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Stewart E, Such E. Evaluating participant experiences of Community Panels to scrutinise policy modelling for health inequalities: the SIPHER Consortium. RESEARCH INVOLVEMENT AND ENGAGEMENT 2024; 10:4. [PMID: 38191500 PMCID: PMC10775539 DOI: 10.1186/s40900-023-00521-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Accepted: 11/27/2023] [Indexed: 01/10/2024]
Abstract
Data-intensive research, including policy modelling, poses some distinctive challenges for efforts to mainstream public involvement into health research. There is a need for learning about how to design and deliver involvement for these types of research which are highly technical, and where researchers are at a distance from the people whose lives data depicts. This article describes our experiences involving members of the public in the SIPHER Consortium, a data-intensive policy modelling programme with researchers and policymakers working together over five years to try to address health inequalities. We focus on evaluating people's experiences as part of Community Panels for SIPHER. Key issues familiar from general public involvement efforts include practical details, careful facilitation of meetings, and payment for participants. We also describe some of the more particular learning around how to communicate technical research to non-academic audiences, in order to enable public scrutiny of research decisions. We conclude that public involvement in policy modelling can be meaningful and enjoyable, but that it needs to be carefully organised, and properly resourced.
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McDonald N, Little N, Kriellaars D, Doupe MB, Giesbrecht G, Pryce RT. Database quality assessment in research in paramedicine: a scoping review. Scand J Trauma Resusc Emerg Med 2023; 31:78. [PMID: 37951904 PMCID: PMC10638787 DOI: 10.1186/s13049-023-01145-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Accepted: 11/05/2023] [Indexed: 11/14/2023] Open
Abstract
BACKGROUND Research in paramedicine faces challenges in developing research capacity, including access to high-quality data. A variety of unique factors in the paramedic work environment influence data quality. In other fields of healthcare, data quality assessment (DQA) frameworks provide common methods of quality assessment as well as standards of transparent reporting. No similar DQA frameworks exist for paramedicine, and practices related to DQA are sporadically reported. This scoping review aims to describe the range, extent, and nature of DQA practices within research in paramedicine. METHODS This review followed a registered and published protocol. In consultation with a professional librarian, a search strategy was developed and applied to MEDLINE (National Library of Medicine), EMBASE (Elsevier), Scopus (Elsevier), and CINAHL (EBSCO) to identify studies published from 2011 through 2021 that assess paramedic data quality as a stated goal. Studies that reported quantitative results of DQA using data that relate primarily to the paramedic practice environment were included. Protocols, commentaries, and similar study types were excluded. Title/abstract screening was conducted by two reviewers; full-text screening was conducted by two, with a third participating to resolve disagreements. Data were extracted using a piloted data-charting form. RESULTS Searching yielded 10,105 unique articles. After title and abstract screening, 199 remained for full-text review; 97 were included in the analysis. Included studies varied widely in many characteristics. Majorities were conducted in the United States (51%), assessed data containing between 100 and 9,999 records (61%), or assessed one of three topic areas: data, trauma, or out-of-hospital cardiac arrest (61%). All data-quality domains assessed could be grouped under 5 summary domains: completeness, linkage, accuracy, reliability, and representativeness. CONCLUSIONS There are few common standards in terms of variables, domains, methods, or quality thresholds for DQA in paramedic research. Terminology used to describe quality domains varied among included studies and frequently overlapped. The included studies showed no evidence of assessing some domains and emerging topics seen in other areas of healthcare. Research in paramedicine would benefit from a standardized framework for DQA that allows for local variation while establishing common methods, terminology, and reporting standards.
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Affiliation(s)
- Neil McDonald
- Winnipeg Fire Paramedic Service, EMS Training, 2546 McPhillips St, Winnipeg, MB, R2P 2T2, Canada.
- Department of Emergency Medicine, Max Rady College of Medicine, University of Manitoba, S203 Medical Services Building, 750 Bannatyne Ave, Winnipeg, MB, R3E 0W2, Canada.
- Applied Health Sciences, University of Manitoba, 202 Active Living Centre, Winnipeg, MB, R3T 2N2, Canada.
| | - Nicola Little
- Winnipeg Fire Paramedic Service, EMS Training, 2546 McPhillips St, Winnipeg, MB, R2P 2T2, Canada
| | - Dean Kriellaars
- College of Rehabilitation Sciences, Rady Faculty of Health Sciences, University of Manitoba, 771 McDermot Ave, Winnipeg, MB, R3E 0T6, Canada
| | - Malcolm B Doupe
- Department of Community Health Sciences, Rady Faculty of Health Sciences, University of Manitoba, 750 Bannatyne Ave, Winnipeg, MB, R3E 0W2, Canada
| | - Gordon Giesbrecht
- Faculty of Kinesiology and Recreation Management, University of Manitoba, 102-420 University Crescent, Winnipeg, MB, R3T 2N2, Canada
| | - Rob T Pryce
- Department of Kinesiology and Applied Health, Gupta Faculty of Kinesiology, University of Winnipeg, 400 Spence St, Winnipeg, MB, R3B 2E9, Canada
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Teodorowski P, Gleason K, Gregory JJ, Martin M, Punjabi R, Steer S, Savasir S, Vema P, Murray K, Ward H, Chapko D. Participatory evaluation of the process of co-producing resources for the public on data science and artificial intelligence. RESEARCH INVOLVEMENT AND ENGAGEMENT 2023; 9:67. [PMID: 37580823 PMCID: PMC10426152 DOI: 10.1186/s40900-023-00480-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Accepted: 07/31/2023] [Indexed: 08/16/2023]
Abstract
BACKGROUND The growth of data science and artificial intelligence offers novel healthcare applications and research possibilities. Patients should be able to make informed choices about using healthcare. Therefore, they must be provided with lay information about new technology. A team consisting of academic researchers, health professionals, and public contributors collaboratively co-designed and co-developed the new resource offering that information. In this paper, we evaluate this novel approach to co-production. METHODS We used participatory evaluation to understand the co-production process. This consisted of creative approaches and reflexivity over three stages. Firstly, everyone had an opportunity to participate in three online training sessions. The first one focused on the aims of evaluation, the second on photovoice (that included practical training on using photos as metaphors), and the third on being reflective (recognising one's biases and perspectives during analysis). During the second stage, using photovoice, everyone took photos that symbolised their experiences of being involved in the project. This included a session with a professional photographer. At the last stage, we met in person and, using data collected from photovoice, built the mandala as a representation of a joint experience of the project. This stage was supported by professional artists who summarised the mandala in the illustration. RESULTS The mandala is the artistic presentation of the findings from the evaluation. It is a shared journey between everyone involved. We divided it into six related layers. Starting from inside layers present the following experiences (1) public contributors had space to build confidence in a new topic, (2) relationships between individuals and within the project, (3) working remotely during the COVID-19 pandemic, (4) motivation that influenced people to become involved in this particular piece of work, (5) requirements that co-production needs to be inclusive and accessible to everyone, (6) expectations towards data science and artificial intelligence that researchers should follow to establish public support. CONCLUSIONS The participatory evaluation suggests that co-production around data science and artificial intelligence can be a meaningful process that is co-owned by everyone involved.
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Affiliation(s)
| | - Kelly Gleason
- Imperial Cancer Research UK Lead Nurse, Department of Surgery and Cancer, Imperial College London, London, UK
| | - Jonathan J Gregory
- Computational Oncology Group, Department of Surgery and Cancer, Faculty of Medicine, Imperial College London, London, UK
| | - Martha Martin
- School of Primary Care and Public Health, Imperial College London, London, UK
| | | | | | | | | | - Kabelo Murray
- School of Public Health, Imperial College London, London, UK
- NIHR Applied Research Collaboration Northwest London, Imperial College London, London, UK
| | - Helen Ward
- School of Public Health, Imperial College London, London, UK
- NIHR Applied Research Collaboration Northwest London, Imperial College London, London, UK
- National Institute for Health Research Imperial Biomedical Research Centre, London, UK
| | - Dorota Chapko
- School of Public Health, Imperial College London, London, UK
- NIHR Applied Research Collaboration Northwest London, Imperial College London, London, UK
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Fisher L, Curtis HJ, Croker R, Wiedemann M, Speed V, Wood C, Brown A, Hopcroft LEM, Higgins R, Massey J, Inglesby P, Morton CE, Walker AJ, Morley J, Mehrkar A, Bacon S, Hickman G, Macdonald O, Lewis T, Wood M, Myers M, Samuel M, Conibere R, Baqir W, Sood H, Drury C, Collison K, Bates C, Evans D, Dillingham I, Ward T, Davy S, Smith RM, Hulme W, Green A, Parry J, Hester F, Harper S, Cockburn J, O'Hanlon S, Eavis A, Jarvis R, Avramov D, Griffiths P, Fowles A, Parkes N, MacKenna B, Goldacre B. Eleven key measures for monitoring general practice clinical activity during COVID-19: A retrospective cohort study using 48 million adults' primary care records in England through OpenSAFELY. eLife 2023; 12:e84673. [PMID: 37498081 PMCID: PMC10374277 DOI: 10.7554/elife.84673] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Accepted: 07/06/2023] [Indexed: 07/28/2023] Open
Abstract
Background The COVID-19 pandemic has had a significant impact on delivery of NHS care. We have developed the OpenSAFELY Service Restoration Observatory (SRO) to develop key measures of primary care activity and describe the trends in these measures throughout the COVID-19 pandemic. Methods With the approval of NHS England, we developed an open source software framework for data management and analysis to describe trends and variation in clinical activity across primary care electronic health record (EHR) data on 48 million adults.We developed SNOMED-CT codelists for key measures of primary care clinical activity such as blood pressure monitoring and asthma reviews, selected by an expert clinical advisory group and conducted a population cohort-based study to describe trends and variation in these measures January 2019-December 2021, and pragmatically classified their level of recovery one year into the pandemic using the percentage change in the median practice level rate. Results We produced 11 measures reflective of clinical activity in general practice. A substantial drop in activity was observed in all measures at the outset of the COVID-19 pandemic. By April 2021, the median rate had recovered to within 15% of the median rate in April 2019 in six measures. The remaining measures showed a sustained drop, ranging from a 18.5% reduction in medication reviews to a 42.0% reduction in blood pressure monitoring. Three measures continued to show a sustained drop by December 2021. Conclusions The COVID-19 pandemic was associated with a substantial change in primary care activity across the measures we developed, with recovery in most measures. We delivered an open source software framework to describe trends and variation in clinical activity across an unprecedented scale of primary care data. We will continue to expand the set of key measures to be routinely monitored using our publicly available NHS OpenSAFELY SRO dashboards with near real-time data. Funding This research used data assets made available as part of the Data and Connectivity National Core Study, led by Health Data Research UK in partnership with the Office for National Statistics and funded by UK Research and Innovation (grant ref MC_PC_20058).The OpenSAFELY Platform is supported by grants from the Wellcome Trust (222097/Z/20/Z); MRC (MR/V015757/1, MC_PC-20059, MR/W016729/1); NIHR (NIHR135559, COV-LT2-0073), and Health Data Research UK (HDRUK2021.000, 2021.0157).
