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The future of research: Emerging trends and new directions in scientific inquiry

The world of research is constantly evolving, and staying on top of emerging trends is crucial for advancing scientific inquiry. With the rapid development of technology and the increasing focus on interdisciplinary research, the future of research is filled with exciting opportunities and new directions.

In this article, we will explore the future of research, including emerging trends and new directions in scientific inquiry. We will examine the impact of technological advancements, interdisciplinary research, and other factors that are shaping the future of research.

One of the most significant trends shaping the future of research is the rapid development of technology. From big data analytics to machine learning and artificial intelligence, technology is changing the way we conduct research and opening up new avenues for scientific inquiry. With the ability to process vast amounts of data in real-time, researchers can gain insights into complex problems that were once impossible to solve.

Another important trend in the future of research is the increasing focus on interdisciplinary research. As the boundaries between different fields of study become more fluid, interdisciplinary research is becoming essential for addressing complex problems that require diverse perspectives and expertise. By combining the insights and methods of different fields, researchers can generate new insights and solutions that would not be possible with a single-discipline approach.

One emerging trend in research is the use of virtual and augmented reality (VR/AR) to enhance scientific inquiry. VR/AR technologies have the potential to transform the way we conduct experiments, visualize data, and collaborate with other researchers. For example, VR/AR simulations can allow researchers to explore complex data sets in three dimensions, enabling them to identify patterns and relationships that would be difficult to discern in two-dimensional representations.

Another emerging trend in research is the use of open science practices. Open science involves making research data, methods, and findings freely available to the public, facilitating collaboration and transparency in the scientific community. Open science practices can help to accelerate the pace of research by enabling researchers to build on each other’s work more easily and reducing the potential for duplication of effort.

The future of research is also marked by scientific innovation, with new technologies and approaches being developed to address some of the world’s most pressing problems. For example, gene editing technologies like CRISPR-Cas9 have the potential to revolutionize medicine by allowing scientists to edit DNA and cure genetic diseases. Similarly, nanotechnology has the potential to create new materials with unprecedented properties, leading to advances in fields like energy, electronics, and medicine.

One new direction in research is the focus on sustainability and the environment. With climate change and other environmental issues becoming increasingly urgent, researchers are turning their attention to developing sustainable solutions to the world’s problems. This includes everything from developing new materials and technologies to reduce greenhouse gas emissions to developing sustainable agricultural practices that can feed the world’s growing population without damaging the environment.

Another new direction in research is the focus on mental health and wellbeing. With mental health issues becoming increasingly prevalent, researchers are exploring new approaches to understanding and treating mental illness. This includes everything from developing new therapies and medications to exploring the role of lifestyle factors like diet, exercise, and sleep in mental health.

In conclusion, the future of research is filled with exciting opportunities and new directions. By staying on top of emerging trends, embracing interdisciplinary research, and harnessing the power of technological innovation, researchers can make significant contributions to scientific inquiry and address some of the world’s most pressing problems.

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The present and future of AI

Finale doshi-velez on how ai is shaping our lives and how we can shape ai.

Finale Doshi-Velez, the John L. Loeb Professor of Engineering and Applied Sciences. (Photo courtesy of Eliza Grinnell/Harvard SEAS)

How has artificial intelligence changed and shaped our world over the last five years? How will AI continue to impact our lives in the coming years? Those were the questions addressed in the most recent report from the One Hundred Year Study on Artificial Intelligence (AI100), an ongoing project hosted at Stanford University, that will study the status of AI technology and its impacts on the world over the next 100 years.

The 2021 report is the second in a series that will be released every five years until 2116. Titled “Gathering Strength, Gathering Storms,” the report explores the various ways AI is  increasingly touching people’s lives in settings that range from  movie recommendations  and  voice assistants  to  autonomous driving  and  automated medical diagnoses .

Barbara Grosz , the Higgins Research Professor of Natural Sciences at the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS) is a member of the standing committee overseeing the AI100 project and Finale Doshi-Velez , Gordon McKay Professor of Computer Science, is part of the panel of interdisciplinary researchers who wrote this year’s report. 

We spoke with Doshi-Velez about the report, what it says about the role AI is currently playing in our lives, and how it will change in the future.  

Q: Let's start with a snapshot: What is the current state of AI and its potential?

Doshi-Velez: Some of the biggest changes in the last five years have been how well AIs now perform in large data regimes on specific types of tasks.  We've seen [DeepMind’s] AlphaZero become the best Go player entirely through self-play, and everyday uses of AI such as grammar checks and autocomplete, automatic personal photo organization and search, and speech recognition become commonplace for large numbers of people.  

In terms of potential, I'm most excited about AIs that might augment and assist people.  They can be used to drive insights in drug discovery, help with decision making such as identifying a menu of likely treatment options for patients, and provide basic assistance, such as lane keeping while driving or text-to-speech based on images from a phone for the visually impaired.  In many situations, people and AIs have complementary strengths. I think we're getting closer to unlocking the potential of people and AI teams.

There's a much greater recognition that we should not be waiting for AI tools to become mainstream before making sure they are ethical.

Q: Over the course of 100 years, these reports will tell the story of AI and its evolving role in society. Even though there have only been two reports, what's the story so far?

There's actually a lot of change even in five years.  The first report is fairly rosy.  For example, it mentions how algorithmic risk assessments may mitigate the human biases of judges.  The second has a much more mixed view.  I think this comes from the fact that as AI tools have come into the mainstream — both in higher stakes and everyday settings — we are appropriately much less willing to tolerate flaws, especially discriminatory ones. There's also been questions of information and disinformation control as people get their news, social media, and entertainment via searches and rankings personalized to them. So, there's a much greater recognition that we should not be waiting for AI tools to become mainstream before making sure they are ethical.

Q: What is the responsibility of institutes of higher education in preparing students and the next generation of computer scientists for the future of AI and its impact on society?

First, I'll say that the need to understand the basics of AI and data science starts much earlier than higher education!  Children are being exposed to AIs as soon as they click on videos on YouTube or browse photo albums. They need to understand aspects of AI such as how their actions affect future recommendations.

But for computer science students in college, I think a key thing that future engineers need to realize is when to demand input and how to talk across disciplinary boundaries to get at often difficult-to-quantify notions of safety, equity, fairness, etc.  I'm really excited that Harvard has the Embedded EthiCS program to provide some of this education.  Of course, this is an addition to standard good engineering practices like building robust models, validating them, and so forth, which is all a bit harder with AI.

I think a key thing that future engineers need to realize is when to demand input and how to talk across disciplinary boundaries to get at often difficult-to-quantify notions of safety, equity, fairness, etc. 

Q: Your work focuses on machine learning with applications to healthcare, which is also an area of focus of this report. What is the state of AI in healthcare? 

A lot of AI in healthcare has been on the business end, used for optimizing billing, scheduling surgeries, that sort of thing.  When it comes to AI for better patient care, which is what we usually think about, there are few legal, regulatory, and financial incentives to do so, and many disincentives. Still, there's been slow but steady integration of AI-based tools, often in the form of risk scoring and alert systems.

In the near future, two applications that I'm really excited about are triage in low-resource settings — having AIs do initial reads of pathology slides, for example, if there are not enough pathologists, or get an initial check of whether a mole looks suspicious — and ways in which AIs can help identify promising treatment options for discussion with a clinician team and patient.

Q: Any predictions for the next report?

I'll be keen to see where currently nascent AI regulation initiatives have gotten to. Accountability is such a difficult question in AI,  it's tricky to nurture both innovation and basic protections.  Perhaps the most important innovation will be in approaches for AI accountability.

Topics: AI / Machine Learning , Computer Science

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The future of clinical trials and drug development: 2050

Timothy c hardman.

1 Niche Science & Technology Ltd., Richmond-upon-Thames, Surrey, UK

Rob Aitchison

2 4C Life Sciences, 4C Associates Ltd., Hammersmith, London, UK

Richard Scaife

3 Vecti Bio AG, Basel, Switzerland

Jean Edwards

4 Devizes, Wiltshire, UK

Gill Slater

5 Leo Pharma, 2 rue Rene Caudron, Saint Quentin en Yvelines Cedex, France

A workshop held at the 18th Annual Conference of the Pharmaceutical Contract Management Group in Krakow on 9 September 2022 asked over 200 delegates what the clinical trial landscape would look like in 2050. Issues considered included who will be running the pharmaceutical industry in 2050; how ‘health chips’, wearables and diagnostics will impact on finding the right patients to study; how will artificial intelligence be designing and controlling clinical trials; and what will the role of the Clinical Research Associate, the critical observer, documenter and conductor of a clinical trial need to look like by 2050. The consensus was that, by 2050, if you are working in clinical trials, you will be a data scientist. We can expect to see an increasing role of new technologies and a new three-phase registration model for novel therapies. The first phase will involve an aspect of quality evaluation and biological proof-of-concept probably involving more preclinical modelling and engineered human cell lines and fewer animal studies than currently used. Once registered, new products will enter a period of adaptive clinical development (delivered as a single study) intended to establish safety. This phase will most likely take around 1–2 years and explore tailored options for administration. Investigations will most likely be conducted in patients, possibly in a ‘patient-in-a-box’ setting (hospital or healthcare centre, virtual or microsite). On completion of safety licencing, drugs will begin an assessment of efficacy in partnership with those responsible for reimbursement – testing will be performed in patients, possibly where individual patient involvement in safety testing will offer some reimbursement deal for future treatment. Change is coming, though its precise form will likely depend on the creativity and vision of sponsors, regulators and payers.

Introduction

A workshop held at the 18th Annual Conference of the Pharmaceutical Contract Management Group (PCMG) in Krakow on 9th September 2022 asked over 200 delegates what the clinical trial landscape would look like in 2050. (Quotes presented throughout the text were obtained from the participant responses.)

As Confucius tells us, if we are to divine the future, we must first study the past. Scientists appreciate the value of extrapolation. In this case, the delegates had amassed 2500+ years of experience over the last 30+ years. Looking back, the last three decades have seen many changes, including the introduction of novel trial designs, the data revolution and transformative technologies. Many of the modifications have come as a series of bolt-ons, and stakeholders might be forgiven for thinking that the current clinical framework built on Good Clinical Practice and the ICH guidelines is struggling to remain fit for purpose. 1 , 2 So, where do we go next?

It is said that true wisdom comes from asking the right questions, and the workshop focused its discussions around four key questions:

Who will be running the pharmaceutical industry in 2050?

How will ‘health chips’, wearables and diagnostics impact on finding the right patients to study.

• How will artificial intelligence be designing and controlling clinical trials?
• What will the role of the Clinical Research Associate (CRA), the critical observer, documenter and conductor of clinical trials need to look like by 2050?

The questions were posed in response to the unprecedented changes that have occurred throughout the industry. 3 – 5 It would be fair to say that predicting what clinical trials will look like in the next 25, 10 or even 5 years must be little more than speculative. However, the question arises as to how an industry that has been reticent and resistant to change for over three decades will respond to the challenges ahead.

Discussions around the question confirmed the understanding that it is the data that sits firmly at the centre of clinical trials. The 2021 Tufts report on the state of clinical trials highlighted however more complex clinical trial designs are collecting much higher volumes of data from a variety of sources. 5 – 9 We have been seeing the consequence of biopharmaceutical companies engaging in more ambitious and customized drug development activity targeting a growing number of rare diseases, stratifying participant subgroups using biomarker and genetic data, and relying on more structured and unstructured patient data coming from an increasing number of sources.

Clinical trial designs are expected to become more complex in the future, generating even greater data volume and diversity. Figure 1 summarizes the discussions around possible future data flows and gives a taste of the types of data all of us will soon be ‘providing’ – irrespective (possibly) of whether we have agreed to take part in a clinical study. 10 It seems that most of us will be ‘voluntarily’ monitored by 2050 and whole populations might serve as trial participants.

Diagram summarizing a proposed interaction between an individual’s data sources and collection, storage, and analytical processes in 2050.

Numbered items identify insensible data collection devices: (1) Implemented device for heart health monitoring. (2) Artificial tooth, monitors temperature, nutrition and oral health. (3) Smart pill, monitors digestive system. (4) Electronic tattoo, monitors activity, steps and falls. (5) Acute (emergency)/chronic drug delivery system. (6) Smart pillow, monitors sleep and breathing patterns. (7) Scales, monitors weight, body mass index and hydration. (8) Contact lens, monitors sugar levels/general eye health.

The general consensus was that, with the expected revolutions in data collection, the clinical study teams that devise and run studies must also change. Equally, the amount and type of data we can expect dictates that it will not be clinical pharmacologists or clinicians but more likely algorithms managed by data scientists that will be driving drug development. It seemed likely to the delegates that the true leadership in development will lie with those who own the algorithms that we will rely on to establish both the safety and efficacy of new treatments. 11 , 12

Mergers such as that between Quintiles and IMS appear to reflect the growing acceptance of the concept of end-to-end data exploitation (development to reimbursement) solutions for pharmaceutical companies. But look closer. More significant change is coming. Over the last few decades, we have seen pharmaceutical companies divest expertise, reducing their employee base and adopting outsourcing models for specialist functions. 13 Larger pharmaceutical companies have coupled this with an emphasis on asset acquisition over in-house development. 14 In focusing on recouping profit over investing in the engine to drive future development it was generally agreed that the pharmaceutical industry has taken its eye off the ball. Clinical development was always about data – and now the Gods of data are coming to call.

“ Who will run pharma? Google, Amazon, Apple – the big tech companies .”

The opinion was that, try as they might, Blue-chip pharmaceutical companies and mega contract research organization (CRO) conglomerates have already lost the data initiative. It was clear from delegate feedback that we are already seeing technology companies come to the fore as pharma’s trillion dollar spend attracts predators like Google, IBM and Microsoft.

