrix for classification of newborns. The number inside each square along the diagonal represents the number of correctly classified samples. The color is coded so lighter colors represent lower numbers

The density plot in Fig.  14 shows the relationship between the actual LoS and the predicted LoS. For a LoS of 2 days, the centroid of the predicted LoS cluster is between 2 and 3 days.

Shows the density plot of the predicted length of stay versus actual length of stay for the classifier model for non-newborns. We used a kernel density estimation with a Gaussian kernel [ 61 ] to generate the plot

A quantitative depiction of our model errors is shown in Fig.  15 . The values in Fig.  15 are interpreted as follows. Referring to the column for LoS = 2, the top row shows that 51% of the predicted LoS values for an actual stay of 2 days is also 2 days (zero error), and that 23% of the predicted values for LoS equal to 2 days have an error of 1 day and so on. The relatively high values in the top row indicates that the model is performing well, with an error of less than 1 day. There are relatively few instances of errors between 2 and 3 days (typically less than 10% of the values show up in this row). The only exception is for the class corresponding to LoS great than 8 days. The truncation of the data to produce this class results in larger model errors specifically for this class.

Shows the distribution of correctly predicted LoS values for each class used in our model. Along the columns, we depict the different classes used in the model, consisting of LoS equal to 1, 2, 3 …8, and more than 8. Each row depicts different errors made in the prediction. For instance, the top row depicts an error of less than or equal to one day between the actual LoS and the predicted Los. The second row from the top depicts an error which is greater than 1 and less than or equal 2 days. And so on for the other rows, for non-newborns

Figures  16 and 17 show the scatter plots for the linear regression models. The exact line represents a line with slope 1, and a perfect model would be one that produced all points lying on this line.

Scatter plot showing an instance of a linear regression fit to the data (newborns). The R 2 score is 0.82. The blue line represents an exact fit, where the predicted LoS equals the actual LoS (slope of the line is 1)

Scatter plot for linear regression. (non-newborns). The R 2 score is 0.42. The blue line represents an exact fit, where the predicted LoS equals the actual LoS (slope of the line is 1)

Figure  18 shows a density plot depicting the relationship between the predicted length of stay and the actual length of stay.

Shows the density plot of the predicted length of stay versus actual length of stay for the classifier model for non-newborns. We used a kernel density estimation with a Gaussian kernel [ 40 ] to generate the plot. The best fit regression line to our predictions is shown in green, whereas the blue line represents the ideal fit (line of slope 1, where actual LoS and predicted LoS are equal)

Most of the existing literature on LoS stay prediction is based on data for specific disease conditions such as cancer or cardiac disease. Hence, in order to understand which CCS diagnosis codes produce good model fits, we produced the plot in Fig.  19 .

This figure shows the three CCS diagnosis codes that produced the top three R 2 scores using linear regression. These are 101, 100 and 109. The three CCS Diagnosis codes that produced the lowest R 2 scores are 159, 657, and 659

We provide the following descriptions in Tables  4  and 5 for the 3 CCS Diagnosis Codes in Fig.  19 with the top R 2 Scores using linear regression.

Similarly, the following table shows the 3 CCS Diagnosis Codes in Fig.  19 for the lowest R 2 Scores using linear regression.

Models with minimal feature sets

We trained a CatBoost Regressor [ 65 ] on the complete dataset in order to determine the relationship between combinations of features and the prediction accuracy as determined by the R 2 correlation score. This is shown in Fig.  20

The labels for each row on the left show combinations of different input features. A CatBoost regression model was developed using the selected combination of features. The R 2 correlation scores for each model is shown in the bar graph

We can infer from Fig.  20 that only four features (‘'APR MDC Code', 'APR Severity of Illness Code', 'APR DRG Code', 'Patient Disposition') are sufficient for the model to reach very close to its maximum performance. We obtain similar concurring results when using other regression models for the same experiment.

Classification trees

We used a random forest tree approach to generate the trees in Figs.  21 and 22 .

A random forest tree that represents a best-fit model to the data for newborns. With 4 levels of the decision tree, the R 2 score is 0.65

A random forest tree using only a tree of depth 3 that represents a best-fit model to the data for non-newborns. The R 2 score is 0.28. We can generate trees with greater depth that better fit the data, but we have shown only a depth of 3 for the sake of readability in the printed version of this paper. Otherwise, the tree would be too large to be legible on this page. The main point in this figure is to showcase the ease of interpretation of the working of the model through rules

We used tenfold cross validation to determine the regression scores. The results are summarized in Tables  6 and 7 .

We computed the multi-class classifier metrics for logistic regression, using one-hot encoding for non-newborns. The results are presented in Table  8 . The first row represents the accuracy of the classifier when Class 0 is compared against the rest of the classes. A similar interpretation applies to the other rows in the table, ie one-versus-rest. The macro average gives the balanced recall and precision, and the resulting F1 score. The weighted average gives a support (number of samples) weighted average of the individual class metric. The overall accuracy is computed by dividing the total number of accurate predictions, which is 49,686 out of a total number of 105,932 samples, which yields a value of 0.47.

For the category of non-newborns, Fig.  23  provides a graphical plot that visualizes the ROC curves for the different multiclass classifiers we developed.

This figure applies to data concerning non-newborns. We show the multiclass ROC curves for the performance of the catboost classifier for the different classes shown. The area under the ROC curve is 0.7844

In Table  9 we compare the performance of our multiclass classifier using logistic regression developed on 2016 SPARCS data against 2017 SPARCS data.

In order to compare the performance of the different classifiers, we computed the AUC measures reported in Table  10 . Figure 24 visualizes the data in Table 10 and Fig. 25 visualizes the data in Table 11 . In Tables 12 and 13 we report the results of computing the Delong test for non-newborns and newborns respectively. In Tables 14 and 15 we report the results of computing the Brier scores for non-new borns and newborns respectively.

A bar chart that depicts the data in Table  10 for non-newborns

A bar chart that depicts the data in Table  11

Model parameters

In Table  16 we present the parameter and hyperparameter values used in the different models.

Additional results shown in the Appendix/Supplementary material

Due to space restrictions, we show additional results in the Appendix/Supplementary Material. These results are in tabular form and describe the R 2 scores for different segmentations of the variables in the dataset, e.g. according to age group, severity of illness code, etc.