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Affiliation(s)
- Louis Fisher
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Helen J Curtis
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Richard Croker
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Milan Wiedemann
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Victoria Speed
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Christopher Wood
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Andrew Brown
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Lisa E M Hopcroft
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Rose Higgins
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Jon Massey
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Peter Inglesby
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Caroline E Morton
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Alex J Walker
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Jessica Morley
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Amir Mehrkar
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Seb Bacon
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - George Hickman
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Orla Macdonald
- Oxford Health Foundation Trust, Warneford Hospital, Oxford, United Kingdom
| | - Tom Lewis
- Royal Devon University Healthcare NHS Foundation Trust, Barnstaple, United Kingdom
| | | | - Martin Myers
- Lancashire Teaching Hospitals NHS Foundation Trust, Chorley, United Kingdom
| | - Miriam Samuel
- Queen Mary University of London, London, United Kingdom
| | | | | | | | - Charles Drury
- Herefordshire and Worcestershire Health and Care NHS Trust, Worcester, United Kingdom
| | | | | | - David Evans
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Iain Dillingham
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Tom Ward
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Simon Davy
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Rebecca M Smith
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - William Hulme
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | - Amelia Green
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
| | | | | | | | | | | | | | | | | | | | | | | | - Brian MacKenna
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
- NHS England, London, United Kingdom
| | - Ben Goldacre
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, United Kingdom
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Teodorowski P, Rodgers SE, Fleming K, Tahir N, Ahmed S, Frith L. Exploring how to improve the involvement of Polish and South Asian communities around big data research. A qualitative study using COM-B model. Int J Popul Data Sci 2023; 8:2130. [PMID: 37670958 PMCID: PMC10476635 DOI: 10.23889/ijpds.v8i1.2130] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/07/2023] Open
Abstract
Introduction Involving public contributors helps researchers to ensure that public views are taken into consideration when designing and planning research, so that it is person-centred and relevant to the public. This paper will consider public involvement in big data research. Inclusion of different communities is needed to ensure everyone's voice is heard. However, there remains limited evidence on how to improve the involvement of seldom-heard communities in big data research. Objectives This study aims to understand how South Asians and Polish communities in the UK can be encouraged to participate in public involvement initiatives in big data research. Methods Forty interviews were conducted with Polish (n=20) and South Asian (n=20) participants on Zoom. The participants were living in the United Kingdom and had not previously been involved as public contributors. Transcribed interviews were analysed using reflexive thematic analysis. Results We identified eight themes. The 'happy to reuse data' theme sets the scene by exploring our participants' views towards big data research and under what circumstances they thought that data could be used. The remaining themes were mapped under the capability-opportunity-motivation-behaviour (COM-B) model, as developed by Michie and colleagues. This allowed us to discuss multiple factors that could influence people's willingness to become public contributors. Conclusions Our study is the first to explore how to improve the involvement and engagement of seldom-heard communities in big data research using the COM-B model. The results have the potential to support researchers who want to identify what can influence members of the public to be involved. By using the COM-B model, it is possible to determine what measures could be implemented to better engage these communities.
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Affiliation(s)
- Piotr Teodorowski
- Department of Public Health, Policy & Systems, University of Liverpool
| | - Sarah E. Rodgers
- Department of Public Health, Policy & Systems, University of Liverpool
| | - Kate Fleming
- National Disease Registration Service, NHS England
| | | | | | - Lucy Frith
- Centre for Social Ethics and Policy, University of Manchester
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Vinnicombe S, Bianchim MS, Noyes J. A review of reviews exploring patient and public involvement in population health research and development of tools containing best practice guidance. BMC Public Health 2023; 23:1271. [PMID: 37391764 PMCID: PMC10311710 DOI: 10.1186/s12889-023-15937-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Accepted: 05/19/2023] [Indexed: 07/02/2023] Open
Abstract
INTRODUCTION Patient and public involvement (PPI) is increasingly seen as something that is integral to research and of importance to research funders. There is general recognition that PPI is the right thing to do for both moral and practical reasons. The aim of this review of reviews is to examine how PPI can be done 'properly' by looking at the evidence that exists from published reviews and assessing it against the UK Standards for Public Involvement in Research, as well as examining the specific features of population health research that can make PPI more challenging. METHODS A review of reviews and development of best practice guidance was carried out following the 5-stage Framework Synthesis method. RESULTS In total 31 reviews were included. There is a lack of current research or clarity around Governance and Impact when findings are mapped against UK Standards for Public Involvement in Research. It was also clear that there is little knowledge around PPI with under-represented groups. There are gaps in knowledge about how to ensure key specific attributes of population health research are addressed for PPI team members - particularly around how to deal with complexity and the data-driven nature of the research. Four tools were produced for researchers and PPI members to further improve their PPI activity within population health research and health research more generally, including a framework of recommended actions to address PPI in population health research, and guidance on integrating PPI based on the UK Standards for Public Involvement in Research. CONCLUSIONS Facilitating PPI in population health research is challenging due to the nature of this type of research and there is far less evidence on how to do PPI well in this context. The tools can help researchers identify key aspects of PPI that can be integrated when designing PPI within projects. Findings also highlight specific areas where more research or discussion is needed.
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Affiliation(s)
- Soo Vinnicombe
- School of Medical and Health Sciences, Bangor University, Bangor, UK
| | - Mayara S Bianchim
- School of Medical and Health Sciences, Bangor University, Bangor, UK
| | - Jane Noyes
- School of Medical and Health Sciences, Bangor University, Bangor, UK.
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Curtis HJ, MacKenna B, Wiedemann M, Fisher L, Croker R, Morton CE, Inglesby P, Walker AJ, Morley J, Mehrkar A, Bacon SC, Hickman G, Evans D, Ward T, Davy S, Hulme WJ, Macdonald O, Conibere R, Lewis T, Myers M, Wanninayake S, Collison K, Drury C, Samuel M, Sood H, Cipriani A, Fazel S, Sharma M, Baqir W, Bates C, Parry J, Goldacre B. OpenSAFELY NHS Service Restoration Observatory 2: changes in primary care clinical activity in England during the COVID-19 pandemic. Br J Gen Pract 2023; 73:e318-e331. [PMID: 37068964 PMCID: PMC10131234 DOI: 10.3399/bjgp.2022.0301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Accepted: 10/14/2022] [Indexed: 02/04/2023] Open
Abstract
BACKGROUND The COVID-19 pandemic has disrupted healthcare activity across a broad range of clinical services. The NHS stopped non-urgent work in March 2020, later recommending services be restored to near-normal levels before winter where possible. AIM To describe changes in the volume and variation of coded clinical activity in general practice across six clinical areas: cardiovascular disease, diabetes, mental health, female and reproductive health, screening and related procedures, and processes related to medication. DESIGN AND SETTING With the approval of NHS England, a cohort study was conducted of 23.8 million patient records in general practice, in situ using OpenSAFELY. METHOD Common primary care activities were analysed using Clinical Terms Version 3 codes and keyword searches from January 2019 to December 2020, presenting median and deciles of code usage across practices per month. RESULTS Substantial and widespread changes in clinical activity in primary care were identified since the onset of the COVID-19 pandemic, with generally good recovery by December 2020. A few exceptions showed poor recovery and warrant further investigation, such as mental health (for example, for 'Depression interim review' the median occurrences across practices in December 2020 was down by 41.6% compared with December 2019). CONCLUSION Granular NHS general practice data at population-scale can be used to monitor disruptions to healthcare services and guide the development of mitigation strategies. The authors are now developing real-time monitoring dashboards for the key measures identified in this study, as well as further studies using primary care data to monitor and mitigate the indirect health impacts of COVID-19 on the NHS.
