thesis healthcare sector

Research Topics & Ideas: Healthcare

F inding and choosing a strong research topic is the critical first step when it comes to crafting a high-quality dissertation, thesis or research project. If you’ve landed on this post, chances are you’re looking for a healthcare-related research topic , but aren’t sure where to start. Here, we’ll explore a variety of healthcare-related research ideas and topic thought-starters across a range of healthcare fields, including allopathic and alternative medicine, dentistry, physical therapy, optometry, pharmacology and public health.

NB – This is just the start…

The topic ideation and evaluation process has multiple steps . In this post, we’ll kickstart the process by sharing some research topic ideas within the healthcare domain. This is the starting point, but to develop a well-defined research topic, you’ll need to identify a clear and convincing research gap , along with a well-justified plan of action to fill that gap.

If you’re new to the oftentimes perplexing world of research, or if this is your first time undertaking a formal academic research project, be sure to check out our free dissertation mini-course. In it, we cover the process of writing a dissertation or thesis from start to end. Be sure to also sign up for our free webinar that explores how to find a high-quality research topic.

Overview: Healthcare Research Topics

• Allopathic medicine
• Alternative /complementary medicine
• Veterinary medicine
• Physical therapy/ rehab
• Optometry and ophthalmology
• Pharmacy and pharmacology
• Public health
• Examples of healthcare-related dissertations

Allopathic (Conventional) Medicine

• The effectiveness of telemedicine in remote elderly patient care
• The impact of stress on the immune system of cancer patients
• The effects of a plant-based diet on chronic diseases such as diabetes
• The use of AI in early cancer diagnosis and treatment
• The role of the gut microbiome in mental health conditions such as depression and anxiety
• The efficacy of mindfulness meditation in reducing chronic pain: A systematic review
• The benefits and drawbacks of electronic health records in a developing country
• The effects of environmental pollution on breast milk quality
• The use of personalized medicine in treating genetic disorders
• The impact of social determinants of health on chronic diseases in Asia
• The role of high-intensity interval training in improving cardiovascular health
• The efficacy of using probiotics for gut health in pregnant women
• The impact of poor sleep on the treatment of chronic illnesses
• The role of inflammation in the development of chronic diseases such as lupus
• The effectiveness of physiotherapy in pain control post-surgery

Topics & Ideas: Alternative Medicine

• The benefits of herbal medicine in treating young asthma patients
• The use of acupuncture in treating infertility in women over 40 years of age
• The effectiveness of homoeopathy in treating mental health disorders: A systematic review
• The role of aromatherapy in reducing stress and anxiety post-surgery
• The impact of mindfulness meditation on reducing high blood pressure
• The use of chiropractic therapy in treating back pain of pregnant women
• The efficacy of traditional Chinese medicine such as Shun-Qi-Tong-Xie (SQTX) in treating digestive disorders in China
• The impact of yoga on physical and mental health in adolescents
• The benefits of hydrotherapy in treating musculoskeletal disorders such as tendinitis
• The role of Reiki in promoting healing and relaxation post birth
• The effectiveness of naturopathy in treating skin conditions such as eczema
• The use of deep tissue massage therapy in reducing chronic pain in amputees
• The impact of tai chi on the treatment of anxiety and depression
• The benefits of reflexology in treating stress, anxiety and chronic fatigue
• The role of acupuncture in the prophylactic management of headaches and migraines

Topics & Ideas: Dentistry

• The impact of sugar consumption on the oral health of infants
• The use of digital dentistry in improving patient care: A systematic review
• The efficacy of orthodontic treatments in correcting bite problems in adults
• The role of dental hygiene in preventing gum disease in patients with dental bridges
• The impact of smoking on oral health and tobacco cessation support from UK dentists
• The benefits of dental implants in restoring missing teeth in adolescents
• The use of lasers in dental procedures such as root canals
• The efficacy of root canal treatment using high-frequency electric pulses in saving infected teeth
• The role of fluoride in promoting remineralization and slowing down demineralization
• The impact of stress-induced reflux on oral health
• The benefits of dental crowns in restoring damaged teeth in elderly patients
• The use of sedation dentistry in managing dental anxiety in children
• The efficacy of teeth whitening treatments in improving dental aesthetics in patients with braces
• The role of orthodontic appliances in improving well-being
• The impact of periodontal disease on overall health and chronic illnesses

Topics & Ideas: Veterinary Medicine

• The impact of nutrition on broiler chicken production
• The role of vaccines in disease prevention in horses
• The importance of parasite control in animal health in piggeries
• The impact of animal behaviour on welfare in the dairy industry
• The effects of environmental pollution on the health of cattle
• The role of veterinary technology such as MRI in animal care
• The importance of pain management in post-surgery health outcomes
• The impact of genetics on animal health and disease in layer chickens
• The effectiveness of alternative therapies in veterinary medicine: A systematic review
• The role of veterinary medicine in public health: A case study of the COVID-19 pandemic
• The impact of climate change on animal health and infectious diseases in animals
• The importance of animal welfare in veterinary medicine and sustainable agriculture
• The effects of the human-animal bond on canine health
• The role of veterinary medicine in conservation efforts: A case study of Rhinoceros poaching in Africa
• The impact of veterinary research of new vaccines on animal health

Topics & Ideas: Physical Therapy/Rehab

• The efficacy of aquatic therapy in improving joint mobility and strength in polio patients
• The impact of telerehabilitation on patient outcomes in Germany
• The effect of kinesiotaping on reducing knee pain and improving function in individuals with chronic pain
• A comparison of manual therapy and yoga exercise therapy in the management of low back pain
• The use of wearable technology in physical rehabilitation and the impact on patient adherence to a rehabilitation plan
• The impact of mindfulness-based interventions in physical therapy in adolescents
• The effects of resistance training on individuals with Parkinson’s disease
• The role of hydrotherapy in the management of fibromyalgia
• The impact of cognitive-behavioural therapy in physical rehabilitation for individuals with chronic pain
• The use of virtual reality in physical rehabilitation of sports injuries
• The effects of electrical stimulation on muscle function and strength in athletes
• The role of physical therapy in the management of stroke recovery: A systematic review
• The impact of pilates on mental health in individuals with depression
• The use of thermal modalities in physical therapy and its effectiveness in reducing pain and inflammation
• The effect of strength training on balance and gait in elderly patients

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Topics & Ideas: Optometry & Opthalmology

• The impact of screen time on the vision and ocular health of children under the age of 5
• The effects of blue light exposure from digital devices on ocular health
• The role of dietary interventions, such as the intake of whole grains, in the management of age-related macular degeneration
• The use of telemedicine in optometry and ophthalmology in the UK
• The impact of myopia control interventions on African American children’s vision
• The use of contact lenses in the management of dry eye syndrome: different treatment options
• The effects of visual rehabilitation in individuals with traumatic brain injury
• The role of low vision rehabilitation in individuals with age-related vision loss: challenges and solutions
• The impact of environmental air pollution on ocular health
• The effectiveness of orthokeratology in myopia control compared to contact lenses
• The role of dietary supplements, such as omega-3 fatty acids, in ocular health
• The effects of ultraviolet radiation exposure from tanning beds on ocular health
• The impact of computer vision syndrome on long-term visual function
• The use of novel diagnostic tools in optometry and ophthalmology in developing countries
• The effects of virtual reality on visual perception and ocular health: an examination of dry eye syndrome and neurologic symptoms

Topics & Ideas: Pharmacy & Pharmacology

• The impact of medication adherence on patient outcomes in cystic fibrosis
• The use of personalized medicine in the management of chronic diseases such as Alzheimer’s disease
• The effects of pharmacogenomics on drug response and toxicity in cancer patients
• The role of pharmacists in the management of chronic pain in primary care
• The impact of drug-drug interactions on patient mental health outcomes
• The use of telepharmacy in healthcare: Present status and future potential
• The effects of herbal and dietary supplements on drug efficacy and toxicity
• The role of pharmacists in the management of type 1 diabetes
• The impact of medication errors on patient outcomes and satisfaction
• The use of technology in medication management in the USA
• The effects of smoking on drug metabolism and pharmacokinetics: A case study of clozapine
• Leveraging the role of pharmacists in preventing and managing opioid use disorder
• The impact of the opioid epidemic on public health in a developing country
• The use of biosimilars in the management of the skin condition psoriasis
• The effects of the Affordable Care Act on medication utilization and patient outcomes in African Americans

Topics & Ideas: Public Health

• The impact of the built environment and urbanisation on physical activity and obesity
• The effects of food insecurity on health outcomes in Zimbabwe
• The role of community-based participatory research in addressing health disparities
• The impact of social determinants of health, such as racism, on population health
• The effects of heat waves on public health
• The role of telehealth in addressing healthcare access and equity in South America
• The impact of gun violence on public health in South Africa
• The effects of chlorofluorocarbons air pollution on respiratory health
• The role of public health interventions in reducing health disparities in the USA
• The impact of the United States Affordable Care Act on access to healthcare and health outcomes
• The effects of water insecurity on health outcomes in the Middle East
• The role of community health workers in addressing healthcare access and equity in low-income countries
• The impact of mass incarceration on public health and behavioural health of a community
• The effects of floods on public health and healthcare systems
• The role of social media in public health communication and behaviour change in adolescents

Examples: Healthcare Dissertation & Theses

While the ideas we’ve presented above are a decent starting point for finding a healthcare-related research topic, they are fairly generic and non-specific. So, it helps to look at actual dissertations and theses to see how this all comes together.

Below, we’ve included a selection of research projects from various healthcare-related degree programs to help refine your thinking. These are actual dissertations and theses, written as part of Master’s and PhD-level programs, so they can provide some useful insight as to what a research topic looks like in practice.

• Improving Follow-Up Care for Homeless Populations in North County San Diego (Sanchez, 2021)
• On the Incentives of Medicare’s Hospital Reimbursement and an Examination of Exchangeability (Elzinga, 2016)
• Managing the healthcare crisis: the career narratives of nurses (Krueger, 2021)
• Methods for preventing central line-associated bloodstream infection in pediatric haematology-oncology patients: A systematic literature review (Balkan, 2020)
• Farms in Healthcare: Enhancing Knowledge, Sharing, and Collaboration (Garramone, 2019)
• When machine learning meets healthcare: towards knowledge incorporation in multimodal healthcare analytics (Yuan, 2020)
• Integrated behavioural healthcare: The future of rural mental health (Fox, 2019)
• Healthcare service use patterns among autistic adults: A systematic review with narrative synthesis (Gilmore, 2021)
• Mindfulness-Based Interventions: Combatting Burnout and Compassionate Fatigue among Mental Health Caregivers (Lundquist, 2022)
• Transgender and gender-diverse people’s perceptions of gender-inclusive healthcare access and associated hope for the future (Wille, 2021)
• Efficient Neural Network Synthesis and Its Application in Smart Healthcare (Hassantabar, 2022)
• The Experience of Female Veterans and Health-Seeking Behaviors (Switzer, 2022)
• Machine learning applications towards risk prediction and cost forecasting in healthcare (Singh, 2022)
• Does Variation in the Nursing Home Inspection Process Explain Disparity in Regulatory Outcomes? (Fox, 2020)

Looking at these titles, you can probably pick up that the research topics here are quite specific and narrowly-focused , compared to the generic ones presented earlier. This is an important thing to keep in mind as you develop your own research topic. That is to say, to create a top-notch research topic, you must be precise and target a specific context with specific variables of interest . In other words, you need to identify a clear, well-justified research gap.

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18 Comments

I need topics that will match the Msc program am running in healthcare research please

Hello Mabel,

I can help you with a good topic, kindly provide your email let’s have a good discussion on this.

Can you provide some research topics and ideas on Immunology?

Thank you to create new knowledge on research problem verse research topic

Help on problem statement on teen pregnancy

This post might be useful: https://gradcoach.com/research-problem-statement/

can you give me research titles that i can conduct as a school nurse

can you provide me with a research topic on healthcare related topics to a qqi level 5 student

Please can someone help me with research topics in public health ?

Hello I have requirement of Health related latest research issue/topics for my social media speeches. If possible pls share health issues , diagnosis, treatment.

I would like a topic thought around first-line support for Gender-Based Violence for survivors or one related to prevention of Gender-Based Violence

Please can I be helped with a master’s research topic in either chemical pathology or hematology or immunology? thanks

Can u please provide me with a research topic on occupational health and safety at the health sector

Good day kindly help provide me with Ph.D. Public health topics on Reproductive and Maternal Health, interventional studies on Health Education

may you assist me with a good easy healthcare administration study topic

May you assist me in finding a research topic on nutrition,physical activity and obesity. On the impact on children

I have been racking my brain for a while on what topic will be suitable for my PhD in health informatics. I want a qualitative topic as this is my strong area.

Hi, may I please be assisted with research topics in the medical laboratory sciences

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Keeping pace with the healthcare transformation: a literature review and research agenda for a new decade of health information systems research

• Research Paper
• Open access
• Published: 17 July 2021
• Volume 31 , pages 901–921, ( 2021 )

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• Nadine Ostern   ORCID: orcid.org/0000-0003-3867-3385 1 ,
• Guido Perscheid 2 ,
• Caroline Reelitz 2 &
• Jürgen Moormann 2  

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A Correction to this article was published on 20 December 2021

This article has been updated

Accelerated by the coronavirus disease 2019 (Covid-19) pandemic, major and lasting changes are occuring in healthcare structures, impacting people's experiences and value creation in all aspects of their lives. Information systems (IS) research can support analysing and anticipating resulting effects.

The purpose of this study is to examine in what areas health information systems (HIS) researchers can assess changes in healthcare structures and, thus, be prepared to shape future developments.

A hermeneutic framework is applied to conduct a literature review and to identify the contributions that IS research makes in analysing and advancing the healthcare industry.

We draw an complexity theory by borrowing the concept of 'zooming-in and out', which provides us with a overview of the current, broad body of research in the HIS field. As a result of analysing almost 500 papers, we discovered various shortcomings of current HIS research.

Contribution

We derive future pathways and develop a research agenda that realigns IS research with the transformation of the healthcare industry already under way.

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Introduction

Particularly since the last decade, IT has opened up new opportunities for ‘ehealth’ through telemedicine and remote patient monitoring, alongside potential improvements in the cost-effectiveness and accessibility of health care (Chiasson & Davidson, 2004 ). Accordingly, health information systems (HIS) research has come to focus on how healthcare organizations invest in and then assimilate HIS, looking in particular at the impact of digitalization on healthcare costs, healthcare quality, and patient privacy (Chen et al., 2019 ; Park, 2016 ).

Less attention has been paid to issues such as mobile health, health information interchange, digital health communities, and services that change customer expectations and may lead to major disruptions (Chen et al., 2019 ; Park, 2016 ). These topics, however, are becoming increasingly important due to the penetration of the user and health market by external players, especially tech companies, providing services such as fitness trackers, and surveillance software for patient monitoring in hospitals (Gantori et al., 2020 ). Modern IT, thus, becomes a catalyst to provide greater operational efficiency, offering new possibilities for tech companies to build new health-centred business models and services (Park, 2016 ).

The ways in which tech companies are entering the healthcare industry can be seen amid the spread of coronavirus disease 2019 (Covid-19), which is pushing healthcare systems to the edge of their capacities (Worldbank, 2020 ). In this extraordinary condition, the pandemic has provided an additional opportunity for tech companies that were hitherto not active or not allowed to enter the healthcare industry (Gantori et al., 2020 ).

We are currently seeing how entering the healthcare market is actually taking place, particularly in the USA, where tech companies are increasingly offering services to help address some of the problems associated with Covid-19. Google’s subsidiary Verily, for instance, facilitates the automation of coronavirus symptom screening and provides actionable, up-to-date information that supports community-based decision-making (Landi, 2020 ). Although the collaboration with Verily assists the US government in tracking cases to identify the spread of the virus, it is reasonable to suggest that Verily probably did not launch the screening tool out of altruism. In fact, to receive preliminary screening results via the Verily app, citizens have to log into their personal Google account (Greenwood, 2020 ). This allows Verily to gain immense value by aggregating huge, structured data sets and analyse them to come up with new health services, such as better tools for disease detection, new data infrastructures, and insurance offerings that – for better or for worse – may outplay current healthcare providers and even disrupt whole healthcare ecosystems (CB Insights, 2018 ). Similarly, Amazon has started to provide cloud space through Amazon Web Services to store health surveillance data for the Australian government’s tracing app (Tillett, 2020 ), and Amazon Care, a division initially responsible for handling internal staff care needs, now cooperates with the Bill and Melinda Gates Foundation to distribute Covid-19 testing kits to US residents (Lee & Nilsson, 2020 ).

