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• v.8(2); 2021 Jul

Artificial intelligence in healthcare: transforming the practice of medicine

Junaid bajwa.

A Microsoft Research, Cambridge, UK

Usman Munir

B Microsoft Research, Cambridge, UK

Aditya Nori

C Microsoft Research, Cambridge, UK

Bryan Williams

D University College London, London, UK and director, NIHR UCLH Biomedical Research Centre, London, UK

Artificial intelligence (AI) is a powerful and disruptive area of computer science, with the potential to fundamentally transform the practice of medicine and the delivery of healthcare. In this review article, we outline recent breakthroughs in the application of AI in healthcare, describe a roadmap to building effective, reliable and safe AI systems, and discuss the possible future direction of AI augmented healthcare systems.

Introduction

Healthcare systems around the world face significant challenges in achieving the ‘quadruple aim’ for healthcare: improve population health, improve the patient's experience of care, enhance caregiver experience and reduce the rising cost of care. 1–3 Ageing populations, growing burden of chronic diseases and rising costs of healthcare globally are challenging governments, payers, regulators and providers to innovate and transform models of healthcare delivery. Moreover, against a backdrop now catalysed by the global pandemic, healthcare systems find themselves challenged to ‘perform’ (deliver effective, high-quality care) and ‘transform’ care at scale by leveraging real-world data driven insights directly into patient care. The pandemic has also highlighted the shortages in healthcare workforce and inequities in the access to care, previously articulated by The King's Fund and the World Health Organization (Box ​ (Box1 1 ). 4,5

Workforce challenges in the next decade

The application of technology and artificial intelligence (AI) in healthcare has the potential to address some of these supply-and-demand challenges. The increasing availability of multi-modal data (genomics, economic, demographic, clinical and phenotypic) coupled with technology innovations in mobile, internet of things (IoT), computing power and data security herald a moment of convergence between healthcare and technology to fundamentally transform models of healthcare delivery through AI-augmented healthcare systems.

In particular, cloud computing is enabling the transition of effective and safe AI systems into mainstream healthcare delivery. Cloud computing is providing the computing capacity for the analysis of considerably large amounts of data, at higher speeds and lower costs compared with historic ‘on premises’ infrastructure of healthcare organisations. Indeed, we observe that many technology providers are increasingly seeking to partner with healthcare organisations to drive AI-driven medical innovation enabled by cloud computing and technology-related transformation (Box ​ (Box2 2 ). 6–8

Quotes from technology leaders

Here, we summarise recent breakthroughs in the application of AI in healthcare, describe a roadmap to building effective AI systems and discuss the possible future direction of AI augmented healthcare systems.

What is artificial intelligence?

Simply put, AI refers to the science and engineering of making intelligent machines, through algorithms or a set of rules, which the machine follows to mimic human cognitive functions, such as learning and problem solving. 9 AI systems have the potential to anticipate problems or deal with issues as they come up and, as such, operate in an intentional, intelligent and adaptive manner. 10 AI's strength is in its ability to learn and recognise patterns and relationships from large multidimensional and multimodal datasets; for example, AI systems could translate a patient's entire medical record into a single number that represents a likely diagnosis. 11,12 Moreover, AI systems are dynamic and autonomous, learning and adapting as more data become available. 13

AI is not one ubiquitous, universal technology, rather, it represents several subfields (such as machine learning and deep learning) that, individually or in combination, add intelligence to applications. Machine learning (ML) refers to the study of algorithms that allow computer programs to automatically improve through experience. 14 ML itself may be categorised as ‘supervised’, ‘unsupervised’ and ‘reinforcement learning’ (RL), and there is ongoing research in various sub-fields including ‘semi-supervised’, ‘self-supervised’ and ‘multi-instance’ ML.

• Supervised learning leverages labelled data (annotated information); for example, using labelled X-ray images of known tumours to detect tumours in new images. 15
• ‘Unsupervised learning’ attempts to extract information from data without labels; for example, categorising groups of patients with similar symptoms to identify a common cause. 16
• In RL, computational agents learn by trial and error, or by expert demonstration. The algorithm learns by developing a strategy to maximise rewards. Of note, major breakthroughs in AI in recent years have been based on RL.
• Deep learning (DL) is a class of algorithms that learns by using a large, many-layered collection of connected processes and exposing these processors to a vast set of examples. DL has emerged as the predominant method in AI today driving improvements in areas such as image and speech recognition. 17,18

How to build effective and trusted AI-augmented healthcare systems?

Despite more than a decade of significant focus, the use and adoption of AI in clinical practice remains limited, with many AI products for healthcare still at the design and develop stage. 19–22 While there are different ways to build AI systems for healthcare, far too often there are attempts to force square pegs into round holes ie find healthcare problems to apply AI solutions to without due consideration to local context (such as clinical workflows, user needs, trust, safety and ethical implications).

We hold the view that AI amplifies and augments, rather than replaces, human intelligence. Hence, when building AI systems in healthcare, it is key to not replace the important elements of the human interaction in medicine but to focus it, and improve the efficiency and effectiveness of that interaction. Moreover, AI innovations in healthcare will come through an in-depth, human-centred understanding of the complexity of patient journeys and care pathways.

In Fig ​ Fig1, 1 , we describe a problem-driven, human-centred approach, adapted from frameworks by Wiens et al , Care and Sendak to building effective and reliable AI-augmented healthcare systems. 23–25

Multi-step, iterative approach to build effective and reliable AI-augmented systems in healthcare.

Design and develop

The first stage is to design and develop AI solutions for the right problems using a human-centred AI and experimentation approach and engaging appropriate stakeholders, especially the healthcare users themselves.

Stakeholder engagement and co-creation

Build a multidisciplinary team including computer and social scientists, operational and research leadership, and clinical stakeholders (physician, caregivers and patients) and subject experts (eg for biomedical scientists) that would include authorisers, motivators, financiers, conveners, connectors, implementers and champions. 26 A multi-stakeholder team brings the technical, strategic, operational expertise to define problems, goals, success metrics and intermediate milestones.

Human-centred AI

A human-centred AI approach combines an ethnographic understanding of health systems, with AI. Through user-designed research, first understand the key problems (we suggest using a qualitative study design to understand ‘what is the problem’, ‘why is it a problem’, ‘to whom does it matter’, ‘why has it not been addressed before’ and ‘why is it not getting attention’) including the needs, constraints and workflows in healthcare organisations, and the facilitators and barriers to the integration of AI within the clinical context. After defining key problems, the next step is to identify which problems are appropriate for AI to solve, whether there is availability of applicable datasets to build and later evaluate AI. By contextualising algorithms in an existing workflow, AI systems would operate within existing norms and practices to ensure adoption, providing appropriate solutions to existing problems for the end user.

Experimentation

The focus should be on piloting of new stepwise experiments to build AI tools, using tight feedback loops from stakeholders to facilitate rapid experiential learning and incremental changes. 27 The experiments would allow the trying out of new ideas simultaneously, exploring to see which one works, learn what works and what doesn't, and why. 28 Experimentation and feedback will help to elucidate the purpose and intended uses for the AI system: the likely end users and the potential harm and ethical implications of AI system to them (for instance, data privacy, security, equity and safety).

Evaluate and validate

Next, we must iteratively evaluate and validate the predictions made by the AI tool to test how well it is functioning. This is critical, and evaluation is based on three dimensions: statistical validity, clinical utility and economic utility.

• Statistical validity is understanding the performance of AI on metrics of accuracy, reliability, robustness, stability and calibration. High model performance on retrospective, in silico settings is not sufficient to demonstrate clinical utility or impact.
• To determine clinical utility, evaluate the algorithm in a real-time environment on a hold-out and temporal validation set (eg longitudinal and external geographic datasets) to demonstrate clinical effectiveness and generalisability. 25
• Economic utility quantifies the net benefit relative to the cost from the investment in the AI system.

Scale and diffuse

Many AI systems are initially designed to solve a problem at one healthcare system based on the patient population specific to that location and context. Scale up of AI systems requires special attention to deployment modalities, model updates, the regulatory system, variation between systems and reimbursement environment.

Monitor and maintain

Even after an AI system has been deployed clinically, it must be continually monitored and maintained to monitor for risks and adverse events using effective post-market surveillance. Healthcare organisations, regulatory bodies and AI developers should cooperate to collate and analyse the relevant datasets for AI performance, clinical and safety-related risks, and adverse events. 29

What are the current and future use cases of AI in healthcare?

AI can enable healthcare systems to achieve their ‘quadruple aim’ by democratising and standardising a future of connected and AI augmented care, precision diagnostics, precision therapeutics and, ultimately, precision medicine (Table ​ (Table1 1 ). 30 Research in the application of AI healthcare continues to accelerate rapidly, with potential use cases being demonstrated across the healthcare sector (both physical and mental health) including drug discovery, virtual clinical consultation, disease diagnosis, prognosis, medication management and health monitoring.

Widescale adoption and application of artificial intelligence in healthcare

Timings are illustrative to widescale adoption of the proposed innovation taking into account challenges / regulatory environment / use at scale.

We describe a non-exhaustive suite of AI applications in healthcare in the near term, medium term and longer term, for the potential capabilities of AI to augment, automate and transform medicine.

AI today (and in the near future)

Currently, AI systems are not reasoning engines ie cannot reason the same way as human physicians, who can draw upon ‘common sense’ or ‘clinical intuition and experience’. 12 Instead, AI resembles a signal translator, translating patterns from datasets. AI systems today are beginning to be adopted by healthcare organisations to automate time consuming, high volume repetitive tasks. Moreover, there is considerable progress in demonstrating the use of AI in precision diagnostics (eg diabetic retinopathy and radiotherapy planning).

AI in the medium term (the next 5–10 years)

In the medium term, we propose that there will be significant progress in the development of powerful algorithms that are efficient (eg require less data to train), able to use unlabelled data, and can combine disparate structured and unstructured data including imaging, electronic health data, multi-omic, behavioural and pharmacological data. In addition, healthcare organisations and medical practices will evolve from being adopters of AI platforms, to becoming co-innovators with technology partners in the development of novel AI systems for precision therapeutics.

AI in the long term (>10 years)

In the long term, AI systems will become more intelligent , enabling AI healthcare systems achieve a state of precision medicine through AI-augmented healthcare and connected care. Healthcare will shift from the traditional one-size-fits-all form of medicine to a preventative, personalised, data-driven disease management model that achieves improved patient outcomes (improved patient and clinical experiences of care) in a more cost-effective delivery system.

Connected/augmented care

AI could significantly reduce inefficiency in healthcare, improve patient flow and experience, and enhance caregiver experience and patient safety through the care pathway; for example, AI could be applied to the remote monitoring of patients (eg intelligent telehealth through wearables/sensors) to identify and provide timely care of patients at risk of deterioration.

In the long term, we expect that healthcare clinics, hospitals, social care services, patients and caregivers to be all connected to a single, interoperable digital infrastructure using passive sensors in combination with ambient intelligence. 31 Following are two AI applications in connected care.

Virtual assistants and AI chatbots

AI chatbots (such as those used in Babylon ( www.babylonhealth.com ) and Ada ( https://ada.com )) are being used by patients to identify symptoms and recommend further actions in community and primary care settings. AI chatbots can be integrated with wearable devices such as smartwatches to provide insights to both patients and caregivers in improving their behaviour, sleep and general wellness.

Ambient and intelligent care

We also note the emergence of ambient sensing without the need for any peripherals.

