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 ◽  
...  

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.

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Kathleen Murphy ◽  
Erica Di Ruggiero ◽  
Ross Upshur ◽  
Donald J. Willison ◽  
Neha Malhotra ◽  
...  

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.


2020 ◽  
Author(s):  
Kathleen Murphy ◽  
Erica Di Ruggiero ◽  
Ross Upshur ◽  
Donald J. Willison ◽  
Neha Malhotra ◽  
...  

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.


2021 ◽  
Author(s):  
Kathleen Murphy ◽  
Erica Di Ruggiero ◽  
Ross Upshur ◽  
Donald J. Willison ◽  
Neha Malhotra ◽  
...  

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.


2020 ◽  
Author(s):  
Kathleen Murphy ◽  
Erica Di Ruggiero ◽  
Ross Upshur ◽  
Donald J. Willison ◽  
Neha Malhotra ◽  
...  

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 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.


Author(s):  
Amal Chakraborty ◽  
Mark Daniel ◽  
Natasha J. Howard ◽  
Alwin Chong ◽  
Nicola Slavin ◽  
...  

The high prevalence of preventable infectious and chronic diseases in Australian Indigenous populations is a major public health concern. Existing research has rarely examined the role of built and socio-political environmental factors relating to remote Indigenous health and wellbeing. This research identified built and socio-political environmental indicators from publicly available grey literature documents locally-relevant to remote Indigenous communities in the Northern Territory (NT), Australia. Existing planning documents with evidence of community input were used to reduce the response burden on Indigenous communities. A scoping review of community-focused planning documents resulted in the identification of 1120 built and 2215 socio-political environmental indicators. Indicators were systematically classified using an Indigenous indicator classification system (IICS). Applying the IICS yielded indicators prominently featuring the “community infrastructure” domain within the built environment, and the “community capacity” domain within the socio-political environment. This research demonstrates the utility of utilizing existing planning documents and a culturally appropriate systematic classification system to consolidate environmental determinants that influence health and disease occurrence. The findings also support understanding of which features of community-level built and socio-political environments amenable to public health and social policy actions might be targeted to help reduce the prevalence of infectious and chronic diseases in Indigenous communities.


BMJ Open ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. e046177
Author(s):  
Julie Polisena ◽  
Maria Ospina ◽  
Omolara Sanni ◽  
Brittany Matenchuk ◽  
Rachel Livergant ◽  
...  

ObjectiveThe main objectives of this study were to synthesise and compare pandemic preparedness strategies issued by the federal and provincial/territorial (P/T) governments in Canada and to assess whether COVID-19 public health (PH) measures were tailored towards priority populations, as defined by relevant social determinants of health.MethodsThis scoping review searched federal and P/T websites on daily COVID-19 pandemic preparedness strategies between 30 January and 30 April 2020. The PROGRESS-Plus equity-lens framework was used to define priority populations. All definitions, policies and guidelines of PH strategies implemented by the federal and P/T governments to reduce risk of SARS-CoV-2 transmission were included. PH measures were classified using a modified Public Health Agency of Canada Framework for Canadian Pandemic Influenza Preparedness.ResultsA total of 722 COVID-19 PH measures were issued during the study period. Of these, home quarantine (voluntary) (n=13.0%; 94/722) and retail/commerce restrictions (10.9%; n=79/722) were the most common measures introduced. Many of the PH orders, including physical distancing, cancellation of mass gatherings, school closures or retail/commerce restrictions began to be introduced after 11 March 2020. Lifting of some of the PH orders in phases to reopen the economy began in April 2020 (6.5%; n=47/722). The majority (68%, n=491/722) of COVID-19 PH announcements were deemed mandatory, while 32% (n=231/722) were recommendations. Several PH measures (28.0%, n=202/722) targeted a variety of groups at risk of socially produced health inequalities, such as age, religion, occupation and migration status.ConclusionsMost PH measures centred on limiting contact between people who were not from the same household. PH measures were evolutionary in nature, reflecting new evidence that emerged throughout the pandemic. Although ~30% of all implemented COVID-19 PH measures were tailored towards priority groups, there were still unintended consequences on these populations.


2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Ashley Weeks ◽  
Lisa Waddell ◽  
Andrea Nwosu ◽  
Christina Bancej ◽  
Shalini Desai ◽  
...  