Looking at information systems (IS) researchers’ previous assessments of state-of-the-art healthcare-related IS literature reveals that most scholars seem to have little or no concern for the beginning of those potentially long-lasting changes that are occurring in the healthcare industry (Chen et al., 2019 ). This is worrying, considering that it is already apparent that the years ahead will be marked by economic volatility and social upheaval as well as direct and indirect health consequences, including sweeping transformations in many of the world’s healthcare systems.

While it is clear that recent developments and the push of tech and platform companies into the healthcare sector can significantly improve the quality of life for billions of people around the world, it will be accompanied by serious challenges for healthcare industries, governments, and individuals (Park, 2016 ). Technological advances are, for instance, giving rise to a plethora of smart, connected products and services, combining sensors, software, data, analytics, and connectivity in all kinds of ways, which in turns leads to a restructuring of health industry boundaries and the empowerment of novel actors, especially tech and platform companies such as IBM, Google, and Amazon (Park, 2016 ).

Observing those changes, we need to develop a general understanding of long-term trends such as digitalization and blurring industry boundaries. As the pandemic is only an amplifier of longer-lasting trends, it is likely that the consequences and exogenous effects on the healthcare industry will go far beyond the time of the current pandemic. Given these observations, we wonder whether the IS research domain is ready to capture, understand, and accompany these developments, which require a holistic view of the healthcare industry, its structures, and the interdependencies between incumbents and new entrants. Thus, we argue that it is now time to develop a more comprehensive understanding of these developments and to determine the role that IS research can play by asking: How can we prepare HIS research to capture and anticipate current developments in the healthcare industry?

To find answers to this question, our paper provides a literature overview of HIS research by ‘zooming in and zooming out’ (Gaskin et al., 2014 ) and by drawing on complexity theory (Benbya et al., 2020 ). Since a healthcare system, like the industry as a whole, can be understood as a complex, digital socio-technical system (Kernick & Mitchell, 2009 ; Therrien et al., 2017 ), zooming in and zooming out is a way to view, capture, and theorize the causes, dynamics, and consequences of a system’s complexity. Complex systems are characterized by adaptiveness, openness (Cilliers, 2001 ), and the diversity of actors and their mutual dependency in the system, meaning that outcomes and research span various levels within these systems, although the boundaries of socio-technical systems are elusive. Assuming that HIS research is just as complex as the socio-technical system investigated, we first zoom in, focusing on concrete research outcomes across levels (i.e., what we can actually observe). Zooming in is followed by zooming out, which means abstracting from the concrete level and embracing the strengths and disparities of overall HIS research on a higher level in which concrete research outcomes are embedded (Benbya et al., 2020 ). Using this approach, we can capture and understand the complexity of HIS research without losing sight of concrete research issues and topics that drive research in this field.

To do this, we chose a hermeneutic framework to guide us in a thorough review and interpretation of HIS literature and lead us to the following overarching observations: (i) The literature review determines the unique contribution that IS research plays in analysing and advancing the healthcare industry. However, it also shows that we are hardly prepared to take up current developments and anticipate their consequences. (ii) The reason for this unpreparedness is that we currently neglect the ecosystem perspective and thus ignore holistic approaches to resolve the striking number of interrelated issues in HIS research. (iii) Based on the unique insights of this literature review, our paper provides a research agenda in which we use complexity theory to discuss the consequences of current developments. This theory assists IS researchers not only to better understand developments and implications thereof for the healthcare industry (and thus HIS research) but also to create a meaningful impact on the future of this industry. Since we have limited our research explicitly to the IS domain, our results may not be generally applicable to other healthcare research domains and we do not claim to provide an overview of the literature in the field of HIS research. However, while IS researchers cannot solve the pandemic directly, preparing them by providing a new research agenda will support them in developing concepts and applications, thereby helping them to overcome the negative effects of the pandemic. In our opinion, it is particularly important that IS research, and especially HIS-related research, obtains a deeper understanding of the needed transformation that is caused by digitalization and the emergence of new players catalysed by the current pandemic.

The remainder of this paper is structured as follows. The next section is concerned with the hermeneutic framework used to conduct the systematic literature review. After explaining the hermeneutic approach and the research steps, we elaborate on the key findings by zooming in; that is, we focus on the key results that emerge from analysing and interpreting the literature for each of the phases defined in the course of the literature sorting process. We then concentrate on zooming out, emphasizing the patterns and interdependencies across phases, which helps us determine the state of HIS research. The results of both parts of the literature review – i.e., zooming in and zooming out (Benbya et al., 2020 ; Gaskin et al., 2014 ) – support us in identifying strengths, as well as drawbacks, in HIS research. On this basis, we develop a research agenda that provides future directions for how HIS research can evolve to anticipate the impending transformation of the healthcare industry.

Literature review: a hermeneutic approach

To answer our research question, we conducted a literature review based on hermeneutic understanding. In particular, we followed Boell and Cecez-Kecmanovic ( 2014 ). They proposed a hermeneutic philosophy as a theoretical foundation and methodological approach that focuses on the inherently interpretive processes in which a reader engages in an ever-expanding and deepening understanding of a relevant body of literature. Adopting a comprehensive literature review approach that addresses well-known issues resulting from applying structured literature review approaches (e.g., Webster & Watson, 2002 ), we strive toward the dual purpose of hermeneutic analysis – i.e., to synthesize and critically assess the body of knowledge (Boell & Cecez-Kecmanovic, 2014 ). We would like to emphasize that the hermeneutic approach to literature reviews is not in opposition to structured approaches. Rather, it addresses the weaknesses of structured approaches (i.e., that they view engagement with the literature as a routine task rather than as a process of intellectual development) and complements them with the hermeneutic perspective to create a holistic approach for conducting literature reviews.

Theoretical underpinning and research method

A methodological means for engaging in reciprocal interpretation of a whole and its constituent elements is the hermeneutic cycle (Bleicher, 2017 ), which consists of a mutually intertwined search and acquisition circle (Circle 1 in Fig.  1 ) and the wider analysis and interpretation circle (Circle 2 in Fig.  1 ) (Boell & Cecez-Kecmanovic, 2014 ). Figure  1 depicts the steps associated with the hermeneutic literature review. The search and acquisition circle is shown on the left of the figure, while the analysis and interpretation circle containing steps of meta and content analysis is depicted on the right. The two circles should be understood as an iterative procedure, the nature of which will be explained in the following. 

Hermeneutic procedure applied to the literature review

Circle 1: Search and acquisition

The hermeneutic literature review starts with the search and acquisition circle, which is aimed at finding, acquiring, and sorting relevant publications. In line with holistic thinking, we began with the identification of a rather small set of highly relevant literature (Boell & Cecez-Kecmanovic, 2014 ) and went on to identify further literature on the basis of progressively emerging keywords. This step is central to the hermeneutic approach and addresses a criticism on structured literature reviews, namely that they downplay the importance of reading and dialogical interaction between the literature and the reader in the literature search process, reducing it to a formalistic search, stifling academic curiosity, and threatening quality and critique in scholarship and research (Boell & Cecez-Kecmanovic, 2014 ; MacLure, 2005 ). Thus, while the search process remains formalized, as in pure structured approaches, the hermeneutic approach allows us to acquire more information about the problem at hand and to identify more relevant sources of information (Boell & Cecez-Kecmanovic, 2014 ).

Given our initial research question and the scope of the review, we began by searching for papers in the Association for Information System’s (AIS’s) eLibrary over a period of 30 years (1990 to 2019). We consider this database to be a source of the most significant publications in the field of HIS research with a focus on the IS research domain. Using the keywords ‘digital health’ and ‘digital healthcare service’, we identified an initial set of 54 papers based on the title, abstract, and keyword search. Engaging in a first round of the hermeneutic search and acquisition circle, we extended and refined these keywords by identifying emerging topics within the literature, as well as using backward and forward search (Webster & Watson, 2002 ). In particular, with each additional paper identified through backward and forward search, we compared keyword references in the papers to our list of keywords and added them if there was sufficient content delimitation. The decision to add a keyword was discussed with all authors until consensus was reached. This led us to a set of 12 keywords, including ‘electronic health’, ‘ehealth’, ‘mobile health’, ‘mhealth’, ‘health apps’, ‘tech health’, ‘healthcare services’, ‘healthcare informatics’, ‘medical informatics’, and ‘health data’.

The selection of publications being considered for our research comprised all journals belonging to the AIS eLibrary, the Senior Scholars’ Basket of Eight Journals (e.g., European Journal of Information Systems, Information Systems Research , and MIS Quarterly ), well-regarded journals following the analyses of Chiasson and Davidson ( 2004 ) and Chen et al. ( 2019 ) (e.g., Business & Information Systems Engineering , Communications of the ACM, and Decision Support Systems ), and the proceedings of the major AIS conferences (e.g., Americas Conference on Information Systems (AMCIS), International Conference on Information Systems (ICIS)). An overview of the selected journals and proceedings is provided in Appendix 1 .

Using our set of keywords, we searched for each keyword individually in the AIS eLibrary and the databases of the respective journals. Subsequently, we created a dataset and filtered out the duplicates, yielding a total number of 1,789 papers to be screened in the search and acquisition circle (Circle 1 in Fig.  1 ). Figure  2 provides an overview of this process by listing the total number of articles identified for each journal individually.

Steps of the search process to create the data set

The resulting 1,789 papers progressively passed through the intertwined hermeneutic circles. Because of the large number, we divided the papers at random into four equally sized groups and assigned them to each of the authors. Each author then screened the paper in his or her group. In the course of several rounds of discussion, decisions on the inclusion of keywords and articles in the literature review were made by all authors, based on the original recommendations of the author responsible for the respective group. To ensure rigor and transparency of the analysis and results, we kept a logbook in which all decisions of the authors and steps of the literature review were recorded (Humphrey, 2011 ).

Given the abundance of topics that were already apparent from titles and abstracts, we began to sort the publications (Boell & Cecez-Kecmanovic, 2014 ). The process of sorting proved to be challenging, as HIS research is diverse and tends to be eclectic (Agarwal et al., 2010 ). This is why researchers have developed frameworks for clustering and analysing HIS research (LeRouge et al., 2007 ). So far, however, no consent on a unified framework has emerged, and sorting is often strongly influenced by the authors’ views on HIS research (Agarwal et al., 2010 ; Fichman et al., 2011 ). For instance, Agarwal et al. ( 2010 ) predetermined health IT adoption and health IT impact as major themes associated with health ITs, acknowledging that this pre-categorization of research topics made a systematic review of the growing and increasingly complex HIS literature unfeasible. Consequently, we decided to sort the articles we had identified into groups inspired by and loosely related to the phases of design science research (DSR) (Peffers et al., 2008 ), which is an essential step in hermeneutics – i.e., defining guidelines to facilitate interpretive explication (Cole & Avison, 2007 ). DSR can be understood as a cumulative endeavour and, therefore, we understood HIS research as accumulative knowledge that can be reconstructed and consolidated using DSR phases as guidance (vom Brocke et al., 2015 ; vom Brocke et al., 2009 ). In particular, this helped us to sort the articles without prejudice to expected HIS research topics and clusters (Grondin, 2016 ).

In the past, researchers have used the DSR process in the context of literature reviews to identify advances in design science-related research outcomes (Offermann et al., 2010 ). In this paper, we use the DSR phases – in the sense of a rough guideline – as a neutral lens to classify articles according to their research outcomes. We thereby assume that HIS literature can be seen as an overall process, where research results and progress are built upon each other and can be classified into phases of problem identification and research issues , definition of research objectives and possible solution space , design and development of solutions , demonstration of research effectiveness, innovativeness and acceptance , and evaluation . These phases served as a guide to achieve an outcome-oriented, first-hand sorting of articles, while this approach also gave us the opportunity to take a bird's-eye view on HIS research. Note that we intentionally omitted the last step of DSR – i.e., communication – as we regard communication as present in all published articles. Based on our initial reading, we assigned all 1,789 papers to the phases and discussed this sorting in multiple rounds until all authors agreed on the assignments.

Simultaneously, we applied criteria for the inclusion and exclusion of articles. We included full papers published in the journals and conference proceedings belonging to our selection. We excluded articles that were abstract-only papers, research-in-progress papers, panel formats, or workshop formats, as well as papers without direct thematic reference to our research objective. Additionally, during the acquisition stage we stored selected papers in a separate database whenever they fulfilled certain quality criteria (e.g., for separate studies using the same dataset, such as a conference publication and a subsequent journal publication, we only used the articles with the most comprehensive reporting of data to avoid over-representation).

The authors read the resulting 489 papers to identify new core terms and keywords that were used in subsequent searches, which not only provided the link to the analysis and interpretation circle but also informed the literature search. For this purpose, each author read the papers and kept notes in the logbook that supported us in systematically recording the review process and allowed us to shift from concentrating on particular papers to focusing on scientific concepts (Boell & Cecez-Kecmanovic, 2014 ; Webster & Watson, 2002 ).

Circle 2: Analysis and interpretation

The search and acquisition circle formed part of the iterative procedure of analysis and interpretation, whereby the reading of individual papers was the key activity linking Circle 1 to the steps of Circle 2 (Boell & Cecez-Kecmanovic, 2014 ). Through orientational reading we gained a general understanding of the literature, thus laying the foundation for the subsequent steps of analysis and interpretation (Boell & Cecez-Kecmanovic, 2014 ).

Within the analysis and interpretation circle, two types of reviews were conducted for all identified and sorted articles: in a first round a meta-review, and in a second round a content analysis of the papers was performed. Meta-reviews are a useful tool for capturing and analysing massive quantities of knowledge using systematic measures and metrics. We followed Palvia et al. ( 2015 ), who proposed a structured method that is integrated into the hermeneutic approach. In particular, having identified and sorted the relevant research articles, we applied proposed review features, including methodological approach, level of observation, sample size, and research focus (Humphrey, 2011 ; Palvia et al., 2015 ) to map, classify, and analyse the publications (Boell & Cecez-Kecmanovic, 2014 ). In doing so, we slightly adapted the classic meta-analysis by focusing on meta-synthesis, which is similar to meta-analysis but follows an interpretive rather than a deductive approach. Whereas a classic meta-analysis tries to increase certainty in cause-and-effect conclusions, meta-synthesis seeks to understand and explain the phenomena of mainly qualitative work (Walsh & Downe, 2005 ). The results of the meta-synthesis provided the basis for our subsequent critical assessment of content. Furthermore, we created a classification matrix based on particularly salient features of the meta-review (i.e., levels of observation and research foci), which facilitated and standardized the content analysis.

Within the matrix, the levels of observation comprised infrastructure (e.g., information exchange systems, electronic health records), individuals (patients and users of digital health services), professionals (e.g., nurses and general practitioners), organizations (hospitals and other medical institutions), and an ecosystem level. The latter is defined as individuals, professionals, organizations, and other stakeholders integrated via a digital infrastructure and aiming to create a digital environment for networked services and organizations with common resources and expectations (Leon et al., 2016 ). To identify the most important concepts used by researchers, we discussed a variety of approaches to the derivation of research foci – i.e., areas containing related or similar concepts that are frequently used in research on HIS. Finally, six research focus areas emerged, covering all relevant research areas. To describe the core HIS research issues addressed by these foci, we used the following questions:

HIS strategy: What are the prerequisites for configuring, implementing, using, maintaining, and finding value in HISs?

HIS creation: How are HISs composed or developed?

HIS implementation: How are HISs implemented and integrated?

HIS use and maintenance: How can HISs be used and maintained once in place?

Consequences and value of HIS: What are the consequences and the added value of HISs?

HIS theorization: What is the intellectual contribution of HIS research?