Perhaps the greatest focus of comment and debate was around the future impact that technologies would have and how that technology would be employed in clinical studies of the future. Over the past few years, the general public have become more aware of the various wearable technologies in the form of sensors and diodes intended to monitor health data such as heart rate, lung capacity and body temperature. Such technology already exists in the guise of smartwatches and smart clothing and, while currently used in professional sports to track progress and fitness, the real capabilities of wearable medical technology are only just emerging.

“ Implanted biosensors will be commonplace by 2050 .”

At the forefront of the discussions on wearable technology is the revolution in and clinical potential of responsive ‘drug delivery’. People with chronic health conditions can often feel that their lives revolve around medication – the appropriate use of medicine is considered to represent better managed disease and relieve the burden on patients. An example offered was that of diabetes. Instead of manual checking of blood sugar and administering insulin as needed, it is proposed that the patient would rely on an electromedical device — not just for monitoring but for administration of correct (and variable) dosages based on specific patient needs.

Although diabetes is a common disease and likely target for new technologies, the delegates considered a broader application, not only for the treatment of disease but in the testing of new agents. It was considered how, during the COVID pandemic, healthcare agencies approved and released more convenient at-home chemotherapy cancer treatments in a bid to minimize possible exposure to infection and keep more hospital spaces open. The consensus was that, although this approach was adopted out of necessity, it showed a reliance on the administrative technology associated with at-home treatments; a definite step in the direction of trust for wearable technology that can be employed in the treatment of a variety of diseases and chronic conditions.

The wearable drug delivery market is exploding – from simple patches and medical wearable devices on the skin to subcutaneous non-needle injectors, the industry is expected to exceed US$240 billion in the next 2 years alone. 15 The innovations coming from this corner of our industry are immense, and the audience considered that the potential offered by technology to manage the disease and promote healing is nothing short of astounding. It was not hard for the delegates to imagine that implantable devices will be monitoring a broad scope of factors both in health and disease, providing live, real-world population data long before 2050.

“ We are already approaching true personalized medicine. The future will see ‘dose’ being a term consigned to the past – instead, dug delivery will be responsive .”

The consensus was that the new technologies will not only facilitate the recruitment of participants but also aid the identification of more appropriate patients, empowered by what could be a long-term ‘baseline’ date. With wearables becoming more commonplace and powerful in their breadth of offerings, they could, when combined with more informed genetic testing, result in earlier and more definitive diagnoses of a broad range of diseases – a real-life realization of the Theranos dream. 16

“ There will be rapid, home diagnostic testing – like Theranos, but that actually works .”

How will AI be designing and controlling clinical trials?

Discussions and opinions regarding how artificial intelligence (AI) may be impacting the design and conduct of clinical trials focused on three parts: optimization, facilitation and simulation.

In terms of optimization, delegates envisaged how historical data available on clinical trial registries such as ClinicalTrials.gov will empower the study design process. This information will be used to reduce the time it currently takes to prepare protocols, minimize error and the need for amendments, and guide clinical teams in the selection of the most appropriate measurements/biomarkers, milestones and endpoints. It was envisaged that the wider adoption of AI would accelerate the process to move studies from the planning stage to delivery. It was also proposed that the introduction of algorithms programmed to deliver protocols would allow clinical teams to explore different design options in ‘real time’, a development that would foster more creative and complex study designs most likely to provide high-value scientific and regulatory data.

Consideration was given to how AI might facilitate trial delivery. It was discussed how various algorithms are already being employed to identify disease-specific centres of excellence, high-performing trialists (those that consistently hit recruitment targets and with low ‘drop-outs’ or failures) and potential opinion leaders. 17 , 18 Delegates recognized that further exploitation, almost down to identification of individual patients, will be necessary to reduce the trial cost burden of sites that fail to recruit. Emphasis was placed on an expected increase in the uptake and utilization of personal health monitoring devices and the rationalization of health data. It was proposed that patient identification could be simplified using algorithms to identify the most appropriate patients for trials (and their medical history and details entered automatically into trial databases), although it was agreed that these proposed reductions in administrative burden would need to fit within the current and any future data privacy framework (assuming that data privacy regulations are still in place by 2050). In addition, this ambition overlooks the use of coding systems in current electronic health record systems to identify medical diagnoses. Currently, data protection and governance guidelines mean that we only have access to coded data and not unstructured text, which encompasses the wider patient history. Finally, it was proposed that trial costs could be further reduced using large-scale (population), real-life, patient pathway data to dispense with the need for the inclusion of placebo treatment groups in clinical trials.

Simulation guided by AI was expected to play a key role in clinical trials by 2050. Although not within the general expertise of the conference attendees, it was generally agreed that simulation would contribute to the preclinical characterization of new therapies; identification of optimal dosing strategies and, through the availability of actual patient data, the modelling of large-scale, simulated/synthetic cohorts; provide instantaneous safety and population profiling; and deliver estimates of economic benefits that could be achieved following the deployment of new medicines.

How will the process of registration respond to innovation?

Debate around how changes in technology are likely to impact on the conduct of clinical research introduced an unplanned question: how will the process of registration respond to innovation? It was clear from the various discussion streams that, even if we continue with our current rate of identifying new therapies, we still have the problem of getting them into the patients. Delegates felt that, if anything needs addressing, it is the process of drug testing in man – it is recognized as being slow, expensive and inefficient. The statistics are well known: it takes 7–10 years to bring a drug to market and the best estimates suggest that only ~1 in 10,000 candidates pass the finishing line. Despite our best efforts, we have not found solutions for the dual curse of attrition and protracted development times. Modifications to the registration pathway intended to facilitate delivery from the FDA have made small, incremental improvements ( Figure 2 ), and even with the modifications suggested earlier, preliminary estimates suggest that they will not make a substantial impact on either timelines or attrition. 19

Summary of the FDA accelerated (expedited) development pathways.

Source data: www.fda.gov/media/86377/download .

“ Given the leaps forward in personalized medicine and genetic screening to identify which drugs will work best for specific patients, this is only going to increase the number of compounds we need to get on to the market to treat the same number of conditions … and as the number of patients each can treat will go down, either we have to increase the unit price of sale, or make the development a whole lot faster and cheaper .”

Delegates suggested that registration of new therapies in 2050 will have moved away from the traditional serial trial approach ( Figure 3 ). We currently segment development into individual phases and trials, each incorporating different treatment groups, often with the focus of the study being to establish some statistical significance. This approach ties up considerable resources. For example, the process of writing and approval for each clinical study protocol can take anything from 5 to 20 weeks; each protocol undergoes (at least) four substantial amendments, with more delays and not insubstantial additional regulatory costs. Depending on the number of countries involved, a substantial amendment can cost between $250k and $750k in submission effort and fees. Once the study has been delivered, data review and analysis must be performed before writing and publishing the final report. If each drug development programme involves anything from 5 to 10 clinical studies, it can be estimated that regulatory ‘administration’ alone takes up over 3 years – a third of the current clinical development time.

A proposed continuous and integrated drug registration and development framework.

It was suggested that regulatory authorities will begin to take a more proactive approach to registration, employing the improvements in data collection coupled with integrated data management systems to empower regulators to regulate in real time. On consideration of current stated regulatory strategy of the EMA, the delegates felt that it already contains many of the necessary components for real time programme coordination and monitoring. 20 It was proposed that, with a few modest modifications it would be possible for protocols to become less rigid, even infinitely amendable, managed online and approved in an ‘as required’ basis. This would introduce the possibility of turning a whole development programme into a single, infinitely adaptive clinical trial, perhaps managed by a global authority. The savings in administration time alone would be significant.

“Technology is not the hurdle to more efficient drug development and registration. Agencies need to work as a global institution .”

Even if a global regulatory authority does not emerge, it is expected that agencies would share access to the same study design information (for example, patient profiling, endpoints and milestones), removing the requirements of sponsors to justify features of the protocols and modifications. It was even proposed that the process of building the registration data package itself could fall fully under the umbrella of the agencies.

What will the role of the CRA need to look like by 2050?

The last of the planned questions focused on the role of the CRA, currently the critical stakeholders of the clinical trial delivery team. The challenge associated with sourcing sufficiently trained and experienced CRAs to deliver clinical trials has been the focus of previous PCMG events over the last 5 years. In line with the law of supply and demand, the salaries of CRAs have seen marked increases. The constantly moving goalposts are driving movement across the clinical trial workforce. Employees are reaping the reward in terms of golden handshakes, salaries and perks. In contrast, sponsors and CROs are finding it difficult to retain teams. 21 , 22

The overall conclusion was that improved data accountability, technology and automation will mean that there would no longer be any need for CRAs. Clinical trials were expected to be managed centrally, with data being provided from a variety of tools (wearables, etc.). Investigations will most likely be conducted directly in patients, doing away with the healthy participants involvement in clinical development programmes. Studies may be conducted in a ‘patient-in-a-box’ setting (hospital or healthcare centre, virtual or microsite, or within the home).

Beyond the four premeditated questions, the discussion sessions derived three additional questions that the delegates felt were important in determining the industry’s future.

What impact will the changes have on reimbursement?

Discussions ranged across all aspects of clinical development and focus often returned to the impact the changes would have on existing reimbursement mechanisms. Our expenditure on healthcare has been increasing and is expected to have doubled between now and 2030 – less than 10 years’ time. 23 Although it was agreed that the proposed changes would likely reduce the time and costs of the development of new treatments, profit will remain a key industry driver. Although increases in healthcare costs will not solely be due to the price of medicines, the pharmaceutical industry and the price of medicines have come under increasing scrutiny. Looking at some of the medicines at the upper end of the market, it is clear that the numbers are many times greater than any single patient could ever afford ( Table 1 ) – and this is for treatments that are often no more than life-extender therapies. It was agreed that, both on an individual and a healthcare service level, these costs are unsustainable.

Most expensive drugs in the world.

Estimates from 2022 (ref. 31 ).

Whichever number you believe, US$1.1 billion or even US$3.2 billion, 24 development of new drugs represents a significant investment for a single organization, especially seeing that success is not guaranteed. In 2016, for example, the FDA approved only 22 novel drugs of the 41 filed.

A 50% success rate is a considerable risk when you are spending a billion dollars. How have sponsor companies responded over the last few decades? We have seen many mergers and acquisitions as companies have attempted to minimize overheads. There has been reduced investment in maintaining internal teams and increased use of outsourcing models. Sponsors are investigating ‘shared risk’ models with CROs and there has been increased investment in specialty/orphan submissions. However, perhaps the most newsworthy has been the drive to increase reimbursement.

The general consensus was that pressures to drive change aimed at reducing the cost of development will continue. One aspect of change may link a patient access to treatment and cost of treatment to their involvement in the development pathway or alternate research studies.

“In the future, rather than paying full price for a product, you allow the ‘developer’ to use your data and you are rewarded with a price reduction .”

Delegates were also convinced that the current speculations over reimbursement models based on efficacy – that is, if your drug does not work for a patient you do not receive payment – will have matured. Such models could see a significant swing in the development landscape. Shifting the efficacy relationship to one between payers and sponsors – a relationship that offers the potential to reduce the burden on regulatory agencies to that of monitoring safety. It was believed that this approach has the potential to slash the time it takes to get drugs into patients.

The future of disease

Another aspect of the pharmaceutical industry landscape touched on by the discussions included how treatments and the practice of medicine will have changed by 2050. It was the consensus of the delegates that our fundamental understanding of disease will itself change. For most of our medical history, we have applied a symptomatic approach to the classification, diagnosis and treatment of disease. Our understanding has been based on what we could measure and what we could observe – for example, we defined high blood pressure as hypertension and increased body mass as obesity. However, daily advances in our understanding of the underlying pathophysiology of disease and the involvement of increasingly complex technology are changing what we know of disease. It is expected that we will start to see many diseases in a new light. The future will no longer be relying on textbook descriptions and diagnoses based on symptoms, our new ‘electronic dissection kit’ will be more subtle than the scalpels of Edwardian clinicians.

“ I suspect we will be re-writing medical textbooks for years to come – except (obviously) there won’t be any textbooks .”

Delegates discussed how we are collecting more data at every level. This is not only newly generated data, we are also gaining access to more and more historical patient data as it is scanned and transcribed into databases or extracted from the bones in plague pits, for example, giving insights into genetic differences between those who survived (and those who did not) and how that has impacted the increased prevalence of autoimmune diseases 700 years later. 25

Following calls for transparency, we are also sharing more and more clinical study data making it available for anyone to access through registries and databanks. With the explosion in biomarkers, we are monitoring more and more parameters 6 ; we have also seen increasing technological granularity as our ‘measuring’ devices are becoming more sensitive, providing continuous multi-layered data streams.

There have been few restrictions in the ability of our technology to process these volumes of data plus that coming from our mobile phones, health apps, wearables and online sources from Amazon to Google to Zoom.

“ The future is all about data .”

Previous PCMG conferences have drawn attention to how an ever-increasing array of computer-based systems are looking for ‘understanding’ at a level that individuals are not equipped to comprehend. We already have a veritable armoury of analytical tools to use in our clinical studies and the computer power to run them. In short, the delegates concluded that our technological wizardries are providing new insights as well as earlier and more precise diagnoses than clinicians can. The utility of these tools is already being reported across the clinical spectra – from acute kidney disease to automatic imaging assessment and patient triaging. 26 – 28

The future of medicines

If the same way that our understanding of the disease is changing, it is only logical to assume that our approach to treatment will also change. Even if you ignore the growing issue of antibacterial resistance, we are in desperate need for new medicines as our population itself is changing. Estimates suggest that more than 1.4 billion people will be over 60 years of age by 2030. 29 Although many of us can expect to remain active, old age is also associated with a plethora of chronic, non-communicable diseases and their associated disabilities that we have failed to understand for decades – hypertension, obesity, diabetes … even ageing itself? By 2030, one in three people over 50 will be suffering from chronic disease. At best, these diseases bring a plethora of morbidities that will require the co-administration a broad range of therapies and will account for 70% of deaths. 30 It seems appropriate that the clinical trial paradigm involving healthy young participants will need to adapt to reflect these changes. Multimorbidity is the new norm, and we need to acknowledge that by testing drugs in populations that better reflect the ultimate recipient of drugs.