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Affiliation(s)
- Helen J Curtis
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford
| | - Brian MacKenna
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford
| | - Milan Wiedemann
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford
| | - Louis Fisher
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford
| | - Richard Croker
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford
| | - Caroline E Morton
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford
| | - Peter Inglesby
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford
| | - Alex J Walker
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford
| | - Jessica Morley
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford
| | - Amir Mehrkar
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford
| | - Sebastian Cj Bacon
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford
| | - George Hickman
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford
| | - David Evans
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford
| | - Tom Ward
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford
| | - Simon Davy
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford
| | - William J Hulme
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford
| | - Orla Macdonald
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford
| | | | - Tom Lewis
- Royal Devon University Healthcare NHS Foundation Trust, Barnstaple
| | - Martin Myers
- Lancashire Teaching Hospitals NHS Foundation Trust, Preston
| | | | | | - Charles Drury
- Herefordshire and Worcestershire Health and Care NHS Trust, Worcester
| | - Miriam Samuel
- Wolfson Institute of Population Health, Queen Mary University of London, London
| | - Harpreet Sood
- University College London Hospitals NHS Foundation Trust, London
| | | | - Seena Fazel
- Department of Psychiatry, University of Oxford, Oxford
| | - Manuj Sharma
- Department of Primary Care and Population Health, University College London, London
| | | | | | | | - Ben Goldacre
- The Bennett Institute for Applied Data Science, Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford
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Ford E, Rees-Roberts M, Stanley K, Goddard K, Giles S, Armes J, Ikhile D, Madzvamuse A, Spencer-Hughes V, George A, Farmer C, Cassell J. Understanding how to build a social licence for using novel linked datasets for planning and research in Kent, Surrey and Sussex: results of deliberative focus groups. Int J Popul Data Sci 2023; 5:2114. [PMID: 37671318 PMCID: PMC10476239 DOI: 10.23889/ijpds.v5i3.2114] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023] Open
Abstract
Introduction Digital programmes in the newly created NHS integrated care boards (ICBs) in the United Kingdom mean that curation and linkage of anonymised patient data is underway in many areas for the first time. In Kent, Surrey and Sussex (KSS), in Southeast England, public health teams want to use these datasets to answer strategic population health questions, but public expectations around use of patient data are unknown. Objectives We aimed to engage with citizens of KSS to gather their views and expectations of data linkage and re-use, through deliberative discussions. Methods We held five 3-hour deliberative focus groups with 79 citizens of KSS, presenting information about potential uses of data, safeguards, and mechanisms for public involvement in governance and decision making about datasets. After each presentation, participants discussed their views in facilitated small groups which were recorded, transcribed and analysed thematically. Results The focus groups generated 15 themes representing participants' views on the benefits, risks and values for safeguarding linked data. Participants largely supported use of patient data to improve health service efficiency and resource management, preventative services and out of hospital care, joined-up services and information flows. Most participants expressed concerns about data accuracy, breaches and hacking, and worried about commercial use of data. They suggested that transparency of data usage through audit trails and clear information about accountability, ensuring data re-use does not perpetuate stigma and discrimination, ongoing, inclusive and valued involvement of the public in dataset decision-making, and a commitment to building trust, would meet their expectations for responsible data use. Conclusions Participants were largely favourable about the proposed uses of patient linked datasets but expected a commitment to transparency and public involvement. Findings were mapped to previous tenets of social license and can be used to inform ICB digital programme teams on how to proceed with use of linked datasets in a trustworthy and socially acceptable way.
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Affiliation(s)
| | | | | | | | - Sarah Giles
- Digital Innovation Theme Public Advisor, NIHR ARC -KSS (Applied Research Collaboration Kent, Surrey, and Sussex)
| | - Jo Armes
- University of Surrey, Guildford, UK
| | | | - Anotida Madzvamuse
- University of Sussex, Brighton, UK
- University of British Columbia, Canada
| | | | | | - Chris Farmer
- Centre for Health Services Studies, University of Kent, Canterbury, Kent, UK
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Leung T, Verheij RA, Francke AL, Tomassen M, Houtzager M, Joling KJ, Oosterveld-Vlug MG. Setting up a Governance Framework for Secondary Use of Routine Health Data in Nursing Homes: Development Study Using Qualitative Interviews. J Med Internet Res 2023; 25:e38929. [PMID: 36696162 PMCID: PMC9909520 DOI: 10.2196/38929] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Revised: 09/07/2022] [Accepted: 11/25/2022] [Indexed: 01/26/2023] Open
Abstract
BACKGROUND In the nursing home sector, reusing routinely recorded data from electronic health records (EHRs) for knowledge development and quality improvement is still in its infancy. Trust in appropriate and responsible reuse is crucial for patients and nursing homes deciding whether to share EHR data for these purposes. A data governance framework determines who may access the data, under what conditions, and for what purposes. This can help obtain that trust. Although increasing attention is being paid to data governance in the health care sector, little guidance is available on development and implementation of a data governance framework in practice. OBJECTIVE This study aims to describe the development process of a governance framework for the "Registry Learning from Data in Nursing Homes," a national registry for EHR data on care delivered by nursing home physicians (in Dutch: specialist ouderengeneeskunde) in Dutch nursing homes-to allow data reusage for research and quality improvement of care. METHODS Relevant stakeholders representing practices, policies, and research in the nursing home sector were identified. Semistructured interviews were conducted with 20 people from 14 stakeholder organizations. The main aim of the interviews was to explore stakeholders' perspectives regarding the Registry's aim, data access criteria, and governing bodies' tasks and composition. Interview topics and analyses were guided by 8 principles regarding governance for reusing health data, as described in the literature. Interview results, together with legal advice and consensus discussions by the Registry's consortium partners, were used to shape the rules, regulations, and governing bodies of the governance framework. RESULTS Stakeholders valued the involvement of nursing home residents and their representatives, nursing home physicians, nursing homes' boards of directors, and scientists and saw this as a prerequisite for a trustworthy data governance framework. For the Registry, involvement of these groups can be achieved through a procedure in which residents can provide their consent or objection to the reuse of the data, transparency about the decisions made, and providing them a position in a governing body. In addition, a data request approval procedure based on predefined assessment criteria indicates that data reuse by third parties aligns with the aims of the Registry, benefits the nursing home sector, and protects the privacy of data subjects. CONCLUSIONS The stakeholders' views, expertise, and knowledge of other frameworks and relevant legislation serve to inform the application of governance principles to the contexts of both the nursing home sector and the Netherlands. Many different stakeholders were involved in the development of the Registry Learning from Data in Nursing Homes' governance framework and will continue to be involved. Engagement of the full range of stakeholders in an early stage of governance framework development is important to generate trust in appropriate and responsible data reuse.
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Affiliation(s)
| | - Robert A Verheij
- Nivel, Netherlands Institute for Health Services Research, Utrecht, Netherlands.,Tranzo, School of Social Sciences and Behavioural Research, Tilburg University, Tilburg, Netherlands
| | - Anneke L Francke
- Nivel, Netherlands Institute for Health Services Research, Utrecht, Netherlands.,Department of Public and Occupational Health, Location Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, Netherlands
| | - Marit Tomassen
- Nivel, Netherlands Institute for Health Services Research, Utrecht, Netherlands
| | - Max Houtzager
- Department of Medicine for Older People, Location Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, Netherlands.,Aging & Later Life, Amsterdam Public Health, Amsterdam, Netherlands
| | - Karlijn J Joling
- Department of Medicine for Older People, Location Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, Netherlands.,Aging & Later Life, Amsterdam Public Health, Amsterdam, Netherlands
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10
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Kotecha D, Asselbergs FW, Achenbach S, Anker SD, Atar D, Baigent C, Banerjee A, Beger B, Brobert G, Casadei B, Ceccarelli C, Cowie MR, Crea F, Cronin M, Denaxas S, Derix A, Fitzsimons D, Fredriksson M, Gale CP, Gkoutos GV, Goettsch W, Hemingway H, Ingvar M, Jonas A, Kazmierski R, Løgstrup S, Thomas Lumbers R, Lüscher TF, McGreavy P, Piña IL, Roessig L, Steinbeisser C, Sundgren M, Tyl B, van Thiel G, van Bochove K, Vardas PE, Villanueva T, Vrana M, Weber W, Weidinger F, Windecker S, Wood A, Grobbee DE. CODE-EHR best practice framework for the use of structured electronic healthcare records in clinical research. Eur Heart J 2022; 43:3578-3588. [PMID: 36208161 PMCID: PMC9452067 DOI: 10.1093/eurheartj/ehac426] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 06/21/2022] [Indexed: 11/29/2022] Open
Abstract
Big data is central to new developments in global clinical science aiming to improve the lives of patients. Technological advances have led to the routine use of structured electronic healthcare records with the potential to address key gaps in clinical evidence. The covid-19 pandemic has demonstrated the potential of big data and related analytics, but also important pitfalls. Verification, validation, and data privacy, as well as the social mandate to undertake research are key challenges. The European Society of Cardiology and the BigData@Heart consortium have brought together a range of international stakeholders, including patient representatives, clinicians, scientists, regulators, journal editors and industry. We propose the CODE-EHR Minimum Standards Framework as a means to improve the design of studies, enhance transparency and develop a roadmap towards more robust and effective utilisation of healthcare data for research purposes.
