scholarly journals Applications of digital health for public health responses to COVID-19: a systematic scoping review of artificial intelligence, telehealth and related technologies

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Dinesh Visva Gunasekeran ◽  
Rachel Marjorie Wei Wen Tseng ◽  
Yih-Chung Tham ◽  
Tien Yin Wong
Keyword(s):  
Public Health ◽  
Artificial Intelligence ◽  
Scoping Review ◽  
Real World ◽  
Digital Health ◽  
Large Body ◽  
Healthcare Services ◽  
Digital Platforms ◽  
Medical Needs ◽  
Wide Range

AbstractThe coronavirus disease 2019 (COVID-19) pandemic has overwhelmed healthcare services, faced with the twin challenges in acutely meeting the medical needs of patients with COVID-19 while continuing essential services for non-COVID-19 illnesses. The need to re-invent, re-organize and transform healthcare and co-ordinate clinical services at a population level is urgent as countries that controlled initial outbreaks start to experience resurgences. A wide range of digital health solutions have been proposed, although the extent of successful real-world applications of these technologies is unclear. This study aims to review applications of artificial intelligence (AI), telehealth, and other relevant digital health solutions for public health responses in the healthcare operating environment amidst the COVID-19 pandemic. A systematic scoping review was performed to identify potentially relevant reports. Key findings include a large body of evidence for various clinical and operational applications of telehealth (40.1%, n = 99/247). Although a large quantity of reports investigated applications of artificial intelligence (AI) (44.9%, n = 111/247) and big data analytics (36.0%, n = 89/247), weaknesses in study design limit generalizability and translation, highlighting the need for more pragmatic real-world investigations. There were also few descriptions of applications for the internet of things (IoT) (2.0%, n = 5/247), digital platforms for communication (DC) (10.9%, 27/247), digital solutions for data management (DM) (1.6%, n = 4/247), and digital structural screening (DS) (8.9%, n = 22/247); representing gaps and opportunities for digital public health. Finally, the performance of digital health technology for operational applications related to population surveillance and points of entry have not been adequately evaluated.

scholarly journals Artificial intelligence for good health: a scoping review of the ethics literature

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Kathleen Murphy ◽  
Erica Di Ruggiero ◽  
Ross Upshur ◽  
Donald J. Willison ◽  
Neha Malhotra ◽  
...  
Keyword(s):  
Public Health ◽  
Artificial Intelligence ◽  
Global Health ◽  
Population Health ◽  
Health Systems ◽  
Scoping Review ◽  
Industrial Revolution ◽  
Ethical Issues ◽  
Grey Literature ◽  
Ethical Implications

Abstract Background Artificial intelligence (AI) has been described as the “fourth industrial revolution” with transformative and global implications, including in healthcare, public health, and global health. AI approaches hold promise for improving health systems worldwide, as well as individual and population health outcomes. While AI may have potential for advancing health equity within and between countries, we must consider the ethical implications of its deployment in order to mitigate its potential harms, particularly for the most vulnerable. This scoping review addresses the following question: What ethical issues have been identified in relation to AI in the field of health, including from a global health perspective? Methods Eight electronic databases were searched for peer reviewed and grey literature published before April 2018 using the concepts of health, ethics, and AI, and their related terms. Records were independently screened by two reviewers and were included if they reported on AI in relation to health and ethics and were written in the English language. Data was charted on a piloted data charting form, and a descriptive and thematic analysis was performed. Results Upon reviewing 12,722 articles, 103 met the predetermined inclusion criteria. The literature was primarily focused on the ethics of AI in health care, particularly on carer robots, diagnostics, and precision medicine, but was largely silent on ethics of AI in public and population health. The literature highlighted a number of common ethical concerns related to privacy, trust, accountability and responsibility, and bias. Largely missing from the literature was the ethics of AI in global health, particularly in the context of low- and middle-income countries (LMICs). Conclusions The ethical issues surrounding AI in the field of health are both vast and complex. While AI holds the potential to improve health and health systems, our analysis suggests that its introduction should be approached with cautious optimism. The dearth of literature on the ethics of AI within LMICs, as well as in public health, also points to a critical need for further research into the ethical implications of AI within both global and public health, to ensure that its development and implementation is ethical for everyone, everywhere.

scholarly journals Leveraging artificial intelligence for pandemic preparedness and response: a scoping review to identify key use cases