“ Many existing medicines may find themselves having a second life, repurposed for ‘new’ indications .”

Over the last 100 years, we have pursued small-molecule solutions to symptomatic relief through the receptor theory of pharmacology. We have had some notable successes, but we have long known the limitations of our approach. People’s responses to medicines are variable – medicines do not work in everyone. All drugs are potential poisons and we have battled with the challenge of getting the right amount of that poison to the right place at the right time. The failure of all trial participants to respond in exactly the same way has meant that the only rational option has been to establish ‘optimal’ doses based on group responses established in relatively small, homogeneous cohorts. It was proposed that we will start to see more and more ‘smart’ medicines – self-regulating therapies that adjust to the dynamic nature of our own pathophysiologies. We have been running trials for some time in novel ‘devices’ that employ creative release mechanisms to provide more subtle and targeted delivery. We are also seeing medications that exploit mechanisms that have progressed far beyond the receptor theory – CRISPR, targeted protein degradation, immuno-oncology treatments, silencing and cellular therapies. Current experience indicates that these treatments take much less time to register.

“ Gene and epigenetic profiling coupled with biomarker-targeted monitoring will see medicines able to ‘hit hard and hit early’ – potentially reducing the burden of chronically under-treated or ‘late to diagnosis’ disease .”

These new medicines raise some interesting questions. Are we equipped to validate these treatments before they are used in actual patients and will regulators believe the industry even if it claims it can? Do such clinical trials tell us anything more than a ‘sanitized’ safety and tolerability profile? It might seem a bit of a leap but, if we are investigating treatments for diseases we did not previously understand and those medicines are exploiting subtle mechanisms we cannot measure, our technology becomes the only way to ‘detect’ changes indicative of benefit (and this would only be achievable in patients).

The consensus was that, by 2050, if you are working in clinical trials and you are not the janitor, then you are a data scientist, probably working at Google. We can expect to see a new, three-phase registration model. The first phase will involve an aspect of quality evaluation and biological proof-of-concept probably involving more preclinical modelling and engineered human cell lines and fewer animal studies.

Once a new product is registered, it will enter a period of adaptive clinical development (delivered as a single study) intended to establish safety. This phase will most likely take ~1–2 years and explore tailored options for administration. Investigations will most likely be conducted in patients, possibly in a ‘patient-in-a-box’ setting (hospital or healthcare centre, virtual, or microsite). On completion of safety licencing, drugs will begin an assessment of efficacy in partnership with those responsible for reimbursement – testing will be performed in patients, possibly where individual patient involvement in safety testing will offer some reimbursement deal for future treatment. Change is coming, though its precise form will likely depend on the creativity and vision of sponsors, regulators and payers. In the words of Vladimir Lenin: “ It is impossible to predict the time and progress of revolution as it is governed by its own mysterious laws .”

Acknowledgements

The Pharmaceutical Contract Management Group (PCMG) is a not-for-profit association of clinical development outsourcing and procurement professionals from across the pharmaceutical and biotechnology industry who seek to advance best outsourcing practices. The contents of this communication are a summary of collated statements from participants of the PCMG 2022 annual meeting and do not represent the views or position of any company whose employees contributed to the event.

Contributions: All authors contributed equally to the preparation of this manuscript. All named authors meet the International Committee of Medical Journal Editors (ICMJE) criteria for authorship for this article, take responsibility for the integrity of the work as a whole and have given their approval for this version to be published.

Disclosure and potential conflicts of interest: The authors declare that they have no conflicts of interest relevant to this manuscript. The International Committee of Medical Journal Editors (ICMJE) Potential Conflicts of Interests form for the authors is available for download at: https://www.drugsincontext.com/wp-content/uploads/2023/05/dic.2023-2-2-COI.pdf

Funding declaration: There was no funding associated with the preparation of this article.

Correct attribution: Copyright © 2023 Hardman TC, Aitchison R, Scaife R, Edwards J, Slater G on behalf of the Committee of the Pharmaceutical Contract Management Group. https://doi.org/10.7573/dic.2023-2-2 . Published by Drugs in Context under Creative Commons License Deed CC BY NC ND 4.0.

Article URL: https://www.drugsincontext.com/the-future-of-clinical-trials-and-drug-development-2050

Provenance: Submitted; externally peer reviewed.

Drugs in Context is published by BioExcel Publishing Ltd. Registered office: 6 Green Lane Business Park, 238 Green Lane, New Eltham, London, SE9 3TL, UK.

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The future of research revealed

April 20, 2022

By Adrian Mulligan

Researchers lay bare the challenges and opportunities they face in a post-COVID world

The research ecosystem has been undergoing rapid and profound change, accelerated by COVID-19. This transformation is being fueled by many factors, including advances in technology, funding challenges and opportunities, political uncertainty, and new pressures on women in research.

At Elsevier, we have been working with the global research community to better understand these changes and what the world of research might look like in the future. The results were published today in Elsevier’s new Research Futures Report 2.0

Commenting on the report, Elsevier Research Director Adrian Mulligan said:

It’s clear from the results of the Research Futures Report 2.0 that we’re at a point of change. There is uncertainty and added pressures on the research community because of the pandemic. Universities, governments, research information providers, and funders working collaboratively are best positioned to help researchers manage that pressure.

Despite this uncertainty, researchers also believe there are long-term opportunities, most notably new levels of collaboration and openness across the research community, plus new sources of funding and technologies, which can help create a bright future for research.

Adrian Mulligan talks about the previous  Research Futures  report with colleagues in New York.

The report builds on a previous Research Futures study in 2019, carried out with the global research agency Ipsos MORI to gather predictions from funders, publishers, technology experts and researchers on what research might look like in 10 years’ time. The aim of the Research Futures project is to gather the views and opinions of researchers across the world to help us better understand the challenges and opportunities they face. Elsevier will use these insights to look at steps we could take to better support the research community in the future.

One point is clear: we can best prepare for the future by working together.

Key findings

Publishing moves faster, with more open knowledge.

The  Research Futures Report 2.0  shows that the past two years have driven progress in both speed and openness in the communication of research. Around two-thirds (67%) of researchers globally now consider preprints a valued source of communication, up from 43% before the pandemic — a shift likely driven by the increased role of preprints in finding ways to tackle COVID-19. While preprints are becoming more popular, they have not benefited from the pivotal role of peer review or had any additional value added to them by publishers. For example, 94% of version-of-record articles published in Elsevier journals have content changes made during the editorial process, and 13% of submissions go through major changes, according to 2021 Elsevier data. Also, 54% of respondents said they planned to publish open access, 6% higher than in 2019.

Funding is harder, but new opportunities emerge

Despite COVID spotlighting the importance of research, funding continues to be a major challenge for researchers, with half (50%) stating there is insufficient funding available in their field. Just one in four (24%) researchers believe there is enough funding for their work; worryingly, this figure has declined from nearly one in three (30%) in 2020. Researchers cite fewer funding sources, increased competition, changing priorities and the diversion of funds to COVID-19 related fields.

Looking ahead, researchers expect more money for research to become available from businesses, with 41% believing that corporate funding for research will increase. Government funding has also increased as a proportion of research budgets since 2019, which has led to a growth of funding across various subjects. For example, Materials Science research has seen the biggest growth in funding satisfaction in 2021, with 35% saying available funding is sufficient — almost triple the percentage (12%) who were satisfied with funding levels in 2020.

Women in research face new pressures — and adapt

While women in research were faster to adapt during the pandemic, they still face unique challenges. Elsevier’s research shows that they are:

Expecting to collaborate more than they did before the pandemic: 64% expect to increase work with researchers across different scientific disciplines, up from 49% in 2020.

Embracing technology faster than their male counterparts: 53% of women scientists think the use of technology in research will accelerate over the next 2 to 5 years versus 46% for men.

More likely to have shared their research with the wider public than men: 60% of women versus 55% of men have shared their research publicly.

Women reported having less time to do research during lockdowns, which could slow or hamper their future career prospects. 62% reported they were finding it difficult to find a good work-life balance during the pandemic, compared to just 50% of male researchers — a trend which could have significant negative long-term effects on the careers of women in research.

Researchers are collaborating more

As teaching, publishing and funding accelerate and increase the pressure on researchers, how they work has changed — and not necessarily for the worse. Researchers are collaborating more. Just over half (52%) state that they are sharing more research data now than 2 to 3 years ago, and the number of researchers who say they are collaborating more than in the past has grown to 63% from 48% pre-pandemic. The gains are across geographies and disciplines. Researchers in Computer Science have seen the biggest rise, with 76% agreeing that there is more collaboration involved in their projects than previously — a substantial rise from the 41% who agreed pre-pandemic.

More researchers are embracing AI

AI has been embraced more than ever during the past two years, though some caution remains. 16% of researchers are extensive users of AI in their research, and while high take-up in Computer Sciences skews that number (64% of computer scientists are heavy users), attitudes across a number of specialties have grown more positive. In Materials Science, which covers the structure and properties of materials and the discovery of new materials and how they are made, 18% are now likely to be extensive users of AI in their research, up from zero a year ago; in Chemistry, the number has grown from 2% to 19% and, in Maths, from 4% to 13% since 2020. Attitudes towards the use of AI in peer review is perhaps where we have seen the greatest shift in attitude: 21% of researchers agree they would read papers peer reviewed by AI — a 5-percentage point increase from 2019. Those age 55 and under are the most willing to read AI-reviewed articles (21%), while those age 56 and over have increased their willingness compared to a year ago (19%, up from 14% last year). At the same time, most researchers surveyed continue to object to AI peer review, with almost two in three unwilling to read such articles (58 percent) — a similar proportion as in 2020.

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Project methodology

In total, over 2,000 researchers responded to two separate global surveys: 1,173 researchers responded in July-August 2021 and 1,066 in July 2020. Responses have been weighted to be representative of the global researcher population by country (UNESCO/OECD data). Base sizes shown in this report are unweighted unless otherwise stated. The full methodology is available in the report.

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Adrian Mulligan

Research Director for Customer Insights, Elsevier

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The present-focused, future-ready R&D organization

Across engineered industries, the explosion in software has increased product complexity by an order of magnitude. Along with rapidly evolving technologies, fast-changing consumer preferences, accelerated product cycles, and the practical realities of globalized operations and markets, R&D departments are under unprecedented strain. As product variation grows and product portfolios expand, updating existing products compounds the already heavy load R&D organizations bear.

Yet amid these 21st-century challenges, R&D units are still following 20th-century models of organization—models not designed for today’s need for speed and the expanding web of interdependencies among all of the moving parts. The traditional component-based approach to R&D is no longer sensible in an era when digital and electronic systems are so thoroughly integrated with hardware. Still many companies struggle to shift toward an approach that focuses more on the function the customer wants, rather than the components that make the desired function work.

There is no one right way to organize R&D. But there are certain fundamentals that can help R&D organizations in advanced industries act more responsively and meet the burgeoning challenges they face today. From our work with clients and our extensive research, we’ve distilled a set of core design principles for R&D organizations and identified the important ones. By following these principles, companies can help their R&D organization serve as engines of innovation for outpacing competitors. And they can foster the agility organizations need in supporting collaboration among remote, distributed teams—as has become more important than ever in response to unpredictable external events.

A growing mismatch between design and function

Determining the right structure for the R&D organization has never been easy. The division of responsibility is a balancing act between the project-management organization and the R&D line organization, with inevitable trade–offs. Today’s R&D teams don’t have the luxury of following a sequential, piece-by-piece approach in which finished, designed components are handed off to testing at the end. Moreover, the teams need to be appropriately protected from the external and internal disruptions that the broader organization experiences, which today come with greater frequency.

As they’ve grown organically, many R&D organizations continue to operate with the same structures and processes they’ve used for years. Despite (or perhaps because of) the increasing inadequacy of those structures and processes, organizations don’t follow them consistently. Pet projects are often hard to kill, even long after their diminished promise becomes apparent. And because research effectiveness is hard to measure—and companies often don’t understand R&D costs or ways of working—the black-box image persists without challenge.

Thus, adhering to an existing structure isn’t enough: the shifting demands, the sheer volume of work and the growing complexity (much of it the result of software integration) make it incumbent on R&D organizations to reappraise their design. Instead, they can create new mechanisms to provide the coordination, transparency, governance, and risk protection R&D needs in the digital era.

A set of winning design principles

In the ideal R&D organization, responsibilities are clearly established, and interfaces between and among teams (internal and external) are seamless and transparent. These requirements, although not new, have become even more important of late, particularly when more teams are working remotely. R&D organizations that fulfill them can better meet further requirements—managing complexity actively and efficiently while staying focused on the future, and also maintaining the tools and capabilities for adapting to change.

Clearly delineate responsibilities for systems and end-to-end work

Historically, the R&D function has been organized according to field of expertise, components, or location, which has the effect of creating silos. Product properties are defined at the start of the development process, without being analyzed according to larger internal systems or user functions. Little attention is given to thinking in terms of the overarching goals customers want to achieve, or to the growing interdependencies as software and digital functions have pervaded almost every engineered product.