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Affiliation(s)
- Dipak Kotecha
- Institute of Cardiovascular Sciences, University of Birmingham, Medical School, Birmingham, UK
- University Hospitals Birmingham NHS Foundation Trust and Health Data Research UK Midlands, Birmingham, UK
- Department of Cardiology, Division of Heart and Lungs, University Medical Centre Utrecht, University of Utrecht, Utrecht, Netherlands
| | - Folkert W Asselbergs
- Department of Cardiology, Division of Heart and Lungs, University Medical Centre Utrecht, University of Utrecht, Utrecht, Netherlands
- Institute of Cardiovascular Science, Faculty of Population Health Sciences, University College London, London, UK
- Health Data Research UK and Institute of Health Informatics, University College London, London, UK
| | - Stephan Achenbach
- Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Stefan D Anker
- Department of Cardiology and Berlin Institute of Health Centre for Regenerative Therapies, German Centre for Cardiovascular Research (DZHK) partner site Berlin; Charité Universitätsmedizin Berlin, Germany
| | - Dan Atar
- Department of Cardiology, Oslo University Hospital, Ulleval, Oslo, Norway
- University of Oslo, Institute of Clinical Medicine, Oslo, Norway
| | - Colin Baigent
- MRC Population Health Research Unit, Nuffield Department of Population Health, Oxford, UK
- Clinical Trial Service Unit and Epidemiological Studies Unit, University of Oxford, Oxford, UK
| | - Amitava Banerjee
- Health Data Research UK and Institute of Health Informatics, University College London, London, UK
- University College London Hospitals NHS Trust, London, UK
| | | | | | - Barbara Casadei
- Division of Cardiovascular Medicine, John Radcliffe Hospital, University of Oxford NIHR Oxford Biomedical Research Centre, Oxford, UK
| | | | - Martin R Cowie
- Royal Brompton Hospital, Division of Guy’s St Thomas’ NHS Foundation Trust, London, UK
- School of Cardiovascular Medicine Sciences, King’s College London, London, UK
| | - Filippo Crea
- Department of Cardiovascular and Pulmonary Sciences, Catholic University of the Sacred Heart, Rome, Italy
- European Heart Journal, Oxford University Press, University of Oxford, Oxford, UK
| | - Maureen Cronin
- Vifor Pharma, Glattbrugg, Switzerland and Ava AG, Zurich, Switzerland
| | - Spiros Denaxas
- Health Data Research UK and Institute of Health Informatics, University College London, London, UK
- Alan Turing Institute, London, UK
- British Heart Foundation Data Science Centre, London, UK
| | | | - Donna Fitzsimons
- School of Nursing and Midwifery, Queen’s University Belfast, Northern Ireland
| | - Martin Fredriksson
- Late Clinical Development, Cardiovascular, Renal and Metabolism (CVRM), Biopharmaceuticals RD, AstraZeneca, Gothenburg, Sweden
| | - Chris P Gale
- Leeds Institute of Cardiovascular and Metabolic Medicine and Leeds Institute for Data Analytics, University of Leeds, Leeds, UK
- Leeds Institute for Data Analytics, University of Leeds, Leeds, UK
- Department of Cardiology, Leeds Teaching Hospitals NHS Trust, Leeds, UK
| | - Georgios V Gkoutos
- University Hospitals Birmingham NHS Foundation Trust and Health Data Research UK Midlands, Birmingham, UK
- College of Medical and Dental Sciences, Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, UK
| | - Wim Goettsch
- National Health Care Institute (ZIN), Diemen, Netherlands
- Division of Pharmacoepidemiology and Clinical Pharmacology, Utrecht Institute for Pharmaceutical Sciences, Utrecht University, Utrecht, Netherlands
| | - Harry Hemingway
- Health Data Research UK and Institute of Health Informatics, University College London, London, UK
| | - Martin Ingvar
- Department of Clinical Neuroscience, Karolinska Institutet, Solna, Sweden
- Department of Neuroradiology, Karolinska University Hospital Stockholm, Stockholm, Sweden
| | - Adrian Jonas
- Data and Analytics Group, National Institute for Health and Care Excellence, London, UK
| | - Robert Kazmierski
- Office of Cardiovascular Devices, US Food and Drug Administration, Silver Spring, MD, USA
| | | | - R Thomas Lumbers
- Health Data Research UK and Institute of Health Informatics, University College London, London, UK
- Barts Health NHS Trust and University College London Hospitals NHS Trust
| | - Thomas F Lüscher
- Centre for Molecular Cardiology, University of Zurich, Zurich, Switzerland
- Research, Education & Development, Royal Brompton and Harefield Hospitals, London, UK
- Faculty of Medicine, Imperial College London, London, UK
| | - Paul McGreavy
- European Society of Cardiology Patient Forum, European Society of Cardiology, Brussels, Belgium
| | - Ileana L Piña
- Central Michigan University College of Medicine, Midlands, MI, USA
- Centre for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, MD, USA
| | | | - Carl Steinbeisser
- Bayer AG, Leverkusen, Germany
- Steinbeisser Project Management, Munich, Germany
| | - Mats Sundgren
- Data Science AI, Biopharmaceuticals RD, AstraZeneca, Gothenburg, Sweden
| | - Benoît Tyl
- Centre for Therapeutic Innovation, Cardiovascular and Metabolic Disease, Institut de Recherches Internationales Servier, Suresnes, France
| | - Ghislaine van Thiel
- Julius Centre for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, Netherlands
| | | | - Panos E Vardas
- Hygeia, Mitera, Hospitals Hellenic Health Group, Athens, Greece
- European Heart Agency, European Society of Cardiology, Brussels, Belgium
| | | | | | | | | | - Stephan Windecker
- Department of Cardiology, Inselspital, University Hospital Bern, Bern, Switzerland
| | - Angela Wood
- Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Diederick E Grobbee
- Department of Epidemiology, University Medical Centre Utrecht, Division Julius Centrum, Utrecht, Netherlands
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Nunn JS, Shafee T, Chang S, Stephens R, Elliott J, Oliver S, John D, Smith M, Orr N, Preston J, Borthwick J, van Vlijmen T, Ansell J, Houyez F, de Sousa MSA, Plotz RD, Oliver JL, Golumbic Y, Macniven R, Wines S, Borda A, da Silva Hyldmo H, Hsing PY, Denis L, Thompson C. Standardised data on initiatives-STARDIT: Beta version. RESEARCH INVOLVEMENT AND ENGAGEMENT 2022; 8:31. [PMID: 35854364 PMCID: PMC9294764 DOI: 10.1186/s40900-022-00363-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Accepted: 06/20/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND AND OBJECTIVE There is currently no standardised way to share information across disciplines about initiatives, including fields such as health, environment, basic science, manufacturing, media and international development. All problems, including complex global problems such as air pollution and pandemics require reliable data sharing between disciplines in order to respond effectively. Current reporting methods also lack information about the ways in which different people and organisations are involved in initiatives, making it difficult to collate and appraise data about the most effective ways to involve different people. The objective of STARDIT (Standardised Data on Initiatives) is to address current limitations and inconsistencies in sharing data about initiatives. The STARDIT system features standardised data reporting about initiatives, including who has been involved, what tasks they did, and any impacts observed. STARDIT was created to help everyone in the world find and understand information about collective human actions, which are referred to as 'initiatives'. STARDIT enables multiple categories of data to be reported in a standardised way across disciplines, facilitating appraisal of initiatives and aiding synthesis of evidence for the most effective ways for people to be involved in initiatives. This article outlines progress to date on STARDIT; current usage; information about submitting reports; planned next steps and how anyone can become involved. METHOD STARDIT development is guided by participatory action research paradigms, and has been co-created with people from multiple disciplines and countries. Co-authors include cancer patients, people affected by rare diseases, health researchers, environmental researchers, economists, librarians and academic publishers. The co-authors also worked with Indigenous peoples from multiple countries and in partnership with an organisation working with Indigenous Australians. RESULTS AND DISCUSSION Over 100 people from multiple disciplines and countries have been involved in co-designing STARDIT since 2019. STARDIT is the first open access web-based data-sharing system which standardises the way that information about initiatives is reported across diverse fields and disciplines, including information about which tasks were done by which stakeholders. STARDIT is designed to work with existing data standards. STARDIT data will be released into the public domain (CC0) and integrated into Wikidata; it works across multiple languages and is both human and machine readable. Reports can be updated throughout the lifetime of an initiative, from planning to evaluation, allowing anyone to be involved in reporting impacts and outcomes. STARDIT is the first system that enables sharing of standardised data about initiatives across disciplines. A working Beta version was publicly released in February 2021 (ScienceforAll.World/STARDIT). Subsequently, STARDIT reports have been created for peer-reviewed research in multiple journals and multiple research projects, demonstrating the usability. In addition, organisations including Cochrane and Australian Genomics have created prospective reports outlining planned initiatives. CONCLUSIONS STARDIT can help create high-quality standardised information on initiatives trying to solve complex multidisciplinary global problems.
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Affiliation(s)
- Jack S Nunn
- Director of Science for All (Education Charity Registered in Australia), Melbourne, Australia.