The most significant result we obtain is shown in Figs.  21 and 22 , which provides an interpretable working of the decision trees using random forest modeling. Figure  21 for newborns shows that the birth weight features prominently in the decision tree, occurring at the root node. Low birth weights are represented on the left side of the tree and are typically associated with longer hospital stays. Higher birth weights occur on the right side of the tree, and the node in the bottom row with 189,574 samples shows that the most frequently occurring predicted stay is 2.66 days. Figure  22 for non-newborns shows that the features of “APR DRG Code”, “APR Severity of Illness Code” and “Patient Disposition” are the most important top-level features to predict the LoS. This provides a relatively simple rule-based model, which can be easily interpreted by healthcare providers as well as patients. For instance, the right-most branch of the tree classifies the input data into a relatively high LoS (46 days) when the branch conditions APR DRG Code is greater than 813.55 and the APR Severity of Illness Code is less than 91.

The results in Fig.  19 and Table  4 show that if we restrict our model to specific CCS Diagnosis descriptions such as “coronary atherosclerosis and other heart disease”, we obtain a good R 2 Score of 0.62. The objective of our work is not to cherry-pick CCS Diagnosis codes that produce good results, but rather to develop a single model for the entire SPARCS dataset to obtain a birds-eye perspective. For future work, we can explicitly build separate models for each CCS Diagnosis code, and that could have relevance to specific medical specialties, such as cardiovascular care.

Similarly, the results in Fig.  19 and Table  5 show that there are CCS Diagnosis codes corresponding to schizophrenia and mood disorders that produce a poor model fit. Factors that contribute to this include the type of data in the SPARCS dataset, where information about patient vitals, medications, or a patient’s income level is not provided, and the inherent variability in treating schizophrenia and mood disorders. Baeza et al. [ 69 ] identified several variables that affect the LoS in psychiatric patients, which include psychiatric admissions in the previous years, psychiatric rating scale scores, history of attempted suicide, and not having sufficient income. Such variables are not provided in the SPARCS dataset. Hence a policy implication is to collect and make such data available, perhaps as a separate dataset focused on mental health issues, which have proven challenging to treat.

Figures  16 and 17 show that a better regression fit is obtained when a specific CCS Diagnosis code is used to build the model, such as “Newborn” in Fig.  16 . To put these results in context, we note that it is difficult to obtain a high R 2 value for healthcare datasets in general, and especially for large numbers of patient samples that span multiple hospitals. For instance, Bertsimas [ 70 ] reported an R 2 value of 0.2 and Kshirsagar [ 71 ] reported an R 2 value of 0.33 for similar types of prediction problems as studied in this paper.

Further details for a segmentation of R 2 scores by the different variable categories are shown in the Appendix/Supplementary Material section. For instance, the table corresponding to Age Groups shows that there is close agreement between the mean of the predicted LoS from our model and the actual LoS. Furthermore, the mean LoS increases steadily from 4.8 days for Age group 0–17 to 6.4 days for ages 70 or older. A discussion of these tables is outside the scope of this paper. However, they are being provided to help other researchers form hypotheses for further investigations or to find supporting evidence for ongoing research.

Table 3 shows that the best encoding scheme is to combine target encoding with one-hot encoding and then apply linear regression. This produces an R 2 score of 0.42 for the non-newborn data, which is the best fit we could obtain. This table also shows that significant improvements can be obtained by exploring the search space which consists of different strategies of feature encoding and regression methods. There is no theoretical framework which determines the optimum choice, and the best method is to conduct an experimental search. An important contribution of the current paper is to explore this search space so that other researchers can use and build upon our methodology.

The distribution of errors in Fig.  15 shows that the truncation we employed at a LoS of 8 days produces artifacts in the prediction model as all stays of greater than 8 days are lumped into one class. Nevertheless, the distribution of LoS values in Fig.  4 shows that a relatively small number of data samples have LoS greater than 8 days. In the future, we will investigate different truncation levels, and this is outside the scope of the current paper. By using our methodology, the truncation level can also be tuned by practitioners in the field, including hospital administrators and other researchers.

Our results in Fig.  7 show that certain features are not useful in predicting the LoS. The SHAP plot shows that features such as race, gender, and ethnicity are not useful in predicting the LoS. It would have been interesting if this were not the case, as that implies that there is systemic bias based on race, gender or ethnicity. For instance, a person with a given race may have a smaller LoS based on their demographic identity. This would be unacceptable in the medical field. It is satisfying to see that a large and detailed healthcare dataset does not show evidence of bias.

To place this finding in context, racial bias is an important area of research in the U.S., especially in fields such as criminology and access to financial services such as loans. In the U.S., it is well known that there is a disproportional imprisonment of black and Hispanic males [ 72 ]. Researchers working on criminal justice have determined that there is racial bias in the process of sentencing and granting parole, with blacks being adversely affected [ 73 ]. This bias is reinforced through any algorithms that are trained on the underlying data. There is evidence that banks discriminate against applicants for loans based on their race or gender [ 74 ].

This does not appear to be the case in our analysis of the SPARCS data. Though we did not specifically investigate the issue of racial bias in the LoS, the feature analysis we conducted automatically provides relevant answers. Other researchers including those in the U.K [ 21 ] have also determined that gender does not have an effect on LoS or costs. Hence the results in the current paper are consistent with the findings of other researchers in other countries working on entirely different datasets.

From Table  6 we see that in the case of data concerning non-newborns, the catboost regression performs the best, with an R 2 score of 0.432. The p -value is less than 0.01, indicating that the correlation between the actual and predicted values of LoS through catboost regression is statistically significant. Similarly, the p -values for linear regression and random forest regression indicate that these models produce predictions that are statistically significant, i.e. they did not occur by random chance.

From Table  7 that refers to data from newborns, the linear regression performs the best, with an R 2 score of 0.82. The p -value is less than 0.01, indicating that the correlation between the actual and predicted values of LoS through linear regression is statistically significant. Similarly, the p -values for random forest regression and catboost regression indicate that these models produce predictions that are statistically significant.