Many of the complications R&D organizations encounter today are the result of organizational interfaces that don’t match the product, along with a lack of transparency between groups. Take, for example, a feature such as lane-assistance for vehicles. Developing further advances in this function depends on a high level of coordination among teams developing steering systems, brake systems, and electrical systems. But too often that coordination occurs only late in design, perhaps even the final testing phase, by which point addressing problems becomes expensive and time-consuming. R&D organizations are more effective when they shift their orientation from components to user function, while keeping platform development stable to ensure a core of commonly used modules.

With such a shift, assigning end-to-end responsibility for functionality has become imperative. Companies can assign responsibility for the complete product, as well as for the individual system layers, under the “V” model shown in Exhibit 1, which imposes oversight as ideas progress from concept through to market release, series development, and finally upgrades.

The process moves from left to right. Under “Conception,” individual systems and their associated software are defined to fit customer demands and budget. Through early testing in the development process, issues and challenges become apparent early on. The right side of the V comprises testing and integration, which are conducted along the system layers. By breaking a product’s properties into requirements for systems and functions, the activities become transparent to everyone involved in development.

This approach enables iterative handshakes—more frequent interactions between concept owners and developers. Teams work together to translate properties into functional requirements. The approach also establishes dedicated responsibilities for complex functions and keeps the development process transparent. To coordinate and manage the interaction points, automotive companies tend to introduce central units that manage the whole integration process along the different development steps. These departments can be seen as a “stable backbone” within a dynamic development process, which helps improve planning for milestones and facilitates early failure detection. One machinery company, for example, set up quarterly integration meetings to align on priorities; the one to two days team members spend planning together gets them aligned for the next quarter’s work.

Perhaps the biggest benefit to assigning end-to-end responsibility is that it enables R&D to manage the interfaces and different development cycles between hardware and software development. Functionality owners coordinate the development of complex and interdependent components and features, creating technical guidelines and specifications that support consistency. They effectively safeguard the implementation and validate that the solution fulfills its requirements over its entire lifecycle.

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Keep functional interfaces across work sites to a minimum.

Companies can most effectively conceive of interfaces in terms of R&D’s geographic footprint—a balance taking into account which activities are performed where, and how the locations must interact. Minimizing functional interfaces across multiple sites and avoiding duplication of similar work are both important as well. Furthermore, to be cost-effective, location design can identify best-cost country sourcing for repetitive tasks, keeping in mind end-to-end responsibilities.

Dividing projects up among sites is usually less ideal, as people who work together in the same place tend to work more efficiently: earlier research found that with each additional development site for a given software product, productivity fell by about 14 percent (Exhibit 2.) Just the difference between one site and three sites amounts to a 37 percent decline in productivity.

Minimizing the number of handovers between sites—and making those that remain as smooth as possible—also helps. Distance between sites is not what matters; without the right management practices, a site across the city can seem as distant to employees as one clear across the country. But virtual teams can be as efficient as co-located teams, as long as the tools supporting the virtual work are utilized properly, communication is adapted accordingly, and everyone can participate on an equal basis.

R&D leaders can consider future roles and competencies when thinking about the physical design of the department. Where should the development of next-generation products start? How could a transition to new products be built for sites currently focused on legacy products? How will cost and availability figure into the overall network structure? The answers form a long-term site strategy that can help avert a talent crunch.

The right footprint model also builds in a detailed understanding of local requirements, such as interactions with suppliers, local regulations, and internally, the interdependencies with other departments or components. The sophistication of the design work, and the degree of conceptual work that will be done in a particular location, will inform the kind of competencies and technologies that will be needed. For example, one white-goods manufacturer carried out almost all development in its home market, later building a few local development centers in key markets to help adjust the products for local preferences, such as for refrigerator and freezer sizes, configurations, and color schemes.

Synchronize software and hardware development

Complexity in all its forms has increased markedly—product variations alone have exploded over the past two to three decades, driven largely by the rise of embedded software and digital capabilities.

But R&D protocols often fail to account for the unique challenges of managing the development of integrated software and hardware. Software and hardware development follow different development cycles and require different approaches to project steering. And when digital features or components aren’t explicitly considered in milestone planning, integration problems and delays are almost inevitable.

As essential as synchronizing development may be, it isn’t easy. In automotive, for example, map software generally takes about a year to develop, with frequent updates, while apps or innovative vehicle-control features (such as autopilot) may be updated monthly, with ongoing development and improvement. Contrast these cycle times with the hardware that runs navigation systems (which take two to three years to design and build), vehicle platforms (about seven years in the making) and basic vehicle components, such as heating systems and airbags—mature components that typically have a 10-year lifespan.

With such wide disparities in cycle times, transparency becomes crucial. The lack of it is a problem not only in concept development, but in delaying product launches as well. For complex functions, such as lane assistance, R&D units may have limited ability to measure how mature the product really is. When changes are made, teams may therefore fail to assess the implications on other features currently in development. Beyond cost overruns, delays, and risks to product integrity, poorly managed complexity invariably leads to finger-pointing among system teams as well as conflict between R&D and the project-management team.

R&D organizations have two options for managing the complexities of synching software and hardware development.

• Embedding software development within existing departments. This approach promotes integrated development—but in practice, processes are often designed from a hardware point of view, and software complexity is not managed effectively.
• Keeping development separate but coordinated. With this arrangement, individual technology components don’t get short shrift. The onus is on leaders to establish synchronization points to identify potential conflicts that would require escalation to senior management.

The approach to take is generally determined by the nature of the product, as well as the organization’s experience with software—bearing in mind that complexity will likely grow. Increasingly, services are developed not only within the engineering department but also within IT, creating still more interfaces and responsibilities, with implications for organization design.

Strike a balance between old and new technologies

When it comes to developing new technologies, R&D managers have three choices: segregate them completely in a separate unit; include them in the R&D organization, but keep them separate; or integrate them fully into the core R&D organization (Exhibit 3).

Taking supplier collaboration to the next level

Segregating the current and new technologies has its advantages. Unfettered by standard processes, separated units are free to realize their full potential. The R&D organization keeps budgets separate and shields the new technology from the noise of existing projects.

But this option can be a hard sell to management, as creating a new unit can be costly, labor-intensive, and harder to absorb into the existing structure. Beyond the break-in time to adapt to existing products and processes, segregation also limits the broader organization’s ability to transfer capabilities and knowledge, particularly given that cutting-edge technologies call for special (at times rare) expertise and training time for employees.

Short of total separation, there are essentially two ways to include new technology development within the R&D organization. Integrating new technologies fully into the existing organization helps transfer knowledge, and lets the new part of the organization tap into existing capabilities and processes, all of which helps in reaching scale faster. However, in this arrangement, there are risks—new technologies could be prematurely quashed by senior management, or if developed according to current methodologies, could yield less-than-optimal results.

An R&D makeover to sustain market leadership

A global production-equipment manufacturer had long viewed its R&D organization as a crucial source of competitive advantage. But the company’s rapid growth and increasingly complex product portfolio meant that more products were being developed in parallel. That led to even greater specialization among engineers and more technical interdependencies across modules. As the number of engineers and management layers grew, so did the number and complexity of interfaces, threatening the company’s rapid growth.

Historically, R&D groups had been organized in two types of departments.

• System functions, which handled the functionalities that met system specs and customer requirements, such as for productivity and machine precision.
• Engineering functions, encompassing specialties such as electronics, mechanics, software, and environmental controls.

These functions were required for developing the system modules and the system architecture needed for the integrity of the assembled machine.

Cutting complexity

As a first step in redesigning the R&D organization, R&D leaders made system function leaders responsible for tangible and testable machine modules. System leaders’ reports were given responsibility for the respective submodules. In that way, every production module and submodule would have a clear owner with end-to-end responsibility, from new-product introduction to third-line field-service support. Whereas before, each engineer worked on multiple products, under the new system each now works on only one business line and handles only one submodule at a time (Exhibit).

System-function departments are now primarily business-line dedicated. Each system function has a central architecture team that promotes commonality in the system modules’ roadmaps and the maximum reuse of module elements among business lines.

Engineering-function teams (such as software teams) are largely dedicated to modules or submodules. Leaders have the authority to deliver their technical roadmap with more stable, focused, and experienced people. Within each engineering function is a central architecture department that’s responsible for system design and standards (the left side of the V in Exhibit 1 in the main text) and for setting guardrails for module design and development. This structure also ensures integrity in the final product.

Responsive and future-ready

To maintain system integrity, shared platforms, and innovation- and knowledge-sharing across business lines, the company established several central teams. To manage competence (and continue building needed skills), the organization developed a taxonomy of critical competencies, assigned to VPs and managers and governed through an annual planning cycle.

The stable, multidisciplinary teams that characterize the new design have created a solid foundation for piloting and scaling agile ways of working in the product development teams. Since the launch of the new organization, more than 2,000 engineers have migrated to agile methods. But engineers aren’t the only ones working in new ways. By forging and executing the redesign as a team, R&D leaders have developed adaptive muscle, with the ability to adjust their organization to fast-changing requirements and environments.

Most often, the best bet is a happy medium, in which new technologies are assigned to a separate team but explored within the current R&D organization (see sidebar, “An R&D makeover to sustain market leadership”).

The right approach is also a function of the situation and the culture. Consider the electric powertrain in the automotive industry—the different manufacturers offer a sample of all of the archetypes.

To be future-ready, adopt new ways of working

The traditional waterfall development model that some organizations still follow is so protracted that products can be obsolete by the time they are released. Long development times become impracticable when businesses factor in the out-of-sync cycle times of software and hardware components. In addition, a siloed and fragmented organizational structure makes it hard to respond nimbly to new process requirements.

Fast-changing customer demands and rapidly evolving technologies have increased the premium for enterprises and their R&D organizations to be adaptable, flexible, and future-oriented. And the coordination, integration, and speed needed in R&D today call for new ways of working. These include agile methods that enable fast iterations and cross-functional, flexible teams that ensure that the concerns of all relevant stakeholders—people from different functional units, as well as the different engineering teams, project managers, and customer representatives—are addressed. For example, a team working on autonomous driving would include not only software engineers but also hardware engineers from the steering, brake-system, and overall car-design teams, as well as those working on user interface design.

To foster a future orientation within the R&D function, companies can adopt certain design features and practices, in particular those structures that promote agility:

• A flat organization in which teams are granted full responsibility to design solutions. This creates a strong sense of ownership among individuals
• End-to-end, cross-functional teams whose talent is drawn from all the relevant and traditional R&D functions. Often, teams are supported by individuals outside of R&D, such as marketing managers or customer representatives. Team membership is stable and changes only when projects are finished or strategic priorities change
• Pools of experts (both internal and external) that support projects with the talent they need
• Resource allocation that is flexible, shifting as needs change
• More co-location time for teams, wherever possible
• Role descriptions and rewards that align with the new organizational structure and targets

These practices usually suggest that the company might consider changing certain roles in the organization—particularly in light of the widespread need for more architects, as leaders are charged with empowering teams to foster innovation more than ever before. In fact, an automotive manufacturer saw its leadership transformation as a driving force for putting in place its new R&D organization.

A further question we are hearing is: how does all of this work in a remote working environment? The bulk of these practices can be implemented in a digitally enabled organization if co-location is not an option, with priority for practical matters such ensuring teams have sufficient bandwidth to connect as often as needed. Clear roles and targets will be especially important as well, as will an emphasis on empowering teams and individuals.

With ever-expanding product portfolios—from more product variation to additional software embedded in engineered products—R&D organizations tell us they are struggling to keep up pace. That makes the shift from a traditional, component-based approach to a functional all the more essential.

Change isn’t easy for this traditionally black-box area of the organization. Engineers themselves struggle with how to reengineer their own work processes, often not knowing where to start. To determine the right blueprint, it helps to step back and reflect on current performance and future needs by asking a few central questions:

• Do we have a clear way of addressing the complexity that comes from interfaces?
• How are we handling interdependencies between systems? Is complexity increasing, and if so, are we well set up for the future demands?
• Do we have what it takes to adapt to a larger proportion of software development in our R&D?
• Are we sufficiently agile and flexible to adjust our focus based on changing demand? Could we handle more frequent changes in demand?
• How prepared are we for future technologies? Do we have the right structure in place to acquire and scale them?
• Do we have sufficiently clear roles, interfaces, and end-to-end responsibilities within R&D between teams and sites and to other departments?

There is no master formula for making this shift—nor could there be, given the differences across industries and from organization to organization—but certain principles prevail. Abiding by the principles outlined here can provide a blueprint needed for integration at the right points, and the much-needed transparency across R&D. If R&D is the company’s engine of innovation, its own transformation is more than a matter of securing market share, it’s about being built for a fast-changing present in order to secure the future.

Anne Hidma is an associate partner in McKinsey’s Amsterdam office, where Vendla Sandström is a consultant, and Sebastian Küchleris a partner in the Munich office.

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What Is Research and Development?

• Understanding R&D
• Types of R&D
• Pros and Cons
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• R&D vs. Applied Research
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The Bottom Line

• Business Essentials

What Is Research and Development (R&D)?

Investopedia / Ellen Lindner

Research and development (R&D) is the series of activities that companies undertake to innovate. R&D is often the first stage in the development process that results in market research product development, and product testing.

Key Takeaways

• Research and development represents the activities companies undertake to innovate and introduce new products and services or to improve their existing offerings.
• R&D allows a company to stay ahead of its competition by catering to new wants or needs in the market.
• Companies in different sectors and industries conduct R&D—pharmaceuticals, semiconductors, and technology companies generally spend the most.
• R&D is often a broad approach to exploratory advancement, while applied research is more geared towards researching a more narrow scope.
• The accounting for treatment for R&D costs can materially impact a company's income statement and balance sheet.