Objective: To create a scoping review on enterovirus D-68 (EV-D68) that will serve as a useful tool to guide future research with the aim of filling critical information gaps and supporting the development of public health preparedness activities.Introduction: EV-D68 is a non-polio enterovirus, primarily resulting in respiratory illness, with clinical symptoms ranging from mild to severe. Infection has also been associated with severe neurological conditions like acute flaccid myelitis (AFM). EV-D68 was first discovered in 1962, with infrequent case reports until 2014 at which point a widespread multi-national outbreak mostly affecting the pediatric population occurred across North America, Europe, Southeast Asia and Africa. This outbreak was associated with an increase in AFM, with cases being reported in Canada, the United States, Norway, and France. With this new and emerging threat, public health and other organizations were called upon to implement response measures such as establishment of case definitions, surveillance mechanisms, and recommendations for clinical and public health management. The response to the 2014 outbreak in Canada highlighted several important EV-D68 evidence gaps including a lack of risk factor and clinical information available for non-severe cases, and uncertainty around seasonal, cyclical and secular trends. Given the increased reporting of EV-D68 cases associated with severe outcomes, it's critical that public health establishes what is known about EV-D68 in order to support decision-making, education and other preparedness activities and to highlight priority areas for future research to fill critical knowledge gaps. Scoping reviews provide a reproducible and updateable synthesis research methodology to identify and characterise all the literature on a broad topic as a means to highlight where evidence exists and where there are knowledge gaps. In order to systematically characterise the EV-D68 knowledge base, a scoping review was conducted to map the current body of evidence.Methods: A literature search of published and grey literature on EV-D68 was conducted on May 1, 2017. A standardized search algorithm was implemented in four bibliographic databases: Medline, Embase, Global Health and Scopus. Relevant grey literature was sought from a prioriidentified sources: the World Health Organization, United States Centers for Disease Control and Prevention, the Public Health Agency of Canada, the European Centre for Disease Prevention and Control, and thesis registries. Two-level relevance screening (title/abstract followed by full-text) was performed in duplicate by two independent reviewers using pretested screening forms. Conflicts between the reviewers were reconciled following group discussion with the study team. English and French articles were included if they reported on EV-D68 as an outcome. There were no limitations by date, publication type, geography or study design. Conference abstracts were excluded if they did not provide sufficient outcome information to characterize. The articles were then characterized by two independent reviewers using a pretested study characterization form. The descriptive characteristics of each article were extracted and categorized into one of the following broad topic categories: 1) Epidemiology and Public Health, 2) Clinical and Infection Prevention and Control (IPC), 3) Guidance Products, 4) Public Health Surveillance, 5) Laboratory, and 6) Impact. The Epidemiology and Public Health category contained citations describing prevalence, epidemiological distribution, outbreak data and public health mitigation strategies. Clinical and IPC citations included details regarding symptoms of EV-D68 infection, patient outcomes, clinical investigation processes, treatment options and infection prevention and control strategies. The Guidance category included citations that assess risk, provide knowledge translation or provide practice guidelines. Public Health Surveillance citations provided details on surveillance systems. Citations in the laboratory category included studies that assessed the genetic characteristics of circulating EV-D68 (phylogeny, taxonomy) and viral characteristics (proteins, viral properties). Lastly, the Impact category contained citations describing the social, economic and resource burden of EV-D68 infection. Each broad topic category was subsequently characterised further into subtopics.Results: The search yielded a total of 384 citations, of which 300 met the inclusion criteria. Twenty-six of forty-three potentially relevant grey literature sources were also included. Preliminary literature characterization suggests that the majority of the published literature fell under the topic categories of Epidemiology, Clinical, and Laboratory. There were limited published articles on public health guidance, IPC, surveillance systems and the impact of EV-D68. The grey literature primarily consisted of webpages directed towards the public (what EV-D68 is, how to prevent it, what to do if ill, etc.). This scoping review work is presently underway and a summary of the full results will be presented at the 2018 Annual Conference.Conclusions: The body of literature on EV-D68 has increased since the 2014 outbreak, but overall remains small and contains knowledge gaps in some areas. To our knowledge, this scoping review is the first to classify the entirety of literature relating to EV-D68. It will serve as a useful tool to guide future research with the aim of filling critical information gaps, and supporting development of public health preparedness activities.


JMIR Nursing ◽  
10.2196/23933 ◽  
2021 ◽  
Vol 4 (1) ◽  
pp. e23933
Author(s):  
Christine Buchanan ◽  
M Lyndsay Howitt ◽  
Rita Wilson ◽  
Richard G Booth ◽  
Tracie Risling ◽  
...  