We examine the performance of classifiers on non-newborn data, as shown in Tables  10 and 12 . The Delong test conducted in Table  12 shows that there is a statistically significant difference between the AUCs of the pairwise comparisons of the models. Hence, we conclude that the catboost classifier performs the best with an average AUC of 0.7844. We also note that there is a marginal improvement in performance when we use the catboost classifier instead of the random forest classifier. Both the catboost classifier and the random forest classifier perform better than logistic regression. We conclude that the best performing model for non-newborns is the catboost classifier, followed by the random forest classifier, and then logistic regression.

In the case of newborn data, we examine the performance of the classifiers as shown in Tables  11 and 13 . From Table 13 , we note that the p -values in all the rows are less than 0.05, except for the binary class “one vs. rest for class 3”, random forests vs. catboost. Hence, for this particular comparison between the random forest classifier and the catboost classifier for “one vs. rest for class 3”, we cannot conclude that there is a statistically significant difference between the performance of these two classifiers. From Table  11 we observe that the AUCs of these two classifiers are very similar. We also note that only about 10% of the dataset consists of newborn cases.

From Table  14 we note that the Brier score for the catboost classifier is the lowest. A lower Brier score indicates better performance. According to the Brier scores for the non-newborn data, the catboost classifier performs the best, followed by the random forest classifier and then logistic regression. Table 15 shows that for newborns, the random forest classifier performs the best, followed by the catboost classifier and logistic regression. The performance of the random forest classifier and catboost classifier are very similar.

From a practical perspective, it may make sense to use a catboost classifier on both newborn and non-newborn data as it simplifies the processing pipeline. The ultimate decision rests with the administrators and implementers of these decision systems in the hospital environment.

Burn et al. observe [ 21 ] that though the U.S. has reported similar declines in LoS as in the U.K, the overall costs of joint replacement have risen. The U.K. government created policies to encourage the formation of specialist centers for joint replacement, which have resulted in reduction in the LoS as well as delivering cost reductions. The results and analysis presented in our current paper can help educate patients and healthcare consumers about trends in healthcare costs and how they can be reduced. An informed and educated electorate can press their elected representatives to make changes to the healthcare system to benefit the populace.

Hachesu et al. examined the LoS for cardiac disease patients [ 22 ] where they used data from around 5000 patients and considered 35 input variables to build a predictive model. They found that the LoS was longer in patients with high blood pressure. In contrast, our method uses data from 2.5 million patients and considers multiple disease conditions simultaneously. We also do not have access to patient vitals such as blood pressure measurements, due to the limitation of the existing New York State SPARCS data.

Garcia et al. [ 23 ] conducted a study of elderly patients (age greater than 60) to understand factors governing the LoS for hip fracture treatment. They used 660 patient records and determined that the most significant variable was the American Society of Anesthesiologists (ASA) classification system. The ASA score ranges from 1–5 and captures the anesthesiologist’s impression of a patient’s health and comorbidities at the time of surgery. Garcia et al. showed a monotonically increasing relationship between the ASA score and the LoS. However, they did not build a specific predictive model. Their work shows that it is possible to find single variables with significant information content in order to estimate the LoS. The New York SPARCS dataset that we used does not contain the ASA score. Hence a policy implication of our research is to alert the healthcare authorities include such variables such as the ASA score where relevant in the datasets released in the future. The additional storage required is very small (one additional byte per patient record).

Arjannikov et al. [ 25 ] developed predictive models by binarizing the data into two categories, e.g. LoS <  = 2 days or LoS > 2 days. In our work, we did not employ such a discretization. In contrast, we used continuous regression techniques as well as classification into more than two bins. It is preferable to stay as close to the actual data as possible.

Almashrafi et al. [ 27 ] and Cots et al. [ 75 ] observed that larger hospitals tended to have longer LoS for patients undergoing cardiac surgery. Though we did not specifically examine cardiac surgery outcomes, our feature analysis indicated that the hospital operating certificate number had lower relevance than other features such as DRG codes. Nevertheless, the SHAP plots in Fig.  7 and Fig.  8 show that the hospital operating certificate number occurs within the top 10 features in order of SHAP values. We will investigate this relationship in more detail in future research, as it requires determining the size of the hospital from the operating certificate number and creating an appropriate machine-learning model. The Appendix contains results that show certain operating certificate numbers that produce a good model fit to the data.

A major focus of our research is on building interpretable and explainable models. Based on the principle of parsimony, it is preferable to utilize models which involve fewer features. This will provide simpler explanations to healthcare professionals as well as patients. We have shown through Fig.  20 that a model with five features performs just as well as a model with seven features. These features also make intuitive sense and the model’s operation can be understood by both patients and healthcare providers.

Patients in the U.S. increasingly have to pay for medical procedures out-of-pocket as insurance payments do not cover all the expenses, leading to unexpectedly large bills [ 76 ]. Many patients also do not possess health insurance in the U.S., with the consequence that they get charged the highest [ 77 ]. Kullgreen et.al. observe that patients in the U.S. need to be discerning healthcare consumers [ 78 ], as they can optimize the value they receive from out-of-pocket spending. In addition to estimating the cost of medical procedures, patients will also benefit from estimating the expected duration for a procedure such as joint replacement. This will allow them to budget adequate time for their medical procedures. Patients and consumers will benefit from obtaining estimates from an unbiased open data source such as New York State SPARCS and the use of our model.

Other researchers have developed specific LoS models for particular health conditions, such as cardiac disease [ 22 ], hip replacement [ 21 ], cancer [ 26 ], or COVID-19 [ 24 ]. In addition, researchers typically assume a prior statistical distribution for the outcomes, such a Weibull distribution [ 24 ]. However, we have not made any assumptions of specific prior statistical distributions, nor have we restricted our analysis to specific diseases. Consequently, our model and techniques should be more widely applicable, especially in the face of rapidly changing disease trajectories worldwide.

Our study is based exclusively on freely available open health data. Consequently, we cannot control the granularity of the data and must use the data as-is. We are unable to obtain more detailed patient information such as their physiological variables such as blood pressure, heartrate variability etc. at the time of admittance and during their stay. Hospitals, healthcare providers, and insurers have access to this data. However, there is no mandate for them to make this available to researchers outside their own organizations. Sometimes they sell de-identified data to interested parties such as pharmaceutical companies [ 79 ]. Due to the high costs involved in purchasing this data, researchers worldwide, especially in developing countries are at a disadvantage in developing AI algorithms for healthcare.