• Emerald ( www.emeraldinno.com ): a wireless, touchless sensor and machine learning platform for remote monitoring of sleep, breathing and behaviour, founded by Massachusetts Institute of Technology faculty and researchers.
• Google nest: claiming to monitor sleep (including sleep disturbances like cough) using motion and sound sensors. 32
• A recently published article exploring the ability to use smart speakers to contactlessly monitor heart rhythms. 33
• Automation and ambient clinical intelligence: AI systems leveraging natural language processing (NLP) technology have the potential to automate administrative tasks such as documenting patient visits in electronic health records, optimising clinical workflow and enabling clinicians to focus more time on caring for patients (eg Nuance Dragon Ambient eXperience ( www.nuance.com/healthcare/ambient-clinical-intelligence.html )).

Precision diagnostics

Diagnostic imaging.

The automated classification of medical images is the leading AI application today. A recent review of AI/ML-based medical devices approved in the USA and Europe from 2015–2020 found that more than half (129 (58%) devices in the USA and 126 (53%) devices in Europe) were approved or CE marked for radiological use. 34 Studies have demonstrated AI's ability to meet or exceed the performance of human experts in image-based diagnoses from several medical specialties including pneumonia in radiology (a convolutional neural network trained with labelled frontal chest X-ray images outperformed radiologists in detecting pneumonia), dermatology (a convolutional neural network was trained with clinical images and was found to classify skin lesions accurately), pathology (one study trained AI algorithms with whole-slide pathology images to detect lymph node metastases of breast cancer and compared the results with those of pathologists) and cardiology (a deep learning algorithm diagnosed heart attack with a performance comparable with that of cardiologists). 35–38

We recognise that there are some exemplars in this area in the NHS (eg University of Leeds Virtual Pathology Project and the National Pathology Imaging Co-operative) and expect widescale adoption and scaleup of AI-based diagnostic imaging in the medium term. 39 We provide two use cases of such technologies.

Diabetic retinopathy screening

Key to reducing preventable, diabetes-related vision loss worldwide is screening individuals for detection and the prompt treatment of diabetic retinopathy. However, screening is costly given the substantial number of diabetes patients and limited manpower for eye care worldwide. 40 Research studies on automated AI algorithms for diabetic retinopathy in the USA, Singapore, Thailand and India have demonstrated robust diagnostic performance and cost effectiveness. 41–44 Moreover, Centers for Medicare & Medicaid Services approved Medicare reimbursement for the use of Food and Drug Administration approved AI algorithm ‘IDx-DR’, which demonstrated 87% sensitivity and 90% specificity for detecting more-than-mild diabetic retinopathy. 45

Improving the precision and reducing waiting timings for radiotherapy planning

An important AI application is to assist clinicians for image preparation and planning tasks for radiotherapy cancer treatment. Currently, segmentation of the images is time consuming and laborious task, performed manually by an oncologist using specially designed software to draw contours around the regions of interest. The AI-based InnerEye open-source technology can cut this preparation time for head and neck, and prostate cancer by up to 90%, meaning that waiting times for starting potentially life-saving radiotherapy treatment can be dramatically reduced (Fig ​ (Fig2 2 ). 46,47

Potential applications for the InnerEye deep learning toolkit include quantitative radiology for monitoring tumour progression, planning for surgery and radiotherapy planning. 47

Precision therapeutics

To make progress towards precision therapeutics, we need to considerably improve our understanding of disease. Researchers globally are exploring the cellular and molecular basis of disease, collecting a range of multimodal datasets that can lead to digital and biological biomarkers for diagnosis, severity and progression. Two important future AI applications include immunomics / synthetic biology and drug discovery.

Immunomics and synthetic biology

Through the application of AI tools on multimodal datasets in the future, we may be able to better understand the cellular basis of disease and the clustering of diseases and patient populations to provide more targeted preventive strategies, for example, using immunomics to diagnose and better predict care and treatment options. This will be revolutionary for multiple standards of care, with particular impact in the cancer, neurological and rare disease space, personalising the experience of care for the individual.

AI-driven drug discovery

AI will drive significant improvement in clinical trial design and optimisation of drug manufacturing processes, and, in general, any combinatorial optimisation process in healthcare could be replaced by AI. We have already seen the beginnings of this with the recent announcements by DeepMind and AlphaFold, which now sets the stage for better understanding disease processes, predicting protein structures and developing more targeted therapeutics (for both rare and more common diseases; Fig ​ Fig3 3 ). 48,49

An overview of the main neural network model architecture for AlphaFold. 49 MSA = multiple sequence alignment.

Precision medicine

New curative therapies.

Over the past decade, synthetic biology has produced developments like CRISPR gene editing and some personalised cancer therapies. However, the life cycle for developing such advanced therapies is still extremely inefficient and expensive.

In future, with better access to data (genomic, proteomic, glycomic, metabolomic and bioinformatic), AI will allow us to handle far more systematic complexity and, in turn, help us transform the way we understand, discover and affect biology. This will improve the efficiency of the drug discovery process by helping better predict early which agents are more likely to be effective and also better anticipate adverse drug effects, which have often thwarted the further development of otherwise effective drugs at a costly late stage in the development process. This, in turn will democratise access to novel advanced therapies at a lower cost.

AI empowered healthcare professionals

In the longer term, healthcare professionals will leverage AI in augmenting the care they provide, allowing them to provide safer, standardised and more effective care at the top of their licence; for example, clinicians could use an ‘AI digital consult’ to examine ‘digital twin’ models of their patients (a truly ‘digital and biomedical’ version of a patient), allowing them to ‘test’ the effectiveness, safety and experience of an intervention (such as a cancer drug) in the digital environment prior to delivering the intervention to the patient in the real world.

We recognise that there are significant challenges related to the wider adoption and deployment of AI into healthcare systems. These challenges include, but are not limited to, data quality and access, technical infrastructure, organisational capacity, and ethical and responsible practices in addition to aspects related to safety and regulation. Some of these issues have been covered, but others go beyond the scope of this current article.

Conclusion and key recommendations

Advances in AI have the potential to transform many aspects of healthcare, enabling a future that is more personalised, precise, predictive and portable. It is unclear if we will see an incremental adoption of new technologies or radical adoption of these technological innovations, but the impact of such technologies and the digital renaissance they bring requires health systems to consider how best they will adapt to the changing landscape. For the NHS, the application of such technologies truly has the potential to release time for care back to healthcare professionals, enabling them to focus on what matters to their patients and, in the future, leveraging a globally democratised set of data assets comprising the ‘highest levels of human knowledge’ to ‘work at the limits of science’ to deliver a common high standard of care, wherever and whenever it is delivered, and by whoever. 50 Globally, AI could become a key tool for improving health equity around the world.

As much as the last 10 years have been about the roll out of digitisation of health records for the purposes of efficiency (and in some healthcare systems, billing/reimbursement), the next 10 years will be about the insight and value society can gain from these digital assets, and how these can be translated into driving better clinical outcomes with the assistance of AI, and the subsequent creation of novel data assets and tools. It is clear that we are at an turning point as it relates to the convergence of the practice of medicine and the application of technology, and although there are multiple opportunities, there are formidable challenges that need to be overcome as it relates to the real world and the scale of implementation of such innovation. A key to delivering this vision will be an expansion of translational research in the field of healthcare applications of artificial intelligence. Alongside this, we need investment into the upskilling of a healthcare workforce and future leaders that are digitally enabled, and to understand and embrace, rather than being intimidated by, the potential of an AI-augmented healthcare system.

Healthcare leaders should consider (as a minimum) these issues when planning to leverage AI for health:

• processes for ethical and responsible access to data: healthcare data is highly sensitive, inconsistent, siloed and not optimised for the purposes of machine learning development, evaluation, implementation and adoption
• access to domain expertise / prior knowledge to make sense and create some of the rules which need to be applied to the datasets (to generate the necessary insight)
• access to sufficient computing power to generate decisions in real time, which is being transformed exponentially with the advent of cloud computing
• research into implementation: critically, we must consider, explore and research issues which arise when you take the algorithm and put it in the real world, building ‘trusted’ AI algorithms embedded into appropriate workflows.

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• Perspective
• Published: 16 January 2023

The next generation of evidence-based medicine

• Vivek Subbiah   ORCID: orcid.org/0000-0002-6064-6837 1 , 2 , 3  

Nature Medicine volume  29 ,  pages 49–58 ( 2023 ) Cite this article

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• Adaptive clinical trial
• Drug development
• Health policy

Recently, advances in wearable technologies, data science and machine learning have begun to transform evidence-based medicine, offering a tantalizing glimpse into a future of next-generation ‘deep’ medicine. Despite stunning advances in basic science and technology, clinical translations in major areas of medicine are lagging. While the COVID-19 pandemic exposed inherent systemic limitations of the clinical trial landscape, it also spurred some positive changes, including new trial designs and a shift toward a more patient-centric and intuitive evidence-generation system. In this Perspective, I share my heuristic vision of the future of clinical trials and evidence-based medicine.

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The last 30 years have witnessed breathtaking, unparalleled advancements in scientific research—from a better understanding of the pathophysiology of basic disease processes and unraveling the cellular machinery at atomic resolution to developing therapies that alter the course and outcome of diseases in all areas of medicine. Moreover, exponential gains in genomics, immunology, proteomics, metabolomics, gut microbiomes, epigenetics and virology in parallel with big data science, computational biology and artificial intelligence (AI) have propelled these advances. In addition, the dawn of CRISPR–Cas9 technologies has opened a tantalizing array of opportunities in personalized medicine.

Despite these advances, their rapid translation from bench to bedside is lagging in most areas of medicine and clinical research remains outpaced. The drug development and clinical trial landscape continues to be expensive for all stakeholders, with a very high failure rate. In particular, the attrition rate for early-stage developmental therapeutics is quite high, as more than two-thirds of compounds succumb in the ‘valley of death’ between bench and bedside 1 , 2 . To bring a drug successfully through all phases of drug development into the clinic costs more than 1.5–2.5 billion dollars (refs. 3 , 4 ). This, combined with the inherent inefficiencies and deficiencies that plague the healthcare system, is leading to a crisis in clinical research. Therefore, innovative strategies are needed to engage patients and generate the necessary evidence to propel new advances into the clinic, so that they may improve public health. To achieve this, traditional clinical research models should make way for avant-garde ideas and trial designs.

Before the COVID-19 pandemic, the conduct of clinical research had remained almost unchanged for 30 years and some of the trial conduct norms and rules, although archaic, were unquestioned. The pandemic exposed many of the inherent systemic limitations in the conduct of trials 5 and forced the clinical trial research enterprise to reevaluate all processes—it has therefore disrupted, catalyzed and accelerated innovation in this domain 6 , 7 . The lessons learned should help researchers to design and implement next-generation ‘patient-centric’ clinical trials.

Chronic diseases continue to impact millions of lives and cause major financial strain to society 8 , but research is hampered by the fact that most of the data reside in data silos. The subspecialization of the clinical profession has led to silos within and among specialties; every major disease area seems to work completely independently. However, the best clinical care is provided in a multidisciplinary manner with all relevant information available and accessible. Better clinical research should harness the knowledge gained from each of the specialties to achieve a collaborative model enabling multidisciplinary, high-quality care and continued innovation in medicine. Because many disciplines in medicine view the same diseases differently—for example, infectious disease specialists view COVID-19 as a viral disease while cardiology experts view it as an inflammatory one—cross-discipline approaches will need to respect the approaches of other disciplines. Although a single model may not be appropriate for all diseases, cross-disciplinary collaboration will make the system more efficient to generate the best evidence.