We used the classification matrix as a tool for assigning publications and finding patterns across research articles and phases. In particular, we used open, axial, and selective coding (Corbin & Strauss, 1990 ) to analyse the content of articles in a second round of the analysis and interpretation circle. Each author individually assigned open codes to text passages while reading the identified research articles, noting their thoughts in the shared digital logbook that was used for constant comparative analysis. Once all authors had agreed on the open codes, axial coding – which is the process of relating the categories and subcategories (including their properties) to each other (Wolfswinkel et al., 2013 ) – was conducted by each author and then discussed until consent on codes was reached. Next, we conducted selective coding and discussed the codes until theoretical saturation was achieved (Corbin & Strauss, 1990 ; Matavire & Brown, 2008 ). For the sake of consistent terminology, we borrowed terms from Chen et al. ( 2019 ), who used multimethod data analysis to investigate the intellectual structure of HIS research. In particular, they proposed 22 major research themes, which we assigned to the initial codes whenever possible. In two rounds of discussion in which we compared the assignment of codes, two additional codes emerged, which left us with a total of 24 theme labels (Appendix 2 ). By discussing the codes at all stages of coding, theoretical saturation emerged, which is the stage at which no additional data are being found or properties of selective codes can be developed (Glaser & Straus, 1968 ; Saunders et al., 2018 ). In fact, independent from each other, all authors saw similar instances occurring over and over again, resulting in the same codes, making us confident that we had reached theoretical saturation (Saunders et al., 2018 ).

Finally, we entered the codes into the classification matrix, which allowed us to identify patterns based on the meta and content analysis. This enabled us to provide insights into the strengths and weaknesses of current HIS research; these are presented in the following section.

Zooming-in: key findings of the phase-based literature analysis

In the following, we ‘zoom in’ (Gaskin et al., 2014 ) by presenting key findings of the literature review for each phase, illustrated by means of the classification matrices. We assigned selective codes that emerged from the content analysis to the fields of the matrices, with the numbers in brackets indicating the frequency with which codes emerged. Note that, for the sake of clarity, we displayed only the most relevant research themes in the matrices and indicated the number of further papers using the reference ‘other themes.’ A complete list of research themes for each phase can be found in the appendix (Appendix 2 ). In the following, each table shows the classification matrix and selective codes that resulted from the meta and content analysis of papers in the respective phase. The shaded areas in the matrix show focused research themes (i.e., selective codes) and characteristics of research articles that gave way to clusters (i.e., collections of themes that appear frequently and/or characteristically for the respective focus).

Phase 1: Problem identification and research issues

Within the first phase, a large body of literature was found (218 articles). This phase encompasses articles that identify problems and novel research issues as a main outcome, with the aim of pointing out shortcomings and provoking further research. For instance, besides behavioural issues such as missing user acceptances or trust in certain HISs, the design and effectiveness of national health programs and/or HIS is a frequently mentioned topic. It should be noted, however, that literature assigned to this phase is extremely diverse in terms of research foci, levels of observation, and research themes, and hardly any gaps can be identified (Table 1 ).

The first cluster (1a) encompasses the research focus of HIS strategy, spanning all levels of observation and totalling 24 publications. HIS strategy appears to be of particular relevance to the levels of organization and infrastructure. Content-wise, the theme of health information interchange is of particular interest, referring, for example, to the development of a common data infrastructure (Ure et al., 2009 ), consumer-oriented health websites (Fisher et al., 2007 ), and security risks of inter-organizational data sharing (Zhang & Pang, 2019 ). HIS productivity and HIS security are the second most salient themes, focusing, for example, on measuring the effectiveness of fitness apps (Babar et al., 2018 ) and presenting challenges with regard to the interoperability of medical devices (Sametinger et al., 2015 ).

The second cluster (1b), comprising 25 publications, represents the ecosystem level and focuses mainly on national and cross-national HIS-related issues such as the relation between ICT penetration and access to ehealth technologies across the European Union (Currie & Seddon, 2014 ), as well as on the collaboration and involvement of different stakeholders (Chang et al., 2009 ; King, 2009 ). Most important here is health information interchange – e.g., the provision, sharing, and transfer of information (Bhandari & Maheshwari, 2009 ; Blinn & Kühne, 2013 ).

Cluster 1c covers the research focus of HIS use and maintenance, as well as the consequences of HIS. Whereas most papers addressing the HIS acceptance theme focus on professionals’ or patients’ acceptance of specific technological solutions, such as telemedicine (Djamsbi et al., 2009 ) or electronic health records (Gabel et al., 2019 ), papers assigned to health information interchange focus on topics related to information disclosure, such as self-tracking applications (Gimpel et al., 2013 ). Finally, the HIS outsourcing and performance theme concentrates on financial aspects in organizations, including potential for quality improvements and cost reductions (Setia et al., 2011 ; Singh et al., 2011 ).

Finally, the fourth cluster (1d) focuses on HIS theorizing with respect to the individual and infrastructure levels of observation. Although this cluster represents a range of theme labels (15), those addressing HIS acceptance, HIS patient-centred care, as well as health analytics and data mining predominate. Papers within the theme label HIS acceptance cover a wide range of topics, such as the acceptance of telehealth (Tsai et al., 2019 ) up to the usage intentions of gamified systems (Hamari & Koivisto, 2015 ). The same applies to the papers assigned to the theme labels of health analytics and data mining. Focusing on the infrastructure level of observation, the identified papers mostly review academic research on data mining in healthcare in general (Werts & Adya, 2000 ), through to the review of articles on the usage of data mining with regard to diabetes self-management (Idrissi et al., 2019 ). Papers on HIS patient-centred care mostly address the challenges and opportunities of patient-centred ehealth applications (Sherer, 2014 ).

Apart from these clusters, quite a few research articles refer to the infrastructure level of observation, addressing information sharing in general (Li et al., 2008 ), electronic medical records (George & Kohnke, 2018 ; Wessel et al., 2017 ), and security and privacy issues (Zafar & Sneha, 2012 ).

Most common in terms of research methods within this phase are case studies (57), followed by quantitative data analyses (50), theoretical discussions (29), and literature studies (14). In particular, case studies dominate when referring to the ecosystem or infrastructure level of observation, whereas quantitative analyses are conducted when individuals or professionals are at the centre of the discussion. However, and unsurprisingly given the considerable diversity of research themes within this phase, the variety of research methods is also quite large, ranging from field studies (Paul & McDaniel, 2004 ), to interviews (Knight et al., 2008 ), to multimethod research designs (Motamarri et al., 2014 ).

Phase 2: Definition of research objectives and solution space

The second phase of HIS research yielded a lower number of articles (45) compared to the phase of problem identification and research issues. The second phase comprises articles that focus on proposing possible solutions to existing problems – i.e., introducing theory-driven, conceptual designs of health ecosystems including health information interchange, as well as scenario analyses anticipating the consequences of HIS implementation on an organizational level. Based on the research foci and levels of observation, we identified three specific thematic clusters, as shown in Table 2 .

The first cluster (2a) comprises the ecosystem level of observation and encompasses eight publications. Besides a strong tendency toward theory-driven research, health information interchange is the most common theme. We found that the need to enable cooperation within networks and to ensure accurate data input was addressed in most of the literature. While a majority of studies focus on the application of HIS in networks within specific boundaries, such as medical emergency coordination (Sujanto et al., 2008 ) or Singapore’s crisis management in the fight against the SARS outbreak in 2003 (Devadoss & Pan, 2004 ), other studies, such as that by Aanestad et al. ( 2019 ), take an overarching perspective, addressing the need to break down silo thinking and to start working in networks. Following the question of why action research fails to persist over time, Braa et al. ( 2004 ) highlighted the role of network alignment, criticizing action research projects for failing to move beyond the prototyping phase and, therefore, failing to have any real impact.

Cluster 2b, encompassing nine publications, was derived from the observation that studies within the organizational level concentrated strongly on HIS use and maintenance and the consequences of HIS research. Herein, a vast array of topics was observed, such as the potential for cost reduction through HIS (Byrd & Byrd, 2009 ), the impact of HIS on product and process innovation in European hospitals (Arvanitis & Loukis, 2014 ), and the perceived effectiveness of security risk management in healthcare (Zafar et al., 2012 ). Moreover, we found that practice-oriented methods, such as mixed-method approaches, surveys, data analyses, and case studies, are used predominantly within this cluster. Focusing on the latter, most studies analyse particular scenarios by using a rather small sample of cases, for instance, Al-Qirim ( 2003 ) analysed factors influencing telemedicine success in psychiatry and dermatology in Norway.

The third cluster (2c) was derived from analysis of the HIS creation research focus (nine publications). Although health information interchange is the most represented in this cluster, a large number of further themes can be observed. Studies within this cluster predominantly address design aspects of system interoperability, focusing on data processing and data interchange between the actors. HISs mostly serve as a tool for the development or enhancement of decision support systems, such as for real-time diagnostics combining knowledge management with specific patient information (Mitsa et al., 2007 ) or clinical learning models incorporating decision support systems in the dosing process of initial drug selection (Akcura & Ozdemir, 2008 ).

Phase 3: Design and development

The design and development phase comprises 84 research articles concerned with the creation of novel IS artefacts (e.g., theories, models, instantiations). We thereby refer to Lee et al.’s ( 2015 ) definition of the IS artefact – i.e., the information, technology, and social artefact that forms an IS artefact by interacting. We assigned to this phase papers that are explicitly concerned with developing solutions for information exchange (e.g., design of messaging systems or knowledge systems in hospitals), technological artefacts (e.g., hardware or software used for generating electronic health records), and social artefacts that relate to social objects (e.g., design of national or international institutions and policies to control specific health settings and patient-centred solutions). Within the design and development phase, the analysis revealed two clusters (Table 3 ).

The first cluster (3a) was identified in the research focus of HIS creation (31 articles). Here, the most frequent research theme is HIS innovation followed by HIS and patient-centred care, HIS productivity, and health analytics and data mining. The focus is on specific contexts, mostly medical conditions and artefacts developed for their treatment, such as in the context of mental health/psychotherapy (Neben et al., 2016 ; Patel et al., 2018 ), diabetes (Lichtenberg et al., 2019 ), or obesity (Pletikosa et al., 2014 ). Furthermore, information infrastructures or architectures – for instance, for the process of drug prescription (Rodon & Silva, 2015 ), or for communication between healthcare providers and patients (Volland et al., 2014 ) – are represented.

The second aggregation of research articles is found in cluster 3b, focusing on theoretical aspects of HIS (32 articles). Again, these studies span all levels of observation (including infrastructure, individual, professional, organization, and ecosystem). Topics in this theme are diverse, ranging from HIS on a national level (Preko et al., 2019 ), to knowledge management in healthcare (Wu & Hu, 2012 ) to security of HIS (Kenny & Connolly, 2016 ).

Beyond both clusters, it is evident that during design and development, researchers do not deal with the consequences of HIS, nor does HIS strategy play an important role. Furthermore, only in the research focus of theorization is the ecosystem level of some relevance to other levels (e.g., the individual level). It should be noted that ecosystems are mostly referred to in terms of nations or communities, without any transnational or global perspective. Furthermore, the term ‘ecosystem’ has not been used in research, and within the other research focus areas, the ecosystem level is barely represented. Moreover, articles combining different perspectives of the single levels of observation on HIS – namely individuals (i.e., patients), professionals (i.e., medical staff), and organizations (e.g., hospitals) – are rare. During design and development, potential users are not typically integrated, whereas it is quite common to derive requirements and an application design from theory, only involving users afterwards – e.g., in the form of a field experiment (e.g., Neben et al., 2016 ).

Surprisingly, theoretical papers outweigh papers on practical project work, whereby the latter mostly focus on a description of the infrastructure or artefact (e.g., Dehling & Sunyaev, 2012 ; Theobalt et al., 2013 ; Varshney, 2004 ) or are based on (mostly single) case studies (e.g., Hafermalz & Riemer, 2016 ; Klecun et al., 2019 ; Ryan et al., 2019 ). Within the design and development phase, the generation of frameworks, research models, or taxonomies is prevalent (e.g., Preko et al., 2019 ; Tokar et al., 2015 ; Yang & Varshney, 2016 ).

Phase 4: Demonstration

This phase includes 35 articles related to presenting and elaborating on proposed solutions – e.g., how HIS can be implemented organization-wide (e.g., via integration into existing hospital-wide information systems), proposed strategies and health policies, as well as novel solutions that focus on health treatment improvements. Within the demonstration phase, we identified two clusters that emerged from the meta and content analyses (Table 4 ).

Cluster 4a (10 articles) is characterized by articles that focus on HIS issues related to the infrastructure level, spanning the research foci of HIS strategy, creation, and deployment. Content-wise, the cluster deals mainly with technical feasibility and desirability of HISs, including topics such as the configuration of modular infrastructures that support a seamless exchange of HISs within and between hospitals (Dünnebeil et al., 2013 ). Moreover, papers in this cluster address HIS practicability by determining general criteria that are important for the design of health information systems (Maheshwari et al., 2006 ) or conduct HIS application tests by carrying out prototypical implementations of communication infrastructures. In particular, the latter are tested and proven to meet specific technical standards to guarantee the frictionless transmission of health information data (Schweiger et al., 2007 ). In contrast, Heine et al. ( 2003 ) upscaled existing HIS solutions and tested the infrastructure in large, realistic scenarios.

Conversely, cluster 4b (11 articles) is mainly concerned with HIS use and maintenance, spanning several levels of observation – i.e., infrastructure, individuals, professionals, and organizations. Interestingly, papers in this cluster aim at efficiency and added value when looking at the infrastructure and organizational levels, whereas researchers are more interested in acceptance when focusing on the individual and professional use of HISs. Overall, cluster 4b is primarily concerned with organizational performance (e.g., increases in efficiency due to better communication and seamless transfer of patient health information) as well as user acceptance of new HISs.

Although the two clusters constitute a diverse set of literature and themes, it is apparent that research taking an ecosystem perspective is very rarely represented. Across the papers, only three are concerned with issues related to the ecosystem level. In particular, Lebcir et al. ( 2008 ) applied computer simulations in a theoretical demonstration as a decision support system for policy and decision-makers in the healthcare ecosystem. Abouzahra and Tan ( 2014 ) used a mixed-methods approach to demonstrate a model that supports clinical health management. Findikoglu and Watson-Manheim ( 2016 ) addressed the consequences of the implementation of electronic health records (EHR) systems in developing countries.

Phase 5: Evaluation

The fifth phase includes 92 publications with a focus on assessing existing or newly introduced HIS artefacts – i.e., concepts, policies, applications, and programs – thereby proving their innovativeness, effectiveness, or user acceptance. As Table 5 shows, three clusters were identified.

The main focus of publications in the evaluation phase is on the infrastructure level, where most papers are related to HIS creation and HIS use and maintenance. Therefore, together with the publications pigeonholed to HIS deployment and consequences of HIS, these articles were summarized as the first cluster (5a, comprising 53 articles). The assessment of national HIS programs, as well as mobile health solutions, are a frequent focus (10 papers). Articles on HIS use and maintenance are largely related to the professional, organizational, and ecosystem levels and were thus grouped as cluster 5b (10 articles). A third cluster (5c – 11 articles) emerged from research articles in HIS theorization. Here, papers at all levels of observation were found. Research focusing on areas such as HIS strategy and consequences of HIS are, with a few exceptions, not covered in the evaluation phase. Methods used include interviews, focus groups, and observations (e.g., Romanow et al., 2018 ). Experiments and simulation are rarely applied (e.g., Mun & Lee, 2017 ). The number of interviews shows a huge spread, starting with 12 and reaching a maximum of 150 persons interviewed.