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Ania Syrowatka ◽  
Masha Kuznetsova ◽  
Ava Alsubai ◽  
Adam L. Beckman ◽  
Paul A. Bain ◽  
...  
Keyword(s):  
Public Health ◽  
Artificial Intelligence ◽  
Real Time ◽  
Scoping Review ◽  
Grey Literature ◽  
Use Cases ◽  
Outbreak Detection ◽  
Response To Treatment ◽  
Pandemic Preparedness ◽  
Modeling Approaches

AbstractArtificial intelligence (AI) represents a valuable tool that could be widely used to inform clinical and public health decision-making to effectively manage the impacts of a pandemic. The objective of this scoping review was to identify the key use cases for involving AI for pandemic preparedness and response from the peer-reviewed, preprint, and grey literature. The data synthesis had two parts: an in-depth review of studies that leveraged machine learning (ML) techniques and a limited review of studies that applied traditional modeling approaches. ML applications from the in-depth review were categorized into use cases related to public health and clinical practice, and narratively synthesized. One hundred eighty-three articles met the inclusion criteria for the in-depth review. Six key use cases were identified: forecasting infectious disease dynamics and effects of interventions; surveillance and outbreak detection; real-time monitoring of adherence to public health recommendations; real-time detection of influenza-like illness; triage and timely diagnosis of infections; and prognosis of illness and response to treatment. Data sources and types of ML that were useful varied by use case. The search identified 1167 articles that reported on traditional modeling approaches, which highlighted additional areas where ML could be leveraged for improving the accuracy of estimations or projections. Important ML-based solutions have been developed in response to pandemics, and particularly for COVID-19 but few were optimized for practical application early in the pandemic. These findings can support policymakers, clinicians, and other stakeholders in prioritizing research and development to support operationalization of AI for future pandemics.

Development of the Key Performance Indicators for Digital Health Interventions: A Scoping Review (Preprint)

2021 ◽  
Author(s):  
Maria Brenner ◽  
Arielle Weir ◽  
Margaret McCann ◽  
Carmel Doyle ◽  
Mary Hughes ◽  
...  
Keyword(s):  
Scoping Review ◽  
Performance Indicators ◽  
Delphi Study ◽  
Current Knowledge ◽  
Digital Health ◽  
Stakeholder Involvement ◽  
Key Performance Indicators ◽  
Healthcare Services ◽  
Health Interventions ◽  
Original Research

BACKGROUND Development of the Key Performance Indicators for Digital Health Interventions: A Scoping Review OBJECTIVE Digital health interventions (DHIs) offer new methods for delivering healthcare, with the potential to innovate healthcare services. Key performance indicators (KPIs) play a role in evaluation, measurement, and improvement in healthcare quality and service performance. The scoping review question was developed following an initial search to identify literature to assist in the development of KPIs for an ongoing DHI initiative. During the initial search, it became clear that there was limited literature on how to develop specific and measurable KPIs that evaluate DHIs. The aim of this scoping review was to identify current knowledge and evidence surrounding the development of KPIs for DHIs. METHODS A rigorous literature search was conducted across ten key databases: AMED - The Allied and Complementary Medicine Database, CINAHL Complete, Health Source: Nursing/Academic Edition, MEDLINE, APA PsycINFO, EMBASE, EBM Reviews - Cochrane Database of Systematic Reviews, EBM Reviews - Database of Abstracts of Reviews of Effects, EBM Reviews - Health Technology Assessment, and IEEE Xplore. A descriptive summary of the literature was performed, and thematic analysis identified important or reoccurring themes. RESULTS Five references (representing four unique publications) were eligible for the review. Of the four included publications, two were articles on original research studies of a specific DHI, and two were overviews of methods for developing DHIs (not specific to a single DHI). All the included reports discussed the involvement of stakeholders in developing KPIs for DHIs. The step of identifying and defining the KPIs was completed using various methodologies, but all centered on a form of stakeholder involvement. Potential options for stakeholder involvement for KPI identification include the use of an elicitation framework, a factorial survey approach, or a Delphi study. Most of the included articles recognised the lack of literature relating to KPI development for DHIs, compared to the breath of literature available on the development of KPIs in other fields like health or informatics CONCLUSIONS Few articles were identified, highlighting a significant gap in the evidence-based knowledge in this domain. All the included articles discussed the involvement of stakeholders in developing KPIs for DHIs, which was performed using various methodologies. The articles acknowledged a lack of literature related to KPI development for DHIs. To allow comparability between KPI initiatives and facilitate work in the field, further research would be beneficial to develop a common methodology for KPI development for DHIs.