Over the next decade, the application of machine learning, deep neural networks and multimodal biomedical AI is poised to reinvigorate clinical research from all angles, including drug discovery, image interpretation, streamlining electronic health records, improving workflow and, over time, advancing public health (Fig. 1 ). In addition, innovations in wearables, sensor technology and Internet of Medical Things (IoMT) architectures offer many opportunities (and challenges) to acquire data 9 . In this Perspective, I share my heuristic vision of the future of clinical trials and evidence generation and deliberate on the main areas that need improvement in the domains of clinical trial design, clinical trial conduct and evidence generation.

The figure represents the timeline from drug discovery to first-in-human phase 1 trials and ultimately FDA approval. Phase 4 studies occur after FDA approval and can go on for several years. There is an urgent need to reinvigorate clinical trials through drug discovery, interpreting imaging, streamlining electronic health records, and improving workflow, over time advancing public health. AI can aid in many of these aspects in all stages of drug development. DNN, deep neural network; EHR, electronic health records; IoMT, internet of medical things; ML, machine learning.

Clinical trial design

Trial design is one of the most important steps in clinical research—better protocol designs lead to better clinical trial conduct and faster ‘go/no-go’ decisions. Moreover, losses from poorly designed, failed trials are not only financial but also societal.

Challenges with randomized controlled trials

Randomized controlled trials (RCTs) have been the gold standard for evidence generation across all areas of medicine, as they allow unbiased estimates of treatment effect without confounders. Ideally, every medical treatment or intervention should be tested via a well-powered and well-controlled RCT. However, conducting RCTs is not always feasible owing to challenges in generating evidence in a timely manner, cost, design on narrow populations precluding generalizability, ethical barriers and the time taken to conduct these trials. By the time they are completed and published, RCTs become quickly outdated and, in some cases, irrelevant to the current context. In the field of cardiology alone, 30,000 RCTs have not been completed owing to recruitment challenges 10 . Moreover, trials are being designed in isolation and within silos, with many clinical questions remaining unanswered. Thus, traditional trial design paradigms must adapt to contemporary rapid advances in genomics, immunology and precision medicine 11 .

Progress in clinical trial design

High-quality evidence is needed for clinical practice, which has traditionally been achieved with RCTs 12 . In the last decade, substantial progress has been made in the design, conduct and implementation of ‘master’ protocols (overarching protocols that apply to several substudies), which has led to many practice changes that have substantially improved the stagnation of RCTs. Moreover, master protocols may involve parallel interventional studies in a single disease or multiple diseases defined by a biomarker or disease entity 12 . Four different classes of studies are included under the master protocols—the umbrella study, basket study, platform study and master observational trial (MOT) (Fig. 2 ). Each of these is a unique trial design that can include independent arms with control interventions and may be analyzed individually and/or collectively, with added flexibility 13 , 14 . The field of oncology has led these efforts more so than any other field, owing to advances in genomics (for identifying molecular alterations), discovery of therapeutics and rapid clinical translation, thus ushering in the precision oncology era.

Four different classes of studies are included under the master protocols—the basket study, umbrella study, platform study and MOT.

Umbrella study

Umbrella trials are study designs that evaluate multiple targeted therapies for the same disease entity, stratified by molecular alteration. Examples include the I-SPY (Investigation of Serial Studies to Predict Your Therapeutic Response With Imaging And Molecular Analysis) breast cancer trial and Lung-MAP (Lung Cancer Master Protocol) 15 , 16 .

Basket (or bucket) trial

Basket trials are tissue-agnostic or histology-independent studies where targeted therapy is evaluated on multiple disease types that all harbor the same underlying molecular aberration. For instance, the VE-Basket study (in which VE denotes vemurafenib) 17 , Rare Oncology Agnostic Research (ROAR) study 18 , ARROW trial 19 and LIBRETTO-001 trials 20 , 21 have led to several drug approvals in specific biomarker-driven populations in a histology-dependent and histology-independent manner.

Platform study

These are multi-arm, multistage study designs that compare several intervention groups with a common control group in the context of the same master protocol. Additionally, they can be perpetual/immortal study designs (with no defined end date) and are more efficient than traditional trials on account of the shared control arm, which ensures that a greater proportion of patients are enrolled in the interventional/experimental arms than in the control arm. The Randomised Evaluation of COVID-19 Therapy (RECOVERY) Platform Study is a prominent example; this practice-changing trial established dexamethasone as an effective treatment for COVID-19 (ref. 22 ) and also showed that hydroxychloroquine was ineffective. Platform studies are flexible by design and do not necessarily need to have a shared control arm; the main idea is that intervention arms may be added to an ongoing trial, for example, as in the The UK Plasma Based Molecular Profiling of Advanced Breast Cancer to Inform Therapeutic CHoices (plasmaMATCH) platform trial 23 . Although the aforementioned trials were designed in the context of drug development in oncology and infectious diseases, the scope of platform trials could be leveraged in other diverse areas such as clinical psychology and neurology 24 . Such trials could also be used for digital mental health interventions and could be readily implemented in resource-constrained settings 24 .

The MOT is a prospective, observational study design that broadly accepts patients independently of biomarker signature and collects comprehensive data on each participant 14 , 25 . The MOT is a combination of the master interventional trial and prospective observational trial designs and attempts to hybridize the power of biomarker-based master interventional protocols with the breadth of real-world data (RWD) 14 , 25 . This approach could be well suited to collect prospective RWD across many specialties; the Registry of Oncology Outcomes Associated with Testing and Treatment (ROOT) MOT is one example 14 .

Development of biomarkers and defining endpoints

Biomarker development has facilitated progress in clinical trial design, with unprecedented advances in genomics and immunology leading to several approvals for biomarker-based targeted therapies and immunotherapy in the last decade. In fact, human genetics evidence provided support for more than two-thirds of the drug approvals in 2021 (ref. 26 ). The fields of oncology and genetics have benefited immensely from these advances, but fields such as cardiology, nephrology and pulmonology are still lagging in biomarker-based drug approvals.

To fast-track drug development and clinical trials in every major disease, we will need to define biomarkers (whether clinical, pathological or physiological) and their context of use for every disease process and delineate clear endpoints for studies 27 . Biomarkers can be diagnostic, prognostic or predictive and can inform early drug development, dose selection and trial design. In addition, biomarkers can help to fast-track basic science and drug discovery—all with the eventual goal of improving patient health 28 . However, the level of evidence for a biomarker largely depends on the context of use.

In addition to biomarkers, every field needs to define areas of top priority for research and identify the most relevant endpoints to answer priority research questions. Endpoints are measures of health and/or disease and serve different purposes depending on the phase of the trial 28 , 29 . Beyond clinical and regulatory endpoints, patient-reported outcomes and digital endpoints are also rapidly emerging.

Digital endpoints

Digital endpoints are sensor-generated data collected outside the clinical environment in the context of patients’ routine living—such as using smartphone microphones to monitor cognitive decline in people with Alzheimer’s disease or smartwatch monitors to evaluate drug effect in people with sickle-cell anemia 29 . This is an area of considerable excitement in medicine as it could permit more realistic real-world tracking of the patient experience. Moreover, with the increase in decentralized trial conduct across many specialties, remote monitoring is poised to increase. For instance, a recent study developed an AI model to detect and track progression of Parkinson’s disease (for which there are no biomarkers) on the basis of nocturnal breathing signals using noninvasive, at-home assessment, providing evidence that AI may be useful in risk assessment before clinical diagnosis of the condition 29 , 30 . Additionally, digital atrial fibrillation screening by smart devices has been evaluated extensively in large-scale studies, including the Apple 31 , Huawei 32 and Fitbit 33 cardiac studies. Altogether, these siteless observational studies enrolled over 1 million participants, an amazing feat, and a randomized study showed the superiority of digital atrial fibrillation detection over usual care 34 .

Digital characterization and assessment of clinical status need to be standardized and harmonized, with interdisciplinary collaboration and regulatory input. Consensus is also needed to identify and characterize intermediate and surrogate endpoints for major chronic diseases. This requires specialty-specific incorporation of multiple levels of data such as genomic, proteomic and genotype–phenotype-based clinical data and disease-specific measurements, in addition to a layer of functional data 26 . The National Institutes of Health (NIH) and Food and Drug Administration (FDA) have developed BEST (Biomarkers, EndpointS and other Tools) resources to clarify the ambiguity in biomarkers and endpoints. This is a ‘living document’ that is continually updated as standards and evidence change 35 and that clarifies important definitions and describes some of the hierarchical relationships, connections and dependencies among the terms.

Clinical trial conduct

The components of clinical trial conduct are protocol implementation; patient selection, recruitment, monitoring and retention; ensuring compliance to safety reporting; and continuing review and data analysis. The pharmaceutical industry and the healthcare sector invest substantial resources into clinical trial conduct, but changes are urgently needed to make the process more seamless. Moreover, the pace at which clinical trials are conducted is too slow to match the research advances in every field; thus, a high-tech transformation of every component in a stepwise manner is needed.

One of the positive sides of the pandemic is that it forced the system to redirect clinical trials to be more patient-centric than before, thus giving more importance to the principal subject of clinical research—the patient 36 (Fig. 3 ). This has led to decentralized trials and digital, remote and ‘virtual’ trials (which allow patients access to trials regardless of their geographic location), as well as ‘hospital-at-home’ and home-based monitoring concepts 37 . Such rapid strides have been aided by guidance from regulatory authorities 38 . Adopting an AI-based approach to enhance the patient experience can further improve high-fidelity assessments and ensure compliance with protocols 39 . Although digitalization, virtualization and decentralization are not cures for clinical research crises, they can create efficiencies that may have a sizeable and long-term downstream impact.

The main constituents of the clinical trial enterprise—patients, academic centers, industry sponsors (big and small pharma), government/cooperative group sponsors, regulatory agencies, patient advocacy organizations and CROs—need to work together, with the patient as the center of this clinical trial universe. AMA, African Medicines Agency; CDSCO, Central Drugs Standard Control Organization (India); CMS, Centers for Medicare and Medicaid Services; ECA, external control arm; EMA, European Medicines Agency; HTA, Health Technology Assessment; NMPA, National Medical Products Administration (China).

Physicians, healthcare team members and clinical investigators at academic sites and other trial enrolling sites contribute immensely to patient recruitment. In addition, high-impact, high-functioning sites (as in major academic centers of excellence) often have a portfolio of trials and screen patients presenting to the system in an efficient manner. Such sites are in the minority, however, and most clinical trial sites are challenged with staffing constraints and other barriers.

Clinical trial research enterprise

Efficiency and collaboration in the clinical trial research enterprise are major components of clinical trial success. The main constituents of the clinical trial enterprise are patients, academic centers, industry sponsors (big and small pharma), government/cooperative group sponsors, regulatory agencies, patient advocacy organizations and contract research organizations (CROs), and all of these need to work together with the patient as the center of the clinical trial universe (Fig. 3 ). Moreover, this whole system needs a digital overhaul as many sites still use protocol binders, pen-and-paper diaries, faxes between sites, unstructured data and decades-old software systems. Registrational clinical trials need to be well managed on a day-to-day basis with rigorous electronic data capture and monitoring. Integration of blockchain technology into the clinical trial management system could conceivably bolster trust in the clinical trial process and facilitate regulatory oversight 40 .

Patient participation in clinical trials is key, as there can be no trials without patients. Clinical trial organizers should make it easier for patients to participate in trials. In addition, physician–patient treatment decisions for major diseases should include clinical trial options as standard. These clinical trials should be easily accessible and should ensure that no patients are unnecessarily excluded; this can be achieved with site-agnostic clinical trial matching and navigation services. In addition, clinical trial training should be a part of medical education so that a diverse pool of trained investigators and personnel from the entire healthcare enterprise can be available for clinical research.