Under the evaluation lens, the ecosystem perspective is covered by seven articles, but only three papers look at cases, while the others focus on theorization or consequences in terms of costs. Overall, popular topics in the evaluation phase include mobile health and the fields of electronic medical records (EMR) and EHR, e.g., Huerta et al. ( 2013 ); Kim and Kwon ( 2019 ). The authors cover these themes mostly from an HIS creation perspective; thus, they deal with concrete concepts, prototypes, or even implemented systems. In the evaluation phase, just nine papers deal with HIS innovation – a good example being Bullinger et al. ( 2012 ), who investigated the adoption of open health platforms. We may conclude that, in most cases, evaluation is related to more established technologies of HIS. As expected, most articles in this phase rely on practice-oriented/empirical work (as opposed to theory-driven/conceptual work). Just two papers (Ghanvatkar & Rajan, 2019 ; Lin et al., 2017 ) deal with health analytics and data mining, one of the emerging topics of HIS.

Zooming out: key findings of the literature analysis across phases

Having elaborated on the key findings within each phase of HIS research, we now ‘zoom out’ (Benbya et al., 2020 ; Gaskin et al., 2014 ) to recognize the bigger picture. Thereby, we ‘black-box’ the concrete research themes (e.g., HIS implementation, health analytics, HIS innovation) to focus on clusters across phases, highlighting the breadth that HIS research encompasses (Leroy et al., 2013 ). In particular, while we focused on analysing the main topics within the different phases of HIS research in the zoom-in section, we now abstract from those to perform a comparative analysis of emerging clusters across those phases by zooming out. We do so by comparing the different clusters, taking into account the aspects of the level of observation and the research foci, which gave us the opportunity to identify areas of strong emphasis and potential gaps.

In particular, each author first conducted this comparative analysis on their own and then discussed and identified the potential weaknesses together. This was done in two rounds of discussion. In particular, it became obvious which areas hold immense potential for further research in healthcare (especially the penetration of new, initially non-healthcare actors, such as tech companies or other providers pushing into the industry). We summarize these potentials for research by proposing four pathways that can help HIS research to broaden its focus so that we can better understand and contribute to current developments. Notably, we expect that these insights will help to assess the state-of-the-art of HIS research and its preparedness for dealing with the consequences of Covid-19 and further pandemics, as well as for coping with associated exogenous shocks.

In zooming out, we identified discrepancies between phase 1 (problem identification and research issues) and the subsequent phases. In particular, the diversity of topics was considerably lower when it came to how researchers determined strategies; created, demonstrated, used, and maintained HISs; and coped with the consequences thereof. We observed that researchers pointed to a diverse set of issues that span all levels of observation, especially in HIS theorization, focusing on topics such as trust in HIS, data analytics, and problems associated with the carrying out of national health programs. Surprisingly, although we can assume that researchers recognized the multidimensionality of issues as a motivation to conduct HIS research, they did not seem to approach HIS research issues in a comprehensive and consistent way.

To illustrate this assertion, we point to the ‘shift of clusters’ that can be observed when comparing the single phases, from problem identification to the evaluation of HIS. We note that clusters increasingly migrate ‘downwards’ (i.e., from the ecosystem level down to the infrastructure level) and become even fewer. In line with Braa et al. ( 2004 ), we suggest that extant HIS research has identified a multitude of interrelated issues but has faced problems in translating these approaches into concrete and holistic solutions. This is reflected in the lower number of, and reduced diversity in, clusters across research themes when we move through the HIS research phases. Thus, we conclude that future HIS research can be broadened by taking into account the following pathway:

HIS research is well-prepared and able to identify and theorize on systemic problems related to the healthcare industry. Nonetheless, it has the potential to address these problems more thoroughly – i.e., to find solutions that are as diverse as the problems and, thus, suitable for coping with issues in the healthcare industry characterized by the involvement of multiple actors, such as governments, healthcare providers, tech companies, and their interactions in diverse ecosystems (pathway 1).

As we have seen, HIS research has tended to focus on important but incremental improvements to existing infrastructures, particularly in the phases of demonstration and evaluation, with the aim of presenting new IS artefacts and conceptual or practical solutions. For instance, Choi and Tulu ( 2017 ) considered improvements in user interfaces to decrease the complexity of mobile health applications using incremental interface design changes and altering touch techniques. Similarly, Roehrig and Knorr ( 2000 ) designed patient-centred access controls that can be implemented in existing infrastructures to increase the privacy and security of EHRs and avoid malicious access and misuse of patient health information by third parties.

While we sincerely acknowledge these contributions and wish to emphasize the multitude of papers that are concerned with enhancements to existing infrastructures, we would like to shift the view to the major challenges in HIS research. These challenges include combating global and fast-spreading diseases (e.g., malaria, tuberculosis, Covid-19) and tracking health statuses accurately and efficiently, especially in developing countries. All of these challenges necessitate global and comprehensive solutions, spanning individuals, organizations, and nations, and have to be embedded in a global ecosystem (Winter & Butler, 2011 ). Such grand challenges are, by nature, not easy to cope with, and the intention to develop a comprehensive solution from the perspective of IS researchers seems almost misguided. However, HIS research is currently missing the opportunity to make an impact, despite the discipline’s natural intersection with essential aspects of the healthcare industry (i.e., its infrastructures, technologies, and stakeholders, and the interdependencies between these components). Thus, we assert that:

HIS research has often focused on necessary and incremental improvements to existing IS artefacts and infrastructures. We see potential in shifting this focus to developing solutions that combine existing IS artefacts to allow for exchange of information and the creation of open systems, which will enhance support for and understanding of the emergence of ecosystems (pathway 2).

By focusing on incremental improvements, HIS research has become extraordinarily successful in solving isolated issues, especially in relation to the problems of patients and health service providers (e.g., hospitals and general practitioners). However, we observed during our analysis that spillover effects were seldom investigated. When, for example, a new decision support system in a hospital was introduced, positive consequences for patients, such as more accurate diagnoses, were rarely of interest to the research. In fact, our meta-analysis revealed that the level of observation for the majority of papers matched the level of analysed effects. While it is valid to investigate productivity and efficiency gains by introducing a hospital-wide decision support system, we are convinced that spillover effects (for instance, on patients) should also be within the focus of HIS research. Therein, we suggest that HIS research has not focused primarily on patients and their well-being but on IS infrastructures and artefacts. However, patient well-being is the ultimate direct (or indirect) goal of any HIS research (by increasing the accuracy and shortening the time of diagnosis, improving treatment success rates, etc.). Thus, we propose that:

HIS research is experienced in solving isolated issues related to the daily processes of healthcare providers; however, we see much potential in considering the value that is delivered by focusing on patient-centricity (pathway 3).

Putting the patient at the centre of HIS research implies shifting the focus of researchers to the patient’s own processes. The question remains as to how HIS researchers can support patient-centricity. While this is only possible by understanding patients’ processes, we also see the need to understand the whole system – i.e., the ecosystem in which patients’ processes are embedded. The ecosystem perspective needs to consider networked services and organizations, including resources and how they interact with stakeholders of the healthcare industry (including patients). To date, we observe, across phases the ecosystem perspective has largely been neglected. To be precise, although HIS research seems to be aware of the multilevel aspects of healthcare issues in the problem identification phase, researchers appear to stop or are hindered from developing solutions that go beyond the development of prototypes (Braa et al., 2004 ). Thus, we find that:

HIS research is capable of theorizing on an ecosystem level (i.e., capturing the complexity of the socio-technical health system), but would benefit from increasing the transfer of these insights into research so as to develop holistic solutions (pathway 4).

Looking at the strengths of HIS research, the reviewed papers accentuate the unique contribution that IS researchers can make to better understand and design IS artefacts for the healthcare context. This has been achieved by analysing empirical data and exploring contextual influences through the application and elaboration of IS theories (LeRouge et al., 2007 ). At the same time, our literature review shows the incredible diversity and high level of complexity of issues related to HISs, indicating that we need solutions characterized by holism and the inclusion of multiple actors (i.e., an integrative ecosystem perspective). So far, by concentrating on incremental improvements to existing infrastructures HIS research has widely failed to reach the necessary holistic level.

We would like to emphasize that we recognize the value of all previous approaches. Yet, it is necessary to ask whether we as IS researchers are in a position to identify current developments in the healthcare industry and to anticipate the consequences triggered by pandemics or other waves of disease. We acknowledge that this will be difficult unless we take a more holistic view and try to understand connections in the health ecosystems. Regarding whether HIS research is in a position to capture and anticipate consequences of the current push of tech companies in the healthcare industry catalysed, for example, by Covid-19, we assert that this is hardly the case, even if IS research is well-placed to interpret the expected socio-technical changes and adaptations within healthcare. Given the enormous potential for disruption caused by, for instance, pandemics and its consequences, such as the intrusion of technology companies into the market, it is now time to question and redefine the role of HIS research so that it can generate decisive impacts on the developments in this industry.

• Research agenda

To support HIS research for the transformation of the healthcare industry, we develop a research agenda that is informed by complexity theory. This theory implies that complex, socio-technical systems such as the healthcare industry can fluctuate between different states, ranging from homogenous forms of coevolution (i.e., a state where emergent structures and processes become similar to each other) to chaotic systems that are characterized by increasing levels of tension, which might result in extreme outcomes such as catastrophes or crises (Benbya et al., 2020 ).

While coevolution and chaos represent possible extreme states, the current situation – i.e., the penetration of tech companies into the healthcare industry – is best described by the dynamic process of emergence. Emergence is characterized by a disequilibrium, which implies unpredictability of outcomes that may lead to new structures, patterns, and properties within a system characterized by self-organization and bursts of amplification (Benbya et al., 2020 ; Kozlowski et al., 2013 ). Given the dynamics resulting from this, it seems impossible to predict the future; however, it is not impossible to prepare for it.

In particular, the current dynamics within the healthcare industry necessitate an understanding of exponential progress, not as the ability to foresee well-defined events in space and time, but as an anticipation of the consequences of emerging states and dynamic adaptive behaviours within the industry (Benbya et al., 2020 ). The following research agenda for HIS research is thus structured along three key issues: anticipating the range of actors’ behaviours, determining boundaries and fostering collaboration in the healthcare industry, and creating sustainable knowledge ecosystems.

According to these key issues, Table 6 offers guiding questions for HIS researchers. Addressing all issues will contribute to an understanding of the entire healthcare industry and the development of holistic solutions for a multitude of health issues by involving different actors (e.g., patients, hospitals, professionals, governments, NGOs). However, we propose approaching the agenda stepwise, in the order of the key issues, first looking at the range of behaviours and consequences of current developments for actors, then focusing on the blurring lines of the healthcare industry, and finally investigating the dissemination and sharing of knowledge, which we see as the ultimate means to connect actors and infrastructures to create a joint ecosystem. Table 6 thereby provides key guiding statements and exemplary research questions for future HIS research that support researchers in taking one of the aforementioned pathways. We structured guiding statements along three major areas of improvement. In addition, we offer exemplary research questions to these statements, as well as inspiring studies from other industries that have faced similar challenges and have been studied and supported by researchers.

Area of improvement 1: Anticipating the range of actor behaviours

As healthcare systems are becoming more open – for example, through the penetration of new market actors and the use of increasingly comprehensive and advanced health technologies – accurately determining the boundaries of an industry and its key actors is becoming more difficult. To model these systems, we must carefully model every interaction in them (Benbya et al., 2020 ), which first requires HIS researchers to identify potential actors in the ecosystem rather than predetermining assumed industry boundaries. As actors are not always evident, we follow Benbya et al. ( 2020 ) in proposing Salthe’s ( 1985 ) three-level specification, assisting researchers in identifying actors at the focal level of what is actually observed (e.g., hospitals, patients, and general practitioners) and its relations with the parts described at the lower level (e.g., administrators and legal professionals), taking into account entities or processes at a higher level in which actors at the focal level are embedded (e.g., national health system structures and supporting industries, such as the pharmaceutical or tech industries). These examples are only illustrative, and criteria for levels have to be suggested and discussed for each research endeavour.

To anticipate future developments in the healthcare industry, we also need to analyse the strategies and interests of actors for joining or staying in the healthcare industry. This is especially important because, like other complex socio-technical systems, the healthcare industry is made up of large numbers of actors that influence each other in nonlinear ways, continually adapting to internal or external tensions (Holland et al., 1996 ). If tension rises above a certain threshold, we might expect chaos or extreme outcomes. As these are not beneficial for the actors in the system, the eventual goal is to align actors’ interests and strategies across a specific range of behaviour to foster coevolution. This allows for multi-layered ecosystems that encourage joint business strategies in competitive landscapes, as well as the alignment of business processes and IT across actors (Lee et al., 2013 ).

Area of improvement 2: Determining boundaries and fostering collaboration

Actors build the cornerstones of the healthcare industry. Thus, if we want to understand and capture its blurring boundaries, there is a need to understand the complex causality of interactions among heterogeneous actors. In particular, scholars have emphasized that, in complex systems, outcomes rarely have a single cause but rather result from the interdependence of multiple conditions, implying that there exist multiple pathways from an input to an output (Benbya et al., 2020 ). To capture interaction, we follow Kozlowski et al. ( 2013 ), who envisioned a positive feedback process including bottom-up dynamic interaction among lower-level actors (upward causation), which over time manifests at higher, collective levels, while higher-level actors influence interaction at lower levels (downward causation). As these kinds of causalities shape interaction within healthcare ecosystems as well as at their boundaries, HIS researchers need to account for multi-directional causality in the form of upward, downward, and circular causality (Benbya et al., 2020 ; Kim, 1992 ).

Understanding casualties among actors in the healthcare industry is important for harnessing the advantages of the blurring of boundaries – e.g., by making use of the emergent ecosystem for launching innovation cycles (Hacklin, 2008 ). However, first, HIS researchers increasingly need to consider the ecosystem perspective by investigating interactions among actors and the role of IS infrastructures in fostering collaborative health innovations. We propose a focus on radical innovation, which is necessary to address the diversity and interdependence of issues present in the healthcare industry by putting the patient at the core of all innovation efforts. HIS researchers, however, need to break down the boundaries between different innovation phases and innovation agencies, including a higher level of unpredictability and overlap in their time horizons (Nambisan et al., 2017 ). Notably, this requires actors in the healthcare industry to discover new meaning around advanced technologies and IS infrastructures whose design needs to facilitate shared meaning among a diverse set of actors, thereby fuelling radical digital innovations (Nambisan et al., 2017 ).

Area of improvement 3: Creating sustainable knowledge ecosystems

We define knowledge dissemination and sharing as the ultimate means of connecting actors and aligning actions within common frameworks to shape an inclusive healthcare ecosystem. Paving the way for inclusive healthcare ecosystems is thus necessary to address the current shortcomings of HIS research as elaborated in the previous section.

Addressing knowledge dissemination and sharing is thereby of the utmost importance as we look at the healthcare industry in the current phase of emergence. This means that the industry might go through several transition phases in which existing actors, structures, and causal relationships dissipate and new ones emerge, resulting in a different set of causal relationships and eventually altering knowledge claims (Benbya et al., 2020 ). Creating a permeable and sustainable knowledge management system is necessary to ensure the transfer of knowledge for the best outcomes for the patient while securing the intellectual property rights and competitive advantages of diverse actors such as hospitals and other healthcare providers.

To be precise, we argue that to design sustainable knowledge management systems, HIS researchers need to implement systems with structures that create mutual benefits – i.e., encourage knowledge dissemination and sharing (e.g., open innovation) by actors in the healthcare industry. In a comprehensive and sustainable knowledge management system, however, not only corporations but also patients should be encouraged to share knowledge. Using this information, researchers and health service providers will be enabled to create optimized infrastructures, processes, and products (e.g., for predictive algorithms that improve treatment accuracy, or for assessing the likelihood of the occurrence of certain diseases and even of pandemics). At the same time, the trustworthiness of predictions and the anonymity of health information (and thus privacy) must be ensured. Bridging this duality of data sharing and knowledge dissemination, on the one hand, and protection of health information, on the other, is therefore essential for future HIS research.