Discovering User Preferences for Digital Health in Well-being Apps (Preprint)

2021 ◽  
Author(s):  
Neta Kela ◽  
Eleanor Eytam ◽  
Adi Katz
Keyword(s):  
Public Health ◽  
Digital Health ◽  
New Technology ◽  
Well Being ◽  
Major Product ◽  
User Preferences ◽  
Healthcare Organizations ◽  
Wide Range ◽  
Range Of Functions ◽  
Mhealth Apps

UNSTRUCTURED The desire for healthcare organizations to reduce the cost of chronic care and to prevent disease from occurring to begin with, has coincided with the development of new technology that is revolutionizing digital health. Numerous health-oriented mobile phone applications (referred to as mHealth apps) have been developed and are available for download into smartphones. These mHealth apps serve a wide range of functions. There are apps that monitor data to treat or avoid chronic illness; apps for managing daily activities and diet; apps promoting healthy choices for people who want to maintain and improve their overall health, and many others. While it is generally recognized that mHealth apps have a significant potential for promoting public health, little research has been done to determine user preferences for such apps. Understanding what users want in their mHealth apps can help increase their acceptability and encourage healthy lifestyles. The research in this article tests the major product qualities of such apps, asking two key questions: Do users seek interaction with a live physician, or are they willing to rely on artificial intelligence to analyze data from their app? Which aspects of their app do they consider as having a positive instrumental, aesthetic, or symbolic value? Next, the research presented here tests how these judgments influence product preference. The contribution of this paper is its focus on user preferences which may help in the design of mHealth apps to better address peoples’ needs—thus encouraging a wide, frequent, and effective use of such tools which promote public health.

Human-Compatible Artificial Intelligence

2021 ◽  
pp. 3-23
Author(s):  
Stuart Russell
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Standard Model ◽  
Real World ◽  
Technical Aspects ◽  
Alan Turing ◽  
Wide Range ◽  
The Standard Model ◽  
Formal Class

Following the analysis given by Alan Turing in 1951, one must expect that AI capabilities will eventually exceed those of humans across a wide range of real-world-decision making scenarios. Should this be a cause for concern, as Turing, Hawking, and others have suggested? And, if so, what can we do about it? While some in the mainstream AI community dismiss the issue, I will argue that the problem is real: we have to work out how to design AI systems that are far more powerful than ourselves while ensuring that they never have power over us. I believe the technical aspects of this problem are solvable. Whereas the standard model of AI proposes to build machines that optimize known, exogenously specified objectives, a preferable approach would be to build machines that are of provable benefit to humans. I introduce assistance games as a formal class of problems whose solution, under certain assumptions, has the desired property.

scholarly journals The digital transformation of the healthcare industry: exploring the rise of emerging platform ecosystems and their influence on the role of patients

2020 ◽  
Vol 13 (3) ◽  
pp. 1033-1069
Author(s):  
Sebastian Hermes ◽  
Tobias Riasanow ◽  
Eric K. Clemons ◽  
Markus Böhm ◽  
Helmut Krcmar
Keyword(s):  
Digital Health ◽  
Academic Research ◽  
Digital Transformation ◽  
Healthcare Industry ◽  
Value Capture ◽  
Digital Platforms ◽  
Systems Research ◽  
Wide Range ◽  
Platform Ecosystems

AbstractWhile traditional organizations create value within the boundaries of their firm or supply chain, digital platforms leverage and orchestrate a platform-mediated ecosystem to create and co-create value with a much wider array of partners and actors. Although the change to two-sided markets and their generalization to platform ecosystems have been adopted among various industries, both academic research and industry adoption have lagged behind in the healthcare industry. To the best of our knowledge current Information Systems research has not yet incorporated an interorganizational perspective of the digital transformation of healthcare. This neglects a wide range of emerging changes, including changing segmentation of industry market participants, changing patient segments, changing patient roles as decision makers, and their interaction in patient care. This study therefore investigates the digital transformation of the healthcare industry by analyzing 1830 healthcare organizations found on Crunchbase. We derived a generic value ecosystem of the digital healthcare industry and validated our findings with industry experts from the traditional and the start-up healthcare domains. The results indicate 8 new roles within healthcare, namely: information platforms, data collection technology, market intermediaries, services for remote and on-demand healthcare, augmented and virtual reality provider, blockchain-based PHR, cloud service provider, and intelligent data analysis for healthcare provider. Our results further illustrate how these roles transform value proposition, value capture, and value delivery in the healthcare industry. We discuss competition between new entrants and incumbents and elaborate how digital health innovations contribute to the changing role of patients.