There is growing recognition that medical researchers need to standardize data formats and tools used for their analysis, and share them openly. One such effort is the organization for Observational Health Data Sciences and Informatics (OHDSI) as described in [ 80 ].

Twitter has demonstrated an interesting path forward, where a small percentage of its data was made available freely to all users for non-commercial purposes through an API [ 81 ]. Recently, Twitter has made a larger proportion of its data available to qualified academic researchers [ 82 ]. In the future, the profit motives of companies need to be balanced with considerations for the greater public good. An advantage of using the Twitter model is that it spurs more academic research and allows universities to train students and the workforce of the future on real-world and relevant datasets.

In the U.S., a new law went into effect in January 2021 requiring hospitals to make pricing data available publicly. The premise is that having this data would provide better transparency into the working of the healthcare system in the U.S. and lead to cost efficiencies. However, most hospitals are not in compliance with this law [ 83 ]. Concerted efforts by government officials as well as pressure by the public will be necessary to achieve compliance. If the eventual release of such data is not accompanied by a corresponding interest shown by academicians, healthcare researchers, policymakers, and the public it is likely that the very premise of the utility of this data will be called into question. Furthermore, merely dumping large quantities of data into the public domain is unlikely to benefit anyone. Hence research efforts such as the one presented in this paper will be valuable in demonstrating the utility of this data to all stakeholders.

Our machine-learning pipeline can easily be applied to new data that will be released periodically by New York SPARCS, and also to hospital pricing data [ 83 ]. Due to our open-source methodology, other researchers can easily extend our work and apply it to extract meaning from open health data. This improves reproducibility, which is an essential aspect of science. We will make our code available on Github to interested researchers for non-commercial purposes.

Limitations of our models

Our models are restricted to the data available through New York State SPARCS, which does not provide detailed information about patient vitals. More detailed physiological data is available through the Multiparameter Intelligent Monitoring in Intensive Care (MIMIC) framework [ 84 ], though for a smaller number of patients. We plan to extend our methodology to handle such data in the future. Another limitation of our study is that it does not account for patient co-morbidities. This arises from the de-identification process used to release the SPARCS data, where patient information is removed. Hence we are unable to analyze multiple hospital admissions for a given patient, possibly for different conditions. The main advantage of our approach is that it uses large-scale population data (2.3 million patients) but at a coarse level of granularity, where physiological data is not available. Nevertheless, our approach provides a high-level view of the operation of the healthcare system, which provides valuable insights.

There is growing interest in using data analytics to increase government transparency and inform policymaking. It is expected that the meaning and insights gained from such evidence-based analysis will translate to better policies and optimal usage of the available infrastructure. This requires cooperation between computer scientists, domain experts, and policy makers. Open healthcare data is especially valuable in this context due to its economic significance. This paper presents an open-source analytics system to conduct evidence-based analysis on openly available healthcare data.

The goal is to develop interpretable machine learning models that identify key drivers and make accurate predictions related to healthcare costs and utilization. Such models can provide actionable insights to guide healthcare administrators and policy makers. A specific illustration is provided via a robust machine learning pipeline that predicts hospital length of stay across 285 disease categories based on 2.3 million de-identified patient records. The length of stay is directly related to costs.

We focused on the interpretability and explainability of input features and the resulting models. Hence, we developed separate models for newborns and non-newborns, given differences in input features. The best performing model for non-newborn data was catboost regression, which used linear regression and achieved an R 2 score of 0.43. The best performing model for newborns and non-newborns respectively was linear regression, which achieved an R 2 score of 0.82. Key newborn predictors included birth weight, while non-newborn models relied heavily on the diagnostic related group classification. This demonstrates model interpretability, which is important for adoption. There is an opportunity to further improve performance for specific diseases. If we restrict our analysis to cardiovascular disease, we obtain an improved R 2 score of 0.62.

The presented approach has several desirable qualities. Firstly, transparency and reproducibility are enabled through the open-source methodology. Secondly, the model generalizability facilitates insights across numerous disease states. Thirdly, the technical framework can easily integrate new data while allowing modular extensions by the research community. Lastly, the evidence generated can readily inform multiple key stakeholders including healthcare administrators planning capacity, policy makers optimizing delivery, and patients making medical decisions.

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Acknowledgements

We are grateful to the New York State SPARCS program for making the data available freely to the public. We greatly appreciate the feedback provided by the anonymous reviewers which helped in improving the quality of this manuscript.

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Jain, R., Singh, M., Rao, A.R. et al. Predicting hospital length of stay using machine learning on a large open health dataset. BMC Health Serv Res 24 , 860 (2024). https://doi.org/10.1186/s12913-024-11238-y

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100% Clean Electricity by 2035 Study

An NREL study shows there are multiple pathways to 100% clean electricity by 2035 that would produce significant benefits exceeding the additional power system costs.

For the study, funded by the U.S. Department of Energy’s Office of Energy Efficiency and Renewable Energy, NREL modeled technology deployment, costs, benefits, and challenges to decarbonize the U.S. power sector by 2035, evaluating a range of future scenarios to achieve a net-zero power grid by 2035.

The exact technology mix and costs will be determined by research and development, among other factors, over the next decade. The results are published in Examining Supply-Side Options To Achieve 100% Clean Electricity by 2035 .

Scenario Approach

To examine what it would take to achieve a net-zero U.S. power grid by 2035, NREL leveraged decades of research on high-renewable power systems, from the Renewable Electricity Futures Study , to the Storage Futures Study , to the Los Angeles 100% Renewable Energy Study , to the Electrification Futures Study , and more.

NREL used its publicly available flagship  Regional Energy Deployment System   capacity expansion model to study supply-side scenarios representing a range of possible pathways to a net-zero power grid by 2035—from the most to the least optimistic availability and costs of technologies.

The scenarios apply a carbon constraint to:

• Achieve 100% clean electricity by 2035 under accelerated demand electrification
• Reduce economywide, energy-related emissions by 62% in 2035 relative to 2005 levels—a steppingstone to economywide decarbonization by 2050.