This paper analyses the HIS literature within the IS research domain, prompted by the question of whether IS researchers are prepared to capture and anticipate exogenous changes and the consequences of current developments in the healthcare industry. While this review is limited to insights into the IS research domain and does not claim to offer insights into the health literature in general or related publications (e.g., governmental publications), we disclose several shortcomings and three key issues. Based on these, we provide initial guidance on how IS research can develop so that it is prepared to capture the expected large and long-lasting changes from current and possible future pandemics as well as the necessary adaptation of global healthcare industries affecting human agencies and experiences in all dimensions. Thus, while adaptations in the healthcare industry are already emerging, IS researchers have yet to develop a more comprehensive view of the healthcare industry. For this purpose, we provide a research agenda that is structured in terms of three areas of improvement: anticipating the range of actors’ behaviours, determining boundaries and fostering collaborations among actors in the healthcare industry, and creating sustainable knowledge management systems. In particular, addressing these areas will assist IS researchers in balancing the shortcomings of current HIS research with the unique contribution that IS research plays in analysing, advancing, and managing the healthcare industry. We are confident that IS research is not only capable of anticipating changes and consequences but also of actively shaping the future of the healthcare industry by promoting sustainable healthcare ecosystems, cultivating structures of mutual benefit and cooperation between actors, and realigning IS research to face the imminent transformation of the healthcare industry. IS research cannot contribute directly to solving the current pandemic problems; however, it can contribute indirectly triggering timely adaptations of novel technologies in global health systems, and proposing new processes, business models, and systematic changes that will prepare health systems to cope with increasing digitalization and emerging players whose push into the market enabled by the exogenous effects triggered by the pandemic.

While we are confident that the proposed research agenda based on the analysis of HIS literature provides fruitful arrays for being prepared in anticipating the future role of IS research for the healthcare industry, our results need to be reflected in light of their shortcomings. First and foremost, we recognize that the selection of literature, which is limited to the IS research domain, excludes other contextual factors that are not primarily considered by IS researchers. Thus, we cannot assume completeness, providing instead a broad overview of current issues in HIS research. In addition, possible biases may have arisen due to the qualitative analysis approach used. By independently coding and discussing codes to the point of theoretical saturation, we are confident that we largely eliminated biases in the thematic analysis. However, data saturation could not be achieved. This means that further insights could have emerged through the addition of other database searches and journals with a broader scope. Additionally, the initial sorting of papers into single defined phases of DSR research restricted multiple assignments that could have led to different results. However, we consider sorting as a necessary step of abstraction, especially given the large number of papers analysed.

We deliberately considered IS research, for which we have developed an agenda for potential future research avenues. For each of those avenues, researchers should go deeper into the subject matter in order to examine the complexity of the paths shown and to include them in the analysis (e.g., through in-depth case studies). However, it is also clear from the issues identified that IS researchers cannot solve current challenges by working on the pathways alone. In fact, the issues identified in the research agenda are only the starting point for further research, which should address the proposed issues step by step and in cooperation with other research disciplines. The latter is likely to generate further and deeper-rooted problems, as well as, in turn, future paths for research. Nevertheless, we are confident that this paper provides an important first step in opening up HIS research to better understand current developments in the healthcare industry. Further, by following and enhancing the proposed research pathways, we believe that HIS research can contribute to and support changes already taking place in the healthcare industry.

Change history

20 december 2021.

A Correction to this paper has been published: https://doi.org/10.1007/s12525-021-00518-8

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Ostern, N., Perscheid, G., Reelitz, C. et al. Keeping pace with the healthcare transformation: a literature review and research agenda for a new decade of health information systems research. Electron Markets 31 , 901–921 (2021). https://doi.org/10.1007/s12525-021-00484-1

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A Systematic Literature Review of Health Information Systems for Healthcare

Ayogeboh epizitone.

1 ICT and Society Research Group, Durban University of Technology, Durban 4001, South Africa

Smangele Pretty Moyane

2 Department of Information and Corporate Management, Durban University of Technology, Durban 4001, South Africa

Israel Edem Agbehadji

3 Centre for Transformative Agricultural and Food Systems, School of Agricultural, Earth and Environmental Sciences, University of KwaZulu-Natal, Pietermaritzburg 3209, South Africa

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Health information system deployment has been driven by the transformation and digitalization currently confronting healthcare. The need and potential of these systems within healthcare have been tremendously driven by the global instability that has affected several interrelated sectors. Accordingly, many research studies have reported on the inadequacies of these systems within the healthcare arena, which have distorted their potential and offerings to revolutionize healthcare. Thus, through a comprehensive review of the extant literature, this study presents a critique of the health information system for healthcare to supplement the gap created as a result of the lack of an in-depth outlook of the current health information system from a holistic slant. From the studies, the health information system was ascertained to be crucial and fundament in the drive of information and knowledge management for healthcare. Additionally, it was asserted to have transformed and shaped healthcare from its conception despite its flaws. Moreover, research has envisioned that the appraisal of the current health information system would influence its adoption and solidify its enactment within the global healthcare space, which is highly demanded.

1. Introduction

Health information systems (HIS) are critical systems deployed to help organizations and all stakeholders within the healthcare arena eradicate disjointed information and modernize health processes by integrating different health functions and departments across the healthcare arena for better healthcare delivery [ 1 , 2 , 3 , 4 , 5 , 6 ]. Over time, the HIS has transformed significantly amidst several players such as political, economic, socio-technical, and technological actors that influence the ability to afford quality healthcare services [ 7 ]. The unification of health-related processes and information systems in the healthcare arena has been realized by HIS. HIS has often been contextualized as a system that improves healthcare services’ quality by supporting management and operation processes to afford vital information and a unified process, technology, and people [ 7 , 8 ]. Several authors assert this disposition of HIS, alluding to its remarkable capabilities in affording seamless healthcare [ 9 ]. Haux [ 10 ] modestly chronicled HIS as a system that handles data to convey knowledge and insights in the healthcare environment. Almunawar and Anshari [ 7 ] incorporated this construed method to describe HIS to be any system within the healthcare arena that processes data and affords information and knowledge. Malaquias and Filho [ 11 ] accentuated the importance of HIS in the same light, highlighting its emergence to tackle the need to store, process, and extract information from the system data for the optimization of processes, enhancing services provided and supporting decision making.

HIS’s definition was popularized by Lippeveld [ 12 ], and reported to be an “integrated effort to collect, process, report and use health information and knowledge to influence policy-making, programme action and research”. Over the course of time, this definition has been adopted and contextualized countlessly by many authors and the World Health Organization (WHO) [ 3 , 8 , 13 , 14 , 15 ]. Although Haule, Muhanga [ 8 ] claimed the definition of HIS varies globally, in actuality, the definition has never changed from its inception, but on the contrary, it has been conceptualized over various contexts. Malaquias and Filho [ 11 ] reiterated this definition in the extant literature. These scholars affirmed HIS as “a set of interrelated components that collect, process, store and distribute information to support the decision-making process and assist in the control of health organizations” [ 11 ]. The same definition is adopted in this paper, and HIS is construed as “a system of interrelated constituents that collect, process, store and distribute data and information to support the decision-making process, assist in the control of health organizations and enhance healthcare applications”. However, it is paramount to note that HIS is broad. In many instances, the definition is of minimal relevance due to its associated incorporation with external applications related to health developments and policy making [ 16 ]. Hence, emphasis should not be placed on the definition but on its contribution to all facets of health development.

The current state of HIS is considered to be inadequate despite its numerus deployment of HIS that has been driven by its potential benefit to uplift healthcare and revolutionize its processes [ 17 , 18 ]. The persistence of many constraints and resistance to technology has resulted to the incapacitation of HIS in the attainment of its objectives. The extant literature reveals several challenges in different categories, such as the inadequacy of human resources and technological convergence within the healthcare [ 18 ], highlighting the evidence of limitations of HIS that restrict their utilization and deployment within the healthcare. Although several authors identified the unique disposition of HIS in integrating care and unifying the health process, these perspectives seems to be marred by the presence of barriers [ 17 , 19 ]. Garcia, De la Vega [ 17 ] alleged that the current HIS deployment is characterized by fragmentation, update instability, and lack of standardization that limit its potential to aid healthcare. Congruently, several authors associated the lack of awareness of HIS potential, the underuse HIS, inadequate communication network, and security and confidentiality concerns among the barriers limiting HIS [ 20 ]. Thus, the need for this paper is set forth: to uncover current and pertinent insights on HIS deployment as a concerted effort to strengthen it and augment its healthcare delivery capabilities. This paper comprehensively explores the extant literature systematically with respect to the overarching objective: to ascertain value insights pertaining to HIS holistically from literature synthesis. To achieve this goal, the following research questions are investigated: What has been the development of the HIS since its conception? How has HIS been deployed? Finally, how does HIS enable information and knowledge management in healthcare?

In this paper, an overview HIS from the extant literature in relation to the health sector is presented with associated related work. It is essential to point out that in spite of the surplus of research work conducted on health information systems, there are still many challenges confronting it within the healthcare area that necessitate the need for this study [ 5 ]. Therefore, the extant literature is explored in this paper systematically to uncover current and pertinent insights surrounding the deployment of the HIS, an integrated information system (IS) for healthcare. This paper is structured into five sections. The paper commences with an introductory background that presents the contextualization of HIS for healthcare, followed by a methodology that details the method and material used in this study. The next section, which is the discussion, presents the discourse of HIS evolution that highlights its progress to date, its structural deployment, and the information system and knowledge management within the healthcare arena as mediated by HIS. The last part of this study focuses on the conclusion that summarizes the discussion presented in this paper.

2. Material and Method

In this paper, a systematic review is conducted to synthesize the extant literature and analyze the content to ascertain the value disposition of HIS in relation to healthcare delivery. Preceding this review, the used of search engines was employed to retrieve related research publications that fit the study scope and contexts. The main database used was the Web of Science . Other databases such as SCOPUS and Google Scholar were also used to obtain additional relevant work associated with the context. For inclusion criteria, only articles containing references to the keywords HIS, information, healthcare, and related healthcare systems were analyzed scrupulously. Research work that did not have these references, did not constitute a journal or conference-proceeding work, and were not written in the English language were excluded. Figure 1 , the PRISMA flow statement, illustrates the methodological phases of this research along with the exclusion and inclusion criteria that were implemented for the study synthesis.

Prisma flow Statement.

3. Discussion

3.1. the evolution of health information systems.

The concept of enhancing healthcare applications has always been the foundation of HIS, which posits that the intercession of information systems with business processes affords better healthcare services [ 7 , 21 ]. According to Almunawar and Anshari [ 7 ], many determinants, such as technological, political, social and economic, have enormously influenced the nature of the healthcare industry. The technological determinant, particularly the computerized component, is thought to be deeply ingrained in the enactment and functioning of HIS. According to Panerai [ 16 ], this single attribute can be held solely responsible for HIS letdowns rather than its accomplishment.

The ownership of HIS has been contested in the literature, with some authors claiming that HIS belongs to the IT industries [ 22 ]. While IT has enabled many developments in various industries, it has also resulted in many dissatisfactions. Recently, there has been an insurgence from many industries, particularly the healthcare industries, who acknowledge the role of IT in optimizing and enhancing health initiatives but want appropriation of their integrated IS. However, according to the definition of HIS, it is presented as “a set of interconnected components that collect, process, store, and distribute information to support decision-making and aid in the control of health organizations”; thus, the disposition of HIS was established. Without bias, the development of HIS was conceived due to unavoidable changes and transformations within the global space.

A good representation and consolidation of this dispute are within the realization that there is a co-existence of different related and non-related components in a system. In this case, the HIS is an entrenched system with several features, including technologies. Panerai [ 16 ] supported this notion and theorized HIS to be broad, stating that the relevance of its definition is contextual. In the study, HIS was reiterated as any kind of “structured repository of data, information, or knowledge” that can be used to support health care delivery or promote health development [ 16 ]. Thus, maintaining a rigid definition is of minimal practical use because many HIS instances are not directly associated with health development, such as the financial and human resource modules. Moreover, several different HIS examples are categorized according to the functions they are dedicated to serving within the healthcare arena. They highlight the instances of the existence of outliers that are not regarded as the normal HIS even though they contain health determinants data, such as socioeconomic and environmental, which can be used to formulate health policies.

The development of HIS over the years has led many to believe they are solely computer technology. This notion has contributed dramatically to the misconception of the origin of HIS and the lack of peculiarity between the HIS conceptual structure and implemented HIS technology. The literature dates back the origin of HIS, which can be associated with the first record of mortality in the 18th century, revealing their existence to be 200 years or older than the invention of computers [ 16 ]. This demonstrates the emergence of digitalized HIS from the availability of commercialized episodes of “electronic medical records” EMR records in the 1970s [ 23 ]. Namageyo-Funa, Aketch [ 24 ] commended the advancement of technologies in the healthcare arena, recounting the implementation of digitalized HIS that significantly revolutionized the recording and accessing of health information. A study by Lindberg, Venkateswaran [ 25 ] highlighted an instance of HIS transition from paper based to digitally based, revealing a streamlined workflow that revolutionized health care applications in the healthcare arena. This HIS transition over the course of time has led to increased adoption of it within the health care arena. Tummers, Tekinerdogan [ 26 ] highlighted the landmark of HIS from its transition to digitalization and reported a current trend in healthcare that has now been extended with the inclusion of block chain technology within the healthcare arena. Malik, Kazi [ 27 ] assessed HIS adoption in terms of technological, organizational, human, and environmental determinants and reported a variation of different degrees of utilization. Despite these facts, the extant literature maintains the need for a resilient and sustainable HIS for health care applications within the healthcare arena at all levels [ 18 , 27 , 28 ].

Figure 2 illustrates the successful adoption of HIS amidst the significant determinants of its effectiveness. From the Figure 2 , the technological, organizational, human, and environmental determinants are the defining concepts along with individual sub-determinants in each domain that influence HIS adoption. At the technological level, the need for digitalization drives HIS adoption, especially for stakeholders such as clinicians and decision makers. The administrative, management, and planning functions are the driving actors within the organization level that endorse the implementation of HIS. The environmental and human determinants are more concerned with the socio-technical components that have been regarded as complex drivers for HIS adoptions. Perceptions, literacy, and usability are known forces within these categories that necessitate the adoption of HIS in many healthcare arenas.

Effective health information system associations with the driving adoption determinants. Source: [ 27 ].

3.2. HIS Structural Deployment

HIS’s unified front is geared toward assimilating and disseminating health gen to enhance healthcare delivery. HIS consists of different sub-systems that serve several actors within the healthcare arena [ 29 ]. These sub-systems are dedicated to specific tasks that perform various functions such as civil registrations, disease surveillance, outbreak notices, interventions, and health information sharing within the healthcare arena. It also supports and links many functions and activities within the healthcare environment, such as recording various data and information for stakeholders, scheduling, billing, and managing. Stakeholders are furnished with health information from diverse HIS scenarios. These include but are not limited to information systems for hospitals and patients, health institution systems, and Internet information systems. Sligo, Gauld [ 30 ] regarded HIS as a panacea within the healthcare ground that improves health care applications. Despite all the limitless capabilities of HIS, it has been reported to be asymmetrical, lacking interactions within subsystems [ 1 , 18 ]. Many decision making methods and policies rely on good health information [ 31 ]. According to Suresh and Singh [ 32 ], the HIS enables stakeholders such as the government and all other players in the healthcare arena to have access to health information, which influences the delivery of healthcare. The sundry literature further reveals accurate health information to be the foundation of decision making and highlights the decisive role of the human constituent [ 29 , 31 , 33 , 34 ].

Furthermore, HIS can be classified into two cogs in today’s era: the computer-related constituent that employs ICT-related tools and the non-computer component, which both operate at different levels. These levels include strategic, tactical, and operational. The deployment of HIS at the strategic level offers intelligence functions such as intelligent decision support, financial estimation, performance assessment, and simulation systems [ 3 , 35 ]. At the tactical level, managerial functions are performed within the system, while at the operational level, functions including recording, invoicing, scheduling, administrative, procurement, automation, and even payroll are carried out. Figure 3 shows the three levels within the healthcare system where HIS deployment is utilized.

Levels of HIS deployment: source authors.