- School of Public Health, La Trobe University, Melbourne, VIC, Australia.
| | - Thomas Shafee
- School of Life Sciences, La Trobe University, Melbourne, VIC, Australia
| | | | - Richard Stephens
- Patient Advocate, Co-Editor-in-Chief, 'Research Involvement and Engagement', London, UK
| | - Jim Elliott
- Public Involvement Lead at Health Research Authority (England), London, UK
| | - Sandy Oliver
- Professor of Public Policy at UCL Social Research Institute, London, UK
- University of Johannesburg, Johannesburg, South Africa
| | - Denny John
- Adjunct Professor, Ramaiah University of Applied Sciences, Bengaluru, India
- Chair, Campbell and Cochrane Economic Methods Group, London, UK
| | | | - Neil Orr
- Department of Linguistics, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, Australia
- Poche Centre Indigenous Health, The University of Sydney, Sydney, Australia
| | - Jennifer Preston
- National Institute for Health and Care Research, Alder Hey Clinical Research Facility, Liverpool, UK
| | | | | | - James Ansell
- Consumers Health Forum of Australia, Deakin, Australia
| | | | - Maria Sharmila Alina de Sousa
- Independent Impact Intelligence Design & Strategy Consultant, Research Impact Academy Brazil Ambassador, Sao Paulo, Brazil
| | - Roan D Plotz
- Applied Ecology and Environmental Change Research Group, Institute for Sustainable Industries and Liveable Cities, Victoria University, Melbourne, Australia
| | | | | | - Rona Macniven
- The Poche Centre for Indigenous Health, Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, 2006, Australia
- School of Population Health, Faculty of Medicine and Health, University of New South Wales, Sydney, Sydney, 2052, Australia
- Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, 2109, Australia
| | | | - Ann Borda
- University of Melbourne, Melbourne, Australia
- University College London, London, UK
| | - Håkon da Silva Hyldmo
- Department of Geography, Norwegian University of Science and Technology, Trondheim, Norway
| | - Pen-Yuan Hsing
- University of Bath, Bath, UK
- MammalWeb Project, London, UK
| | - Lena Denis
- Johns Hopkins University, Baltimore, USA
| | - Carolyn Thompson
- University College London, London, UK
- Institute of Zoology, Zoological Society of London, London, UK
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12
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Morley J, Murphy L, Mishra A, Joshi I, Karpathakis K. Governing Data and Artificial Intelligence for Health Care: Developing an International Understanding. JMIR Form Res 2022; 6:e31623. [PMID: 35099403 PMCID: PMC8844981 DOI: 10.2196/31623] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Revised: 10/12/2021] [Accepted: 10/13/2021] [Indexed: 01/04/2023] Open
Abstract
Background Although advanced analytical techniques falling under the umbrella heading of artificial intelligence (AI) may improve health care, the use of AI in health raises safety and ethical concerns. There are currently no internationally recognized governance mechanisms (policies, ethical standards, evaluation, and regulation) for developing and using AI technologies in health care. A lack of international consensus creates technical and social barriers to the use of health AI while potentially hampering market competition. Objective The aim of this study is to review current health data and AI governance mechanisms being developed or used by Global Digital Health Partnership (GDHP) member countries that commissioned this research, identify commonalities and gaps in approaches, identify examples of best practices, and understand the rationale for policies. Methods Data were collected through a scoping review of academic literature and a thematic analysis of policy documents published by selected GDHP member countries. The findings from this data collection and the literature were used to inform semistructured interviews with key senior policy makers from GDHP member countries exploring their countries’ experience of AI-driven technologies in health care and associated governance and inform a focus group with professionals working in international health and technology to discuss the themes and proposed policy recommendations. Policy recommendations were developed based on the aggregated research findings. Results As this is an empirical research paper, we primarily focused on reporting the results of the interviews and the focus group. Semistructured interviews (n=10) and a focus group (n=6) revealed 4 core areas for international collaborations: leadership and oversight, a whole systems approach covering the entire AI pipeline from data collection to model deployment and use, standards and regulatory processes, and engagement with stakeholders and the public. There was a broad range of maturity in health AI activity among the participants, with varying data infrastructure, application of standards across the AI life cycle, and strategic approaches to both development and deployment. A demand for further consistency at the international level and policies was identified to support a robust innovation pipeline. In total, 13 policy recommendations were developed to support GDHP member countries in overcoming core AI governance barriers and establishing common ground for international collaboration. Conclusions AI-driven technology research and development for health care outpaces the creation of supporting AI governance globally. International collaboration and coordination on AI governance for health care is needed to ensure coherent solutions and allow countries to support and benefit from each other’s work. International bodies and initiatives have a leading role to play in the international conversation, including the production of tools and sharing of practical approaches to the use of AI-driven technologies for health care.
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Affiliation(s)
- Jessica Morley
- Oxford Internet Institute, University of Oxford, Oxford, United Kingdom
| | | | - Abhishek Mishra
- Uehiro Centre for Practical Ethics, University of Oxford, Oxford, United Kingdom
| | | | - Kassandra Karpathakis
- Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, United States
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13
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Ethics as a Service: A Pragmatic Operationalisation of AI Ethics. Minds Mach (Dordr) 2021; 31:239-256. [PMID: 34720418 PMCID: PMC8550007 DOI: 10.1007/s11023-021-09563-w] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2021] [Accepted: 06/01/2021] [Indexed: 11/06/2022]
Abstract
As the range of potential uses for Artificial Intelligence (AI), in particular machine learning (ML), has increased, so has awareness of the associated ethical issues. This increased awareness has led to the realisation that existing legislation and regulation provides insufficient protection to individuals, groups, society, and the environment from AI harms. In response to this realisation, there has been a proliferation of principle-based ethics codes, guidelines and frameworks. However, it has become increasingly clear that a significant gap exists between the theory of AI ethics principles and the practical design of AI systems. In previous work, we analysed whether it is possible to close this gap between the ‘what’ and the ‘how’ of AI ethics through the use of tools and methods designed to help AI developers, engineers, and designers translate principles into practice. We concluded that this method of closure is currently ineffective as almost all existing translational tools and methods are either too flexible (and thus vulnerable to ethics washing) or too strict (unresponsive to context). This raised the question: if, even with technical guidance, AI ethics is challenging to embed in the process of algorithmic design, is the entire pro-ethical design endeavour rendered futile? And, if no, then how can AI ethics be made useful for AI practitioners? This is the question we seek to address here by exploring why principles and technical translational tools are still needed even if they are limited, and how these limitations can be potentially overcome by providing theoretical grounding of a concept that has been termed ‘Ethics as a Service.’
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Teodorowski P, Jones E, Tahir N, Ahmed S, Frith L. Public involvement and engagement in big data research: protocol for a scoping review and a systematic review of delivery and effectiveness of strategies for involvement and engagement. BMJ Open 2021; 11:e050167. [PMID: 34413107 PMCID: PMC8378392 DOI: 10.1136/bmjopen-2021-050167] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Accepted: 07/27/2021] [Indexed: 12/05/2022] Open
Abstract
INTRODUCTION Big data research has grown considerably over the last two decades. This presents new ethical challenges around consent, data storage and anonymisation. Big data research projects require public support to succeed and it has been argued that one way to achieve this is through public involvement and engagement. To better understand the role public involvement and engagement can play in big data research, we will review the current literature. This protocol describes the planned review methods. METHODS AND ANALYSIS Our review will be conducted in two stages. In the first stage, we will conduct a scoping review using Arksey and O'Malley methodology to comprehensively map current evidence on public involvement and engagement in big data research. Databases (CINAHL, Health Research Premium Collection, PubMed, Scopus, Web of Science) and grey literature will be searched for eligible papers. We provide a narrative description of the results based on a thematic analysis. In the second stage, out of papers found in the scoping review which discuss involvement and engagement strategies, we will conduct a systematic review following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, exploring the delivery and effectiveness of these strategies. We will conduct a qualitative synthesis. Relevant results from the quantitative studies will be extracted and placed under qualitative themes. Individual studies will be appraised through Mixed Methods Appraisal Tool (MMAT), we will then assess the overall confidence in each finding through Confidence in the Evidence from Reviews of Qualitative research (GRADE-CERQual). Results will be reported in a thematic and narrative way. ETHICS AND DISSEMINATION This protocol sets out how the review will be conducted to ensure rigour and transparency. Public advisors were involved in its development. Ethics approval is not required. Review findings will be presented at conferences and published in peer-reviewed journals.
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Affiliation(s)
- Piotr Teodorowski
- Institute of Population Health, University of Liverpool, Liverpool, UK
| | - Elisa Jones
- Institute of Population Health, University of Liverpool, Liverpool, UK
| | | | | | - Lucy Frith
- Departments of Law and Philosophy, University of Liverpool, Liverpool, UK
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15
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Muller SHA, Kalkman S, van Thiel GJMW, Mostert M, van Delden JJM. The social licence for data-intensive health research: towards co-creation, public value and trust. BMC Med Ethics 2021; 22:110. [PMID: 34376204 PMCID: PMC8353823 DOI: 10.1186/s12910-021-00677-5] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Accepted: 08/03/2021] [Indexed: 11/10/2022] Open
Abstract
Background The rise of Big Data-driven health research challenges the assumed contribution of medical research to the public good, raising questions about whether the status of such research as a common good should be taken for granted, and how public trust can be preserved. Scandals arising out of sharing data during medical research have pointed out that going beyond the requirements of law may be necessary for sustaining trust in data-intensive health research. We propose building upon the use of a social licence for achieving such ethical governance. Main text We performed a narrative review of the social licence as presented in the biomedical literature. We used a systematic search and selection process, followed by a critical conceptual analysis. The systematic search resulted in nine publications. Our conceptual analysis aims to clarify how societal permission can be granted to health research projects which rely upon the reuse and/or linkage of health data. These activities may be morally demanding. For these types of activities, a moral legitimation, beyond the limits of law, may need to be sought in order to preserve trust. Our analysis indicates that a social licence encourages us to recognise a broad range of stakeholder interests and perspectives in data-intensive health research. This is especially true for patients contributing data. Incorporating such a practice paves the way towards an ethical governance, based upon trust. Public engagement that involves patients from the start is called for to strengthen this social licence. Conclusions There are several merits to using the concept of social licence as a guideline for ethical governance. Firstly, it fits the novel scale of data-related risks; secondly, it focuses attention on trustworthiness; and finally, it offers co-creation as a way forward. Greater trust can be achieved in the governance of data-intensive health research by highlighting strategic dialogue with both patients contributing the data, and the public in general. This should ultimately contribute to a more ethical practice of governance. Supplementary Information The online version contains supplementary material available at 10.1186/s12910-021-00677-5.