Understanding Research and Development (R&D)

The concept of research and development is widely linked to innovation both in the corporate and government sectors. R&D allows a company to stay ahead of its competition. Without an R&D program, a company may not survive on its own and may have to rely on other ways to innovate such as engaging in mergers and acquisitions (M&A) or partnerships. Through R&D, companies can design new products and improve their existing offerings.

R&D is distinct from most operational activities performed by a corporation. The research and/or development is typically not performed with the expectation of immediate profit. Instead, it is expected to contribute to the long-term profitability of a company. R&D may often allow companies to secure intellectual property, including patents , copyrights, and trademarks as discoveries are made and products created.

Companies that set up and employ departments dedicated entirely to R&D commit substantial capital to the effort. They must estimate the risk-adjusted return on their R&D expenditures, which inevitably involves risk of capital. That's because there is no immediate payoff, and the return on investment (ROI) is uncertain. As more money is invested in R&D, the level of capital risk increases. Other companies may choose to outsource their R&D for a variety of reasons including size and cost.

Companies across all sectors and industries undergo R&D activities. Corporations experience growth through these improvements and the development of new goods and services. Pharmaceuticals, semiconductors , and software/technology companies tend to spend the most on R&D. In Europe, R&D is known as research and technical or technological development.

Many small and mid-sized businesses may choose to outsource their R&D efforts because they don't have the right staff in-house to meet their needs.

Types of Research and Development (R&D)

There are several different types of R&D that exist in the corporate world and within government. The type used depends entirely on the entity undertaking it and the results can differ.

Basic Research

There are business incubators and accelerators, where corporations invest in startups and provide funding assistance and guidance to entrepreneurs in the hope that innovations will result that they can use to their benefit.

M&As and partnerships are also forms of R&D as companies join forces to take advantage of other companies' institutional knowledge and talent.

Applied Research

One R&D model is a department staffed primarily by engineers who develop new products —a task that typically involves extensive research. There is no specific goal or application in mind with this model. Instead, the research is done for the sake of research.

Development Research

This model involves a department composed of industrial scientists or researchers, all of who are tasked with applied research in technical, scientific, or industrial fields. This model facilitates the development of future products or the improvement of current products and/or operating procedures.

The largest companies may also be the ones that drive the most R&D spend. For example, Amazon has reported $1.147 billion of research and development value on its 2023 annual report.

Advantages and Disadvantages of R&D

There are several key benefits to research and development. It facilitates innovation, allowing companies to improve existing products and services or by letting them develop new ones to bring to the market.

Because R&D also is a key component of innovation, it requires a greater degree of skill from employees who take part. This allows companies to expand their talent pool, which often comes with special skill sets.

The advantages go beyond corporations. Consumers stand to benefit from R&D because it gives them better, high-quality products and services as well as a wider range of options. Corporations can, therefore, rely on consumers to remain loyal to their brands. It also helps drive productivity and economic growth.

Disadvantages

One of the major drawbacks to R&D is the cost. First, there is the financial expense as it requires a significant investment of cash upfront. This can include setting up a separate R&D department, hiring talent, and product and service testing, among others.

Innovation doesn't happen overnight so there is also a time factor to consider. This means that it takes a lot of time to bring products and services to market from conception to production to delivery.

Because it does take time to go from concept to product, companies stand the risk of being at the mercy of changing market trends . So what they thought may be a great seller at one time may reach the market too late and not fly off the shelves once it's ready.

Facilitates innovation

Improved or new products and services

Expands knowledge and talent pool

Increased consumer choice and brand loyalty

Economic driver

Financial investment

Shifting market trends

R&D Accounting

R&D may be beneficial to a company's bottom line, but it is considered an expense . After all, companies spend substantial amounts on research and trying to develop new products and services. As such, these expenses are often reported for accounting purposes on the income statement and do not carry long-term value.

There are certain situations where R&D costs are capitalized and reported on the balance sheet. Some examples include but are not limited to:

• Materials, fixed assets, or other assets have alternative future uses with an estimable value and useful life.
• Software that can be converted or applied elsewhere in the company to have a useful life beyond a specific single R&D project.
• Indirect costs or overhead expenses allocated between projects.
• R&D purchased from a third party that is accompanied by intangible value. That intangible asset may be recorded as a separate balance sheet asset.

R&D Considerations

Before taking on the task of research and development, it's important for companies and governments to consider some of the key factors associated with it. Some of the most notable considerations are:

• Objectives and Outcome: One of the most important factors to consider is the intended goals of the R&D project. Is it to innovate and fill a need for certain products that aren't being sold? Or is it to make improvements on existing ones? Whatever the reason, it's always important to note that there should be some flexibility as things can change over time.
• Timing: R&D requires a lot of time. This involves reviewing the market to see where there may be a lack of certain products and services or finding ways to improve on those that are already on the shelves.
• Cost: R&D costs a great deal of money, especially when it comes to the upfront costs. And there may be higher costs associated with the conception and production of new products rather than updating existing ones.
• Risks: As with any venture, R&D does come with risks. R&D doesn't come with any guarantees, no matter the time and money that goes into it. This means that companies and governments may sacrifice their ROI if the end product isn't successful.

Research and Development vs. Applied Research

Basic research is aimed at a fuller, more complete understanding of the fundamental aspects of a concept or phenomenon. This understanding is generally the first step in R&D. These activities provide a basis of information without directed applications toward products, policies, or operational processes .

Applied research entails the activities used to gain knowledge with a specific goal in mind. The activities may be to determine and develop new products, policies, or operational processes. While basic research is time-consuming, applied research is painstaking and more costly because of its detailed and complex nature.

R&D Tax Credits

The IRS offers a R&D tax credit to encourage innovation and significantly reduction their tax liability. The credit calls for specific types of spend such as product development, process improvement, and software creation.

Enacted under Section 41 of the Internal Revenue Code, this credit encourages innovation by providing a dollar-for-dollar reduction in tax obligations. The eligibility criteria, expanded by the Protecting Americans from Tax Hikes (PATH) Act of 2015, now encompass a broader spectrum of businesses. The credit tens to benefit small-to-midsize enterprises.

To claim R&D tax credits, businesses must document their qualifying expenses and complete IRS Form 6765 (Credit for Increasing Research Activities). The credit, typically ranging from 6% to 8% of annual qualifying expenses, offers businesses a direct offset against federal income tax liabilities. Additionally, businesses can claim up to $250,000 per year against their payroll taxes.

Example of Research and Development (R&D)

One of the more innovative companies of this millennium is Apple Inc. As part of its annual reporting, it has the following to say about its research and development spend:

In 2023, Apple reported having spent $29.915 billion. This is 8% of their annual total net sales. Note that Apple's R&D spend was reported to be higher than the company's selling, general and administrative costs (of $24.932 billion).

Note that the company doesn't go into length about what exactly the R&D spend is for. According to the notes, the company's year-over-year growth was "driven primarily by increases in headcount-related expenses". However, this does not explain the underlying basis carried from prior years (i.e. materials, patents, etc.).

Research and development refers to the systematic process of investigating, experimenting, and innovating to create new products, processes, or technologies. It encompasses activities such as scientific research, technological development, and experimentation conducted to achieve specific objectives to bring new items to market.

What Types of Activities Can Be Found in Research and Development?

Research and development activities focus on the innovation of new products or services in a company. Among the primary purposes of R&D activities is for a company to remain competitive as it produces products that advance and elevate its current product line. Since R&D typically operates on a longer-term horizon, its activities are not anticipated to generate immediate returns. However, in time, R&D projects may lead to patents, trademarks, or breakthrough discoveries with lasting benefits to the company. 

Why Is Research and Development Important?

Given the rapid rate of technological advancement, R&D is important for companies to stay competitive. Specifically, R&D allows companies to create products that are difficult for their competitors to replicate. Meanwhile, R&D efforts can lead to improved productivity that helps increase margins, further creating an edge in outpacing competitors. From a broader perspective, R&D can allow a company to stay ahead of the curve, anticipating customer demands or trends.

There are many things companies can do in order to advance in their industries and the overall market. Research and development is just one way they can set themselves apart from their competition. It opens up the potential for innovation and increasing sales. However, it does come with some drawbacks—the most obvious being the financial cost and the time it takes to innovate.

Amazon. " 2023 Annual Report ."

Internal Revenue Service. " Research Credit ."

Internal Revenue Service. " About Form 6765, Credit for Increasing Research Activities ."

Apple. " 2023 Annual Report ."

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U.S. Geological Survey climate science plan—Future research directions

Executive Summary 

Climate is the primary driver of environmental change and is a key consideration in defining science priorities conducted across all mission areas in the U.S. Geological Survey (USGS). Recognizing the importance of climate change to its future research agenda, the USGS’s Climate Science Steering Committee requested the development of a Climate Science Plan to identify future research directions. Subject matter experts from across the Bureau formed the USGS Climate Science Plan Writing Team, which convened in September 2022 to identify and outline the major climate science topics of future concern and develop an integrated approach to conducting climate science in support of the USGS and U.S. Department of the Interior missions. The resulting USGS Climate Science Plan identifies three major priorities under which USGS climate science proceeds: (1) characterize climate change and associated impacts, (2) assess climate change risks and develop approaches to mitigate climate change, and (3) provide climate science tools and support. The Climate Science Plan identifies 12 specific goals to achieve the outcomes of the three priorities.

• Conduct long-term, broad-scale, and multidisciplinary measurements and monitoring and research activities to define, quantify, and predict the impacts of climate change on natural and human systems;
• Provide leadership to standardize measuring, monitoring, reporting, and verifying greenhouse gas emissions, lateral carbon fluxes, and carbon sinks across lands managed by the U.S. Department of the Interior (DOI);
• Provide science capacity, training, tools, and infrastructure to Tribal partners; support Tribal-led science initiatives;
• Conduct climate change research in partnership with the broader climate science community;
• Develop improved data synthesis methods through collaborative and open science across mission areas and between the USGS and agency partners;
• Translate climate change impacts into risk assessments in support of risk management strategies;
• Develop new and improved risk assessments, models, and approaches for mitigating climate change, adapting to its impacts, and reducing uncertainties; design early warning systems for risk mitigation;
• Investigate climate change mitigation strategies and create decision science support tools to inform climate change mitigation and adaptation;
• Provide a framework that facilitates knowledge co-production needed to inform policy decisions;
• Provide access to USGS data and information through novel integration and visualization approaches;
• Build capacity within USGS and DOI through development of scientific training curricula; and
• Coordinate science and capacity building efforts broadly across the Federal Government.

To achieve these goals, the USGS Climate Science Plan also outlines climate science guidelines—key elements for conducting climate-based research—as well as emerging opportunities to support successful climate science. The USGS Climate Science Plan provided in this circular will guide future research priorities and science-support investments, as well as continued development of the climate workforce for decades to come, ensuring that the USGS continues to serve as one of the Nation’s leading climate science agencies.

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Tamara wilson, research geographer, deputy director, ryan boyles, ph.d., senior scientist, casc climate adaptation technical support, nicole m. decrappeo, ph.d., regional administrator, northwest casc, judith z. drexler, research hydrologist, kevin d kroeger, phd, research chemist, rachel a loehman, ph.d., research landscape ecologist, john m. pearce, ph.d., associate center director for ecosystems, mark p waldrop, ph.d., research soil scientist, peter d. warwick, ph.d., supervisory research geologist, anne m wein, ph.d., operations research analyst, sara l zeigler, ph.d, t. douglas beard, jr., ph.d., senior administrator, national casc.

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FUTURE RESEARCH

Types of future research suggestion.

The Future Research section of your dissertation is often combined with the Research Limitations section of your final, Conclusions chapter. This is because your future research suggestions generally arise out of the research limitations you have identified in your own dissertation. In this article, we discuss six types of future research suggestion. These include: (1) building on a particular finding in your research; (2) addressing a flaw in your research; examining (or testing) a theory (framework or model) either (3) for the first time or (4) in a new context, location and/or culture; (5) re-evaluating and (6) expanding a theory (framework or model). The goal of the article is to help you think about the potential types of future research suggestion that you may want to include in your dissertation.

Before we discuss each of these types of future research suggestion, we should explain why we use the word examining and then put or testing in brackets. This is simply because the word examining may be considered more appropriate when students use a qualitative research design; whereas the word testing fits better with dissertations drawing on a quantitative research design. We also put the words framework or model in brackets after the word theory . We do this because a theory , framework and model are not the same things. In the sections that follow, we discuss six types of future research suggestion.

Addressing research limitations in your dissertation

Building on a particular finding or aspect of your research, examining a conceptual framework (or testing a theoretical model) for the first time, examining a conceptual framework (or testing a theoretical model) in a new context, location and/or culture.

• Expanding a conceptual framework (or testing a theoretical model)

Re-evaluating a conceptual framework (or theoretical model)

In the Research Limitations section of your Conclusions chapter, you will have inevitably detailed the potential flaws (i.e., research limitations) of your dissertation. These may include:

An inability to answer your research questions

Theoretical and conceptual problems

Limitations of your research strategy

Problems of research quality

Identifying what these research limitations were and proposing future research suggestions that address them is arguably the easiest and quickest ways to complete the Future Research section of your Conclusions chapter.

Often, the findings from your dissertation research will highlight a number of new avenues that could be explored in future studies. These can be grouped into two categories:

Your dissertation will inevitably lead to findings that you did not anticipate from the start. These are useful when making future research suggestions because they can lead to entirely new avenues to explore in future studies. If this was the case, it is worth (a) briefly describing what these unanticipated findings were and (b) suggesting a research strategy that could be used to explore such findings in future.