3.3. Health Information Systems Benefits

HIS, as an interrelated system, houses several core processes and branches in the healthcare arena, affording many benefits. Among these are the ease of access to patients and medical records, reduction of costs and time, and evidence-based health policies and interventions [ 8 , 21 , 36 , 37 , 38 ]. Several authors revealed the benefits of HIS to be widely known and influential within the healthcare domain [ 38 ]. Furthermore, many health organizations are drawn to HIS because of these numerous advantages [ 22 , 39 ]. Moreover, investment in HIS has enabled effective decision making, real-time comprehensive health information for quality health care applications, effective policies in the healthcare arena, scaled-up monitoring and evaluation, health innovations, resource allocations, surveillance services, and enhanced governance and accountability [ 36 , 40 , 41 , 42 ]. Ideally, HIS is pertinent for data, information, and broad knowledge sharing in the healthcare environment. HIS critical features are now cherished due to their incorporation with diverse technology [ 16 , 43 ]. The extant literature reveals the role of HIS to extend beyond its reimbursement. Table 1 presents a summarized extract of various HIS benefits as captured in the literature and some of its core enabling components or instances.

HIS core enabling components and its benefits.

3.4. Information System and Knowledge Management in the Healthcare Arena

The presence of modernized information systems (IS) in the healthcare arena is alleged by scholars to be a congested domain that seldom fosters stakeholders’ multifaceted and disputed relationships [ 48 ]. On the other hand, it is believed that a significant amount of newly acquired knowledge in the field of healthcare is required for the improvement of health care [ 49 ]. Ascertaining and establishing the role of IS and knowledge management is an important step in the development of HIS for healthcare. Flora, Margaret [ 5 ] posited that efficient IS and data usage are crucial for an effective healthcare system. Bernardi [ 50 ] alleged that the underpinning inkling of a “robust and efficient” HIS enables healthcare stakeholders such as managers and providers to leverage health information to commendably plan and regulate healthcare, which could result in enhanced survival rates. As a result, it is imperative to ground these ideas within the context of the healthcare industry to provide a foundation for developing a robust and sustainable HIS for use in the context of health care applications.

3.4.1. Information System

The assimilation and dissimilation of health information and data within the healthcare system is an important task that influences healthcare outcome. Within the healthcare setting, IS plays a significant role in the assimilation and dissimilation of health information needed by healthcare stakeholders. Many continents endorse the deployment of IS mainly to consolidate mutable information from different sources within the systems. The primary objective for these systems’ deployment has been centered on bringing together unique and different components such as institutions, people, processes, and technology in the system under one umbrella [ 5 , 51 ]. An overview of the extant literature reveals that this has rarely been easy, as integration within this system has always been difficult in many contexts. In the context of HIS, many reported the integration phenomena to be problematic, attributing this to the global transformation within the healthcare arena [ 52 , 53 ]. This revolution, coupled with the advancement of the healthcare arena, has resulted in the need for robust allied health IS systems that incorporates different IS and information technology [ 5 , 22 ]. These allied health information systems are necessary to consolidate independent information systems within their healthcare arena use to enhance healthcare applications [ 54 , 55 ]. Organizations in the healthcare arena expect these systems to be sustainable and resilient; however, in order to satisfy these requirements, an integrated information system is needed to unify all independent, agile, and flexible health IS to mitigate challenges for HIS [ 56 ].

An aligned HIS that is allied is essential, as it supports health information networks (HIN) that subsequently enhance and improve healthcare applications [ 44 , 57 ]. Thus, many organizations within the healthcare settings are fine-tuning their HIS to be resilient and sustainable. However, the realization of a robust information system within the healthcare arena is challenging and depends on the flow of information as a crucial constituent for suave and efficient functioning [ 58 , 59 ].

3.4.2. Knowledge Management

The process of constructing value and generating a maintainable edge for an industry with capitalization on building, communicating, and knowledge applications procedures to realize set aspirations is denoted as knowledge management [ 60 ]. The literature reveals knowledge management as an important contributor to organizational performance through its knowledge-sharing capabilities [ 61 ]. In the healthcare industry, there is a high demand for knowledge to enhance healthcare applications [ 49 , 62 ]. Several studies reported that the deployment of knowledge management in the healthcare arena is set to enhance healthcare treatment effectiveness [ 49 , 58 , 61 ]. Many stakeholders such as governments, World Health Organization (WHO), and healthcare workers rely on the management of healthcare knowledge to complement healthcare applications. According to Kim, Newby-Bennett [ 61 ], the focus of knowledge management is to efficaciously expedite knowledge sharing. However, integrating knowledge from different sources is challenging and requires an enabler [ 61 ].

The HIS is an indispensable enabler of health knowledge generated from amalgamated health information within the healthcare arena [ 63 , 64 , 65 ]. Dixon, McGowan [ 66 ] asserted that efficacious modifications in the healthcare arena are made possible by knowledge codification and collaboration from information technologies. Similarly, some authors have pinpointed information and communication technologies within the healthcare arena to be a major determinant in the attainment of a sustainable health system development [ 58 ]. The knowledge management relationship with HIS is considered complementary and balanced, as it enables the availability of knowledge that can be shared. The importance of knowledge management is relevant for the realization of an enhanced healthcare application via HIS. Soltysik-Piorunkiewicz and Morawiec [ 58 ] claimed that the information society effectively uses HIS as an information system for management, patient knowledge, health knowledge, healthcare unit knowledge, and drug knowledge. The authors herein demonstrated how HIS facilitates knowledge management in the healthcare sector to improve healthcare applications.

The role of HIS as an integrated IS and key enabler of healthcare knowledge management highlights its potential within the healthcare arena. From the conception of HIS and the records of its evolution, significant achievements have been attained that are demonstrated at different levels of its structural deployment. HIS deployment in several settings of healthcare have positively influenced clinical processes and patients’ outcomes [ 17 ]. Globally, the need for HIS within the healthcare system is critical in the enhancement of healthcare. Many healthcare actions are dependent on the use of HIS [ 67 , 68 , 69 ]. This demand is substantiated by the offerings of HIS in tackling the transformation and digitalization confronting the healthcare system. However, despite the need for HIS and its potential within healthcare, several barriers limit its optimization. Some authors posited the role and involvement of healthcare professionals such as physicians to be important measure that is paramount to decreasing the technical and personal barriers sabotaging HIS deployment [ 20 ]. Nonetheless, the design of HIS is accentuated on augmenting health and is considered to be lagging behind in attaining quality healthcare [ 70 ].

Although there are equal blessings as well as challenges with HIS deployment, this study appraisal of HIS highlights its capabilities and attributes that enhance healthcare in many ways. From its conception, HIS has evolved significantly to enable the digitalization of many healthcare processes. Its deployment structurally has facilitated many healthcare applications at all levels within the health system where it has been implemented. Many benefits such as ease of access to medical records, cost reduction, data and information management, precision medicine, and autonomous and intelligent decisions have been enabled by HIS deployment. Primarily, HIS is the core enabler of the healthcare information system and knowledge management within the healthcare arena. Ascertaining the attributes and development of HIS is a paramount to driving its implementation and realizing its potential. Many deployments of HIS can be anchored on this study as a reference for planning and executing HIS implementation. The extant literature points out the need for the role of technology such HIS to be ascertained, as little is known in this regard, which as a result has adversely influenced healthcare coordination [ 19 ]. Additionally, among the barriers of HIS, the presence of inadequate planning that fails to cater to the needs of those adopting it hinders the optimization of these systems within the healthcare arena [ 71 ]. Cawthon, Mion [ 72 ] associated the lack of health literacy incorporation in deployed HIS to increased cost and poorer health outcomes. Hence, the insight from this study can be incorporated and associated with HIS initiatives to mitigate these issues. Thus, the findings of this study can be employed to strategize HIS deployment and plans as well as augment its potential to enhance healthcare. Furthermore, the competency of healthcare stakeholders such as patients can be enhanced with the findings of this study that accentuate the holistic representation of HIS in the dissimilation and assimilation of health data and information.

4. Conclusions

In the healthcare information and knowledge arena, assimilation and dissemination is a facet that influences healthcare delivery. The conception and evolution of HIS has positioned this system within the healthcare arena to arbitrate information interchange for its stakeholders. HIS deployment within healthcare has not only enabled information and knowledge management, but it has also enabled and driven many healthcare agendas and continues to maintain a solidified presence within the healthcare space. However, its deployment and enactment globally has been marred and plagued with several challenges that hinder its optimization and defeat its purpose. Phenomena such as the occurrences of pandemics such as COVID-19, which are uncertain, and the advancement of technology that cannot be controlled have caused disputed gradients regarding the positioning of HIS. These phenomena have not only influenced the adoption of HIS but have also limited its ability to be fully utilized. Although much research on HIS has been conducted, the presence of these phenomena and many other inherent challenges such as fragmentation and cost still maintain a constant, prominent presence, which has led to the need for this study.

Consequently, the starting point for this study was to provide insight and expertise regarding the discourse of HIS for healthcare applications. This paper presents current and pertinent insights regarding the deployment of the HIS that, when adopted, can positively aid its employment. This paper investigated the existing HIS literature to accomplish the objective set forth in the introduction. This study’s synthesis derived key insights relevant to the holistic view of HIS through a thorough systematic review of the various extant literature on HIS and healthcare. According to the study’s findings, HIS are critical and foundational in the drive of information and knowledge management for healthcare. The contribution of HIS to healthcare has been and continues to be groundbreaking since its conception and through its consequent evolution. Nevertheless, despite the presence of some limitations that are external and inherent, it is claimed to have transformed and changed healthcare from the start. Similarly, the evaluation of the current HIS is expected to impact its adoption and strengthen its implementation within the global healthcare space, which is greatly desired. These findings are of great importance to the healthcare stakeholders that directly and indirect interact with HIS. Additionally, scholars and healthcare researchers can benefit from this study by incorporating the findings in future works that plan HIS for healthcare.

Funding Statement

This research received no external funding.

Author Contributions

Conceptualization, A.E.; methodology, A.E.; software, A.E.; validation, A.E.; formal analysis, A.E.; investigation, A.E.; resources, A.E.; data curation, A.E.; writing—original draft preparation, A.E.; writing—review and editing, A.E.; visualization, A.E.; supervision, S.P.M. and I.E.A.; project administration, A.E., S.P.M. and I.E.A.; funding acquisition, A.E., S.P.M. and I.E.A. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

Informed consent statement, data availability statement, conflicts of interest.

The authors declare there are no conflict of interest.

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• Research article
• Open access
• Published: 10 April 2021

The role of artificial intelligence in healthcare: a structured literature review

• Silvana Secinaro 1 ,
• Davide Calandra 1 ,
• Aurelio Secinaro 2 ,
• Vivek Muthurangu 3 &
• Paolo Biancone 1  

BMC Medical Informatics and Decision Making volume  21 , Article number:  125 ( 2021 ) Cite this article

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Background/Introduction

Artificial intelligence (AI) in the healthcare sector is receiving attention from researchers and health professionals. Few previous studies have investigated this topic from a multi-disciplinary perspective, including accounting, business and management, decision sciences and health professions.

The structured literature review with its reliable and replicable research protocol allowed the researchers to extract 288 peer-reviewed papers from Scopus. The authors used qualitative and quantitative variables to analyse authors, journals, keywords, and collaboration networks among researchers. Additionally, the paper benefited from the Bibliometrix R software package.

The investigation showed that the literature in this field is emerging. It focuses on health services management, predictive medicine, patient data and diagnostics, and clinical decision-making. The United States, China, and the United Kingdom contributed the highest number of studies. Keyword analysis revealed that AI can support physicians in making a diagnosis, predicting the spread of diseases and customising treatment paths.

Conclusions

The literature reveals several AI applications for health services and a stream of research that has not fully been covered. For instance, AI projects require skills and data quality awareness for data-intensive analysis and knowledge-based management. Insights can help researchers and health professionals understand and address future research on AI in the healthcare field.

Peer Review reports

Artificial intelligence (AI) generally applies to computational technologies that emulate mechanisms assisted by human intelligence, such as thought, deep learning, adaptation, engagement, and sensory understanding [ 1 , 2 ]. Some devices can execute a role that typically involves human interpretation and decision-making [ 3 , 4 ]. These techniques have an interdisciplinary approach and can be applied to different fields, such as medicine and health. AI has been involved in medicine since as early as the 1950s, when physicians made the first attempts to improve their diagnoses using computer-aided programs [ 5 , 6 ]. Interest and advances in medical AI applications have surged in recent years due to the substantially enhanced computing power of modern computers and the vast amount of digital data available for collection and utilisation [ 7 ]. AI is gradually changing medical practice. There are several AI applications in medicine that can be used in a variety of medical fields, such as clinical, diagnostic, rehabilitative, surgical, and predictive practices. Another critical area of medicine where AI is making an impact is clinical decision-making and disease diagnosis. AI technologies can ingest, analyse, and report large volumes of data across different modalities to detect disease and guide clinical decisions [ 3 , 8 ]. AI applications can deal with the vast amount of data produced in medicine and find new information that would otherwise remain hidden in the mass of medical big data [ 9 , 10 , 11 ]. These technologies can also identify new drugs for health services management and patient care treatments [ 5 , 6 ].

Courage in the application of AI is visible through a search in the primary research databases. However, as Meskò et al. [ 7 ] find, the technology will potentially reduce care costs and repetitive operations by focusing the medical profession on critical thinking and clinical creativity. As Cho et al. and Doyle et al. [ 8 , 9 ] add, the AI perspective is exciting; however, new studies will be needed to establish the efficacy and applications of AI in the medical field [ 10 ].

Our paper will also concentrate on AI strategies for healthcare from the accounting, business, and management perspectives. The authors used the structured literature review (SLR) method for its reliable and replicable research protocol [ 11 ] and selected bibliometric variables as sources of investigation. Bibliometric usage enables the recognition of the main quantitative variables of the study stream [ 12 ]. This method facilitates the detection of the required details of a particular research subject, including field authors, number of publications, keywords for interaction between variables (policies, properties and governance) and country data [ 13 ]. It also allows the application of the science mapping technique [ 14 ]. Our paper adopted the Bibliometrix R package and the biblioshiny web interface as tools of analysis [ 14 ].

The investigation offers the following insights for future researchers and practitioners:

bibliometric information on 288 peer-reviewed English papers from the Scopus collection.

Identification of leading journals in this field, such as Journal of Medical Systems, Studies in Health Technology and Informatics, IEEE Journal of Biomedical and Health Informatics, and Decision Support Systems.

Qualitative and quantitative information on authors’ Lotka’s law, h-index, g-index, m-index, keyword, and citation data.

Research on specific countries to assess AI in the delivery and effectiveness of healthcare, quotes, and networks within each region.

A topic dendrogram study that identifies five research clusters: health services management, predictive medicine, patient data, diagnostics, and finally, clinical decision-making.

An in-depth discussion that develops theoretical and practical implications for future studies.

The paper is organised as follows. Section  2 lists the main bibliometric articles in this field. Section  3 elaborates on the methodology. Section  4 presents the findings of the bibliometric analysis. Section  5 discusses the main elements of AI in healthcare based on the study results. Section  6 concludes the article with future implications for research.

Related works and originality

As suggested by Zupic and Čater [ 15 ], a research stream can be evaluated with bibliometric methods that can introduce objectivity and mitigate researcher bias. For this reason, bibliometric methods are attracting increasing interest among researchers as a reliable and impersonal research analytical approach [ 16 , 17 ]. Recently, bibliometrics has been an essential method for analysing and predicting research trends [ 18 ]. Table  1 lists other research that has used a similar approach in the research stream investigated.

The scientific articles reported show substantial differences in keywords and research topics that have been previously studied. The bibliometric analysis of Huang et al. [ 19 ] describes rehabilitative medicine using virtual reality technology. According to the authors, the primary goal of rehabilitation is to enhance and restore functional ability and quality of life for patients with physical impairments or disabilities. In recent years, many healthcare disciplines have been privileged to access various technologies that provide tools for both research and clinical intervention.