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Affiliation(s)
- Sam H A Muller
- Department of Medical Humanities, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Universiteitsweg 100, 3584 CX, Utrecht, The Netherlands.
| | - Shona Kalkman
- Department of Medical Humanities, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Universiteitsweg 100, 3584 CX, Utrecht, The Netherlands
| | - Ghislaine J M W van Thiel
- Department of Medical Humanities, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Universiteitsweg 100, 3584 CX, Utrecht, The Netherlands
| | - Menno Mostert
- Department of Medical Humanities, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Universiteitsweg 100, 3584 CX, Utrecht, The Netherlands
| | - Johannes J M van Delden
- Department of Medical Humanities, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Universiteitsweg 100, 3584 CX, Utrecht, The Netherlands
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Bunting KV, Stanbury M, Tica O, Kotecha D. Transforming clinical research by involving and empowering patients- the RATE-AF randomized trial. Eur Heart J 2021; 42:2411-2414. [PMID: 33655299 DOI: 10.1093/eurheartj/ehab098] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Affiliation(s)
- Karina V Bunting
- Cardiac Physiologist & Postdoctoral Fellow, University of Birmingham & University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
| | - Mary Stanbury
- Lead for the Patient & Public Involvement Team, RATE-AF trial
| | - Otilia Tica
- Cardiologist & Postdoctoral Fellow, University of Birmingham, Birmingham, UK
| | - Dipak Kotecha
- Professor of Cardiology, Institute of Cardiovascular Sciences, University of Birmingham & University Hospitals Birmingham NHS Foundation Trust, Vincent Drive, Birmingham B15 2TT, UK
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Coeli CM, Saraceni V, Medeiros PM, da Silva Santos HP, Guillen LCT, Alves LGSB, Hone T, Millett C, Trajman A, Durovni B. Record linkage under suboptimal conditions for data-intensive evaluation of primary care in Rio de Janeiro, Brazil. BMC Med Inform Decis Mak 2021; 21:190. [PMID: 34130670 PMCID: PMC8204416 DOI: 10.1186/s12911-021-01550-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2020] [Accepted: 06/03/2021] [Indexed: 01/15/2023] Open
Abstract
Background Linking Brazilian databases demands the development of algorithms and processes to deal with various challenges including the large size of the databases, the low number and poor quality of personal identifiers available to be compared (national security number not mandatory), and some characteristics of Brazilian names that make the linkage process prone to errors. This study aims to describe and evaluate the quality of the processes used to create an individual-linked database for data-intensive research on the impacts on health indicators of the expansion of primary care in Rio de Janeiro City, Brazil. Methods We created an individual-level dataset linking social benefits recipients, primary health care, hospital admission and mortality data. The databases were pre-processed, and we adopted a multiple approach strategy combining deterministic and probabilistic record linkage techniques, and an extensive clerical review of the potential matches. Relying on manual review as the gold standard, we estimated the false match (false-positive) proportion of each approach (deterministic, probabilistic, clerical review) and the missed match proportion (false-negative) of the clerical review approach. To assess the sensitivity (recall) to identifying social benefits recipients’ deaths, we used their vital status registered on the primary care database as the gold standard. Results In all linkage processes, the deterministic approach identified most of the matches. However, the proportion of matches identified in each approach varied. The false match proportion was around 1% or less in almost all approaches. The missed match proportion in the clerical review approach of all linkage processes were under 3%. We estimated a recall of 93.6% (95% CI 92.8–94.3) for the linkage between social benefits recipients and mortality data. Conclusion The adoption of a linkage strategy combining pre-processing routines, deterministic, and probabilistic strategies, as well as an extensive clerical review approach minimized linkage errors in the context of suboptimal data quality.
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Affiliation(s)
- Claudia Medina Coeli
- Instituto de Estudos em Saúde Coletiva, Universidade Federal do Rio de Janeiro, Avenida Horácio Macedo, s/n Ilha do Fundão - Cidade Universitária, Rio de Janeiro, RJ, CEP 21941-598, Brasil.
| | - Valeria Saraceni
- Secretaria Municipal de Saúde do Rio de Janeiro, Rio de Janeiro, Brazil
| | - Paulo Mota Medeiros
- Instituto de Estudos em Saúde Coletiva, Universidade Federal do Rio de Janeiro, Avenida Horácio Macedo, s/n Ilha do Fundão - Cidade Universitária, Rio de Janeiro, RJ, CEP 21941-598, Brasil
| | - Helena Pereira da Silva Santos
- Instituto de Estudos em Saúde Coletiva, Universidade Federal do Rio de Janeiro, Avenida Horácio Macedo, s/n Ilha do Fundão - Cidade Universitária, Rio de Janeiro, RJ, CEP 21941-598, Brasil
| | - Luis Carlos Torres Guillen
- Instituto de Estudos em Saúde Coletiva, Universidade Federal do Rio de Janeiro, Avenida Horácio Macedo, s/n Ilha do Fundão - Cidade Universitária, Rio de Janeiro, RJ, CEP 21941-598, Brasil
| | - Luís Guilherme Santos Buteri Alves
- Instituto de Estudos em Saúde Coletiva, Universidade Federal do Rio de Janeiro, Avenida Horácio Macedo, s/n Ilha do Fundão - Cidade Universitária, Rio de Janeiro, RJ, CEP 21941-598, Brasil
| | - Thomas Hone
- Public Health Policy Evaluation Unit, Imperial College London, London, UK
| | - Christopher Millett
- Public Health Policy Evaluation Unit, Imperial College London, London, UK.,Department of Preventive Medicine, School of Medicine, University of São Paulo, São Paulo, 01246-903, Brazil.,Center of Data and Knowledge Integration for Health (CIDACS), Instituto Gonçalo Muniz, Fundação Oswaldo Cruz, Salvador, Brazil
| | - Anete Trajman
- Programa de Pós-Graduação em Clínica Médica e Mestrado Profissional em Atenção Primária à Saúde, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil.,TB International Centre, McGill University, Quebec, Canada
| | - Betina Durovni
- Centro de Estudos Estratégicos, Fundação Oswaldo Cruz, Rio de Janeiro, Brazil
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Burton JK, Goodman C, Guthrie B, Gordon AL, Hanratty B, Quinn TJ. Closing the UK care home data gap - methodological challenges and solutions. Int J Popul Data Sci 2021; 5:1391. [PMID: 34046529 PMCID: PMC8138869 DOI: 10.23889/ijpds.v5i4.1391] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/31/2022] Open
Abstract
UK care home residents are invisible in national datasets. The COVID-19 pandemic has exposed data failings that have hindered service development and research for years. Fundamental gaps, in terms of population and service demographics coupled with difficulties identifying the population in routine data are a significant limitation. These challenges are a key factor underpinning the failure to provide timely and responsive policy decisions to support care homes. In this commentary we propose changes that could address this data gap, priorities include: (1) Reliable identification of care home residents and their tenure; (2) Common identifiers to facilitate linkage between data sources from different sectors; (3) Individual-level, anonymised data inclusive of mortality irrespective of where death occurs; (4) Investment in capacity for large-scale, anonymised linked data analysis within social care working in partnership with academics; (5) Recognition of the need for collaborative working to use novel data sources, working to understand their meaning and ensure correct interpretation; (6) Better integration of information governance, enabling safe access for legitimate analyses from all relevant sectors; (7) A core national dataset for care homes developed in collaboration with key stakeholders to support integrated care delivery, service planning, commissioning, policy and research. Our suggestions are immediately actionable with political will and investment. We should seize this opportunity to capitalise on the spotlight the pandemic has thrown on the vulnerable populations living in care homes to invest in data-informed approaches to support care, evidence-based policy making and research.