For each scenario, NREL modeled the least-cost option to maintain safe and reliable power during all hours of the year.

Key Findings

Technology deployment must rapidly scale up.

In all modeled scenarios, new clean energy technologies are deployed at an unprecedented scale and rate to achieve 100% clean electricity by 2035. As modeled, wind and solar energy provide 60%–80% of generation in the least-cost electricity mix in 2035, and the overall generation capacity grows to roughly three times the 2020 level by 2035—including a combined 2 terawatts of wind and solar.

To achieve those levels would require rapid and sustained growth in installations of solar and wind generation capacity. If there are challenges with siting and land use to be able to deploy this new generation capacity and associated transmission, nuclear capacity helps make up the difference and more than doubles today’s installed capacity by 2035.

Across the four scenarios, 5–8 gigawatts of new hydropower and 3–5 gigawatts of new geothermal capacity are also deployed by 2035. Diurnal storage (2–12 hours of capacity) also increases across all scenarios, with 120–350 gigawatts deployed by 2035 to ensure demand for electricity is met during all hours of the year.

Seasonal storage becomes important when clean electricity makes up about 80%–95% of generation and there is a multiday to seasonal mismatch of variable renewable supply and demand. Across the scenarios, seasonal capacity in 2035 ranges about 100–680 gigawatts.

Significant additional research is needed to understand the manufacturing and supply chain associated with the unprecedent deployment envisioned in the scenarios.

Significant Additional Transmission Capacity

In all scenarios, significant transmission is also added in many locations, mostly to deliver energy from wind-rich regions to major load centers in the eastern United States. As modeled, the total transmission capacity in 2035 is one to almost three times today’s capacity, which would require between 1,400 and 10,100 miles of new high-capacity lines per year, assuming new construction starts in 2026.

Climate and Health Benefits of Decarbonization Offset the Costs

NREL finds in all modeled scenarios the health and climate benefits associated with fewer emissions offset the power system costs to get to 100% clean electricity.

Decarbonizing the power grid by 2035 could total $330 billion to $740 billion in additional power system costs, depending on restrictions on new transmission and other infrastructure development. However, there is substantial reduction in petroleum use in transportation and natural gas in buildings and industry by 2035. As a result, up to 130,000 premature deaths are avoided by 2035, which could save between $390 billion to $400 billion in avoided mortality costs.

When factoring in the avoided cost of damage from floods, drought, wildfires, and hurricanes due to climate change, the United States could save over an additional $1.2 trillion—totaling an overall net benefit to society ranging from $920 billion to $1.2 trillion.

Necessary Actions To Achieve 100% Clean Electricity

The transition to a 100% clean electricity U.S. power system will require more than reduced technology costs. Several key actions will need to take place in the coming decade:

• Dramatic acceleration of electrification and increased efficiency in demand
• New energy infrastructure installed rapidly throughout the country
• Expanded clean technology manufacturing and the supply chain
• Continued research, development, demonstration, and deployment to bring emerging technologies to the market.

Failing to achieve any of the key actions could increase the difficulty of realizing the scenarios outlined in the study.

Study Resources

Full report, supporting materials.

Download the technical report, Examining Supply-Side Options To Achieve 100% Clean Electricity by 2035 .

Download the report overview infographic and a 1-slide summary brief deck or a 10-slide summary brief deck .

Paul Denholm

Principal Energy Analyst

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The prevalence of spontaneous resolution among pediatric trigger thumb: a systematic review and meta-analysis

• QingSong Tang 1   na1 ,
• XinLing Miao 2   na1 ,
• Kang Zhao 1 ,
• Jie Hu 1 &
• Xiang Ren 1  

Journal of Orthopaedic Surgery and Research volume  19 , Article number:  461 ( 2024 ) Cite this article

Metrics details

Trigger thumb is a prevalent hand condition observed in children, and its management remains a topic of considerable debate, ranging from mere observation to surgical intervention. In recent times, there has been a growing interest in exploring nonoperative treatments as alternatives to surgical procedures for managing pediatric trigger thumb. Gaining insight into the prevalence of spontaneous resolution in pediatric trigger thumb is of paramount importance. However, the literature presents a wide variation in estimates regarding the prevalence of this spontaneous resolution, highlighting the need for further investigation and consensus. The aim of this review was to estimate the overall prevalence of spontaneous resolution among pediatric trigger thumb.

This study meticulously followed the PRISMA guidelines and registered in the PROSPERO. The PubMed, Embase, and Cochrane Library databases were searched for all relevant studies up to May 2024.Inclusion criteria were studies reported only observation spontaneous resolution pediatric trigger thumb, aged up to 14 years, reported at least 10 thumbs and followed up time at least 3 months. Confounded intervention treatment measure studies were excluded. To synthesize the prevalence rates from individual studies, we employed a random-effects meta-analysis. In order to uncover the sources of heterogeneity and to compare prevalence estimates across different groups, we performed sensitivity and subgroup analyses. To meticulously evaluate the quality of the included studies, the Joanna Briggs Institute’s quality assessment checklist was employed. Furthermore, to assess the heterogeneity among the studies, both Cochran’s Q test and the I² statistic were utilized.

A total of eleven studies were included for the final analysis, with 599 pediatric trigger thumbs. Our final meta-analysis showed that more than one-third of these pediatric trigger thumb cases resolved spontaneously, with a resolution rate of 43.5% (95% CI 29.6–58.6). Subgroup analyses showed that in terms of age at the first visit, the prevalence of spontaneous resolution in the less than 24 months group and in the 24 months or older group was 38.7%(95% CI 18.1–64.4)and 45.8%(95% CI 27.4–65.4), respectively. There was no significant difference between the two groups( P  = 0.690). When analyzing follow up time, the prevalence of spontaneous resolution in the 24 months or longer group and in the less than 24 months group was 58.9%(95% CI 41.6–74.2)and 26.8%(95% CI 14.7–43.8), respectively.There was significant statistical differences between the two groups( P  = 0.009). Based on the initial severity of interphalangeal (IP) joint flexion contracture, the prevalence of spontaneous resolution in the 30 degrees or less group and in the other measurements group was 54.1%(95% CI 31.5–75.1)and 37.1%(95% CI 21.9–55.4), respectively.There was no significant difference between the two groups( P  = 0.259).