It is about time

Clinical development timelines for drug candidates are a race against time from when patents are filed to final FDA approval 41 . Drug development timelines, on average, are approximately 10 years (Fig. 1 ). The swiftness of the development of the COVID-19 mRNA vaccines and the oral COVID-19 treatment nirmatrelvir/ritonavir tablets, both of which were developed within a year using a ‘lightspeed approach’, should not be an outlier 42 . The lessons learned should provide a model for multiple therapeutic areas of unmet need. The two small molecules that hold the record for the shortest timeline in drug development, osimertinib for EGFR -mutant non-small-cell lung cancer (NSCLC) (984 days via accelerated approval) and elexacaftor for cystic fibrosis (1,043 days via the regular path) 41 , in nonpandemic times demonstrate that this is possible.

The regulatory logjams slowing drug development necessitated the creation of programs such as the FDA’s accelerated approval pathway, which was introduced in 1992 to address the HIV and AIDS crisis and has since benefitted highly specialized areas such as precision oncology 43 . Multiple programs have been created to shorten timelines for the premarket process, including priority review, fast-track designation, breakthrough designation and orphan designation 44 . Beyond these programs, however, the timelines are still slow and there is an urgent need to address this for all diseases as drug development speed is crucial for patients, physicians and drug development stakeholders alike.

Globalizing drug development, harmonization and transportability

Although the mandate of the FDA is to the US population, their influence is global and, functionally, the FDA is the de facto regulator for the world. Other regulatory authorities such as the European Medicines Agency, the National Medical Products Administration in China and the Central Drugs Standard Control Organization in India, which in total serve more than 3 billion of the world’s population, are also evolving as key players in the global pharmaceutical sector. In addition, the newly established African Medicines Agency was set up (in 2019) to speed up timelines for vaccines and medicine approvals and to improve access to drugs, especially for emerging infectious diseases endemic to the continent 45 . All of these agencies need to be able to stand alone. In addition, there is an urgent need for global harmonization across regulatory authorities to address the substantial inequities in access to medicines. Ideally, clinical trials for new therapies should be conducted globally, for access and generalizability 46 . However, the reality is that clinical trials, including RCTs, cannot be conducted in every country to generate specific evidence for that country’s population. Evidence generation using transportability analysis is gaining traction and refers to the ability to generalize inferences from a study sample in one country to a target population in another country where the study was not conducted 47 , 48 . Transportability analyses may offer some evidence of external validity with implications for local regulatory and health technology assessments 48 .

Evidence generation in clinical research

Clinical studies of rare diseases.

As scientific advances drive clinical trials forward, trials on cancers and many rare diseases are being designed and conducted in small genetically defined or biomarker-defined subsets. Moreover, new methods to generate evidence of clinical benefit may accelerate clinical trial conduct and provide individuals with rare diseases access to new therapeutic compounds. Rare diseases affect an estimated 263 million–446 million people globally at any given time and are increasingly becoming a huge public health burden 49 . Clinical trials in this context come with their own challenges stemming from the rarity of the conditions and incomplete natural history data 50 . However, remarkable advances in molecular biology coupled with legislation to spur orphan disease developmental therapeutics have led to progress. There is increasing regulatory flexibility to use programs such as the accelerated approval program, and there are case scenarios whereby trials have used external control arms based on RWD.

As an example, the FDA granted accelerated approval to alpelisib (Vijoice, Novartis) for adults and children over 2 years of age who require systemic therapy for PIK3CA-related overgrowth spectrum, which includes a group of rare disorders linked to mutations in the PIK3CA gene 51 . Interestingly, efficacy was evaluated using a retrospective chart review of RWD from EPIK-P1 ( NCT04285723 ), a single-arm clinical study in which individuals with PIK3CA-related overgrowth spectrum received alpelisib as part of an expanded access program for compassionate use. The application for this approval used the Real-Time Oncology Review pilot program 52 , which streamlined data submission before filing of the entire clinical application, and Assessment Aid 53 , a voluntary submission from the applicant to facilitate assessment by the FDA. As a result, this application was granted priority review, breakthrough designation and orphan drug designation 51 .

N-of-1 trials

In the era of individualized genomic medicine, N-of-1 trials are emerging as a tool to study potentially fatal rare diseases. The N-of-1 trial is a single-patient clinical trial using the individual person as a unit of investigation to evaluate the efficacy and/or adverse events of different interventions through objective data-driven criteria 54 . For example, an antisense oligonucleotide therapy was designed for, and evaluated in, a single patient who had a fatal genetic neurodegenerative disorder known as CLN7 neuronal ceroid lipofuscinosis (a form of Batten’s disease) 55 . Another patient (who happened to be a physician) with idiopathic Castleman’s disease refractory to IL-6-blocking therapy identified the causative molecular alteration in his own disease to develop a personalized therapy 56 . In yet another example, rapid dose escalation with a selective RET inhibitor was evaluated in a single patient with highly refractory medullary thyroid carcinoma, to overcome a resistance mechanism specific to that patient 57 .

These sensational new drug discovery–translation paradigms raise important questions, such as what level of evidence is needed before exposing a human to a new drug, what evidence this approach might generate for the next patient and what challenges might exist with generalizability 58 . The concept of medical analog patient-specific ‘digital twins’ is an emerging area of research that has the potential to combine polynomial data (mechanistic data, medical history, with the power of AI) and may perhaps serve to enhance N-of-1 trials in the future, to further personalize medicine 37 , 59 , 60 .

RWD and real-world evidence

One of the major criticisms of all clinical trial research is that clinical trials do not represent the ‘real-world’ population; often, the restrictive criteria of clinical trials and the limited analyses framed to answer specific questions may not apply to real-world patients. A wide gap therefore exists between the trial world and the real world, and attempts have been made to close this gap 61 . Conventional trials have been designed on the basis of the misconception that regulatory bodies may not accommodate more modern and diverse evidence from RWD, which is no longer the case 61 , 62 .

It is important to distinguish between RWD, which refers to data generated from routine, standard care of patients 62 , and real-world ‘evidence’ (RWE), which is the evidence generated from RWD regarding the potential use of a product. RWE is generated by trial designs or analysis and is not restricted to randomized trials; instead, it comes from pragmatic trials and prospective and/or retrospective observational studies 62 , 63 .

In this purview of RWD and RWE, all stakeholders look to regulators for guidance. Consequently, regulators have taken a hands-on approach and provided guidance and a comprehensive framework launched through the 21st Century Cures Act 62 , 64 . Moreover, the FDA uses RWD and RWE for postmarketing safety monitoring, and insurance agencies have started to use such data for coverage decisions 62 . This has been necessitated by rapidly accelerating data input from multiple streams and layers into electronic health records, as well as wearables and biosensors, in parallel with new analytical capabilities (multimodal AI) to analyze the vast amount of data.

Evidence from synthetic or external control arms

RCTs are considered the gold standard for drug development and evidence as they allow for estimation of treatment effects that can be assigned to the experimental arm of interest. The randomization in these studies curtails the concern for confounding bias by removing systematic imbalances between arms in measured and unmeasured prognostic factors 65 . However, advances in the genomics of rare diseases and the discovery of rare oncogene-driven cancers have led to specific targeted therapies, for which evaluation in RCTs may not be feasible or ethical and may delay patient access to promising or lifesaving therapies.

In such cases, synthetic control arms are emerging as options for generating comparator arms that can ‘mimic’ the comparator arms of RCTs. Synthetic control arms are external to the study in question, and most are derived from RWD 65 . Moreover, RWD are obtained from electronic health records, administrative claims data, natural history registries and patient-generated data from many sources, including wearable devices 65 . Synthetic control arms may also be generated from previous clinical trial data (single or pooled trials). This is an emerging area primed for innovation as so much data are now available from multiple sources.

NSCLC is increasingly being divided into small oncogene-driven subsets, making it more challenging to conduct randomized trials 66 , and recent developments in the NSCLC trial landscape illustrate the utility of synthetic control arms. For instance, RET fusions are genomic drivers in 1–2% of NSCLCs, and pralsetinib is a selective RET-targeted therapy showing promising responses even in individuals with advanced disease. The ARROW study ( NCT03037385 ) was a single-arm registrational trial, conducted globally, to evaluate pralsetinib in RET fusion-positive individuals with NSCLC 67 , 68 . This trial showed a relative survival benefit with the drug when compared to an external standard-of-care control arm consisting of RWD cohorts derived from two Flatiron Health databases 66 . A template for future studies of this nature using quantitative bias analyses showed that comparisons between the external control arm and the trial arm are robust and able to withstand issues such as data missingness, potentially poorer outcomes in RWD and residual confounding 66 . Overall, the study provided evidence in favor of pralsetinib as a first-line treatment for RET fusion-positive NSCLC.

The use of synthetic control arms can accelerate drug development, and initial skepticism about them arose mainly from a lack of precedence and direction from regulatory authorities. These concerns are now being dispelled as synthetic control arms have been used recently for drug approvals for ultra-rare diseases. For example, neurofibromatosis is a rare disease seen in 1 in 3,000 births. Patients develop plexiform neurofibroma lesions that are painful and debilitating, causing motor and neuronal dysfunction. The MEK inhibitor selumetinib was approved for pediatric patients with symptomatic, inoperable plexiform neurofibromas on the basis of a dataset of 50 patients from Selumetinib in Pediatric Neurofibroma Trial (SPRINT)—a single-arm phase 2 trial showing a durable objective response rate and improvements in functional symptoms 65 , 69 , 70 . Comparator arms from two previously conducted trials provided evidence for the natural history of the disease and were submitted as an external control arm, which helped confirm that spontaneous regressions were uncommon and that the observed responses and symptom improvement represented a genuine treatment effect 69 .

Despite this progress, external control arms are still an emerging concept and they have mainly been used to investigate the natural history of disease and have not generally been included as primary evidence or in product labels. However, in the future, I can envision such comparative effectiveness analysis and comparator arms as primary evidence to support drug approval. Challenges mainly arise from data quality and data missingness, as well as uncertainty of whether external control data are fit for purpose. However, some of these concerns can be mitigated by quantitative bias analysis and other methodologies 66 , 71 .

Pediatric clinical trials

Although pediatric research has been at the forefront of major advances in medicine (extracorporeal membrane oxygenation 72 is a notable example) and has pushed the boundaries of modern oncology (for instance, in treating pediatric leukemia), innovations in new drug development are often delayed. Many rare and orphan diseases occur mainly in the pediatric population, and drug development in this population has always been operationally, ethically, statistically and methodologically challenging 73 , 74 . This is compounded by the limited understanding of basic biology, the ontology of disease manifestations, and the acute and long-term safety of products 73 , 74 . In addition, there is considerable off-label use of products in very young children, infants and neonates where clinical trials have not been feasible, and it is imperative that high-level evidence be generated by creative methods. Programs such as the Best Pharmaceuticals for Children Act (in 2002) and the Pediatric Research Equity Act (in 2003), made permanent in 2012 under the FDA Safety and Innovation Act, have incentivized and enhanced the development of pediatric therapeutics 73 . Innovative trial designs, RWD and leveraging data from other resources may help with risk–benefit assessment and drug approval, such as the approval for neurofibromatosis type 1 (NF1) 73 .