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Affiliation(s)
- J K Burton
- Academic Geriatric Medicine, Institute of Cardiovascular and Medical Sciences, University of Glasgow
| | - C Goodman
- Centre for Research in Public health and Community Care (CRIPACC), University of Hertfordshire.,NIHR Applied Research Collaboration East of England
| | - B Guthrie
- Advanced Care Research Centre, Usher Institute, College of Medicine and Veterinary Medicine, University of Edinburgh
| | - A L Gordon
- Division of Medical Sciences and Graduate Entry Medicine, University of Nottingham.,NIHR Applied Research Collaboration East Midlands
| | - B Hanratty
- Population Health Sciences Institute, Newcastle University.,NIHR Applied Research Collaboration North East and North Cumbria
| | - T J Quinn
- Academic Geriatric Medicine, Institute of Cardiovascular and Medical Sciences, University of Glasgow
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19
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Lounsbury O, Roberts L, Goodman JR, Batey P, Naar L, Flott KM, Lawrence-Jones A, Ghafur S, Darzi A, Neves AL. Opening a "Can of Worms" to Explore the Public's Hopes and Fears About Health Care Data Sharing: Qualitative Study. J Med Internet Res 2021; 23:e22744. [PMID: 33616532 PMCID: PMC7939935 DOI: 10.2196/22744] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2020] [Revised: 11/27/2020] [Accepted: 01/16/2021] [Indexed: 12/25/2022] Open
Abstract
BACKGROUND Evidence suggests that health care data sharing may strengthen care coordination, improve quality and safety, and reduce costs. However, to achieve efficient and meaningful adoption of health care data-sharing initiatives, it is necessary to engage all stakeholders, from health care professionals to patients. Although previous work has assessed health care professionals' perceptions of data sharing, perspectives of the general public and particularly of seldom heard groups have yet to be fully assessed. OBJECTIVE This study aims to explore the views of the public, particularly their hopes and concerns, around health care data sharing. METHODS An original, immersive public engagement interactive experience was developed-The Can of Worms installation-in which participants were prompted to reflect about data sharing through listening to individual stories around health care data sharing. A multidisciplinary team with expertise in research, public involvement, and human-centered design developed this concept. The installation took place in three separate events between November 2018 and November 2019. A combination of convenience and snowball sampling was used in this study. Participants were asked to fill self-administered feedback cards and to describe their hopes and fears about the meaningful use of data in health care. The transcripts were compiled verbatim and systematically reviewed by four independent reviewers using the thematic analysis method to identify emerging themes. RESULTS Our approach exemplifies the potential of using interdisciplinary expertise in research, public involvement, and human-centered design to tell stories, collect perspectives, and spark conversations around complex topics in participatory digital medicine. A total of 352 qualitative feedback cards were collected, each reflecting participants' hopes and fears for health care data sharing. Thematic analyses identified six themes under hopes: enablement of personal access and ownership, increased interoperability and collaboration, generation of evidence for better and safer care, improved timeliness and efficiency, delivery of more personalized care, and equality. The five main fears identified included inadequate security and exploitation, data inaccuracy, distrust, discrimination and inequality, and less patient-centered care. CONCLUSIONS This study sheds new light on the main hopes and fears of the public regarding health care data sharing. Importantly, our results highlight novel concerns from the public, particularly in terms of the impact on health disparities, both at international and local levels, and on delivering patient-centered care. Incorporating the knowledge generated and focusing on co-designing solutions to tackle these concerns is critical to engage the public as active contributors and to fully leverage the potential of health care data use.
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Affiliation(s)
- Olivia Lounsbury
- Patient Safety Translational Research Centre, Institute of Global Health Innovation, London, United Kingdom
- Patient Safety Movement Foundation, Irvine, CA, United States
| | - Lily Roberts
- Patient Safety Translational Research Centre, Institute of Global Health Innovation, London, United Kingdom
| | - Jonathan R Goodman
- Patient Safety Translational Research Centre, Institute of Global Health Innovation, London, United Kingdom
| | - Philippa Batey
- The Helix Centre, Institute of Global Health Innovation, London, United Kingdom
| | - Lenny Naar
- The Helix Centre, Institute of Global Health Innovation, London, United Kingdom
| | - Kelsey M Flott
- Patient Safety Translational Research Centre, Institute of Global Health Innovation, London, United Kingdom
| | - Anna Lawrence-Jones
- Patient Safety Translational Research Centre, Institute of Global Health Innovation, London, United Kingdom
| | - Saira Ghafur
- Centre for Health Policy, Institute of Global Health Innovation, Imperial College London, London, United Kingdom
| | - Ara Darzi
- Patient Safety Translational Research Centre, Institute of Global Health Innovation, London, United Kingdom
| | - Ana Luisa Neves
- Patient Safety Translational Research Centre, Institute of Global Health Innovation, London, United Kingdom
- Center for Health Technology and Services Research / Department of Community Medicine, Health Information and Decision, Faculty of Medicine, University of Porto, Porto, Portugal
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20
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Meurk C, Wittenhagen L, Steele ML, Ferris L, Edwards B, Bosley E, Heffernan E. Examining the Use of Police and Ambulance Data in Suicide Research. CRISIS 2020; 42:386-395. [PMID: 33241741 DOI: 10.1027/0227-5910/a000739] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
Background: Police and paramedics are often the first to respond to individuals in suicide crisis and have an important role to play in facilitating optimal care pathways. Yet, little evidence exists to inform these responses. Data linkage provides one approach to examining this knowledge gap. Aim: We identified studies that examined suicide behaviors and linked to police or ambulance data. Method: A systematic search of PubMed and Scopus was undertaken to identify data linkage studies that: (1) examined suicide behaviors, and (2) included police or ambulance data. Studies were reviewed to identify: aims; suicide behaviors examined; how these were measured; how the cohort was defined; topic area; and what datasets were linked. Results: Eight studies met the inclusion criteria. Six studies included police data, and two studies included ambulance data. No study included both. Two topic areas were identified: (1) suicide-related contact with police or ambulance services; and (2) associations between suicidal behaviors and violence, victimization, and criminality. Limitations: Limitations to the review include the potential to have missed studies that investigated or reported on suicidality under the guise of mental health problems; complexities and nuances arising from the role of police data in coronial investigations; and limitations in the number of databases searched. Conclusion: Police and ambulance data represent a currently underutilized source of valuable information relevant to suicide crises, and further research should aim to address this gap.
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Affiliation(s)
- Carla Meurk
- Forensic Mental Health Group, Queensland Centre for Mental Health Research, Wacol, QLD, Australia.,School of Public Health, The University of Queensland, Brisbane, QLD, Australia
| | - Lisa Wittenhagen
- Forensic Mental Health Group, Queensland Centre for Mental Health Research, Wacol, QLD, Australia.,Queensland Forensic Mental Health Service, Queensland Health, Brisbane, QLD, Australia
| | - Megan L Steele
- Forensic Mental Health Group, Queensland Centre for Mental Health Research, Wacol, QLD, Australia
| | - Laura Ferris
- School of Psychology, The University of Queensland, Brisbane, QLD, Australia
| | | | - Emma Bosley
- Queensland Ambulance Service, Kedron, QLD, Australia.,School of Clinical Sciences, Queensland University of Technology, Brisbane, QLD, Australia
| | - Ed Heffernan
- Forensic Mental Health Group, Queensland Centre for Mental Health Research, Wacol, QLD, Australia.,School of Public Health, The University of Queensland, Brisbane, QLD, Australia.,Queensland Forensic Mental Health Service, Queensland Health, Brisbane, QLD, Australia
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21
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Degeling C, Chen G, Gilbert GL, Brookes V, Thai T, Wilson A, Johnson J. Changes in public preferences for technologically enhanced surveillance following the COVID-19 pandemic: a discrete choice experiment. BMJ Open 2020; 10:e041592. [PMID: 33208337 PMCID: PMC7677347 DOI: 10.1136/bmjopen-2020-041592] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/12/2020] [Revised: 10/18/2020] [Accepted: 10/29/2020] [Indexed: 01/06/2023] Open
Abstract
OBJECTIVES As governments attempt to navigate a path out of COVID-19 restrictions, robust evidence is essential to inform requirements for public acceptance of technologically enhanced communicable disease surveillance systems. We examined the value of core surveillance system attributes to the Australian public, before and during the early stages of the current pandemic. DESIGN A discrete choice experiment was conducted in Australia with a representative group of respondents, before and after the WHO declared COVID-19 a Public Health Emergency of International Concern. We identified and investigated the relative importance of seven attributes associated with technologically enhanced disease surveillance: respect for personal autonomy; privacy/confidentiality; data certainty/confidence; data security; infectious disease mortality prevention; infectious disease morbidity prevention; and attribution of (causal) responsibility. Specifically, we explored how the onset of the COVID-19 outbreak influenced participant responses. SETTING AND PARTICIPANTS 2008 Australians (general public) completed the experiment: 793 before COVID-19 outbreak onset (mean age 45.9 years, 50.2% male) and 1215 after onset (mean age 47.2 years, 49% male). RESULTS All seven attributes significantly influenced respondents' preferences for communicable disease surveillance systems. After onset, participants demonstrated greater preference for a surveillance system that could prevent a higher number of illnesses and deaths, and were less concerned about their personal autonomy. However, they also increased their preference for a system with high data security. CONCLUSIONS Public acceptance of technology-based communicable disease surveillance is situation dependent. During an epidemic, there is likely to be greater tolerance of technologically enhanced disease surveillance systems that result in restrictions on personal activity if such systems can prevent high morbidity and mortality. However, this acceptance of lower personal autonomy comes with an increased requirement to ensure data security. These findings merit further research as the pandemic unfolds and strategies are put in place that enable individuals and societies to live with SARS-CoV-2 endemicity.