Sometimes, dissertations manage to address all aspects of the research questions that were set. However, this is seldom the case. Typically, there will be aspects of your research questions that could not be answered. This is not necessarily a flaw in your research strategy, but may simply reflect that fact that the findings did not provide all the answers you hoped for. If this was the case, it is worth (a) briefly describing what aspects of your research questions were not answered and (b) suggesting a research strategy that could be used to explore such aspects in future.

You may want to recommend that future research examines the conceptual framework (or tests the theoretical model) that you developed. This is based on the assumption that the primary goal of your dissertation was to set out a conceptual framework (or build a theoretical model). It is also based on the assumption that whilst such a conceptual framework (or theoretical model) was presented, your dissertation did not attempt to examine (or test) it in the field . The focus of your dissertations was most likely a review of the literature rather than something that involved you conducting primary research.

Whilst it is quite rare for dissertations at the undergraduate and master's level to be primarily theoretical in nature like this, it is not unknown. If this was the case, you should think about how the conceptual framework (or theoretical model) that you have presented could be best examined (or tested) in the field . In understanding the how , you should think about two factors in particular:

What is the context, location and/or culture that would best lend itself to my conceptual framework (or theoretical model) if it were to be examined (or tested) in the field?

What research strategy is most appropriate to examine my conceptual framework (or test my theoretical model)?

If the future research suggestion that you want to make is based on examining your conceptual framework (or testing your theoretical model) in the field , you need to suggest the best scenario for doing so.

More often than not, you will not only have set out a conceptual framework (or theoretical model), as described in the previous section, but you will also have examined (or tested) it in the field . When you do this, focus is typically placed on a specific context, location and/or culture.

If this is the case, the obvious future research suggestion that you could propose would be to examine your conceptual framework (or test the theoretical model) in a new context, location and/or culture. For example, perhaps you focused on consumers (rather than businesses), or Canada (rather than the United Kingdom), or a more individualistic culture like the United States (rather than a more collectivist culture like China).

When you propose a new context, location and/or culture as your future research suggestion, make sure you justify the choice that you make. For example, there may be little value in future studies looking at different cultures if culture is not an important component underlying your conceptual framework (or theoretical model). If you are not sure whether a new context, location or culture is more appropriate, or what new context, location or culture you should select, a review the literature will often help clarify where you focus should be.

Expanding a conceptual framework (or theoretical model)

Assuming that you have set out a conceptual framework (or theoretical model) and examined (or tested) it in the field , another series of future research suggestions comes out of expanding that conceptual framework (or theoretical model).

We talk about a series of future research suggestions because there are so many ways that you can expand on your conceptual framework (or theoretical model). For example, you can do this by:

Examining constructs (or variables) that were included in your conceptual framework (or theoretical model) but were not focused.

Looking at a particular relationship aspect of your conceptual framework (or theoretical model) further.

Adding new constructs (or variables) to the conceptual framework (or theoretical model) you set out (if justified by the literature).

It would be possible to include one or a number of these as future research suggestions. Again, make sure that any suggestions you make have are justified , either by your findings or the literature.

With the dissertation process at the undergraduate and master's level lasting between 3 and 9 months, a lot a can happen in between. For example, a specific event (e.g., 9/11, the economic crisis) or some new theory or evidence that undermines (or questions) the literature (theory) and assumptions underpinning your conceptual framework (or theoretical model). Clearly, there is little you can do about this. However, if this happens, reflecting on it and re-evaluating your conceptual framework (or theoretical model), as well as your findings, is an obvious source of future research suggestions.

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• Published: 19 April 2022

Focus Issue: The Future Of Cancer Research

Nature Medicine volume  28 ,  page 601 ( 2022 ) Cite this article

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New treatments and technologies offer exciting prospects for cancer research and care, but their global impact rests on widespread implementation and accessibility.

Cancer care has advanced at an impressive pace in recent years. New insights into tumor immunology and biology, combined with advances in artificial intelligence, nano tools, genetic engineering and sequencing — to name but a few — promise ever-more-powerful capabilities in the prevention, diagnosis and personalized treatment of cancer. How do we harness and build on these advances? How do we make them work in different global settings? In this issue, we present a Focus dedicated to the future of cancer research, in which we take stock of progress and explore ways to deliver research and care that is innovative, sustainable and patient focused.

This year brought news that two of the first patients with leukemia to receive chimeric antigen receptor (CAR) T cell treatment remain in remission more than a decade later . Writing in this issue, Carl June — who helped to treat these first patients — and colleagues reflect on how early transplant medicine laid a solid foundation for CAR T cell development in blood cancers, and how this is now paving the way for the use of engineered cell therapies in solid cancers. In a noteworthy step toward this goal, Haas and colleagues present results of a phase 1 trial of CAR T cells in metastatic, castration-resistant prostate cancer — a disease that has seen relatively few new treatment options in recent years.

Up to now, CAR T cells have been used only in the context of relapsed or refractory hematological malignancies, but in this issue, Neelapu et al . present phase 2 study data that suggest CAR T cell therapy could be beneficial when used earlier in certain high-risk patients. In addition, prospective data from van den Brink et al . support a role for the gut microbiome composition in CAR T cell therapy outcomes, highlighting new avenues of research to help maximize therapeutic benefit.

Although the idea that the gut microbiome influences CAR T cell therapy outcomes may be relatively new, it has been known for some time that it has a role in the response to checkpoint-inhibitor immunotherapy. A plethora of microbe-targeting therapies are now under investigation for cancer treatment; in this issue, Pal and colleagues describe one such strategy — whereby the combination of a defined microbial supplement with checkpoint blockade led to improved responses in patients with advanced kidney cancer. In their Review, Jennifer Wargo and colleagues take stock of the latest research in this field, and predict that microbial targeting could become a pillar of personalized cancer care over the next decade.

The theme for this year’s World Cancer Day was ‘Close the care gap’ — a message that is woven through several pieces in this issue. Early detection strategies have enormous potential to make a difference in this area; reviewing the latest advances, Rebecca Fitzgerald and colleagues ask who should be tested, and how — and outline their vision for personalized, risk-based screening, keeping in mind practicality and clinical implementation. Journalist Carrie Arnold reports on an emerging strategy known as ‘theranostics’ that aims to both diagnose and treat cancers in a unified approach, highlighting the growing commercial interest in this field. Of course, commercial interest does not equate to widespread availability or equal access to new therapies, and increasingly sophisticated technologies — although beneficial for some — can serve to widen existing inequalities.

Pediatric cancers lag far behind adult cancers in terms of drug development and approval. Nancy Goodman, a patient advocate whose son died from a childhood cancer, argues that market failures are largely to blame for the gap — but that legislative changes can correct this. Although in some cases there is a strong mechanistic rationale for testing promising adult cancer therapies or combinations in children, translational research is also needed to identify new therapeutic targets — such as the approach taken by Behjati and colleagues , which sheds new light on the molecular characteristics of an aggressive form of infant leukemia.

Meanwhile, for adult cancers, countless new therapeutic modalities are on the horizon , and drug approvals based on genomic biomarkers have accelerated in recent years. Unfortunately, their implementation into routine clinical care is progressing at a much slower pace. In their Perspective, Emile Voest and colleagues point out that bridging this gap will require investment in health infrastructure, as well as in education and decision-support tools, among other things.

Perhaps the most striking gap is that between high-income countries and low- and middle-income countries, not only in terms of cancer survival outcomes but also in terms of resources and infrastructure for impactful research. In their Perspective, CS Pramesh and colleagues outline their top priorities for cancer research in low- and middle-income countries, arguing that cancer research must be regionally relevant and geared toward reducing the number of patients diagnosed with advanced disease. Practicality is key — a sentiment echoed by Bishal Gyawali and Christopher Booth, who call for a “ common sense revolution ” in oncology, and regulatory policies and trial designs that serve patients better.

To realize this goal, clinical trial endpoints and outcome measures should be designed to minimize the burden on patients and maximize the potential for improving on the standard of care. This should go beyond survival outcomes; systemic effects, including cachexia and pain, have a major impact on quality of life and mental health during and after treatment. Two articles in this issue highlight the enormous psychological burden associated with a cancer diagnosis; increased risks of depression, self-harm and suicide emphasize the need for psychosocial interventions and a holistic approach to treatment.

As noted by members of the Bloomberg New Economy International Cancer Coalition in their Comment , the widespread adoption of telemedicine and remote monitoring in response to the COVID-19 pandemic could, if retained, help to make cancer trials more patient centered. Therefore, as health systems and research infrastructures adapt to the ongoing pandemic, there exists an unprecedented opportunity to reshape the landscape of cancer research.

We at Nature Medicine are committed to helping shape this transformation. We are issuing a call for research papers that utilize innovative approaches to address current challenges in cancer prevention, detection, diagnosis and treatment — both clinical trials and population-based studies with global implications. Readers can find more information about publishing clinical research in Nature Medicine at https://www.nature.com/nm/clinicalresearch .

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Sep 02, 2024

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Panasonic HD develops “Technology Future Vision” aiming to create a sympathetic society built on thoughtful choices

Osaka, Japan, September 2, 2024 – Panasonic Holdings Corporation Co., Ltd. (“Panasonic HD”) today announces its “Technology Future Vision” developed through its Corporate Technology Sector. The vision outlines research and development direction to achieve an ideal year 2040 as the company sees it. This vision sets out to create a “co-caring” society where individual choice naturally leads to caring for each other and the planet, with a 2040 goal for enacting this vision. Panasonic HD's Corporate Technology Sector hopes the vision will serve as a guide for listening to customers and collaborating with partners to shape the future for the next generation.

A graphic visualizing the Technology Future Vision* 1

The Technology Future Vision focuses on three key elements to achieve its goals: Maximizing resource value of energy, goods, and food by ensuring safe, affordable and green energy and resources circulate in daily life; Creating meaningful time to live each day with a sense of fulfillment by nurturing the use of time ; And caring for the self and tolerant relationships with others, harmonious state of mind and body that encourages co-caring and tolerant relationships.

The first element focuses on sustainability, aiming to distribute safe, affordable and green energy and resources widely. The second and third elements, building on the first, focus on well-being, aiming to spread sense of fulfillment and compassion in relationships with oneself, friends, family, community, and nature. These circular efforts are expected to drive solutions to social issues.

In developing the Technology Future Vision, Panasonic HD's Corporate Technology Sector employed a new framework based on the company's proprietary design management method, the “Future Vision Program.”* 2 The team involved in developing the vision consisted of experts in business, technology, and creative fields to incorporate diverse perspectives. They analyzed what they called “Future Sign Cards” listing emerging social changes and “Technology Drivers,” organizing information on technological advances with significant social impact. The team formulated the vision through discussions focused around a human-centric, future-driven approach.

Panasonic HD's Corporate Technology Sector will strive to continue refining this vision while listening to customers’ feedback. They aim to implement technologies and create new businesses needed by society through dialogue and collaboration with diverse companies and organizations. This effort aims to contribute to solving urgent environmental issues and support the lifelong health, safety, and comfort of consumers, in line with the Panasonic Group's goal of creating an “ideal society with affluence both in matter and mind.”

*1: An artistic rendition of a bird’s eye view depicting a “society where individual choice naturally leads to caring for each other and the planet” *2: This approach aims to identify the gap between current situations and the future, starting from the viewpoint of a “future that is meaningful to society” that Panasonic HD hopes to create, and to continue a virtuous cycle centered on a future-driven and people-, life-, society- and environment-centric philosophy to strengthen the intrinsic competitiveness of our business.

For more on the Technology Future Vision, click the link below: https://holdings.panasonic/global/corporate/technology/technology-future-vision.html

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Development of a basic evaluation model for manual therapy learning in rehabilitation students based on the Delphi method

• Wang Ziyi 1 , 2 ,
• Zhou Supo 3 &
• Marcin Białas 1  

BMC Medical Education volume  24 , Article number:  964 ( 2024 ) Cite this article

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Manual therapy is a crucial component in rehabilitation education, yet there is a lack of models for evaluating learning in this area. This study aims to develop a foundational evaluation model for manual therapy learning among rehabilitation students, based on the Delphi method, and to analyze the theoretical basis and practical significance of this model.

An initial framework for evaluating the fundamentals of manual therapy learning was constructed through a literature review and theoretical analysis. Using the Delphi method, consultations were conducted with young experts in the field of rehabilitation from January 2024 to March 2024. Fifteen experts completed three rounds of consultation. Each round involved analysis using Dview software, refining and adjusting indicators based on expert opinions, and finally summarizing all retained indicators using Mindmaster.

The effective response rates for the three rounds of questionnaires were 88%, 100%, and 100%, respectively. Expert familiarity scores were 0.91, 0.95, and 0.95; coefficient of judgment were 0.92, 0.93, and 0.93; authority coefficients were 0.92, 0.94, and 0.94, respectively. Based on three rounds of consultation, the model established includes 3 primary indicators, 10 secondary indicators, 17 tertiary indicators, and 9 quaternary indicators. A total of 24 statistical indicators were finalized, with 8 under the Cognitive Abilities category, 10 under the Practical Skills category, and 6 under the Emotional Competence category.

This study has developed an evaluation model for manual therapy learning among rehabilitation students, based on the Delphi method. The model includes multi-level evaluation indicators covering the key dimensions of Cognitive Abilities, Practical Skills, and Emotional Competence. These indicators provide a preliminary evaluation framework for manual therapy education and a theoretical basis for future research.