Reimagining the future of clinical trials

The landscape of AI in medicine has transformed recently, and AI is poised to become ubiquitous. Several RCTs have quantified the benefits of AI in specialties that use pattern recognition and interpretation of images, such as radiology (mammography and lung cancer screening), cardiology (interpreting electrocardiograms (EKGs), cardiac functional assessment and atrial fibrillation screening), gastroenterology (interpreting colonoscopies), pathology (cancer diagnosis), neurology (tracking disease evolution of amyotrophic lateral sclerosis and Parkinson’s disease), dermatology (diagnosing lesions) and ophthalmology (eye disease screening) 75 . However, most AI research focuses on ‘clinical care delivery’ applications and not ‘clinical trial research’ 76 .

The integration of AI into clinical trial research has been slower than expected, mainly owing to the (perceived) friction between AI versus human intelligence. Nevertheless, trials of data generation and interpretation should be conducted, and AI should be used to augment human intelligence—not seen as something to replace it 77 . Next-generation clinical trials using AI should consider AI + human rather than AI versus human scenarios 75 , 78 . The clinical trial guidelines for protocols (Standard Protocol Items: Recommendations for Interventional Trials–Artificial Intelligence (SPIRIT-AI) extension) and publications (Consolidated Standards of Reporting Trials–Artificial Intelligence (CONSORT-AI) extension) 79 , 80 are intended to achieve standardized and transparent reporting for randomized clinical trials involving AI, and these are just the beginning of a new phase of clinical research modernization.

Given the time and cost involved in developing a drug, every failed drug in the market represents a considerable loss to the drug development ecosystem. In addition, inferior trial designs, suboptimal patient recruitment, poor infrastructure to run trials, and inefficiency in trial conduct and monitoring have plagued the system for decades. AI has the potential to augment all phases of drug development, from drug design to the complete drug development cycle (Fig. 1 ).

Clinical trial conduct is still rudimentary in many ways. For instance, in oncology trials, a few aspects of two-dimensional lesions are measured and followed over time and effectiveness of the drug is evaluated by shrinkage of these lesions. Automated quantitative assessments and artificial neural networks can aid in automated rapid processing of multiple lesions 81 . In cardiology trials, vital signs are measured once a week in clinic, and, in neurology, patient questionnaires are administered in clinic. Now, these data can all be tracked dynamically in real time using wearable sensor technology. The application of AI to such areas can have a transformational near-term impact. In addition, pattern recognition using deep neural networks can help with reading scans, pathology images and EKGs, among others 37 , 78 .

The current evidence-based medicine pyramid represents the tip of the iceberg and barely provides shallow evidence to care for a generic patient (Fig. 4 ). Hence, a deep synthesis and amalgamation of all available data is needed to achieve next-generation, ‘deep’ evidence-based medicine. The main challenge in the next two decades will be to tap the potential of multidimensional evidence generation 82 by extracting, collating and mining large sets of natural history data, genomics and all other omics analysis, all published clinical studies, RWD, data from ubiquitous smart devices and amassed data from the IoMT to provide next-generation evidence for deep medicine.

The current evidence-based medicine (EBM) pyramid represents the tip of the iceberg and barely provides enough shallow evidence to care for a generic patient. Hence, a deep synthesis and amalgamation of all available data is needed to achieve next-generation, deep evidence-based medicine. The main challenge ahead in the next two decades will be extracting, collating and mining large sets of natural history data, genomics and all omics analyses, all published clinical studies, RWD and amassed data from the IoMT to provide next-generation evidence for deep medicine. PRO, patient-reported outcomes.

Partnerships in drug development

Currently, the pharma industry is the main driver of drug development, and their expenditures far exceed investments from any national agency such as the National Institutes of Health 61 . There are two domains of clinical trials. The first of these is from ‘big pharma’, which uses CROs to run trials; such trials are very often approved for registration by the FDA. The second domain encompasses academic clinical trials, which often operate on a very limited budget, do not often evaluate new compounds and, thus, rarely result in FDA registration. In this era of reduced federal funding for research, more partnerships are needed for drug development. Academic centers and community sites are crucial for patient enrollment; however, a siloed mentality has impacted drug development and delayed access to lifesaving therapies. Therefore, collaborations among specific disease organizations, academic institutions, federal agencies and patient advocacy groups are crucial for betterment of the health of populations (Fig. 3 ). Because the pharma industry is hesitant to invest huge amounts with limited financial return, especially in rare diseases, federal agencies have developed programs to incentivize rare disease drug development 1 . Moreover, disease-focused organizations have collaborated with the pharma industry, federal agencies and academia to form ‘venture philanthropy’ with risk-sharing financial models to de-risk drug development 1 . Many academic institutions are entering into risk-sharing strategic alliances with the pharma industry to collaborate across preclinical and clinical development phases. Such successful innovative partnership models have set a precedent in diseases such as cystic fibrosis, multiple myeloma, type 1 diabetes mellitus and other rare diseases 1 . These collaborations have effectively catalyzed innovation through all phases of drug development and provided a compelling reason to sustain and foster more of these sorts of programs.

Social media and online community research

Social media outlets (Twitter, Facebook and so on) can influence patient accrual in clinical trials. They can strongly influence and address historical clinical trial challenges, including the lack of awareness among patients and physicians about available trials and the lack of community engagement. More than 4.48 billion people use social media globally, and this number is projected to increase to almost 6 billion in 2027 (ref. 83 ). Over 70% of Americans are on social media, including rural dwellers and adolescent and young adult populations who have always been under-represented in clinical trials. Although many older adults do not use social media, their caregivers are likely to.

People with terminal diseases often self-experiment with drugs, and online patient communities can provide environments for sharing and monitoring such drug usage. This can allow for observational studies to be planned around quantitative, internet-based outcome data. For example, researchers developed an algorithm to dissect the data reported on the PatientsLikeMe website by people with amyotrophic lateral sclerosis who experimented with lithium carbonate treatment 84 . This analysis reached the same conclusion as an ensuing RCT, suggesting that data from online patient behavior can help accelerate drug development and evaluate the effectiveness of drugs already in use.

An increase in engagement from patients and patient advocacy groups can aid patient education and outreach and can facilitate patient-partnered research, as well as allowing for incorporation of patients’ perspectives in the design of clinical research—ultimately generating research that is driven by the needs of real people with the disease under investigation. Moreover, social media breaks open silos dividing researchers and clinicians, creating enormous potential to influence all areas of medicine 85 .

The success of future clinical trials requires a fundamental transformation in how trials are designed, conducted, monitored, adapted, reported and regulated to generate the best evidence. The status quo model is unsustainable. Instead, preventive, personalized, pragmatic and patient-participatory medicine is needed, and paradigm shifts are required to get there via sustainable growth. Silos need to be broken. Standards of care and clinical trials are currently viewed in different realms; however, the overarching goal of both is to improve health outcomes. The COVID-19 pandemic created an opportunity to observe how routine clinical care and clinical trials can work synergistically to generate evidence 86 . Pragmatic platform trials such as the RECOVERY trial should be a model and guide for trial efficiency and real-time impact.

Current paradigms must be continuously challenged by emerging technology and by all stakeholders (the new generations of scientists, physicians, the pharma industry, regulatory authorities and, most importantly, patients). Disruptive innovation should lead to every clinical site being a research site, with all necessary quality checks and research as part of the standard of care. The healthcare system should be integrated into an intuitive RWE-generation system, with clinical research and clinical care going hand in hand. Beyond an ad hoc creative flash of genius (necessitated by a pandemic), sustained momentum will be needed to leverage the knowledge gained from programs such as ‘Operation Warp Speed’ (initiated by the US government to accelerate COVID-19 vaccine development). My personal view is that every major disease needs a ‘Moonshot’ program and every rare disease should have an ‘Operation Warp Speed’—both with clearly identified, sustainable goals to improve population health and address equity, diversity and global access to therapies. Methodological advances and future AI-based analyses of all data will provide deep evidence to realize the goal of personalized medicine— that is, to offer the right treatment to the right patient at the right time.

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Acknowledgements

V.S. is an Andrew Sabin Family Foundation fellow at the University of Texas MD Anderson Cancer Center. V.S. acknowledges the support of the Jacquelyn A. Brady Fund. V.S. thanks the team at Draw Impacts for figures. V.S. is supported by the US National Institutes of Health (NIH) (grants R01CA242845 and R01CA273168); the MD Anderson Cancer Center Department of Investigational Cancer Therapeutics is supported by the Cancer Prevention and Research Institute of Texas (grant RP1100584), the Sheikh Khalifa Bin Zayed Al Nahyan Institute for Personalized Cancer Therapy (grant 1U01CA180964), NCATS (Center for Clinical and Translational Sciences) (grant UL1TR000371) and the MD Anderson Cancer Center Support (grant P30CA016672).

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• Published: 26 February 2018

The use of advanced medical technologies at home: a systematic review of the literature

• Ingrid ten Haken 1 ,
• Somaya Ben Allouch 1 &
• Wim H. van Harten 2 , 3  

BMC Public Health volume  18 , Article number:  284 ( 2018 ) Cite this article

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The number of medical technologies used in home settings has increased substantially over the last 10–15 years. In order to manage their use and to guarantee quality and safety, data on usage trends and practical experiences are important. This paper presents a literature review on types, trends and experiences with the use of advanced medical technologies at home.

The study focused on advanced medical technologies that are part of the technical nursing process and ‘hands on’ processes by nurses, excluding information technology such as domotica. The systematic review of literature was performed by searching the databases MEDLINE, Scopus and Cinahl. We included papers from 2000 to 2015 and selected articles containing empirical material.

The review identified 87 relevant articles, 62% was published in the period 2011–2015. Of the included studies, 45% considered devices for respiratory support, 39% devices for dialysis and 29% devices for oxygen therapy. Most research has been conducted on the topic ‘user experiences’ (36%), mainly regarding patients or informal caregivers. Results show that nurses have a key role in supporting patients and family caregivers in the process of homecare with advanced medical technologies and in providing information for, and as a member of multi-disciplinary teams. However, relatively low numbers of articles were found studying nurses perspective.

Conclusions

Research on medical technologies used at home has increased considerably until 2015. Much is already known on topics, such as user experiences; safety, risks, incidents and complications; and design and technological development. We also identified a lack of research exploring the views of nurses with regard to medical technologies for homecare, such as user experiences of nurses with different technologies, training, instruction and education of nurses and human factors by nurses in risk management and patient safety.

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As a result of demographic changes and the rapidly increasing number of older patients, there is a need for cost savings and health reforms, which include an increased move from inpatient to outpatient care in most industrialized countries over the last 10–15 years [ 1 , 2 ]. As a consequence, the transfer of advanced medical devices into home settings was considerable and it is expected that there will be a further increase in the near future [ 1 , 2 , 3 , 4 , 5 , 6 , 7 ].

When ‘an increase’ in the number of medical technologies used at home is mentioned, it is not clear which and how many technologies are involved. Today, there are an estimated 500,000 different kinds and types of medical devices available on the world market [ 8 , 9 ]. The European Commission (EC) publishes data regarding legislation and regulations for medical devices, but the actual figures for medical technologies in outpatient practice are not available [ 10 ]. The U.S. National Center for Health Statistics (NCHS) stated that technologies have shifted from hospitals into the home, but it too does not illustrate its findings with statistics [ 11 ]. We searched for data with regard to the actual number of medical technologies used in home settings and it proved difficult to find any systematic data sets available throughout the international landscape.