Hao et al. [ 20 ] focus on text mining in medical research. As reported, text mining reveals new, previously unknown information by using a computer to automatically extract information from different text resources. Text mining methods can be regarded as an extension of data mining to text data. Text mining is playing an increasingly significant role in processing medical information. Similarly, the studies by dos Santos et al. [ 21 ] focus on applying data mining and machine learning (ML) techniques to public health problems. As stated in this research, public health may be defined as the art and science of preventing diseases, promoting health, and prolonging life. Using data mining and ML techniques, it is possible to discover new information that otherwise would be hidden. These two studies are related to another topic: medical big data. According to Liao et al. [ 22 ], big data is a typical “buzzword” in the business and research community, referring to a great mass of digital data collected from various sources. In the medical field, we can obtain a vast amount of data (i.e., medical big data). Data mining and ML techniques can help deal with this information and provide helpful insights for physicians and patients. More recently, Choudhury et al. [ 23 ] provide a systematic review on the use of ML to improve the care of elderly patients, demonstrating eligible studies primarily in psychological disorders and eye diseases.

Tran et al. [ 2 ] focus on the global evolution of AI research in medicine. Their bibliometric analysis highlights trends and topics related to AI applications and techniques. As stated in Connelly et al.’s [ 24 ] study, robot-assisted surgeries have rapidly increased in recent years. Their bibliometric analysis demonstrates how robotic-assisted surgery has gained acceptance in different medical fields, such as urological, colorectal, cardiothoracic, orthopaedic, maxillofacial and neurosurgery applications. Additionally, the bibliometric analysis of Guo et al. [ 25 ] provides an in-depth study of AI publications through December 2019. The paper focuses on tangible AI health applications, giving researchers an idea of how algorithms can help doctors and nurses. A new stream of research related to AI is also emerging. In this sense, Choudhury and Asan’s [ 26 ] scientific contribution provides a systematic review of the AI literature to identify health risks for patients. They report on 53 studies involving technology for clinical alerts, clinical reports, and drug safety. Considering the considerable interest within this research stream, this analysis differs from the current literature for several reasons. It aims to provide in-depth discussion, considering mainly the business, management, and accounting fields and not dealing only with medical and health profession publications.

Additionally, our analysis aims to provide a bibliometric analysis of variables such as authors, countries, citations and keywords to guide future research perspectives for researchers and practitioners, as similar analyses have done for several publications in other research streams [ 15 , 16 , 27 ]. In doing so, we use a different database, Scopus, that is typically adopted in social sciences fields. Finally, our analysis will propose and discuss a dominant framework of variables in this field, and our analysis will not be limited to AI application descriptions.

Methodology

This paper evaluated AI in healthcare research streams using the SLR method [ 11 ]. As suggested by Massaro et al. [ 11 ], an SLR enables the study of the scientific corpus of a research field, including the scientific rigour, reliability and replicability of operations carried out by researchers. As suggested by many scholars, the methodology allows qualitative and quantitative variables to highlight the best authors, journals and keywords and combine a systematic literature review and bibliometric analysis [ 27 , 28 , 29 , 30 ]. Despite its widespread use in business and management [ 16 , 31 ], the SLR is also used in the health sector based on the same philosophy through which it was originally conceived [ 32 , 33 ]. A methodological analysis of previously published articles reveals that the most frequently used steps are as follows [ 28 , 31 , 34 ]:

defining research questions;

writing the research protocol;

defining the research sample to be analysed;

developing codes for analysis; and

critically analysing, discussing, and identifying a future research agenda.

Considering the above premises, the authors believe that an SLR is the best method because it combines scientific validity, replicability of the research protocol and connection between multiple inputs.

As stated by the methodological paper, the first step is research question identification. For this purpose, we benefit from the analysis of Zupic and Čater [ 15 ], who provide several research questions for future researchers to link the study of authors, journals, keywords and citations. Therefore, RQ1 is “What are the most prominent authors, journal keywords and citations in the field of the research study?” Additionally, as suggested by Haleem et al. [ 35 ], new technologies, including AI, are changing the medical field in unexpected timeframes, requiring studies in multiple areas. Therefore, RQ2 is “How does artificial intelligence relate to healthcare, and what is the focus of the literature?” Then, as discussed by Massaro et al. [ 36 ], RQ3 is “What are the research applications of artificial intelligence for healthcare?”.

The first research question aims to define the qualitative and quantitative variables of the knowledge flow under investigation. The second research question seeks to determine the state of the art and applications of AI in healthcare. Finally, the third research question aims to help researchers identify practical and theoretical implications and future research ideas in this field.

The second fundamental step of the SLR is writing the research protocol [ 11 ]. Table  2 indicates the currently known literature elements, uniquely identifying the research focus, motivations and research strategy adopted and the results providing a link with the following points. Additionally, to strengthen the analysis, our investigation benefits from the PRISMA statement methodological article [ 37 ]. Although the SLR is a validated method for systematic reviews and meta-analyses, we believe that the workflow provided may benefit the replicability of the results [ 37 , 38 , 39 , 40 ]. Figure  1 summarises the researchers’ research steps, indicating that there are no results that can be referred to as a meta-analysis.

Source : Authors’ elaboration on Liberati et al. [ 37 ]

PRISMA workflow.

The third step is to specify the search strategy and search database. Our analysis is based on the search string “Artificial Intelligence” OR “AI” AND “Healthcare” with a focus on “Business, Management, and Accounting”, “Decision Sciences”, and “Health professions”. As suggested by [ 11 , 41 ] and motivated by [ 42 ], keywords can be selected through a top-down approach by identifying a large search field and then focusing on particular sub-topics. The paper uses data retrieved from the Scopus database, a multi-disciplinary database, which allowed the researchers to identify critical articles for scientific analysis [ 43 ]. Additionally, Scopus was selected based on Guo et al.’s [ 25 ] limitations, which suggest that “future studies will apply other databases, such as Scopus, to explore more potential papers” . The research focuses on articles and reviews published in peer-reviewed journals for their scientific relevance [ 11 , 16 , 17 , 29 ] and does not include the grey literature, conference proceedings or books/book chapters. Articles written in any language other than English were excluded [ 2 ]. For transparency and replicability, the analysis was conducted on 11 January 2021. Using this research strategy, the authors retrieved 288 articles. To strengthen the study's reliability, we publicly provide the full bibliometric extract on the Zenodo repository [ 44 , 45 ].

The fourth research phase is defining the code framework that initiates the analysis of the variables. The study will identify the following:

descriptive information of the research area;

source analysis [ 16 ];

author and citation analysis [ 28 ];

keywords and network analysis [ 14 ]; and

geographic distribution of the papers [ 14 ].

The final research phase is the article’s discussion and conclusion, where implications and future research trends will be identified.

At the research team level, the information is analysed with the statistical software R-Studio and the Bibliometrix package [ 15 ], which allows scientific analysis of the results obtained through the multi-disciplinary database.

The analysis of bibliometric results starts with a description of the main bibliometric statistics with the aim of answering RQ1, What are the most prominent authors, journal keywords and citations in the field of the research study?, and RQ2, How does artificial intelligence relate to healthcare, and what is the focus of the literature? Therefore, the following elements were thoroughly analysed: (1) type of document; (2) annual scientific production; (3) scientific sources; (4) source growth; (5) number of articles per author; (6) author’s dominance ranking; (7) author’s h-index, g-index, and m-index; (8) author’s productivity; (9) author’s keywords; (10) topic dendrogram; (11) a factorial map of the document with the highest contributions; (12) article citations; (13) country production; (14) country citations; (15) country collaboration map; and (16) country collaboration network.

Main information

Table  3 shows the information on 288 peer-reviewed articles published between 1992 and January 2021 extracted from the Scopus database. The number of keywords is 946 from 136 sources, and the number of keywords plus, referring to the number of keywords that frequently appear in an article’s title, was 2329. The analysis period covered 28 years and 1 month of scientific production and included an annual growth rate of 5.12%. However, the most significant increase in published articles occurred in the past three years (please see Fig.  2 ). On average, each article was written by three authors (3.56). Finally, the collaboration index (CI), which was calculated as the total number of authors of multi-authored articles/total number of multi-authored articles, was 3.97 [ 46 ].

Source : Authors’ elaboration

Annual scientific production.

Table  4 shows the top 20 sources related to the topic. The Journal of Medical Systems is the most relevant source, with twenty-one of the published articles. This journal's main issues are the foundations, functionality, interfaces, implementation, impacts, and evaluation of medical technologies. Another relevant source is Studies in Health Technology and Informatics, with eleven articles. This journal aims to extend scientific knowledge related to biomedical technologies and medical informatics research. Both journals deal with cloud computing, machine learning, and AI as a disruptive healthcare paradigm based on recent publications. The IEEE Journal of Biomedical and Health Informatics investigates technologies in health care, life sciences, and biomedicine applications from a broad perspective. The next journal, Decision Support Systems, aims to analyse how these technologies support decision-making from a multi-disciplinary view, considering business and management. Therefore, the analysis of the journals revealed that we are dealing with an interdisciplinary research field. This conclusion is confirmed, for example, by the presence of purely medical journals, journals dedicated to the technological growth of healthcare, and journals with a long-term perspective such as futures.

The distribution frequency of the articles (Fig.  3 ) indicates the journals dealing with the topic and related issues. Between 2008 and 2012, a significant growth in the number of publications on the subject is noticeable. However, the graph shows the results of the Loess regression, which includes the quantity and publication time of the journal under analysis as variables. This method allows the function to assume an unlimited distribution; that is, feature can consider values below zero if the data are close to zero. It contributes to a better visual result and highlights the discontinuity in the publication periods [ 47 ].

Source growth. Source : Authors’ elaboration

Finally, Fig.  4 provides an analytical perspective on factor analysis for the most cited papers. As indicated in the literature [ 48 , 49 ], using factor analysis to discover the most cited papers allows for a better understanding of the scientific world’s intellectual structure. For example, our research makes it possible to consider certain publications that effectively analyse subject specialisation. For instance, Santosh’s [ 50 ] article addresses the new paradigm of AI with ML algorithms for data analysis and decision support in the COVID-19 period, setting a benchmark in terms of citations by researchers. Moving on to the application, an article by Shickel et al. [ 51 ] begins with the belief that the healthcare world currently has much health and administrative data. In this context, AI and deep learning will support medical and administrative staff in extracting data, predicting outcomes, and learning medical representations. Finally, in the same line of research, Baig et al. [ 52 ], with a focus on wearable patient monitoring systems (WPMs), conclude that AI and deep learning may be landmarks for continuous patient monitoring and support for healthcare delivery.

Factorial map of the most cited documents.

This section identifies the most cited authors of articles on AI in healthcare. It also identifies the authors’ keywords, dominance factor (DF) ranking, h-index, productivity, and total number of citations. Table  5 identifies the authors and their publications in the top 20 rankings. As the table shows, Bushko R.G. has the highest number of publications: four papers. He is the editor-in-chief of Future of Health Technology, a scientific journal that aims to develop a clear vision of the future of health technology. Then, several authors each wrote three papers. For instance, Liu C. is a researcher active in the topic of ML and computer vision, and Sharma A. from Emory University Atlanta in the USA is a researcher with a clear focus on imaging and translational informatics. Some other authors have two publications each. While some authors have published as primary authors, most have published as co-authors. Hence, in the next section, we measure the contributory power of each author by investigating the DF ranking through the number of elements.

Authors’ dominance ranking

The dominance factor (DF) is a ratio measuring the fraction of multi-authored articles in which an author acts as the first author [ 53 ]. Several bibliometric studies use the DF in their analyses [ 46 , 54 ]. The DF ranking calculates an author’s dominance in producing articles. The DF is calculated by dividing the number of an author’s multi-authored papers as the first author (Nmf) by the author's total number of multi-authored papers (Nmt). This is omitted in the single-author case due to the constant value of 1 for single-authored articles. This formulation could lead to some distortions in the results, especially in fields where the first author is entered by surname alphabetical order [ 55 ].

The mathematical equation for the DF is shown as:

Table  6 lists the top 20 DF rankings. The data in the table show a low level of articles per author, either for first-authored or multi-authored articles. The results demonstrate that we are dealing with an emerging topic in the literature. Additionally, as shown in the table, Fox J. and Longoni C. are the most dominant authors in the field.

Authors’ impact

Table  7 shows the impact of authors in terms of the h-index [ 56 ] (i.e., the productivity and impact of citations of a researcher), g-index [ 57 ] (i.e., the distribution of citations received by a researcher's publications), m-index [ 58 ] (i.e., the h-index value per year), total citations, total paper and years of scientific publication. The H-index was introduced in the literature as a metric for the objective comparison of scientific results and depended on the number of publications and their impact [ 59 ]. The results show that the 20 most relevant authors have an h-index between 2 and 1. For the practical interpretation of the data, the authors considered data published by the London School of Economics [ 60 ]. In the social sciences, the analysis shows values of 7.6 for economic publications by professors and researchers who had been active for several years. Therefore, the youthfulness of the research area has attracted young researchers and professors. At the same time, new indicators have emerged over the years to diversify the logic of the h-index. For example, the g-index indicates an author's impact on citations, considering that a single article can generate these. The m-index, on the other hand, shows the cumulative value over the years.

The analysis, also considering the total number of citations, the number of papers published and the year of starting to publish, thus confirms that we are facing an expanding research flow.

Authors’ productivity

Figure  5 shows Lotka’s law. This mathematical formulation originated in 1926 to describe the publication frequency by authors in a specific research field [ 61 ]. In practice, the law states that the number of authors contributing to research in a given period is a fraction of the number who make up a single contribution [ 14 , 61 ].

Lotka’s law.

The mathematical relationship is expressed in reverse in the following way:

where y x is equal to the number of authors producing x articles in each research field. Therefore, C and n are constants that can be estimated in the calculation.

The figure's results are in line with Lotka's results, with an average of two publications per author in a given research field. In addition, the figure shows the percentage of authors. Our results lead us to state that we are dealing with a young and growing research field, even with this analysis. Approximately 70% of the authors had published only their first research article. Only approximately 20% had published two scientific papers.

Authors’ keywords

This section provides information on the relationship between the keywords artificial intelligence and healthcare . This analysis is essential to determine the research trend, identify gaps in the discussion on AI in healthcare, and identify the fields that can be interesting as research areas [ 42 , 62 ].

Table  8 highlights the total number of keywords per author in the top 20 positions. The ranking is based on the following elements: healthcare, artificial intelligence, and clinical decision support system . Keyword analysis confirms the scientific area of reference. In particular, we deduce the definition as “Artificial intelligence is the theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages” [ 2 , 63 ]. Panch et al. [ 4 ] find that these technologies can be used in different business and management areas. After the first keyword, the analysis reveals AI applications and related research such as machine learning and deep learning.

Additionally, data mining and big data are a step forward in implementing exciting AI applications. According to our specific interest, if we applied AI in healthcare, we would achieve technological applications to help and support doctors and medical researchers in decision-making. The link between AI and decision-making is the reason why we find, in the seventh position, the keyword clinical decision support system . AI techniques can unlock clinically relevant information hidden in the massive amount of data that can assist clinical decision-making [ 64 ]. If we analyse the following keywords, we find other elements related to decision-making and support systems.

The TreeMap below (Fig.  6 ) highlights the combination of possible keywords representing AI and healthcare.

Keywords treemap.

The topic dendrogram in Fig.  7 represents the hierarchical order and the relationship between the keywords generated by hierarchical clustering [ 42 ]. The cut in the figure and the vertical lines facilitate an investigation and interpretation of the different clusters. As stated by Andrews [ 48 ], the figure is not intended to find the perfect level of associations between clusters. However, it aims to estimate the approximate number of clusters to facilitate further discussion.

Topic dendrogram.

The research stream of AI in healthcare is divided into two main strands. The blue strand focuses on medical information systems and the internet. Some papers are related to healthcare organisations, such as the Internet of Things, meaning that healthcare organisations use AI to support health services management and data analysis. AI applications are also used to improve diagnostic and therapeutic accuracy and the overall clinical treatment process [ 2 ]. If we consider the second block, the red one, three different clusters highlight separate aspects of the topic. The first could be explained as AI and ML predictive algorithms. Through AI applications, it is possible to obtain a predictive approach that can ensure that patients are better monitored. This also allows a better understanding of risk perception for doctors and medical researchers. In the second cluster, the most frequent words are decisions , information system , and support system . This means that AI applications can support doctors and medical researchers in decision-making. Information coming from AI technologies can be used to consider difficult problems and support a more straightforward and rapid decision-making process. In the third cluster, it is vital to highlight that the ML model can deal with vast amounts of data. From those inputs, it can return outcomes that can optimise the work of healthcare organisations and scheduling of medical activities.