Peer Review reports

Introduction

The term “manual therapy” has traditionally been associated with physical therapists who examine and treat patients who have disorders related to the musculoskeletal system [ 1 ]. In vocational colleges in China, manual therapy techniques are an essential part of the rehabilitation education curriculum, integrating traditional Chinese medicine and modern medical teaching methods. These techniques include methods such as neurological rehabilitation, and the level of proficiency in these skills directly impacts the professional capabilities of students after graduation. In documents related to rehabilitation competency by the World Health Organization [ 2 , 3 , 4 ], it is noted that traditional teaching implicitly links the health needs of the population to the curriculum content. It also introduces competency-based education, which explicitly connects the health needs of the population to the competencies required of learners. The Rehabilitation Competency Framework (RCF) suggests a methodology for developing a rehabilitation education and training program and curriculum that can support competency-based education [ 5 ]. Research indicates that manual therapy education needs reform [ 6 ]. The existing evaluation models for manual therapy learning among rehabilitation students face several challenges: the use of equipment for objective assessments is cumbersome, the aspects of evaluation are not comprehensive, and there is a gap between the data from expert practices and the guidance provided to students. Some existing research has proposed models in specific manual therapy instruction. For example, the “Sequential Partial Task Practice (SPTP) strategy” was introduced in spinal manipulation (SM) teaching [ 7 ], and studies focusing on force-time characteristics [ 8 , 9 ] to summarize manual techniques for subsequent teaching. Some approaches apply specific techniques to specific diseases [ 10 ]. However, in terms of overall talent development, we may still need a more comprehensive and practical model.

Learning rehabilitation therapy techniques involves comprehensive skill development. Although some studies [ 11 , 12 ] have addressed the mechanisms of manual therapy, manual therapy based on mechanical actions should be considered one of the most important skills for rehabilitation therapists to focus on [ 13 ]. Currently, the training of rehabilitation students in vocational colleges primarily relies on course grades, clinical practice, and final-year exams to assess students before they enter society. However, these assessments often fail to meet the evaluation needs of employers, schools, teachers, patients/customers, and the students themselves regarding their rehabilitation capabilities. We lack a model for evaluating students’ manual therapy skills, especially for beginners. Developing a foundational evaluation model that integrates existing courses and clinical practice, in line with the World Health Organization’s Rehabilitation Competency Framework, holds significant practical and instructional value. This study aims to construct a foundational evaluation model for manual therapy learning among vocational school rehabilitation students through expert consultation. We present the following article following the CREDES reporting checklist (available at https://figshare.com/s/2886b42de467d58bd631 ) and the survey was performed according to the Delphi studies criteria [ 14 ].

This study employs the Delphi method for the following reasons [ 5 , 15 , 16 , 17 , 18 ]: Different experts have different emphases in manual therapy evaluation, and we need to collect a wide range of opinions and suggestions; unlike a focus group discussion, the anonymity of the Delphi method can reduce some disturbances in achieving consensus; the Delphi method allows for multiple rounds of consultation, facilitating the optimization of the model and flexible adjustment of issues that arise during consultation; the Delphi method is also used in constructing competency models for rehabilitation and has been maturely applied in closely related fields such as nursing. The research is mainly carried out in three stages: (1) Preparatory phase; (2) Delphi phase; (3) Reach consensus (Fig.  1 ).

The flow chart of the research

Literature review

We utilized databases from PubMed to search for and collect literature focused on the theme of rehabilitation education. With the MeSH terms related to “manual therapy” and “education” were used in PubMed. We also studied the World Health Organization’s (WHO) guidelines on rehabilitation competencies, gathered score sheets from national rehabilitation skills competitions, and collected training programs for students of rehabilitation therapy technology in vocational colleges in Jiangsu Province. This helped us to identify and organize the indicators that may be involved in the basic manual therapy learning of students.

Design consulting framework

The selection of experts for the study followed the principle of representativeness, considering factors such as educational qualifications, years of professional experience, and the type of workplace, which included schools, hospitals, and studios. It was ensured that each round included at least 15 experts [ 15 ]. Each round of questionnaires sent to experts is reviewed and tested. An initial list of 20 experts was created, and after a preliminary survey, the consultation list for the first round was determined randomly. The second round was organized based on the feedback and the collection of expert questionnaires from the first round, and the third round was set up following the second round’s feedback and questionnaire collection, continuing until the criteria for concluding the study were met. Inclusion criteria for experts included: (1) having a bachelor’s degree or higher; (2) at least two years of experience in teaching or mentoring; (3) achievements in provincial or national rehabilitation skills competitions or having guided students to such achievements; (4) high level of enthusiasm; and adherence to the principles of informed consent and voluntariness.

The main contents of expert consultation include the experts’ evaluation of the importance of the basic assessment indicators for students’ manual therapy learning, suggestions for building the model, basic information about the experts, and self-evaluations of the “basis for expert judgment” and “familiarity level”. Importance evaluation follows the Likert five-point rating scale, ranging from “very important” to “not important,” with scores assigned from 5 to 1, respectively. Expert Judgment Basis Coefficient (Ca): This includes aspects of work experience, theoretical analysis, understanding of domestic and international peers, and intuitive feelings, scored at three levels: high, medium, and low, with coefficients of 0.4, 0.3, 0.2 (work experience), 0.3, 0.2, 0.1 (theoretical analysis), 0.2, 0.1, 0.1 (understanding of peers), and 0.1, 0.1, 0.1 (intuitive feelings).Expert Familiarity Score (Cs): Rated over five levels: very familiar (1.0), familiar (0.8), moderately familiar (0.5), unfamiliar (0.2), and very unfamiliar (0.0). Expert Authority Coefficient (Cr): Indicates the level of expert authority, represented by the average of the Expert Judgment Basis Coefficient and Expert Familiarity Score. The prediction accuracy increases with the level of expert authority; an Expert Authority Coefficient ≥ 0.70 is considered acceptable, while this study uses an Expert Authority Coefficient > 0.8.

Statistical analysis

In this study, Excel and Dview software were used to analyze and process the data generated in each round. The degree of agreement among experts was analyzed using the coefficient of concordance and the coefficient of variation. The Kendall’s W coefficient of concordance, calculated through Dview software, is represented by W, which ranges from 0 to 1. A higher W value indicates better agreement among experts, and vice versa. If the P -value corresponding to W is less than 0.05, it can be considered that there is consistency in the experts’ ratings of the indicator system. The coefficient of variation (CV) is the ratio of the mean importance score of a certain indicator to its standard deviation; a smaller CV indicates a higher degree of agreement among experts about this indicator. This paper uses the coefficient of variation (CV) and Kendall’s W (W) to assess the level of agreement among expert opinions. A CV < 0.25 suggests a tendency towards consensus among experts. The concentration of expert opinions is represented by the arithmetic mean and the frequency of maximum scores. The arithmetic mean is the average of the experts’ importance scores for a particular indicator; a higher mean indicates greater importance of the indicator in the system. The frequency of maximum scores is the ratio of the number of experts who gave the highest score to an indicator to the total number of experts who rated that indicator; a higher frequency of maximum scores indicates greater importance of the indicator in the system.

A clear and transparent guide for action

During the indicator selection process, this paper adopts the “threshold method” for selecting indicators. The threshold calculation formulas used are as follows: For maximum score frequency and arithmetic mean, the threshold is calculated as “Threshold = Mean - Standard Deviation.” We will select indicators that score above this threshold. For the coefficient of variation, the threshold is calculated as “Threshold = Mean + Standard Deviation.” We will select indicators that score below this threshold. To ensure that key indicators are not eliminated, we will discard indicators that do not meet all three criteria. For indicators that do not meet one or two criteria, we will modify or discuss selection based on principles of rationality and systematicity. Modifications to the model content are generally confirmed by discussions between two experts. If the two experts cannot reach a consensus, a voting process is introduced for the disputed parts, and consensus is formed through expert voting. The process ends when all consulting experts no longer propose new suggestions for the overall model, and all indicators meet the inclusion criteria.

Basic principles of the model and model presentation

This study establishes two basic principles before constructing the target model. (1) The comprehensiveness of the model, where the dimensions of the assessment indicators built into the model are relatively comprehensive. (2) The flexibility of using the model, allows for flexible application across different scenarios, techniques, and personnel. Additionally, the model can be continuously supplemented and developed through further research. After consensus is reached, use MindMaster software to draw the final model.

Ethical considerations

The assignment for technical design, informed consent form, and data report form were approved by the Research Ethical Committee of Yancheng TCM Hospital Affiliated to Nanjing University of Chinese Medicine according to the World Medical Association Declaration of Helsinki Ethical. Approval number: KY230905-02.

Basic information of experts

In this study, an initial list of 20 experts was drafted. After a preliminary survey of their intentions, one expert who did not respond was excluded, and two with insufficient participation intentions were also excluded. This confirmed a list of 17 experts for the first round of consultation. After the first round, two experts whose authority coefficients were less than 0.8 were excluded, resulting in a final selection of 15 young experts from rehabilitation therapy-related schools, hospitals, and studios in Jiangsu Province (Table  1 ). The average age was 34.1 ± 6.6 years, and the average teaching tenure was 8.8 ± 7.7 years. Among them, one had an undergraduate degree, and 14 had graduate degrees or higher. All completed all three rounds of the survey. The level of expert engagement was indicated by the response rate of the expert consultation form, reflecting their concern for the study. The effective response rates were 88% for the first round, and 100% for the second and third rounds, all well above the 70% considered excellent. The average familiarity of the experts with the rounds was 0.91, 0.95, and 0.95 respectively, and the judgment basis coefficients were 0.92, 0.93, and 0.93. The authority coefficients were 0.92, 0.94, and 0.94 respectively.

Results of three rounds of the Delphi phase

The experts’ scoring data was organized in Excel and imported into DView software to calculate Kendall’s coefficient of concordance W, the progressive significance P value, chi-square, mean, coefficient of variation, and the frequency of full marks. The degree of opinion coordination and concentration of expert opinions across three rounds was summarized. The threshold method combined with expert views was applied to refine the model after three rounds of indicator screening. The table (Table  2 ) shows that the experts’ scoring on the indicator system was consistent across all three rounds.

The first Delphi round results

This round still included input from experts number 6 and 9 (Table  1 ). After the first round of consultation, according to the threshold principle (Table  3 ), the arithmetic mean and full score frequency of the primary indicator “Knowledge” in “On-campus” under “Relevant course scores” and “Off-campus” under “Relevant Skills Knowledge” did not meet the threshold. In the primary indicator “Skill”, under “Force” for “Quantitative (Instrument)” the coefficient of variation did not meet the threshold (Table  4 ). These findings suggest that the indicators set under “Knowledge” and “Skill” require significant modification, combined with the feedback from the consolidated advice of the 17 experts. There were 7 suggestions for optimizing the “Knowledge” indicator, 4 suggestions for “Skill”, 6 suggestions for “Emotion,” and 7 suggestions for the overall framework. We have redefined the “Knowledge” category as “Cognition” to broaden its conceptual scope [ 19 ], incorporating the indicator evaluation dimension of “Clinical Reasoning in Rehabilitation“ [ 20 , 21 , 22 ]. For the “Skill” category, we included “Proficiency“ [ 23 , 24 ] and “Subject Evaluation/Effectiveness“ [ 25 ] as indicator evaluation dimensions and divided “Applicability Judgment“ [ 26 , 27 , 28 , 29 ] and “Positioning selection” into four levels of indicators. For the “Emotion” category, we revised the indicators of “Car” and “Respect” to “Conduct and Demeanor” and “Professional Conduct,” dividing “Conduct and Demeanor” into four levels and “Professional Conduct” into three levels [ 30 ]. These recommendations were integrated into the design of the second-round consultation form to further explore the scientific nature of the model.

The second Delphi round results

After the second round of consultation, according to the threshold principle (Table  5 ), the primary indicator “Cognition” under “On-campus” for “Related Course Scores” did not meet the threshold for the arithmetic mean, and the coefficient of variation for “Clinical Practice Site Assessment” under “Off-campus” did not meet the threshold. Additionally, the average and full score frequency for “Related Skills and Knowledge Learning Ability Assessment” under “Off-campus” did not meet the threshold. For the primary indicator “Emotion”, under “Conduct and Demeanor”, the average and full score frequency for “Appearance and Dress” and the coefficient of variation for “Preparation of Materials” did not meet the threshold (Table  6 ). We consolidated the feedback from 15 experts and optimized the model. There were 11 optimization suggestions for the “Cognition” indicator, 3 for “Skill”, and 3 for “Emotion.” Regarding whether the tertiary indicator “Core Courses Scores” should be detailed into “Theoretical scores” and “Practical scores”, 13 experts chose “yes,” one chose “no,” and one was uncertain, thus it was adopted. Concerning whether to divide the tertiary indicators “Communication” and “Conduct and Behavior” into quaternary indicators, 7 experts chose “yes,” 7 chose “no,” and one was uncertain. Considering the actual application scenario and the simplicity of the model, we retained the quaternary indicators for “Communication” and removed the related quaternary indicators for “Conduct and Behavior”. Additionally, in the “Cognition” part of the “Clinical Reasoning in Rehabilitation”, we added “Science Popularization and Patient Education Awareness“ [ 31 , 32 ]; in “Skill”, we added “Palpation identification“ [ 33 , 34 , 35 ]; and in “Emotion” under “Professional Conduct,” we replaced “Respectful and Compassionate Thinking” with “Benevolent Physician Mindset”. After considering the content covered by nouns and the need for translation understanding, we further adjusted some expressions in the whole framework. The primary indicator “Cognitive”, “Skill” and “Emotion” were changed into “Cognitive Abilities”, “Practical Skills” and “Emotional Competence”. The secondary indicators “On-campus” and “Off-campus” are replaced by “Academic Performance” and “External Assessment”, and some other details are adjusted. These recommendations were integrated into the design of the third-round consultation form.