An important condition for the application of medical technology in the home setting is that quality of care and patient safety must be guaranteed [ 6 ]. From a historical perspective medical technologies were designed for hospital settings [ 12 , 13 ]. This means that specific factors regarding the implementation and use at home now need to be taken into account [ 7 , 14 , 15 ]. In general, risks with medical technologies can be classified regarding (a) environmental factors; (b) human factors and (c) technological factors [ 16 ]. Human factors, however, are very important in patient safety in both hospital and in home settings [ 1 , 6 , 12 ]. For example, a major risk factor is the number of users and handovers in the chain of care. In home settings, a sometimes impressive number of different users of medical technology, often with various levels of training, instruction or education, are involved. Although patient empowerment moves control to the patient and/or relatives, an important user group is that of professional nurses. Understanding user experiences and information about adverse events and near incidents are important aspects for developing knowledge regarding implementation and use in home care setting. Sharing this knowledge can support patients and caregivers, and especially nurses in their professional work and will also contribute to patient safety and quality of care.

Therefore, there is a need to address the question first, which types of technologies are used at home; second, how frequently are they used and third, what trends can be distinguished. Additional research questions are whether there are any scientific data regarding particular user experiences; training, instruction and education; safety and risks, and finally, what can be concluded about the role of nurses in using medical technologies in the home environment. The objective of this paper therefore is to present a systematic literature search on the international state of art concerning various aspects of the use of advanced medical technologies at home.

Definitions

First, we want to clarify some definitions. In general, ‘health technology’ refers to the application of organized knowledge and skills in the form of devices, medicines, vaccines, procedures and systems developed to solve a health problem and improve quality of life [ 17 ]. The World Health Organization [ 8 ] uses the definition of ‘medical device’ as ‘An article, instrument, apparatus or machine that is used in the prevention, diagnosis or treatment of illness or disease, or for detecting, measuring, restoring, correcting or modifying the structure or function of the body for some health purpose …….’. A specification for a home use medical device is: ‘A medical device intended for users in any environment outside of a professional healthcare facility. This includes devices intended for use in both professional healthcare facilities and homes’ [ 18 ].

The landscape of medical devices is diverse with technologies varying from relatively simple to very complex devices. Wagner et al. [ 19 ] stated that ‘high-tech dependency’ (for children) matches with ‘technology-dependency’ if it concerns ‘a medical device to compensate for the loss of a vital bodily function and substantial and ongoing nursing care to avert death or further disability’. ‘The needs of these patients may vary from the continuous assistance of a device and highly trained caretaker to less frequent treatment and intermittent nursing care’ [ 20 ]. Although patients dependent of advanced medical technologies at home are often medically stable, they sometimes have high technical needs and may be expected to need long-term recovery. They also require skilled nursing [ 21 ] and a considerable degree of advanced decision making, planning, training and oversight [ 22 ]. An overall definition of ‘advanced medical technology’ is: ‘Medical devices and software systems that are complex, provide critical patient data, or that directly implement pharmacologic or life-support processes whereby inadvertent misuse or use error could present a known probability of patient harm’ [ 23 ]. Examples of advanced medical technologies used at home include ventilators for respiratory support, systems for haemo- or peritoneal dialysis and infusion pumps to provide nutrition or medication.

In the Netherlands, the National Institute for Public Health and the Environment (RIVM) [ 24 ] uses the following definition:

Advanced medical technology or high-tech technology in the home setting is defined as technology that is part of the technical skills in nursing and meets the following conditions:

technology that is advanced or high-tech, for example equipment with a plug, an on/off switch, an alarm button and a pause button;

technology that had been applied formerly only in hospital care, but that is now also often applied in home settings;

technology that can be categorized as ‘supporting physiological functions’, ‘administration’ or ‘monitoring’.

Within the Dutch classification of advanced medical technologies 19 different devices are identified (see Table  1 ), which will be used in this review as a basis to categorize the technologies. It is a classification format in which specific advanced technologies are defined. Terms as ‘advanced medical technology’ (from now on abbreviated as AMT) will be used consistently as synonyms for ‘complex medical technology’ and ‘high-tech medical technology’. The term ‘technology’ will be used in the meaning of ‘device’ or ‘equipment’. The target is on technologies that are instrumental and ‘hands on’ use by nurses in the care for patients. This means that information technology (IT) based technologies as domotica (automation for a home) are not part of the study.

Eligibility and search strategy

The systematic review of the literature was conducted early 2016. Key concepts for the review were ‘medical technologies’ or ‘medical devices’, and ‘home settings’. The concept of ‘home settings’ is related to the terms ‘home nursing’ and ‘home care service’, of which the stem is ‘home’. Combining the key concepts provided the search string: (‘medical technology’ OR ‘medical device’). As domotica is not part of the study, the search string was extended with: AND NOT (eHealth OR telecare OR telemedicine). The exact search string is (“medical technology” OR “medical devices”) AND home AND NOT (ehealth OR telecare OR telemedicine). Online databases MEDLINE, Scopus and Cinahl were searched electronically using the search string to obtain data.

Inclusion and exclusion criteria

Criteria for selection were defined prior to the search process. General criteria for inclusion were:

Year of publication: 2000–2015.

An abstract or an article (with or without abstract) has to be available, containing reference to AMT information.

The article is published in English, German, French or Dutch/Flemish language.

If medical technology is cited, it has to conform to the definition of ‘advanced medical technology’ [ 24 ].

The abstract or the article has to contain empirical material. For the purpose of this review, ‘empirical material’ has been defined as: AMT which is designed for the home setting, or where the design or choices took into account the setting of the home, or where the medical technology has been tested for the home or if the medical technology is already on the market and being used in the home setting.

For further selection, inclusion criteria related to the key concepts for title and abstract were applied, such as ‘advanced medical technology’, ‘high-tech medical technology’, ‘home-centred health-enabling technology’ and ‘care at home’. The classification of the RIVM (see Table 1 ) has been taken as a basis to categorize technologies in this review. Domotica and telemonitoring technologies scored under ‘monitoring’, such as fetal cardiotocography, and respiratory and circulatory monitoring, were left out. If the abstract or article was about electronic health records, ‘smart home’, ambient intelligence, pervasive computing, software of devices, smartphone or surgical robots, the article was also removed from selection. Technologies as ‘VAD (ventricular assist device)’, ‘dental devices’ and ‘AED (automatic external defibrillator)’ were not seen as part of the technical nursing process and these records were left out as well. Studies conducted in the hospital, hospice or nursing home settings were also excluded. An overview of all inclusion and exclusion criteria can be found in Table  2 .

Screening process

The search in the online databases using the search string, identified a total of 1287 references. After checking for duplicates, 1070 articles remained. Those articles were reviewed by a reviewer for titles and abstracts on basis of the inclusion and exclusion criteria. A double check was performed by two reviewers, who independently screened random samples of 20% of the articles. There was an initial agreement of 88%. In case of disagreement about the inclusion of an article, the decision was based on a joint discussion by all three reviewers to an agreement of 100% and the resulting screening policy was applied to the rest of the abstracts. Based on the selected titles and/or abstracts, articles were retrieved or requested in full text and assessed for eligibility. Some articles were excluded from further study, for reasons of ‘full text not available’ or the article contained no empirical material. Finally, 87 studies remained which were included in the analysis (see Table  3 ). A graphical representation of the screening process has been included in Fig.  1 .

PRISMA flowchart

Appraisal of selected studies

To conduct the systematic literature search on the international state of art concerning various aspects of the use of advanced medical technologies at home, several sources are consulted. To guarantee a scientific standard, only articles were retrieved from academic databases. MEDLINE refers to journals for biomedical literature from around the world; Cinahl contains an index of nursing and research journals covering nursing, biomedicine, health sciences librarianship, alternative medicine, allied health and more. These databases related to discipline have been supplemented with Scopus, which is considered to be the largest abstract and citation database of peer-reviewed literature. Grey literature, such as national and international reports on regulations and safety of medical technologies, is also used to illustrate the background of the problem statement and describe definitions. The Classification of advanced medical technologies in the Netherlands according to the National Institute for Public Health and the Environment (RIVM) has been used as a framework to categorise the medical technologies in the selected articles. No methodological conditions of selected studies were applied in advance and the quality criterion we applied was that of the article had to contain empirical material, as we wanted to obtain an comprehensive overview of published studies of any design and to get insight in a variety of contents.

Categorization of included articles

The characteristics of the included articles are outlined in Table  3 . All included articles were categorized by year of publication and the type of research, like the designs, methods and used instruments in the studies. Research features were synthesized where possible into overarching categories. For example, ‘systematic review’ and ‘narrative review’ were scored as ‘review’ and instruments as ‘semi-structured interview’ and ‘in-depth individual interview’ were both assigned to the category ‘interview’.

For each study, the medical technology or technologies on which the study was based was scored. The categorization was in accordance with the classification of AMTs (see Table 1 ). For example, the devices ‘continuous positive airway pressure (CPAP)’ and ‘negative pressure ventilation (NPV) have both been categorized as ‘respiratory support’; and the devices ‘jejeunostomy tube’ and ‘gastronomy tube’ as ‘enteral nutrition’. With regard to the category ‘dialysis’, further subdivision was made by using ‘haemo dialysis’ and ‘peritoneal dialysis’. If in an article a medical technology was mentioned as an example, but was no subject of study, then the technology was not scored.

‘Medical diagnosis (or diagnoses)’ as mentioned in the studies, was included in the analysis only if it was related to the medical technology as the subject of study, not if it has been mentioned as an example. In some cases, an underlying cause of diagnosis was indicated. For example, ‘chronic respiratory failure due to congenital myopathy’, in itself a neurological disorder, has been scored as ‘neurological disorder’. Diseases or disorders have been classified as much as possible under the overarching name. For example ‘pneumonia’ and ‘cystic fibrosis’ are categorized under ‘respiratory failure’, and ‘gastroparesis’ and ‘Crohns disease’ under ‘gastrointestinal disorder’. The category ‘other’ contains diagnoses which occur only once, such as ‘chromosomal anomaly’, or which are not yet determined, like ‘chronic diseases’ or ‘congenital abnormalities’.

In relation to the research questions, articles were classified regarding one of the following categories and, where appropriate, into subcategories:

User experiences

Training, instruction and education, safety, risks, incidents and complications.

From an analysis of the articles, additional categories of content emerged:

Design and technological development

Application with regard to certain diseases or disorders, indication for and extent of use

Policy and management

Types of medical technologies used, frequency of use and trends.

In four of the 87 articles (5%) there were no specific medical technologies mentioned as a subject of study (see Table  4 ). Almost half of the studies (45%) considered medical technologies for respiratory support and 39% devices for dialysis, either haemo- ( n  = 18), peritoneal- ( n  = 15) or dialysis not specified ( n  = 1). Of the studies, 29% reported on devices for oxygen therapy. In addition, there has been relatively more research conducted on equipment for ‘infusion therapy’ ( n  = 19; 22%), parenteral nutrition and enteral nutrition with a score of 20% each ( n  = 17). Relatively little research has been carried out on suction devices (8%), external electrostimulation (5%), nebulizer (5%), insulin pump therapy (3%), sleep apnea treatment (2%), patient lifting hoists (2%), vacuum assisted wound closure (1%) and continuous passive motion (1%). None of de studies considered medical technologies with regard to decubitus treatment, skeletal traction or UV (ultraviolet) therapy.

Table 4 shows that on the years 2000 and 2001 no relevant articles on the subject were found. Over the period 2000–2005, 17 articles were published, the same number over 2006–2010, and there has been a substantial increase in the number of publications to 54 over the years 2011–2015. In general, it can be concluded that more frequent investigated technologies show a fairly even distribution of publications over the years 2000–2015. Technologies, on which little research had been done, except for nebulizers, have been mainly investigated since 2010. An increase of published articles over the years 2000–2015 is apparent particularly for haemo dialysis and to a lesser extent, for devices for enteral- and parenteral nutrition. As mentioned before, several studies reported on the increase of the number of medical technologies used in home settings, but concrete data are not available. However, the number of studies and the visible trends may be indicative of the frequency of use.