scholarly journals Artificial Intelligence for Good Health: A Scoping Review of the Ethics Literature

2020 ◽  
Author(s):  
Kathleen Murphy ◽  
Erica Di Ruggiero ◽  
Ross Upshur ◽  
Donald J. Willison ◽  
Neha Malhotra ◽  
...  
Keyword(s):  
Public Health ◽  
Artificial Intelligence ◽  
Global Health ◽  
Population Health ◽  
Health Systems ◽  
Scoping Review ◽  
Industrial Revolution ◽  
Ethical Issues ◽  
Grey Literature ◽  
Ethical Implications

Abstract Background Artificial intelligence (AI) has been described as the “fourth industrial revolution” with transformative and global implications, including in healthcare, public health, and global health. AI approaches hold promise for improving health systems worldwide, as well as individual and population health outcomes. While AI may have the potential to advance health equity within and between countries, we must consider the ethical implications of its deployment in order to mitigate its potential harms, particularly for the most vulnerable. This scoping review addresses the following question: What ethical issues have been identified in relation to AI in the field of health, including from a global health perspective? Methods Eight electronic databases were searched for peer reviewed and grey literature using the overarching concepts of health, ethics, and AI, and their related terms. Records were independently screened by two reviewers and were included if they reported on AI in relation to health and ethics and were written in the English language. Data was charted on a piloted data abstraction form, and a descriptive and thematic analysis was performed. Results Upon reviewing 12,722 articles, 103 met the predetermined inclusion criteria. The literature was primarily focused on the ethics of AI in health care, particularly on carer robots, diagnostics, and precision medicine, but was largely silent on ethics of AI in public and population health. The literature highlighted a number of common ethical concerns related to privacy, trust, accountability, and bias. Largely missing from the reviewed literature was the ethics of AI in global health, particularly in the context of low- and middle-income countries (LMICs). Conclusions The ethical issues surrounding AI in the field of health are both vast and complex. While AI holds the potential to improve health and health systems, our analysis suggests that its introduction should be approached with cautious optimism. The dearth of literature on the ethics of AI within LMICs, as well as in public health, also points to a critical need for further research into the ethical implications of AI within both global and public health, to ensure that its development and implementation is ethical for everyone, everywhere.

scholarly journals Role of Digital Health and Artificial Intelligence in Inflammatory Bowel Disease: A Scoping Review

Genes ◽  
2021 ◽  
Vol 12 (10) ◽  
pp. 1465
Author(s):  
Kamila Majidova ◽  
Julia Handfield ◽  
Kamran Kafi ◽  
Ryan D. Martin ◽  
Ryszard Kubinski
Keyword(s):  
Artificial Intelligence ◽  
Scoping Review ◽  
Digital Health ◽  
Medication Management ◽  
Standard Of Care ◽  
Preliminary Evidence ◽  
Clinical Environment ◽  
Inflammatory Bowel ◽  
The Impact

Inflammatory bowel diseases (IBD), subdivided into Crohn’s disease (CD) and ulcerative colitis (UC), are chronic diseases that are characterized by relapsing and remitting periods of inflammation in the gastrointestinal tract. In recent years, the amount of research surrounding digital health (DH) and artificial intelligence (AI) has increased. The purpose of this scoping review is to explore this growing field of research to summarize the role of DH and AI in the diagnosis, treatment, monitoring and prognosis of IBD. A review of 21 articles revealed the impact of both AI algorithms and DH technologies; AI algorithms can improve diagnostic accuracy, assess disease activity, and predict treatment response based on data modalities such as endoscopic imaging and genetic data. In terms of DH, patients utilizing DH platforms experienced improvements in quality of life, disease literacy, treatment adherence, and medication management. In addition, DH methods can reduce the need for in-person appointments, decreasing the use of healthcare resources without compromising the standard of care. These articles demonstrate preliminary evidence of the potential of DH and AI for improving the management of IBD. However, the majority of these studies were performed in a regulated clinical environment. Therefore, further validation of these results in a real-world environment is required to assess the efficacy of these methods in the general IBD population.