Our study demonstrates that a significant proportion of pediatric trigger thumbs resolve spontaneously. This finding highlights the benefits of early observation in managing this condition. By prioritizing non-operative observation, both parents and surgeons are better equipped to make informed decisions regarding the treatment of pediatric trigger thumb, potentially reducing the need for surgical intervention.

Introduction

Research findings indicate that the prevalence of pediatric trigger thumb (PTT) ranges from 0 to 3.3 per 1,000 live births [ 1 , 2 ].The etiology of pediatric trigger thumb remains ambiguous, with some scholars suggesting a combination of congenital and acquired factors [ 2 , 3 ]. It is plausible that the actual prevalence acquired may be higher than congenital, as numerous cases manifest after the age of 12 months. Typically, the average age at diagnosis falls between 6 and 24 months [ 4 ].The primary concern revolves around a developmental discordance between the flexor pollicis longus tendon and its encompassing sheath. Historically, the surgical release of the A1 pulley has been the conventional approach for treating pediatric trigger thumb. However, emerging research indicates that a non-surgical observational strategy may result in spontaneous resolution of the condition. Study has reported over half of the affected children can anticipate a natural recovery without surgical intervention, typically within an average follow up time of approximately four years [ 5 ]. In addition, surgical intervention involving A1 pulley release has demonstrated successful outcomes for children over the age of five, irrespective of their age at the time of the procedure [ 6 ]. However, national data reveal that the surgical management of pediatric trigger thumb is often conducted more frequently and at younger ages than what the current literature advocates. This tendency toward over-treatment not only poses potential harm to patients but also imposes unwarranted financial burdens on healthcare systems [ 7 ].

Therefore, comprehending the authentic rate of spontaneous resolution in pediatric trigger thumbs is of paramount importance. This understanding is essential for considering early observation as a viable approach, which could alleviate suffering and mitigate associated negative outcomes. Broadly speaking, the reported prevalence rates of spontaneous resolution exhibit considerable variability across different studies, ranging from no occurrences to nearly four out of every five cases resolving on their own [ 8 , 9 , 10 ].

No systematic review or meta-analysis has thoroughly estimated the consolidated incidence of spontaneous resolution in pediatric trigger thumb. Evidence derived from such a meta-analysis would offer robust insights into the epidemiology of spontaneous resolution in pediatric trigger thumb. This information could be invaluable for both parents and surgeons, aiding them in making informed decisions regarding the choice between nonoperative and operative treatments for pediatric trigger thumb.Therefore, the objective of this review is to undertake a comprehensive analysis of existing literature on the prevalence of spontaneous resolution in pediatric trigger thumb, employing both qualitative and quantitative methodologies.

Research design and method

This study meticulously followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines throughout its protocols. The systematic process of searching, assessing eligibility, evaluating quality, extracting data, and analyzing data was rigorously executed based on a predesigned protocol. Moreover, the study protocol was duly registered on the International Prospective Register of Systematic Reviews (PROSPERO) prior to the commencement of data extraction (Registration CRD42024550745).

Data source and selection process

The literature search was conducted on May, 2024, utilizing three major databases: PubMed, Embase, and The Cochrane Library. The search strategy employed a combination of MeSH terms and keywords, specifically targeting the following phrases: “spontaneous resolution” or “observe” or “conservative” and “child” or “infant” or “newborn” or “pediatric”and “trigger finger” or “trigger thumb”.To ensure comprehensive coverage, additional relevant studies were meticulously identified by thoroughly reviewing the reference lists of the selected eligible studies.

Eligibility criteria and study selection

In this comprehensive review, we included studies that adhered to the following criteria: (i) The study participants were children diagnosed with pediatric trigger thumb, aged up to 14 years, reported at least 10 thumbs and followed up time at least 3 months; (ii) The study either reported the prevalence of spontaneous resolution or provided sufficient data to calculate this prevalence; and (iii) The study was published in the English language. We excluded followed up time less than 3 months, confounded intervention treatment measure studies, reviews, commentaries, case reports, and studies conducted on animal subjects. Additionally, letters to the editor, conference papers, books, editorials, and notes were also excluded from our analysis.

Methods for data extraction and quality assessment

Two independent authors meticulously extracted pertinent data from the selected studies. The information collected from each study encompassed the following details: the name of the first author(s), the sample size, the year of publication, the age at the initial visit, the follow up time, the initial severity of interphalangeal (IP) joint flexion contracture, as well as the number of spontaneous resolution among pediatric trigger thumb and their corresponding prevalence estimates.

All search results were aggregated and subsequently subjected to an independent eligibility screening by two of the authors. In instances of disagreement, a third author intervened during a meeting with the screening authors to mediate and resolve conflicts. This was achieved by attentively listening to the arguments presented and fostering discussions until a consensus was reached. The screening process entailed a meticulous review of the abstracts for each result. Following the initial screening and elimination of duplicates, the studies deemed potentially relevant were subjected to a thorough full-text review to ascertain their suitability for inclusion.

To assess the quality of the studies incorporated into the final analysis, we employed the Joanna Briggs Institute Quality Assessment Tool. This tool evaluates individual studies based on frequency scales, with responses categorized as ‘yes,’ ‘no,’ ‘not clear,’ and ‘not applicable.’ The total quality score for each study was meticulously calculated by summing the number of positive responses.

Data synthesis and analysis

In this research, all statistical analyses were meticulously performed utilizing the Comprehensive Meta-Analysis Software, version 3.0. The prevalence rates derived from the individual studies were amalgamated through the application of a random-effects meta-analysis model [ 11 ].To evaluate the degree of heterogeneity between the studies, the I² statistic was employed [ 11 ].The interpretation of the I² values is as follows: a value of 75% indicates high heterogeneity, 50% signifies medium heterogeneity, and 25% denotes low heterogeneity [ 12 ]. To assess potential sources of heterogeneity across the studies, we considered three key factors: the age at the initial visit, the duration of follow-up, and the initial severity of interphalangeal (IP) joint flexion contracture. To evaluate the risk of publication bias, we employed Egger’s regression tests and funnel plots. For all statistical analyses, a P-value of 0.05 was established as the statistical significance.