In 63% of the cases ( n  = 55), a medical diagnosis (or diagnoses) was mentioned in the article. Where a diagnosis has been mentioned, in almost half of the studies ( n  = 26; 47%) it concerned diagnoses in the field of respiratory failure (see Fig.  2 ). This is not surprising, since ‘respiratory support’ is the medical technology most commonly found in the articles, similarly ‘oxygen therapy’ has also been considered relatively often. Diagnoses with regard to neurological disorders occurred in 42% of the studies ( n  = 23). Just over a quarter of the studies (27%) considered diagnoses ‘other’, such as ‘sepsis’, ‘chromosomal anomaly’ or other not specified medical disorders, nearly a quarter (24%) considered ‘cancer’ and 22% kidney disorders ( n =  12).

Number of medical diagnoses mentioned in articles on AMTs ( n  = 87, multiple answers possible)

An analysis of the used research designs identified that 64% ( n  = 56) of the studies used an observational (non-experimental) design and only a small part of the studies ( n  = 5; 6%) used an experimental design, such as a Randomized Control Trial (RCT). Of the included studies 19 were reviews and 8 were essays. A quantitative design ( n  = 37) was used more frequently than a qualitative design ( n  = 25); and only one study applied ‘mixed methods’ (quantitative and qualitative). Just over one-third of the studies (35%) used a descriptive design, and a similar number used a cross-sectional study (36%). Case series were used in 12% of the articles and a cohort-study in 9%. A phenomenological approach was applied in 16% of the records. Research instruments most frequently used were interviews (33%) and survey/questionnaires (21%). In 10% of the cases other instruments were used, including different types of assessments or tests.

With regard to the categories of content, most research has been carried out on ‘user experiences’ (see Fig.  3 ): just over one-third of the articles ( n  = 31; 36%) focused on this topic. Of these articles almost all studies focused on experiences of patients or informal caregivers ( n  = 29) and only a small number ( n  = 2) considered the user experiences of nurses or other professionals (see Table  5 ). More than half of the studies ( n  = 19) used a qualitative research design; of these 13 used a phenomenological approach. The goal of these studies was to elicit the essence of human phenomena as experienced by the users. Seven studies used a quantitative design and one an integrated mixed method. Three of the studies applied a grounded theory approach and two an experimental design (randomized controlled trial). The research instruments in this content category to collect data were interviews, either semi-structured or in-depth, and a survey. About two-thirds of the articles regarding ‘user experiences’ were published in the period 2011–2015, with an accent on the psychosocial impact of patients or informal caregivers.

Number of articles on AMTs with main content categories ( n  = 87)

Relatively little research was found on ‘training, instruction, education’ ( n  = 7), for the use of AMTs in home settings. It was remarkable that all the studies identified as focusing on this topic, concentrated on one category of AMT. Respiratory support was the subject of study in four instances and in the other three, the focus was on technologies for enteral nutrition, haemo dialysis and external electro-stimulation. Four of the seven articles utilized quantitative methods, among which three of them used an observational non-experimental design and one was an experimental randomized double-blind clinical trial. Another study within the initial seven articles used a qualitative observational non-experimental design, one was a review and another was in essay format.

In total, 22% of the articles discussed topics on safety, risks, incidents and complications ( n  = 19). In the majority of cases ( n  = 13) general aspects about the subject, for instance safe use, factors affecting safety, a safe transfer of the equipment and monitoring of assessing safety were considered. One article described technological factors with regard to safety, three articles reported on environmental factors and two explored human factors. Safety aspects were explored over a wide range of medical technologies. Five articles were reviews and one an essay. Quantitative methods were used in ten of the cases, particularly for monitoring, evaluating and assessing safety, technological and environmental factors. Only three studies used a qualitative design. Retrospective chart reviews or case series were used to collect data in some cases of unforeseen events. Table 5 shows about a doubling of published articles in the period 2011–2015 regarding this content category, compared to the previous period 2000–2010.

Approximately 20% of the selected articles considered the content category ‘design and technological development of the medical device’ ( n  = 17). The studies each focused on only one type of AMT and treated a relative wide range of eight different categories, such as ‘respiratory support’, ‘oxygen therapy’, ‘haemo dialysis’, ‘infusion therapy’, ‘insulin pump therapy’ and ‘enteral nutrition’, but also ‘external electrostimulation’ and ‘patient lifting hoists’. Interestingly, in this group of articles, relatively often ( n  = 6) no medical diagnosis was mentioned. Around half of the studies ( n  = 8) referring to this topic were in review or essay format. All other studies used a quantitative research design and throughout the search no application of qualitative designs were found. Two studies used an experimental study design (randomized crossover trial) to obtain data and two described a prospective cohort study. The majority of papers ( n  = 11) were published in the period 2011–2015 and six in the preceding period up to and including 2010.

Seven articles concerned the application of AMTs, all of them devices with regard to at least respiratory support and/or nutritional support. Five studies used a non-experimental quantitative design including the analysis of clinical data, such as record reviews or cohort studies, and two articles were reviews. Most articles on this subject ( n  = 5) were published in the period 2012–2015.

Six articles described policy or management systems in different countries regarding the use of AMTs at home. The majority of the articles ( n = 4 ) were in essay or review format. The other papers concerned a qualitative cross-sectional case study analysis and an observational quantitative study in which data are collected prospectively using a database. The categories of content will now be discussed in greater detail.

Content description and trends to secondary research questions

In this category, 22 articles described the psychosocial impact on patients or informal caregivers from the use of medical technologies at home. Living at home with the assistance of medical technology needs a range of adjustments. Fex et al. [ 25 , 26 ] state that self-care is more than mastering the technology, in terms of the health-illness transition, it requires ‘…. an active learning process of accepting, managing, adjusting and improving technology’. When it comes to children, they have to learn to incorporate disability, illness and technology actively within their process of growing up [ 27 ]. It seems that the use of medical technologies in the home can have both a positive and a negative psychosocial impact on patients and their families, which in turn causes ambivalence in experiences [ 27 , 28 ]. On the one hand, patients in general gain more independence, an enhanced overall health and a better quality of life [ 29 , 30 , 31 , 32 , 33 , 34 ]. On the other hand, for some patients the experience is one of dependency on others for executing daily activities, and these circumstances, to some extent, a social restricted live and perceived stigmatization [ 29 , 30 ]. The situation in which patients need to use medical technology at home also affects family functioning and requires next of kin responsibilities [ 35 , 36 , 37 ]. As a result, next of kin caregivers are frequently faced with poor sleep quality and quantity, and/−or other significant psychosocial effects [ 38 , 39 , 40 , 41 ]. Nevertheless, family members had a positive attitude to the concept of bringing the technology into the home [ 42 ]. Knowledge of how to use the technology and permanent access to support from healthcare professionals and significant others, enabled next of kin caregivers to take responsibility for providing necessary care and to facilitate patients learning to provide self-care [ 25 , 36 , 42 , 43 , 44 ]. Bezruczko et al. [ 45 , 46 ] developed a measure of mothers’ confidence to care for children assisted with medical technologies in their homes. To provide high quality sustainable care, nurses have to recognize and understand the psychosocial dimensions for both patients and family members which arise as a result of changing role and providing care for the patients. The need to provide emotional support and support with appropriate coping strategies is a key professional role [ 25 , 26 , 47 ]. Insight into the psychosocial effects on those involved can be used to assist designers of medical devices to find strategies to better facilitate the integration of these technologies into the home [ 28 ].

Seven articles reported on the usability, barriers and accessibility experienced by patients or informal caregivers. Findings in these studies showed that several technologies were rarely perceived as user-friendly and that home medical devices inadequately met the needs of individuals with physical or sensory deficits [ 48 , 49 ]. An accessible design which meets the diversity of individual user needs, characteristics and features would be better able to help patients manage their own treatment and so could contribute to the quality of care and safety of patients and lay users [ 50 , 51 ]. Munck et al. [ 52 ] stated that restricted patients were reminded daily of the medical technology and were more dependent on assistance from healthcare professionals than masterful patients.

In contrast to the group of patients or informal caregivers, only two papers in this content category focused on the user experiences of nurses or other professional caregivers. The review demonstrates that to maintain patient safety, more education on application of medical devices for users is needed together with improved awareness and understanding of how to use the medical technology correctly in a patient-safe way [ 53 , 54 ]. More collaboration between all involved ‘actors’ in the process of care is also requisite. Continuity among carers, trust between patient and carers and supportive communication between informal and professional caregivers are important factors for the successful implementation of medical technologies in the home environment while maintaining patient safety [ 44 , 51 , 53 , 54 , 55 ].

Three articles regarding this topic focused on nurses or other professionals and four on the patients or informal caregivers. The results showed that successful use of advanced medical technologies at home requires adequate staff education and training programmes. Although many topics in educational programmes are suitable for different types of professionals in care provision, the focus for the level and application of information can vary for Registered Nurses and unregistered care staff. In addition, for overall learning experiences to be of maximum benefit there is a need for a clear focus on the specific client groups [ 56 ]. According to Sunwoo et al. [ 57 ], in the case of home non-invasive ventilation the degree of clinical support needed is extremely variable given the mixed indications for this respiratory support. A relatively simple procedure, such as the replacement of a feeding tube, can be performed by nurses, the patient and informal caregivers, provided they are trained well [ 58 ]. However, several studies revealed the complexity of the education needed by patients and informal caregivers for the use of advanced medical technologies at home [ 59 , 60 ]. Nevertheless, the studies revealed that a structured education programme, specific training, or the support of a dedicated discharge coordinator has several advantages [ 59 , 61 , 62 ]. It was evident that good preparation by patients or informal caregivers may result in a shorter length of stay in hospital, a better performance with regard to the use of the equipment or less requests by patients and/or families for assistance.

Most articles regarding this topic ( n  = 13) reported on safety in general, like aspects of safe use, factors affecting safety, complications and prevention of incidents in the home. Some identified the risk factors and the complications that may arise [ 63 , 64 , 65 ], where Stieglitz et al. [ 66 ] also emphasize that human error is the main reason for critical incidents and that regular instruction for medical staff and patients is necessary. To prevent untoward and adverse events, evidence based guidelines, recommendations on the preferred methods for managing the equipment, troubleshooting techniques for potential complications and monitoring activities are necessary [ 67 , 68 ]. Faratro et al. [ 68 ] added that key performance and quality indicators are important mechanisms to ensure patient safety when using a medical device in the home. Methods to address or evaluate patient safety issues are for example, a home visit audit tool, a nationwide adverse event reporting system, programs such as the Medical Product Safety Network HomeNet, or, in the case of peripherally inserted central catheters (PICCs) a central catheter stabilization system [ 69 , 70 , 71 , 72 ]. However, a study conducted by Pourrat and Neuville [ 73 ] in France found that there are very few internal medical devices vigilance reports found within organizations that deliver devices for home parenteral nutrition and that safety management could be improved. The safe transfer of medical devices from a hospital setting to the home and vice versa, comes with several challenges regarding technological, environmental and human factors [ 14 ]. While many hospitals have developed policies to control the pathways of home-used devices in the hospitals, in case patients take them into the hospital when they are admitted for treatment [ 74 ]. Improvement of the safety of devices intended for use in home settings, implies also improvement of safety when their transfer to the hospital settings is urgently needed.