scholarly journals Artificial Intelligence for Good Health: A Scoping Review of the Ethics Literature

2021 ◽  
Author(s):  
Kathleen Murphy ◽  
Erica Di Ruggiero ◽  
Ross Upshur ◽  
Donald J. Willison ◽  
Neha Malhotra ◽  
...  
Keyword(s):  
Public Health ◽  
Artificial Intelligence ◽  
Global Health ◽  
Population Health ◽  
Health Systems ◽  
Scoping Review ◽  
Industrial Revolution ◽  
Ethical Issues ◽  
Grey Literature ◽  
Ethical Implications

Abstract Background: Artificial intelligence (AI) has been described as the “fourth industrial revolution” with transformative and global implications, including in healthcare, public health, and global health. AI approaches hold promise for improving health systems worldwide, as well as individual and population health outcomes. While AI may have potential for advancing health equity within and between countries, we must consider the ethical implications of its deployment in order to mitigate its potential harms, particularly for the most vulnerable. This scoping review addresses the following question: What ethical issues have been identified in relation to AI in the field of health, including from a global health perspective? Methods: Eight electronic databases were searched for peer reviewed and grey literature published before April 2018 using the concepts of health, ethics, and AI, and their related terms. Records were independently screened by two reviewers and were included if they reported on AI in relation to health and ethics and were written in the English language. Data was charted on a piloted data charting form, and a descriptive and thematic analysis was performed. Results: Upon reviewing 12,722 articles, 103 met the predetermined inclusion criteria. The literature was primarily focused on the ethics of AI in health care, particularly on carer robots, diagnostics, and precision medicine, but was largely silent on ethics of AI in public and population health. The literature highlighted a number of common ethical concerns related to privacy, trust, accountability and responsibility, and bias. Largely missing from the literature was the ethics of AI in global health, particularly in the context of low- and middle-income countries (LMICs). Conclusions: The ethical issues surrounding AI in the field of health are both vast and complex. While AI holds the potential to improve health and health systems, our analysis suggests that its introduction should be approached with cautious optimism. The dearth of literature on the ethics of AI within LMICs, as well as in public health, also points to a critical need for further research into the ethical implications of AI within both global and public health, to ensure that its development and implementation is ethical for everyone, everywhere.

scholarly journals Explainable artificial intelligence models using real-world electronic health record data: a systematic scoping review

2020 ◽  
Vol 27 (7) ◽  
pp. 1173-1185 ◽  
Author(s):  
Seyedeh Neelufar Payrovnaziri ◽  
Zhaoyi Chen ◽  
Pablo Rengifo-Moreno ◽  
Tim Miller ◽  
Jiang Bian ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Electronic Health Record ◽  
Scoping Review ◽  
Real World ◽  
Research Trend ◽  
Health Record ◽  
Electronic Health Record Data ◽  
Explainable Artificial Intelligence ◽  
Record Data ◽  
Electronic Health

Abstract Objective To conduct a systematic scoping review of explainable artificial intelligence (XAI) models that use real-world electronic health record data, categorize these techniques according to different biomedical applications, identify gaps of current studies, and suggest future research directions. Materials and Methods We searched MEDLINE, IEEE Xplore, and the Association for Computing Machinery (ACM) Digital Library to identify relevant papers published between January 1, 2009 and May 1, 2019. We summarized these studies based on the year of publication, prediction tasks, machine learning algorithm, dataset(s) used to build the models, the scope, category, and evaluation of the XAI methods. We further assessed the reproducibility of the studies in terms of the availability of data and code and discussed open issues and challenges. Results Forty-two articles were included in this review. We reported the research trend and most-studied diseases. We grouped XAI methods into 5 categories: knowledge distillation and rule extraction (N = 13), intrinsically interpretable models (N = 9), data dimensionality reduction (N = 8), attention mechanism (N = 7), and feature interaction and importance (N = 5). Discussion XAI evaluation is an open issue that requires a deeper focus in the case of medical applications. We also discuss the importance of reproducibility of research work in this field, as well as the challenges and opportunities of XAI from 2 medical professionals’ point of view. Conclusion Based on our review, we found that XAI evaluation in medicine has not been adequately and formally practiced. Reproducibility remains a critical concern. Ample opportunities exist to advance XAI research in medicine.

Sign in / Sign up

Export Citation Format

Share Document