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Affiliation(s)
- Chris Degeling
- Australian Centre for Health Engagement, Evidence and Values, University of Wollongong, Wollongong, New South Wales, Australia
| | - Gang Chen
- Centre for Health Economics, Monash Business School, Monash University, Caufield East, Victoria, Australia
| | - Gwendolyn L Gilbert
- Sydney Health Ethics, Sydney School of Public Health, The University of Sydney Faculty of Medicine and Health, Sydney, New South Wales, Australia
- Marie Bashir Institute for Emerging Infectious Disease and Biosecurity, The University of Sydney, Sydney, New South Wales, Australia
| | - Victoria Brookes
- School of Animal and Veterinary Sciences, Charles Sturt University, Wagga Wagga, New South Wales, Australia
| | - Thi Thai
- Centre for Health Economics, Monash Business School, Monash University, Caufield East, Victoria, Australia
| | - Andrew Wilson
- Menzies Centre for Health Policy, The University of Sydney, Sydney, New South Wales, Australia
| | - Jane Johnson
- Marie Bashir Institute for Emerging Infectious Disease and Biosecurity, The University of Sydney, Sydney, New South Wales, Australia
- Department of Philosophy, Macquarie University, Sydney, New South Wales, Australia
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22
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McCradden MD, Sarker T, Paprica PA. Conditionally positive: a qualitative study of public perceptions about using health data for artificial intelligence research. BMJ Open 2020; 10:e039798. [PMID: 33115901 PMCID: PMC7594363 DOI: 10.1136/bmjopen-2020-039798] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/26/2020] [Revised: 08/05/2020] [Accepted: 10/08/2020] [Indexed: 01/10/2023] Open
Abstract
OBJECTIVES Given widespread interest in applying artificial intelligence (AI) to health data to improve patient care and health system efficiency, there is a need to understand the perspectives of the general public regarding the use of health data in AI research. DESIGN A qualitative study involving six focus groups with members of the public. Participants discussed their views about AI in general, then were asked to share their thoughts about three realistic health AI research scenarios. Data were analysed using qualitative description thematic analysis. SETTINGS Two cities in Ontario, Canada: Sudbury (400 km north of Toronto) and Mississauga (part of the Greater Toronto Area). PARTICIPANTS Forty-one purposively sampled members of the public (21M:20F, 25-65 years, median age 40). RESULTS Participants had low levels of prior knowledge of AI and mixed, mostly negative, perceptions of AI in general. Most endorsed using data for health AI research when there is strong potential for public benefit, providing that concerns about privacy, commercial motives and other risks were addressed. Inductive thematic analysis identified AI-specific hopes (eg, potential for faster and more accurate analyses, ability to use more data), fears (eg, loss of human touch, skill depreciation from over-reliance on machines) and conditions (eg, human verification of computer-aided decisions, transparency). There were mixed views about whether data subject consent is required for health AI research, with most participants wanting to know if, how and by whom their data were used. Though it was not an objective of the study, realistic health AI scenarios were found to have an educational effect. CONCLUSIONS Notwithstanding concerns and limited knowledge about AI in general, most members of the general public in six focus groups in Ontario, Canada perceived benefits from health AI and conditionally supported the use of health data for AI research.
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Affiliation(s)
- Melissa D McCradden
- Department of Bioethics, Hospital for Sick Children, Toronto, Ontario, Canada
| | - Tasmie Sarker
- Health Team, Vector Institute, Toronto, Ontario, Canada
| | - P Alison Paprica
- Health Team, Vector Institute, Toronto, Ontario, Canada
- Institute for Health Policy, Management and Evaluation, University of Toronto, Toronto, Ontario, Canada
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Morley J, Machado CCV, Burr C, Cowls J, Joshi I, Taddeo M, Floridi L. The ethics of AI in health care: A mapping review. Soc Sci Med 2020; 260:113172. [PMID: 32702587 DOI: 10.1016/j.socscimed.2020.113172] [Citation(s) in RCA: 131] [Impact Index Per Article: 32.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Revised: 06/22/2020] [Accepted: 06/23/2020] [Indexed: 02/06/2023]
Abstract
This article presents a mapping review of the literature concerning the ethics of artificial intelligence (AI) in health care. The goal of this review is to summarise current debates and identify open questions for future research. Five literature databases were searched to support the following research question: how can the primary ethical risks presented by AI-health be categorised, and what issues must policymakers, regulators and developers consider in order to be 'ethically mindful? A series of screening stages were carried out-for example, removing articles that focused on digital health in general (e.g. data sharing, data access, data privacy, surveillance/nudging, consent, ownership of health data, evidence of efficacy)-yielding a total of 156 papers that were included in the review. We find that ethical issues can be (a) epistemic, related to misguided, inconclusive or inscrutable evidence; (b) normative, related to unfair outcomes and transformative effectives; or (c) related to traceability. We further find that these ethical issues arise at six levels of abstraction: individual, interpersonal, group, institutional, and societal or sectoral. Finally, we outline a number of considerations for policymakers and regulators, mapping these to existing literature, and categorising each as epistemic, normative or traceability-related and at the relevant level of abstraction. Our goal is to inform policymakers, regulators and developers of what they must consider if they are to enable health and care systems to capitalise on the dual advantage of ethical AI; maximising the opportunities to cut costs, improve care, and improve the efficiency of health and care systems, whilst proactively avoiding the potential harms. We argue that if action is not swiftly taken in this regard, a new 'AI winter' could occur due to chilling effects related to a loss of public trust in the benefits of AI for health care.
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Affiliation(s)
- Jessica Morley
- Oxford Internet Institute, University of Oxford, 1 St Giles, Oxford, OX1 3JS, UK.
| | - Caio C V Machado
- Oxford Internet Institute, University of Oxford, 1 St Giles, Oxford, OX1 3JS, UK
| | - Christopher Burr
- Oxford Internet Institute, University of Oxford, 1 St Giles, Oxford, OX1 3JS, UK
| | - Josh Cowls
- Oxford Internet Institute, University of Oxford, 1 St Giles, Oxford, OX1 3JS, UK; Alan Turing Institute, British Library, 96 Euston Rd, London, NW1 2DB, UK
| | - Indra Joshi
- NHSX, Skipton House, 80 London Road, SE1 6LH, UK
| | - Mariarosaria Taddeo
- Oxford Internet Institute, University of Oxford, 1 St Giles, Oxford, OX1 3JS, UK; Alan Turing Institute, British Library, 96 Euston Rd, London, NW1 2DB, UK; Department of Computer Science, University of Oxford, 15 Parks Rd, Oxford, OX1 3QD, UK
| | - Luciano Floridi
- Oxford Internet Institute, University of Oxford, 1 St Giles, Oxford, OX1 3JS, UK; Alan Turing Institute, British Library, 96 Euston Rd, London, NW1 2DB, UK; Department of Computer Science, University of Oxford, 15 Parks Rd, Oxford, OX1 3QD, UK
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24
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Hobbs G, Tully MP. Realist evaluation of public engagement and involvement in data-intensive health research. RESEARCH INVOLVEMENT AND ENGAGEMENT 2020; 6:37. [PMID: 32612850 PMCID: PMC7325137 DOI: 10.1186/s40900-020-00215-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/05/2020] [Accepted: 06/18/2020] [Indexed: 06/11/2023]
Abstract
BACKGROUND High quality public engagement and involvement (PEI) in data-intensive health research is seen as one way of ensuring that social legitimacy, i.e. a social license, is conferred through public acceptance of the need for research use of their data. This is a complex research area, and portfolios of involvement have been suggested, but not yet evaluated, to support the role of public contributors. The study aim was to evaluate if and how membership of a data-intensive research public forum can act as a mechanism for enhancing members' personal development. Our objective was to understand the circumstances and mechanisms that help to explain how, why and for whom involvement with a public forum enhanced those members' personal development. METHOD Qualitative data were collected from 15 current and previous members, via semi-structured interviews, notes from meetings, and consultations with and feedback from members. Data were critically compared, contrasted and reviewed until no new themes could be discerned and then condensed into context-mechanism-outcome (CMO) configurations. Realist evaluation was used to generate a theoretical and empirical appreciation of the contextual circumstances and mechanisms which help to explain the extent to which involvement with a public forum would enhance members' personal development and, if so, how, why, and for whom. RESULTS Three CMO configurations were identified. All of them showed that using the portfolio facilitated growth in forum members' personal development, but only where the members valued using the portfolio. This was particularly so for female members. Members valued the portfolio in one or more of three ways: as a tool to record and evidence activities, to facilitate reflective practice or as a guiding framework. CONCLUSIONS Data analysis and consideration of the three CMO configurations suggests a refined middle range theory that 'The use of a portfolio as a framework for learning in a public forum will facilitate members' personal development if they value its use as a framework for learning'. Further work is needed to confirm these findings both elsewhere in data-intensive health research and in other complex research areas using public forums for PEI. PLAIN ENGLISH SUMMARY Public engagement and involvement in health research is now well established and makes a valuable contribution to the research process. However, little is known about its impact on participants. This article investigates how involvement in a data-intensive health research public forum impacts on public forum members, rather than the research process. Personal involvement portfolios were used to support their involvement work and help evaluate if and how involvement in research activities enhanced members' personal development. Taking a realist evaluation approach, 'Context-Mechanism-Outcome' configurations were used to explore how membership of a public forum might enhance public forum members' personal development. The Context-Mechanism-Outcome configuration refers to an exploration of what influences the extent to which an intervention is successful or unsuccessful in producing positive outcomes and tries to identify the reasons why it is successful for some and unsuccessful for others. However, evidence from this realist evaluation recommends that engagement and involvement should always be underpinned by procedures which ensure that public contributors receive ongoing and tailored guidance and support throughout the process.
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Affiliation(s)
- Georgina Hobbs
- Evidence and Research Manager, Manchester Health and Care Commissioning, Parkway 3, Princess Rd, Manchester, M14-7LU UK
| | - Mary P. Tully
- Division of Pharmacy and Optometry, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK
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25
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Empowerment or Engagement? Digital Health Technologies for Mental Healthcare. THE 2019 YEARBOOK OF THE DIGITAL ETHICS LAB 2020. [DOI: 10.1007/978-3-030-29145-7_5] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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