Furthermore, the word cloud in Fig.  8 highlights aspects of AI in healthcare, such as decision support systems, decision-making, health services management, learning systems, ML techniques and diseases. The figure depicts how AI is linked to healthcare and how it is used in medicine.

Word cloud.

Figure  9 represents the search trends based on the keywords analysed. The research started in 2012. First, it identified research topics related to clinical decision support systems. This topic was recurrent during the following years. Interestingly, in 2018, studies investigated AI and natural language processes as possible tools to manage patients and administrative elements. Finally, a new research stream considers AI's role in fighting COVID-19 [ 65 , 66 ].

Keywords frequency.

Table  9 represents the number of citations from other articles within the top 20 rankings. The analysis allows the benchmark studies in the field to be identified [ 48 ]. For instance, Burke et al. [ 67 ] writes the most cited paper and analyses efficient nurse rostering methodologies. The paper critically evaluates tangible interdisciplinary solutions that also include AI. Immediately thereafter, Ahmed M.A.'s article proposes a data-driven optimisation methodology to determine the optimal number of healthcare staff to optimise patients' productivity [ 68 ]. Finally, the third most cited article lays the groundwork for developing deep learning by considering diverse health and administrative information [ 51 ].

This section analyses the diffusion of AI in healthcare around the world. It highlights countries to show the geographies of this research. It includes all published articles, the total number of citations, and the collaboration network. The following sub-sections start with an analysis of the total number of published articles.

Country total articles

Figure  9 and Table  10 display the countries where AI in healthcare has been considered. The USA tops the list of countries with the maximum number of articles on the topic (215). It is followed by China (83), the UK (54), India (51), Australia (54), and Canada (32). It is immediately evident that the theme has developed on different continents, highlighting a growing interest in AI in healthcare. The figure shows that many areas, such as Russia, Eastern Europe and Africa except for Algeria, Egypt, and Morocco, have still not engaged in this scientific debate.

Country publications and collaboration map

This section discusses articles on AI in healthcare in terms of single or multiple publications in each country. It also aims to observe collaboration and networking between countries. Table  11 and Fig.  10 highlight the average citations by state and show that the UK, the USA, and Kuwait have a higher average number of citations than other countries. Italy, Spain and New Zealand have the most significant number of citations.

Articles per country.

Figure  11 depicts global collaborations. The blue colour on the map represents research cooperation among nations. Additionally, the pink border linking states indicates the extent of collaboration between authors. The primary cooperation between nations is between the USA and China, with two collaborative articles. Other collaborations among nations are limited to a few papers.

Collaboration map.

Artificial intelligence for healthcare: applications

This section aims to strengthen the research scope by answering RQ3: What are the research applications of artificial intelligence for healthcare?

Benefiting from the topical dendrogram, researchers will provide a development model based on four relevant variables [ 69 , 70 ]. AI has been a disruptive innovation in healthcare [ 4 ]. With its sophisticated algorithms and several applications, AI has assisted doctors and medical professionals in the domains of health information systems, geocoding health data, epidemic and syndromic surveillance, predictive modelling and decision support, and medical imaging [ 2 , 9 , 10 , 64 ]. Furthermore, the researchers considered the bibliometric analysis to identify four macro-variables dominant in the field and used them as authors' keywords. Therefore, the following sub-sections aim to explain the debate on applications in healthcare for AI techniques. These elements are shown in Fig.  12 .

Dominant variables for AI in healthcare.

Health services management

One of the notable aspects of AI techniques is potential support for comprehensive health services management. These applications can support doctors, nurses and administrators in their work. For instance, an AI system can provide health professionals with constant, possibly real-time medical information updates from various sources, including journals, textbooks, and clinical practices [ 2 , 10 ]. These applications' strength is becoming even more critical in the COVID-19 period, during which information exchange is continually needed to properly manage the pandemic worldwide [ 71 ]. Other applications involve coordinating information tools for patients and enabling appropriate inferences for health risk alerts and health outcome prediction [ 72 ]. AI applications allow, for example, hospitals and all health services to work more efficiently for the following reasons:

Clinicians can access data immediately when they need it.

Nurses can ensure better patient safety while administering medication.

Patients can stay informed and engaged in their care by communicating with their medical teams during hospital stays.

Additionally, AI can contribute to optimising logistics processes, for instance, realising drugs and equipment in a just-in-time supply system based totally on predictive algorithms [ 73 , 74 ]. Interesting applications can also support the training of personnel working in health services. This evidence could be helpful in bridging the gap between urban and rural health services [ 75 ]. Finally, health services management could benefit from AI to leverage the multiplicity of data in electronic health records by predicting data heterogeneity across hospitals and outpatient clinics, checking for outliers, performing clinical tests on the data, unifying patient representation, improving future models that can predict diagnostic tests and analyses, and creating transparency with benchmark data for analysing services delivered [ 51 , 76 ].

Predictive medicine

Another relevant topic is AI applications for disease prediction and diagnosis treatment, outcome prediction and prognosis evaluation [ 72 , 77 ]. Because AI can identify meaningful relationships in raw data, it can support diagnostic, treatment and prediction outcomes in many medical situations [ 64 ]. It allows medical professionals to embrace the proactive management of disease onset. Additionally, predictions are possible for identifying risk factors and drivers for each patient to help target healthcare interventions for better outcomes [ 3 ]. AI techniques can also help design and develop new drugs, monitor patients and personalise patient treatment plans [ 78 ]. Doctors benefit from having more time and concise data to make better patient decisions. Automatic learning through AI could disrupt medicine, allowing prediction models to be created for drugs and exams that monitor patients over their whole lives [ 79 ].

• Clinical decision-making

One of the keyword analysis main topics is that AI applications could support doctors and medical researchers in the clinical decision-making process. According to Jiang et al. [ 64 ], AI can help physicians make better clinical decisions or even replace human judgement in healthcare-specific functional areas. According to Bennett and Hauser [ 80 ], algorithms can benefit clinical decisions by accelerating the process and the amount of care provided, positively impacting the cost of health services. Therefore, AI technologies can support medical professionals in their activities and simplify their jobs [ 4 ]. Finally, as Redondo and Sandoval [ 81 ] find, algorithmic platforms can provide virtual assistance to help doctors understand the semantics of language and learning to solve business process queries as a human being would.

Patient data and diagnostics

Another challenging topic related to AI applications is patient data and diagnostics. AI techniques can help medical researchers deal with the vast amount of data from patients (i.e., medical big data ). AI systems can manage data generated from clinical activities, such as screening, diagnosis, and treatment assignment. In this way, health personnel can learn similar subjects and associations between subject features and outcomes of interest [ 64 ].

These technologies can analyse raw data and provide helpful insights that can be used in patient treatments. They can help doctors in the diagnostic process; for example, to realise a high-speed body scan, it will be simpler to have an overall patient condition image. Then, AI technology can recreate a 3D mapping solution of a patient’s body.

In terms of data, interesting research perspectives are emerging. For instance, we observed the emergence of a stream of research on patient data management and protection related to AI applications [ 82 ].

For diagnostics, AI techniques can make a difference in rehabilitation therapy and surgery. Numerous robots have been designed to support and manage such tasks. Rehabilitation robots physically support and guide, for example, a patient’s limb during motor therapy [ 83 ]. For surgery, AI has a vast opportunity to transform surgical robotics through devices that can perform semi-automated surgical tasks with increasing efficiency. The final aim of this technology is to automate procedures to negate human error while maintaining a high level of accuracy and precision [ 84 ]. Finally, the -19 period has led to increased remote patient diagnostics through telemedicine that enables remote observation of patients and provides physicians and nurses with support tools [ 66 , 85 , 86 ].

This study aims to provide a bibliometric analysis of publications on AI in healthcare, focusing on accounting, business and management, decision sciences and health profession studies. Using the SLR method of Massaro et al. [ 11 ], we provide a reliable and replicable research protocol for future studies in this field. Additionally, we investigate the trend of scientific publications on the subject, unexplored information, future directions, and implications using the science mapping workflow. Our analysis provides interesting insights.

In terms of bibliometric variables, the four leading journals, Journal of Medical Systems , Studies in Health Technology and Informatics , IEEE Journal of Biomedical and Health Informatics , and Decision Support Systems , are optimal locations for the publication of scientific articles on this topic. These journals deal mainly with healthcare, medical information systems, and applications such as cloud computing, machine learning, and AI. Additionally, in terms of h-index, Bushko R.G. and Liu C. are the most productive and impactful authors in this research stream. Burke et al.’s [ 67 ] contribution is the most cited with an analysis of nurse rostering using new technologies such as AI. Finally, in terms of keywords, co-occurrence reveals some interesting insights. For instance, researchers have found that AI has a role in diagnostic accuracy and helps in the analysis of health data by comparing thousands of medical records, experiencing automatic learning with clinical alerts, efficient management of health services and places of care, and the possibility of reconstructing patient history using these data.

Second, this paper finds five cluster analyses in healthcare applications: health services management, predictive medicine, patient data, diagnostics, and finally, clinical decision-making. These technologies can also contribute to optimising logistics processes in health services and allowing a better allocation of resources.

Third, the authors analysing the research findings and the issues under discussion strongly support AI's role in decision support. These applications, however, are demonstrated by creating a direct link to data quality management and the technology awareness of health personnel [ 87 ].

The importance of data quality for the decision-making process

Several authors have analysed AI in the healthcare research stream, but in this case, the authors focus on other literature that includes business and decision-making processes. In this regard, the analysis of the search flow reveals a double view of the literature. On the one hand, some contributions belong to the positivist literature and embrace future applications and implications of technology for health service management, data analysis and diagnostics [ 6 , 80 , 88 ]. On the other hand, some investigations also aim to understand the darker sides of technology and its impact. For example, as Carter [ 89 ] states, the impact of AI is multi-sectoral; its development, however, calls for action to protect personal data. Similarly, Davenport and Kalakota [ 77 ] focus on the ethical implications of using AI in healthcare. According to the authors, intelligent machines raise issues of accountability, transparency, and permission, especially in automated communication with patients. Our analysis does not indicate a marked strand of the literature; therefore, we argue that the discussion of elements such as the transparency of technology for patients is essential for the development of AI applications.

A large part of our results shows that, at the application level, AI can be used to improve medical support for patients (Fig.  11 ) [ 64 , 82 ]. However, we believe that, as indicated by Kalis et al. [ 90 ] on the pages of Harvard Business Review, the management of costly back-office problems should also be addressed.

The potential of algorithms includes data analysis. There is an immense quantity of data accessible now, which carries the possibility of providing information about a wide variety of medical and healthcare activities [ 91 ]. With the advent of modern computational methods, computer learning and AI techniques, there are numerous possibilities [ 79 , 83 , 84 ]. For example, AI makes it easier to turn data into concrete and actionable observations to improve decision-making, deliver high-quality patient treatment, adapt to real-time emergencies, and save more lives on the clinical front. In addition, AI makes it easier to leverage capital to develop systems and facilities and reduce expenses at the organisational level [ 78 ]. Studying contributions to the topic, we noticed that data accuracy was included in the debate, indicating that a high standard of data will benefit decision-making practitioners [ 38 , 77 ]. AI techniques are an essential instrument for studying data and the extraction of medical insight, and they may assist medical researchers in their practices. Using computational tools, healthcare stakeholders may leverage the power of data not only to evaluate past data ( descriptive analytics ) but also to forecast potential outcomes ( predictive analytics ) and to define the best actions for the present scenario ( prescriptive analytics ) [ 78 ]. The current abundance of evidence makes it easier to provide a broad view of patient health; doctors should have access to the correct details at the right time and location to provide the proper treatment [ 92 ].

Will medical technology de-skill doctors?

Further reflection concerns the skills of doctors. Studies have shown that healthcare personnel are progressively being exposed to technology for different purposes, such as collecting patient records or diagnosis [ 71 ]. This is demonstrated by the keywords (Fig.  6 ) that focus on technology and the role of decision-making with new innovative tools. In addition, the discussion expands with Lu [ 93 ], which indicates that the excessive use of technology could hinder doctors’ skills and clinical procedures' expansion. Among the main issues arising from the literature is the possible de-skilling of healthcare staff due to reduced autonomy in decision-making concerning patients [ 94 ]. Therefore, the challenges and discussion we uncovered in Fig.  11 are expanded by also considering the ethical implications of technology and the role of skills.

Implications

Our analysis also has multiple theoretical and practical implications.

In terms of theoretical contribution, this paper extends the previous results of Connelly et al., dos Santos et al, Hao et al., Huang et al., Liao et al. and Tran et al. [ 2 , 19 , 20 , 21 , 22 , 24 ] in considering AI in terms of clinical decision-making and data management quality.

In terms of practical implications, this paper aims to create a fruitful discussion with healthcare professionals and administrative staff on how AI can be at their service to increase work quality. Furthermore, this investigation offers a broad comprehension of bibliometric variables of AI techniques in healthcare. It can contribute to advancing scientific research in this field.

Limitations

Like any other, our study has some limitations that could be addressed by more in-depth future studies. For example, using only one research database, such as Scopus, could be limiting. Further analysis could also investigate the PubMed, IEEE, and Web of Science databases individually and holistically, especially the health parts. Then, the use of search terms such as "Artificial Intelligence" OR "AI" AND "Healthcare" could be too general and exclude interesting studies. Moreover, although we analysed 288 peer-reviewed scientific papers, because the new research topic is new, the analysis of conference papers could return interesting results for future researchers. Additionally, as this is a young research area, the analysis will be subject to recurrent obsolescence as multiple new research investigations are published. Finally, although bibliometric analysis has limited the subjectivity of the analysis [ 15 ], the verification of recurring themes could lead to different results by indicating areas of significant interest not listed here.

Future research avenues

Concerning future research perspectives, researchers believe that an analysis of the overall amount that a healthcare organisation should pay for AI technologies could be helpful. If these technologies are essential for health services management and patient treatment, governments should invest and contribute to healthcare organisations' modernisation. New investment funds could be made available in the healthcare world, as in the European case with the Next Generation EU programme or national investment programmes [ 95 ]. Additionally, this should happen especially in the poorest countries around the world, where there is a lack of infrastructure and services related to health and medicine [ 96 ]. On the other hand, it might be interesting to evaluate additional profits generated by healthcare organisations with AI technologies compared to those that do not use such technologies.

Further analysis could also identify why some parts of the world have not conducted studies in this area. It would be helpful to carry out a comparative analysis between countries active in this research field and countries that are not currently involved. It would make it possible to identify variables affecting AI technologies' presence or absence in healthcare organisations. The results of collaboration between countries also present future researchers with the challenge of greater exchanges between researchers and professionals. Therefore, further research could investigate the difference in vision between professionals and academics.

In the accounting, business, and management research area, there is currently a lack of quantitative analysis of the costs and profits generated by healthcare organisations that use AI technologies. Therefore, research in this direction could further increase our understanding of the topic and the number of healthcare organisations that can access technologies based on AI. Finally, as suggested in the discussion section, more interdisciplinary studies are needed to strengthen AI links with data quality management and AI and ethics considerations in healthcare.

In pursuing the philosophy of Massaro et al.’s [ 11 ] methodological article, we have climbed on the shoulders of giants, hoping to provide a bird's-eye view of the AI literature in healthcare. We performed this study with a bibliometric analysis aimed at discovering authors, countries of publication and collaboration, and keywords and themes. We found a fast-growing, multi-disciplinary stream of research that is attracting an increasing number of authors.

The research, therefore, adopts a quantitative approach to the analysis of bibliometric variables and a qualitative approach to the study of recurring keywords, which has allowed us to demonstrate strands of literature that are not purely positive. There are currently some limitations that will affect future research potential, especially in ethics, data governance and the competencies of the health workforce.

Availability of data and materials

All the data are retrieved from public scientific platforms.

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Secinaro, S., Calandra, D., Secinaro, A. et al. The role of artificial intelligence in healthcare: a structured literature review. BMC Med Inform Decis Mak 21 , 125 (2021). https://doi.org/10.1186/s12911-021-01488-9

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