The third Delphi round results

After the third round of consultation, according to the threshold principle (Table  7 ), the average for “Related Course Grades” under “Academic Performance” in the primary indicator “Cognition Abilities” did not meet the threshold, nor did the average and full score frequency for “Science Popularization and Patient Education Awareness” under “Clinical Reasoning in Rehabilitation”. Additionally, the coefficient of variation for “Professional Expression” under “Communication” in “Conduct and Demeanor” within “Emotional Competence” did not meet the threshold (Table  8 ). After expert discussion, it was considered acceptable that these three indicator thresholds were exceptional. The 15 experts did not suggest further modifications to the model’s framework or content of indicators, indicating a stable and ideal concentration of opinions. Consequently, it was decided not to proceed with a fourth round of questionnaire survey.

Model presentation and external review

After the third round of research and investigation, we used Mindmaster software to draw the final model diagram (Fig.  2 ). Ultimately, three primary indicators, ten secondary indicators, seventeen tertiary indicators, and nine quaternary indicators were identified. Six experts evaluated the final model, and all agreed that it is relatively well-developed. Three experts raised concerns about the weighting of indicators, which may be the focus of our next phase of research. Additionally, one expert expressed great anticipation for feedback from the actual teaching application scenarios of this model.

The final model diagram

The design of teaching assessments for manual therapy education

A key aspect of manual therapy education in rehabilitation lies in understanding the “practice and case” paradigm [ 36 , 37 , 38 ]. Students transition from classroom learning in school to stage-wise assessment of their learning outcomes before entering the professional sphere, where their clinical practice mindset may evolve [ 20 ] but remain consistent in principle throughout. In our model, there is a concept of a “simulated patient”, which involves simulating assessments using standardized patients or cases representing various types of illnesses. This allows beginners to quickly narrow the gap in operational skills compared to experts [ 25 ]. The advancement of teaching philosophies has posed challenges in integrating the biopsychosocial model into manual therapy practices [ 30 ]. Students’ expectations regarding manual skills in physical therapy, along with reflections on the experiences of touch, both receiving and administering, can foster an understanding of the philosophical aspects of science, ethics, and communication [ 19 ]. The COVID-19 pandemic has altered the clinical practice and education of manual therapy globally [ 39 ]. Past classical teaching methods, such as Peyton’s four-step approach to teaching complex spinal manipulation techniques, have been found superior to standard teaching methods, effectively imparting intricate spinal manipulation skills regardless of gender [ 40 ]. Additionally, other methods involving the integration of teaching with clinical practice [ 38 ], interdisciplinary group learning approaches [ 41 ], and utilization of instructional videos instead of live demonstrations [ 42 ] have also been explored. From the initial use of closed-circuit television in massage education [ 43 ], we have progressed to leveraging the internet to learn the operational strategies and steps of exemplary therapists worldwide. This includes practices such as utilizing Computer-Assisted Clinical Case (CACC) SOAP note exercises to assess students’ application of principles and practices in osteopathic therapy [ 44 ] or employing interactive interdisciplinary online teaching tools for biomechanics and physiology instruction [ 45 ]. Establishing an online practice community to support evidence-based physical therapy practices in manual therapy is also pivotal [ 46 ]. Moreover, the integration of real-time feedback tools and teaching aids has significantly enhanced the depth and engagement of learning [ 9 ].

Designing teaching assessments is considered an “art”, and with the enrichment of teaching methods and tools, feedback strategies [ 47 ] in teaching are continuously optimized. The development of rehabilitation professional courses remains a focal point and challenge for educators. Reubenson A and Elkins MR summarize the models of clinical education for Australian physiotherapy students and analyze the current status of entry-level physiotherapy assessments, along with suggesting future directions for physiotherapy education [ 48 ]. Their study underscores the inclusivity of indicator construction in model development, enabling students from different internship sites to evaluate their manual therapy learning progress using the model. Moreover, the model can be utilized for assessment even in non-face-to-face scenarios. Tai J, Ajjawi R, et al.‘s study [ 49 ] summarized the historical development of teaching assessment, highlighting the transition of assessment models from simple knowledge or skill evaluation to more complex “complex appraisal.” This reflects the increased dimensions of educational assessment, the evolution of methods, and the emphasis on quality. From the Delphi outcomes, Sizer et al. identified eight key skill sets essential for proficiency in orthopedic manual therapy (OMT), as distilled through principal component factor analysis: manual joint assessment, fine sensorimotor proficiency, manual patient management, bilateral hand-eye coordination, gross manual characteristics of the upper extremity, gross manual characteristics of the lower extremity, control of self and patient movement, and discriminate touch [ 50 ]. Additionally, Rutledge CM et al.‘s study [ 51 ] focuses on developing remote health capabilities for nursing education and practice. Caliskan SA et al. [ 52 ]. established a consensus on artificial intelligence (AI)-related competencies in medical education curricula. These breakthroughs in teaching assessment concepts and formats that transcend spatial limitations are worth noting for the future. While existing research has established quantitative models for some challenging manual therapy operations, such as teaching and assessment of high-speed, low-amplitude techniques for the spine [ 53 ], a more comprehensive model is needed to assist beginners in manual therapy education.

The key elements in the manual therapy evaluation model

In 1973, McClelland DC first introduced the concept of competence, emphasizing “Testing for competence rather than for intelligence,” highlighting the importance of distinguishing individual performance levels within specific job contexts [ 54 ]. In 2021, the World Health Organization introduced a competence model for rehabilitation practitioners, defining competence in five dimensions: Practice, Professionalism, Learning and Development, Management and Leadership, and Research. Each dimension outlines specific objectives from the perspectives of Competencies and Activities, with requirements for rehabilitation practitioners varying from basic to advanced levels, encompassing simple to more comprehensive skills, under general principles of talent development [ 2 ]. Our model draws inspiration and insights from the framework and concepts proposed by the World Health Organization, as well as the scoring criteria of the Rehabilitation Skills Competition. When constructing primary indicators, we initially identified three dimensions: knowledge, skills, and emotions. Subsequently, adjustments were made during three rounds of the Delphi method. The content within the three modules can be independently referenced or utilized for novice practitioners to conduct self-assessment or peer evaluation before entering the workplace.

In the Cognitive Abilities module, the model incorporates Academic Performance, External Assessment, and Clinical Reasoning in Rehabilitation. Apart from the conventional Core Course Grades and Related Course Grades from the school curriculum, it also integrates evaluations from students’ internship processes, including Clinical Practice Site Assessment and Related Skills and Knowledge Learning Ability Assessment. To emphasize the significance of professional course learning in school, we further divide Core Course Grades into Theoretical Grades and Practical Grades, aligning with the current pre-clinical internship assessments at our institution. Regarding health education, this model focuses on areas consistent with some related research directions [ 32 , 55 , 56 ]. The model highlights the importance of Clinical Reasoning in Rehabilitation by emphasizing Problem Analysis and Problem Solving in clinical practice, while also addressing the importance of Science Popularization and Patient Education Awareness.

In the Practical Skills module, this model allows for demonstration assessment based on simulated clinical scenarios, where students perform maneuvers on standardized patients, with evaluation conducted by instructors or other experts. During the operation process, we may involve assessment criteria such as Selection of techniques, Palpation Identification, Force Application, Proficiency, and ultimately, Subject Evaluation/Effectiveness. The selection of techniques involves assessing the condition of the subject, determining specific maneuvers, and appropriateness of progression and regression during maneuvers. Additionally, the selection also considers the positioning of both the operator and the subject. In assessing Force Application, besides traditional subjective evaluations, objective assessments can also be facilitated with the aid of instrumentation. Finally, for assessing Proficiency in operation, evaluations can be provided for the Overall Diagnostic and Treatment Process and Overall Operation Status. This serves as a complement to further standardizing the manual therapy process [ 16 , 53 ], as the model can be applied in evaluating the procedures of certain specialized manual techniques.

In the Emotional Competence module, the model is divided into Conduct and Demeanor, and Professional Conduct. We believe that the therapeutic process between therapists and patients inherently involves interpersonal communication, hence focusing on Conduct and Behavior. Therefore, in conjunction with score sheets from national rehabilitation skills competitions, we may introduce more detailed requirements for Fluent Expression, Professional Expression, and Clear and Comprehensive Response. Furthermore, from the perspective of rehabilitation therapists’ professional roles and in alignment with the competence model, we emphasize the importance of Professional Conduct. We consider aspects such as Benevolent Physician Mindset and Scientific Diagnostic and Therapeutic Reasoning to be particularly noteworthy.

The scope and prospects of application of manual therapy evaluation model

The assessment model we designed holds relevance for skills or disciplines involving manual manipulation. Reviewing the literature on Manual Therapy [ 1 , 57 , 58 ] reveals that several terms are used interchangeably, such as Manipulative Therapy [ 59 ], Hands-on Therapy [ 31 ], Massage Therapy [ 24 , 60 ], Manipulative Physiotherapy [ 36 ], the Chiropractic Profession [ 61 ], and Osteopathy [ 62 ]. Threlkeld AJ once stated that manual therapy encompasses a broad range of techniques used to treat neuromusculoskeletal dysfunctions, primarily aiming to relieve pain and enhance joint mobility [ 58 ]. From a professional perspective, practitioners are often referred to as Physical Therapists [ 30 , 59 ], Manual Therapists [ 63 ], Manipulative Physiotherapists [ 33 ], and Massage Therapists [ 24 , 37 , 64 ]. Differences between Chiropractors and Massage Therapists have also been discussed in the literature [ 65 ]. The evolution of specific manual techniques such as Joint Mobilization [ 66 ], Osteopathic Manipulative Treatment (OMT) [ 67 , 68 ], Spinal Manipulation Therapy (SMT) [ 69 , 70 , 71 ], Posterior-to-Anterior (PA) High-Velocity-Low-Amplitude (HVLA) Manipulations [ 72 ], and Cervical Spine Manipulation [ 73 ] has provided more precise guidance for addressing common diseases and disorders. Furthermore, researchers have highlighted that the development of motor skills is an essential component of clinical training across various health disciplines including surgery, dentistry, obstetrics, chiropractic, osteopathy, and physical therapy [ 47 ]. In current rehabilitation education, manual therapy is a crucial component of physical therapy. We categorize physical therapy into physiotherapy and physical therapy exercises. Physiotherapy typically requires the use of special devices to perform interventions involving sound, light, electricity, heat, and magnetism. On the other hand, physical therapy exercises are generally performed manually, with some techniques occasionally requiring the use of simple assistive tools. As researchers have suggested with the concept of motor skills [ 47 ], our physical therapy exercises in teaching may not only be beneficial for a single discipline but could also enhance all disciplines that require “hands-on“ [ 31 ] or “human touch“ [ 13 ] operations.

In the prospects of manual therapy education, the comprehensive neurophysiological model has revealed that manual therapy produces effects through multiple mechanisms [ 11 , 12 ]. Studies have indicated [ 12 , 74 ] that the correlation between manual assessments and clinical outcomes, mechanical measurements, and magnetic resonance imaging is poor. As measurement methodologies enrich, our teaching assessment methods will also continuously evolve. Moreover, the close connection of manual therapy with related disciplines such as anatomy and physiology [ 75 , 76 , 77 ] provides physical therapists with a comprehensive biomedical background, enhancing their clinical capabilities and multidisciplinary collaboration skills [ 13 ]. Secondly, the development of educational resources should emphasize the integration of practice and theory. Drawing on the educational content packaging model of dispatcher-assisted cardiopulmonary resuscitation (DA-CPR) [ 78 ], combining e-learning with practical training, and computer-related teaching models will enrich offline teaching [ 74 ], providing students with a comprehensive learning experience. This model not only increases flexibility and accessibility but also optimizes learning outcomes through continuous performance assessment. Finally, with the development of artificial intelligence and advanced simulation technologies [ 79 ], future manual therapy education could simulate complex human biomechanics and neurocentral processes, providing deeper and more intuitive learning tools. This will further enhance educational quality and lay a solid foundation for the lifelong learning and career development of physical therapy professionals.

Limitations

The panel of experts consulted in this study is relatively concentrated among middle-aged and young professionals and exhibits noticeable regional characteristics. Consequently, the conclusions drawn may exhibit certain regional specificities. Moreover, during the translation process of professional terminology, some terms in the Chinese consultation form were uniform; however, modifications were made to ensure comprehension in the English context.

Conclusions

This study comprehensively utilized theoretical research, literature analysis, and the Delphi expert consultation method. The selected experts are highly authoritative, and there was a good level of activity across three rounds of consultations, with well-coordinated expert opinions. The model includes multi-level evaluation indicators covering the key dimensions of Cognitive Abilities, Practical Skills, and Emotional Competence. This research systematically and preliminarily constructed an evaluation system for foundational manual therapy learning in rehabilitation students.

Availability of data and materials

The datasets generated and/or analysed during the current study are available in the “figshare” repository, available at https://figshare.com/s/2886b42de467d58bd631 .

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Acknowledgements

Thanks to Dong Xinchun, Gu Zhongke, Lu Honggang, Li Le, Sun Wudong, Wang Yudi, Wu Wenlong, Zhao Xinyu and other experts for their assistance and patient analysis during the Delphi consultation process, and some experts chose to remain anonymous, we would like to express our gratitude once again.

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

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Both authors contributed to the creation of the manuscript. WZ designed and conceptualized the review and wrote the draft manuscript. ZS assisted with the Delphi consultation process and article writing. MB was involved in designing and implementing the project as a supervisor.

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Correspondence to Marcin Białas .

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Ziyi, W., Supo, Z. & Białas, M. Development of a basic evaluation model for manual therapy learning in rehabilitation students based on the Delphi method. BMC Med Educ 24 , 964 (2024). https://doi.org/10.1186/s12909-024-05932-y

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Received : 14 May 2024

Accepted : 20 August 2024

Published : 04 September 2024

DOI : https://doi.org/10.1186/s12909-024-05932-y

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