One article considered the technological factors, three the environmental and two the human factors. An example of research on the technological factors of safety related aspects of medical technologies used in home settings by Hilbers et al. [ 75 ] found that manufacturers pay insufficient attention to safety-related items in technical documentation for the use in the home setting. For instance, the environmental factor of electricity blackout leads to electrically powered medical devices failing. Studies show that this type of event causes a dramatic increase in appeal for access to emergency or hospital facilities, and that disaster preparation needs to include the specific needs of patients reliant on electrically driven devices [ 76 , 77 , 78 ]. Regarding human factors impacting on safety aspects, one article assessed the suitability of a particular theoretical framework for understanding safety-critical interactions of patients using medical devices in the home [ 79 ], while Tennankore et al. [ 80 ] described adverse events in home haemodialysis by the use of patients. It was remarkable that none of the articles focused on human factors with regard to the use of medical technologies at home by nurses or other professional caregivers.

Of those articles that focused on this topic, ten reported on the comparison between different types of medical technologies, or their advantages and disadvantages. The comparison of different devices for oxygen therapy was made by two articles [ 81 , 82 ] and one reported on the comparison of two types of enteral nutrition tubes [ 83 ]. Some studies regarding respiratory support considered the process of making a choice between different types of devices [ 84 , 85 , 86 ] while one paper considered the conditions for home-based haemo dialysis [ 87 ]. A minority, explored the individual characteristics and the clinical applications of several devices for respiratory support [ 88 , 89 ] and one considered devices for insulin pump therapy [ 90 ]. Seven papers discussed the technological development or effectiveness of medical technologies. The testing of devices for external electro-stimulation was described in two papers [ 91 , 92 ], with the testing of a new design patient lift was subject of one study [ 93 ]. Hanada and Kudou [ 94 ] explored the current status of electromagnetic interference with medical devices in the home setting, an issue of importance as more devices are considered for home use. The technological development of respiratory support for home use was part of one study [ 95 ], as were the possibilities of solar-assisted home haemo dialysis [ 96 ]. While the study by Pourtier [ 97 ] describes the advantages of analgesia pumps that can be read remotely by nurses, but also emphasizes the central position of a professional nurse in the transfer of information within a multi-disciplinary team.

Application with regard to certain diseases or disorders, indications for and extent of use

All articles described several aspects that need to be considered for use, such as clinical characteristics of the patients, indications for the use in the home setting, the technical availability of devices, the extent of their use at home or eventual complications and morbidity. It was important to note that all but one article ( n  = 6) were about children or related to adults with what are usually regarded as paediatric diseases. Results show that the use of AMTs at home among children after hospital discharge is common (in 20%–60% of cases), or is standard for patients with some disorders [ 98 , 99 , 100 , 101 ]. The timely application of advanced home medical technology benefits patients and can help to reduce respiratory morbidity [ 102 ]. Nevertheless, the rate of death of patients with Möbius syndrome using the devices at home was high (30%) [ 98 ], as was that of patients with intestinal failure dependent on home parental nutrition therapy in Brazil (75% for 5 years) [ 103 ]. The average cumulative survival of children needing home ventilation was found to be between 75 and 90%, depending on the medical diagnosis [ 104 ].

Three of the papers were concerned with costs and/or reimbursement. The application of medical technologies in the home environment can be cost-effective when compared to institutionalized care [ 22 , 105 , 106 ]. Nevertheless, successful employment of medical technologies in the home necessitates medical guidelines for the indicators for use, careful identification of patients as well as careful planning and attention to details [ 105 , 106 , 107 ]. Two studies concerned the dilemma’s for implementation of the technologies in home healthcare and emphasized the importance of cooperation in the chain of key stakeholders to maximize efficiency of high-tech healthcare at home, one with regard to the purchasing policy of medical technologies [ 108 ] and one with regard to the interventions of local community service centres and hospitals supporting optimal use of these technologies in the home setting [ 5 ].

The use of medical technologies in the home setting has drawn increased attention in health care over the last 15 years, as the feasibility of this type of medical support has rapidly grown. This article systematically reviewed the international literature with regard to the state of the art on this subject, in order to provide a comprehensive overview.

Trend analysis over the period 2000–2015 shows that most research has been conducted about respiratory support, dialysis and oxygen therapy; relatively little about vacuum assisted wound closure and continuous passive motion, and no about decubitus treatment, skeletal traction and UV therapy. A substantial increase in publications was found in the period 2011–2015. Although the number of studies on technologies is indicative of the extent to which they are used in home settings, however, no firm conclusions can be drawn about this.

This review also identified that most research is conducted with regard to ‘user experiences’ of medical technologies in the home, ‘safety, risks, incidents and complications’, and ‘design and technological development of medical technologies’. There have been relatively few studies which have explored the topic of training, instruction and education. Content analysis showed that the use of AMTs in the home setting can have both a positive and a negative psychosocial impact on the patients and their families, and that it has become part of self-management and patient empowerment. Successful use of advanced equipment requires adequate education and training programmes for both patients, informal caregivers and nurses or other professionals. When trying to maximize or assure safety, technological, environmental and human factors have to be taken into account, and it is evident that human factors are the main reason for critical incidents. Studies on the design and technological development of medical technologies emphasize that research is necessary to improve its possibilities and effectiveness. The research found on the application of the technologies focused predominantly on children and the results indicate that the rate of the use of home medical devices among children after hospital discharge is common. Also that when compared to institutionalized care, the application of medical technologies in the home environment can be cost-effective. Much is known, but information on several key issues is limited or lacking.

An important finding was that in almost all the reviewed articles, the study subjects were patients or informal caregivers with very few studies focused on the role and activities of nurses or other professionals as users. This was unexpected as nurses are the main group of users of AMTs at home and they have to transfer knowledge and skills on how to use the devices to patients and other caregivers. Nurses also have a key role in setting up and maintaining collaboration between all actors involved in the process of care with regard to the use of home medical technologies and in giving support to patients and family members in this respect. There is need to initiate further in depth research on AMTs use at home focusing on the role of specifically nurses.

Another interesting result was that, despite the fact that most adverse events with AMTs at home are caused by human factors, hardly any studies conducted on this subject were found. None of the articles focused on related human factors regarding the use by nurses or other professional caregivers, although this is the main user group. Research on this area could contribute to improved patient safety and quality of care. The results also revealed the tension between the advantages and disadvantages of medical technologies as experienced by patients at home. Important aspects needed to promote the benefits include improving the user-friendliness of the devices and attuning their designs for the use in home settings. This emphasizes the importance of professionals (and patient groups) working together with the designers with regard to sharing knowledge and user experiences of the use of AMTs at home in order to improve quality of care and patient safety. This collaboration emerged as of key importance in the successful use of AMTs in the home as well.

Although all included articles were retrieved from academic databases and served our purpose, there was considerable heterogeneity of quality of the studies. Most of the studies have explicitly described their research design, albeit to a greater or lesser extent. On the other hand, there were a few studies that did not even mention their methodological approach, though it could be derived from the description. Most included reviews are of moderate quality. Although findings are almost always described clearly, the search strategy and selection criteria used are often lacking. The quantitative studies are generally well described in different methodological aspects, such as selection of respondents, research design, data collection methods and analyses. Studies of qualitative nature show more variation in the depth with which the design is described. However, almost all qualitative studies have described the research instruments very well, such as semi-structured interviews or questionnaires. Despite the varying quality of the studies, we believe that the whole of different methodological approaches and the relatively large number of included studies ( n  = 87) has yielded a fairly reliable overview on the international state of art concerning various aspects of the use of advanced medical technologies at home. For future research, we recommend to emphasize the development of a more detailed methodological design, zooming in on specific technologies, using large databases or conducting large surveys, and focusing on specific groups of respondents. Both in quantitative and in qualitative studies, a good definition of the research question(s), selection of respondents, development of instruments and analysis of findings, contributes to validity, consistency and neutrality.

Some limitations do have to be taken into account with this review. Although we used the RIVM-definition of ‘advanced medical technology’, not all devices are considered as ‘complex devices’ by nurses in practice. For example, the use of an anti-decubitus mattress in the context of ‘decubitus treatment’ and ‘patient lifting hoists’ are considered by nurses as being of less or lower complexity. However, overall the RIVM-classification was found to be a good starting point, and provided a practical and useful framework from which to work to gain an insight and overview of available medical technologies. Of some of the chosen technologies defined using the RIVM-classification of AMTs, questions do have to be asked as to whether they really are part of the technical skills in nursing process. For example, ‘external electrostimulation’ and ‘continuous passive motion’ are mainly applied by physiotherapists, although with appropriate training nurses can apply them. Then too, devices regarded as only ‘monitoring’ were excluded from the review.

This systematic review study was designed to fill a gap in the current research by investigating what is known about different aspects of medical technologies used in the home. From the results it is obvious that a wide and growing range of medical technologies are used at home. Different types of technologies have been subject of study, increasingly –also in scope- over the period 2011–2015.

Professional nurses have a central role in the process of homecare which has to be recognized when considering use of AMTs at home. Nurses have to support patients and family caregivers and in consequence have a key role in providing information for, and as a member of multi-disciplinary teams. Closer collaboration by all actors involved in the process of care and feedback of user experiences to the designers is essential for the provision of high quality of care and patient safety.

This review also identified a lack of research exploring the perspectives of nurses in the processes involved in introducing and maintaining technology in homecare. Most of the research has been conducted regarding the experiences of patient experience and how informal caregivers perceive their role in using medical technologies at home. The few studies that were found, demonstrate the need for more research focused on the experiences of nurses working with advanced technologies in the home. The same applies to research on training, instruction and education to use medical technologies, as in these areas too, there was limited available research so here again there is need for further research. Despite the fact that most adverse events with medical technologies in home settings are caused by human factors, our findings also identified a lack of research in this area for nurses.

This study demonstrates that, although there is increasing attention on and recognition of the need for the use of medical technologies in the environment of the home, the research has not kept pace with the advances in care. Subjects such as user experiences of nurses with different technologies, training, instruction and education of nurses and human factors by nurses in risk management and patient safety urgently need to be investigated by further research.

Abbreviations

Automatic external defibrillator

Advanced medical technology

Continuous positive airway pressure

European Commission

Information technology

National Center for Health Statistics

Negative pressure ventilation

Peripherally inserted central catheters

Randomized Control Trial

National Institute for Public Health and the Environment

Ultraviolet

Ventricular assist device

World Health Organization

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Acknowledgements

The authors thank Ronnie van de Riet, head of the Medical Technical Care Team of the hospital ZiekenhuisGroep Twente, for his time and commitment to this project.

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All authors meet the criteria for authorship and all those entitled to authorship are listed as authors. ITH made the conception and design of the study; acquisition, analysis and interpretation of data; and drafting the article. SBA and WVH have made substantial contributions to the conception and design of the study; the analysis and interpretation of data; and revising the article critically for important intellectual content. All authors have approved the final article, this submission and its publication.

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Ingrid ten Haken is researcher in the research group Technology, Health & Care at Saxion University of Applied Sciences, Enschede, The Netherlands. Somaya Ben Allouch is head of the research group. Wim van Harten is professor at the University of Twente, Faculty Behavioural, Management and Social Sciences, department Health Technology & Services Research and CEO of Rijnstate general hospital, Arnhem, The Netherlands.

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ten Haken, I., Ben Allouch, S. & van Harten, W.H. The use of advanced medical technologies at home: a systematic review of the literature. BMC Public Health 18 , 284 (2018). https://doi.org/10.1186/s12889-018-5123-4

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Received : 03 October 2017

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Published : 26 February 2018

DOI : https://doi.org/10.1186/s12889-018-5123-4

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