Identifcation of relevant studies

Our comprehensive and meticulous search process initially identified a total of 123 studies. However, upon closer examination, we found that 52 of these were duplicates and consequently excluded them from our analysis. Subsequently, during the evaluation phase focusing on titles and abstracts, we removed an additional 47 records—24 based on their titles and 23 based on their abstracts—as they failed to meet our stringent inclusion criteria. As a result, we retained the full texts of 24 publications for more in-depth scrutiny. Ultimately, out of these, 11 publications were deemed suitable and qualified for inclusion in our current systematic review and meta-analysis(Fig.  1 ).

PRISMA flowchart of review search

Characteristics of included studies

The fundamental attributes of the studies encompassed in this systematic review and meta-analysis are delineated in Table  1 . In total, 11 articles [ 5 , 8 , 9 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 ] were incorporated into the final analysis, encompassing 599 cases of pediatric trigger thumb that exhibited spontaneous resolution. The studies reviewed were published over a substantial period, spanning from 1974 to 2021. The sample sizes of these included studies varied significantly, ranging from as few as 12 pediatric cases to as many as 107. Furthermore, the age of the children at their initial visit for these studies spanned from 18 months to 57 months. The mean follow-up duration for the studies included in our analysis varied significantly, ranging from 3 months to an extended period of 59 months. Among these studies, four meticulously documented the average initial flexion deformity of the interphalangeal joint. This deformity was precisely measured using a goniometer, ensuring accuracy and consistency in the recorded data.

Quality of included studies

Table  2 presents a detailed analysis of the quality and potential biases inherent in the studies included in this review. Notably, four studies, representing 36% of the total, utilized an adequate sample size to accurately determine the prevalence of spontaneous resolution. Furthermore, approximately six studies, accounting for 54.5%, received positive evaluations concerning their response rates. Remarkably, all of the studies, encompassing 100%, employed appropriate statistical analyses to investigate the prevalence of spontaneous resolution. Drawing upon the Joanna Briggs Institute’s quality evaluation checklist, the articles selected for the final analysis exhibited a mean quality score of 7.36, with individual scores spanning from five to nine. Notably, five studies, constituting 45% of the total, were classified as high-quality, each achieving a score of 7.36 or above. The remaining articles, which scored between five and 7.36, were deemed to be of fair quality.

The prevalence of spontaneous resolution among pediatric trigger thumb (metaanalysis)

The pooled prevalence estimate of spontaneous resolution among pediatric trigger thumb was determined to be 43.5% (95% CI: 29.6–58.6), follow up time ranged from 3 to 59 months. However, significant heterogeneity was observed across the studies included in this analysis (I² = 90.462%; P  = 0.000) (Fig.  2 ). Subgroup analyses showed that in terms of age at the first visit, the prevalence of spontaneous resolution in the less than 24 months group and in the 24 months or older group was 38.7%(95% CI 18.1–64.4)and 45.8%(95% CI 27.4–65.4), respectively. There was no significant difference between the two groups( P  = 0.690)(Table  3 ). When analyzing follow up time, the prevalence of spontaneous resolution in the 24 months or longer group and in the less than 24 months group was 58.9%(95% CI 41.6–74.2)and 26.8%(95% CI 14.7–43.8), respectively.There was significant statistical differences between the two groups( P  = 0.009)(Table  3 ). Based on the initial severity of interphalangeal (IP) joint flexion contracture, the prevalence of spontaneous resolution in the 30 degrees or less group and in the other measurements group was 54.1%(95% CI 31.5–75.1)and 37.1%(95% CI 21.9–55.4), respectively.There was no significant difference between the two groups( P  = 0.259) (Table  3 ).

The prevalence of spontaneous resolution among pediatric trigger thumb: a random-effect meta-analysis (I 2  = 90.462%; P  = 0.000;based on random effect model)

Sensitivity analysis

To identify potential sources of heterogeneity across the studies and to examine the differences between groups estimating spontaneous resolution among pediatric trigger thumb, we conducted a stratified analysis by categorizing participants based on three key variables: age at the first visit (less than 24 months versus 24 months or older), follow-up duration (less than 24 months versus 24 months or longer), and the initial severity of interphalangeal (IP) joint flexion contracture (30 degrees or less versus other measurements).

This analysis revealed that the observed variation in the prevalence of spontaneous resolution among pediatric trigger thumb, when examined across the aforementioned three variables (groups), did not show statistical significance for age at the first visit (less than 24 months versus 24 months or older) and initial severity of IP joint flexion contracture (30 degrees or less versus other measurements) ( P  > 0.05)(Table  3 ). However, the follow up time (less than 24 months versus 24 months or longer) demonstrated a statistically significant difference ( P  = 0.009)(Table  3 ). To further investigate the potential sources of heterogeneity among the studies included in our analysis, we conducted a leave-one-out sensitivity analysis. This rigorous approach demonstrated that the primary findings are robust and not unduly influenced by any single study. Upon excluding each study one at a time, the pooled estimated prevalence of spontaneous resolution among pediatric trigger thumb ranged from 39.6% (95% CI: 27.2–53.6) to 47.8% (95% CI: 34.8–61.2), thus affirming the stability of our results.

Publication bias

In our comprehensive systematic review and meta-analysis, we uncovered no indications of potential publication bias concerning the prevalence of spontaneous resolution in pediatric trigger thumb. This conclusion is substantiated by the symmetrical appearance of the funnel plot and corroborated by the results of regression tests associated with the funnel plot analysis(Egger’s test) (B = − 789, SE = 3.87, P  = 0.843)(Fig.  3 ).

Funnel plot of the risk of publication bias for the prevalence of spontaneous resolution among pediatric trigger thumb

Key findings

To the best of our knowledge, this study represents the first comprehensive systematic review and meta-analysis aimed at estimating the prevalence of spontaneous resolution in pediatric trigger thumb. Our review encompassed 11 studies that investigated this phenomenon. Both our qualitative and quantitative analyses revealed that the existing scientific evidence on the prevalence of spontaneous resolution in pediatric trigger thumb exhibits substantial variability depending on the follow up time, the age at the initial consultation, and the initial severity of interphalangeal (IP) joint flexion contracture.