Background It is predicted that artificial intelligence (AI) will transform nursing across all domains of nursing practice, including administration, clinical care, education, policy, and research. Increasingly, researchers are exploring the potential influences of AI health technologies (AIHTs) on nursing in general and on nursing education more specifically. However, little emphasis has been placed on synthesizing this body of literature. Objective A scoping review was conducted to summarize the current and predicted influences of AIHTs on nursing education over the next 10 years and beyond. Methods This scoping review followed a previously published protocol from April 2020. Using an established scoping review methodology, the databases of MEDLINE, Cumulative Index to Nursing and Allied Health Literature, Embase, PsycINFO, Cochrane Database of Systematic Reviews, Cochrane Central, Education Resources Information Centre, Scopus, Web of Science, and Proquest were searched. In addition to the use of these electronic databases, a targeted website search was performed to access relevant grey literature. Abstracts and full-text studies were independently screened by two reviewers using prespecified inclusion and exclusion criteria. Included literature focused on nursing education and digital health technologies that incorporate AI. Data were charted using a structured form and narratively summarized into categories. Results A total of 27 articles were identified (20 expository papers, six studies with quantitative or prototyping methods, and one qualitative study). The population included nurses, nurse educators, and nursing students at the entry-to-practice, undergraduate, graduate, and doctoral levels. A variety of AIHTs were discussed, including virtual avatar apps, smart homes, predictive analytics, virtual or augmented reality, and robots. The two key categories derived from the literature were (1) influences of AI on nursing education in academic institutions and (2) influences of AI on nursing education in clinical practice. Conclusions Curricular reform is urgently needed within nursing education programs in academic institutions and clinical practice settings to prepare nurses and nursing students to practice safely and efficiently in the age of AI. Additionally, nurse educators need to adopt new and evolving pedagogies that incorporate AI to better support students at all levels of education. Finally, nursing students and practicing nurses must be equipped with the requisite knowledge and skills to effectively assess AIHTs and safely integrate those deemed appropriate to support person-centered compassionate nursing care in practice settings. International Registered Report Identifier (IRRID) RR2-10.2196/17490


2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Anna-Katharina Heuschen ◽  
Guangyu Lu ◽  
Oliver Razum ◽  
Alhassan Abdul-Mumin ◽  
Osman Sankoh ◽  
...  

Abstract Background The COVID-19 pandemic has resulted in unprecedented challenges to health systems worldwide, including the control of non-COVID-19 diseases. Malaria cases and deaths may increase due to the direct and indirect effects of the pandemic in malaria-endemic countries, particularly in sub-Saharan Africa (SSA). This scoping review aims to summarize information on public health-relevant effects of the COVID-19 pandemic on the malaria situation in SSA. Methods Review of publications and manuscripts on preprint servers, in peer-reviewed journals and in grey literature documents from 1 December, 2019 to 9 June, 2021. A structured search was conducted on different databases using predefined eligibility criteria for the selection of articles. Results A total of 51 papers have been included in the analysis. Modelling papers have predicted a significant increase in malaria cases and malaria deaths in SSA due to the effects of the COVID-19 pandemic. Many papers provided potential explanations for expected COVID-19 effects on the malaria burden; these ranged from relevant diagnostical and clinical aspects to reduced access to health care services, impaired availability of curative and preventive commodities and medications, and effects on malaria prevention campaigns. Compared to previous years, fewer country reports provided data on the actual number of malaria cases and deaths in 2020, with mixed results. While highly endemic countries reported evidence of decreased malaria cases in health facilities, low endemic countries reported overall higher numbers of malaria cases and deaths in 2020. Conclusions The findings from this review provide evidence for a significant but diverse impact of the COVID-19 pandemic on malaria in SSA. There is the need to further investigate the public health consequences of the COVID-19 pandemic on the malaria burden. Protocol registered on Open Science Framework: https://doi.org/10.17605/OSF.IO/STQ9D


2021 ◽  
Vol 136 (1_suppl) ◽  
pp. 40S-46S
Author(s):  
Benjamin D. Hallowell ◽  
Laura C. Chambers ◽  
Jason Rhodes ◽  
Melissa Basta ◽  
Samara Viner-Brown ◽  
...  

Objective No case definition exists that allows public health authorities to accurately identify opioid overdoses using emergency medical services (EMS) data. We developed and evaluated a case definition for suspected nonfatal opioid overdoses in EMS data. Methods To identify suspected opioid overdose–related EMS runs, in 2019 the Rhode Island Department of Health (RIDOH) developed a case definition using the primary impression, secondary impression, selection of naloxone in the dropdown field for medication given, indication of medication response in a dropdown field, and keyword search of the report narrative. We developed the case definition with input from EMS personnel and validated it using an iterative process of random medical record review. We used naloxone administration in consideration with other factors to avoid misclassification of opioid overdoses. Results In 2018, naloxone was administered during 2513 EMS runs in Rhode Island, of which 1501 met our case definition of a nonfatal opioid overdose. Based on a review of 400 randomly selected EMS runs in which naloxone was administered, the RIDOH case definition accurately identified 90.0% of opioid overdoses and accurately excluded 83.3% of non–opioid overdose–related EMS runs. Use of the case definition enabled analyses that identified key patterns in overdose locations, people who experienced repeat overdoses, and the creation of hotspot maps to inform outbreak detection and response. Practice Implications EMS data can be an effective tool for monitoring overdoses in real time and informing public health practice. To accurately identify opioid overdose–related EMS runs, the use of a comprehensive case definition is essential.


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