Our comprehensive meta-analysis revealed the pooled prevalence estimate of spontaneous resolution among pediatric trigger thumb was determined to be 43.5%. Notably, the follow up time played a crucial role, the prevalence of spontaneous resolution in the 24 months or longer group and in the less than 24 months group was 58.9%(95% CI 41.6–74.2)and 26.8%(95% CI 14.7–43.8), respectively.There was significant statistical differences between the two groups( P  = 0.009). This notable difference in resolution rates can greatly assist both parents and surgeons in making informed decisions regarding the preference for nonoperative treatments over surgical interventions for managing pediatric trigger thumb.

Comparisons with the existing evidence

The current study’s prevalence estimates for spontaneous resolution of pediatric trigger thumb stand at 43.5%. Notably, when the follow up time in the 24 months or longer group, this rate increases to 58.9%. These figures are significantly higher than those reported by some authors, who found no cases of spontaneous recovery during their follow-up periods [ 10 , 21 ]. The findings of this study suggest that the duration of nonoperative care can be extensive and should be a topic of discussion, allowing parents to actively participate in the decision-making process regarding the choice between nonoperative and operative treatments. One plausible explanation for this phenomenon may be attributed to the fact that some surgeons do not adhere to the recommended 24 months waiting period. Instead, they may choose to pursue alternative therapeutic approaches if spontaneous resolution is not achieved within a shorter timeframe, as indicated by previous research.

This substantial variation can be attributed to several factors including (i) There are notable variations in the characteristics of the children involved in these studies. These differences encompass the initial severity of the interphalangeal joint flexion contracture associated with trigger thumb [ 20 ], the duration of follow up time [ 18 ], as well as the age at which the condition first presented and whether it affected the right or left side [ 20 , 22 ]; (ii) Significant discrepancies exist in the definitions of spontaneous resolution of trigger thumb across various studies [ 5 , 20 ]; (iii) The clinical characteristics of participants exhibit considerable variation, encompassing factors such as metacarpophalangeal joint laxity issues [ 4 ] and the diverse ethnic and cultural backgrounds of the participants [ 20 ].

This study has unveiled significant heterogeneity among studies investigating the spontaneous resolution of pediatric trigger thumb. This observed variability can be attributed to differences in participant characteristics as well as the methodologies employed in the included studies. Concerning methodological disparities, the studies varied in several respects: sample size, the instruments utilized to measure outcomes, the sampling procedures, as well as the source of population.

Strength and limitations

The present study has several strengths. Firstly, this systematic review and meta-analysis aims to determine the prevalence of spontaneous resolution in pediatric trigger thumb cases. By consolidating existing research, it provides a foundational understanding of this medical phenomenon. Secondly, the study estimates the prevalence rates of spontaneous resolution by considering specific subgroups based on critical factors such as the age at the first visit, follow up time, and the initial severity of interphalangeal (IP) joint flexion contracture. This approach allows for a more detailed and accurate assessment of the condition across different patient demographics. Thirdly, the study incorporates subgroup and sensitivity analyses to identify and mitigate potential biases, ensuring the reliability and validity of the findings. These methodological strengths collectively enhance the study’s contribution to the field of pediatric orthopedics, offering valuable insights for clinicians and researchers alike.

Several limitations inherent in this systematic review and meta-analysis warrant careful consideration. Firstly, the majority of the included studies had relatively small sample sizes. This limitation raises concerns that the reported prevalence of spontaneous resolution among pediatric trigger thumb cases in our current analysis may not accurately reflect the true prevalence in the broader population. Secondly, our review exclusively included studies published in the English language. Consequently, there is a possibility that relevant studies conducted in other languages were overlooked, potentially introducing a language bias into our findings.

The implication of the findings

The current study bears profound implications for both research and clinical practice. To begin with, future investigations are imperative to explore the underlying reasons for the elevated prevalence rates of spontaneous resolution observed in pediatric trigger thumb cases when the follow up time in the 24 months or longer group, as compared to those with in the less than 24 months group. Additionally, it is essential that subsequent studies encompass larger sample sizes and incorporate segmentation based on gender and the affected side, thereby ensuring a more comprehensive understanding of this condition. Finally, to mitigate the need for surgical interventions and alleviate the associated suffering, early screening and public education on the spontaneous resolution of pediatric trigger thumb should be prioritized through coordinated and integrated public health strategies.

This study has demonstrated that a significant proportion of pediatric trigger thumbs resolve spontaneously. The prevalence of spontaneous resolution in pediatric trigger thumb cases is significantly higher when the follow up time in the 24 months or longer group, compared to cases with in the less than 24 months group, suggesting substantial benefits associated with an early observation approach for this condition. By opting for a nonoperative strategy initially, both parents and surgeons may be better equipped to make informed decisions about the treatment plan, potentially avoiding unnecessary surgical interventions.

The average initial flexion deformity of the interphalangeal joint, was measured with use of a goniometer, with the wrist held in neutral extension, the thumb in 20 of palmar abduction, and the metacarpophalangeal joint in neutral extension.

Data availability

No datasets were generated or analysed during the current study.

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Acknowledgements

We thank research office staff at Chengdu Women’s and Children’s Central Hospital.

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QingSong Tang and XinLing Miao equally to this work as co-first authors.

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Chengdu Women’s and Children’s Central Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, 611731, China

QingSong Tang, Kang Zhao, Jie Hu & Xiang Ren

School of Nursing, Chengdu university, Chengdu, China

XinLing Miao

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QingSong Tang and XinLing Miao equally to this work as co-first authors. Tang QS conceptualized the study, performed the search conducted analyses, conducted the quality assessment, write-up and approval of the final manuscript. Miao XL was involved in data extraction, read and approved the final manuscript. Ren X, Zhao K, andHu J was participated in discussion and consensus and approved the final manuscript. All authors read and approved the final manuscript.

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Tang, Q., Miao, X., Zhao, K. et al. The prevalence of spontaneous resolution among pediatric trigger thumb: a systematic review and meta-analysis. J Orthop Surg Res 19 , 461 (2024). https://doi.org/10.1186/s13018-024-04960-0

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