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Im E, Kim H, Lee H, Jiang X, Kim JH. Exploring the tradeoff between data privacy and utility with a clinical data analysis use case. BMC Med Inform Decis Mak 2024; 24:147. [PMID: 38816848 PMCID: PMC11137882 DOI: 10.1186/s12911-024-02545-9] [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: 06/01/2023] [Accepted: 05/21/2024] [Indexed: 06/01/2024] Open
Abstract
BACKGROUND Securing adequate data privacy is critical for the productive utilization of data. De-identification, involving masking or replacing specific values in a dataset, could damage the dataset's utility. However, finding a reasonable balance between data privacy and utility is not straightforward. Nonetheless, few studies investigated how data de-identification efforts affect data analysis results. This study aimed to demonstrate the effect of different de-identification methods on a dataset's utility with a clinical analytic use case and assess the feasibility of finding a workable tradeoff between data privacy and utility. METHODS Predictive modeling of emergency department length of stay was used as a data analysis use case. A logistic regression model was developed with 1155 patient cases extracted from a clinical data warehouse of an academic medical center located in Seoul, South Korea. Nineteen de-identified datasets were generated based on various de-identification configurations using ARX, an open-source software for anonymizing sensitive personal data. The variable distributions and prediction results were compared between the de-identified datasets and the original dataset. We examined the association between data privacy and utility to determine whether it is feasible to identify a viable tradeoff between the two. RESULTS All 19 de-identification scenarios significantly decreased re-identification risk. Nevertheless, the de-identification processes resulted in record suppression and complete masking of variables used as predictors, thereby compromising dataset utility. A significant correlation was observed only between the re-identification reduction rates and the ARX utility scores. CONCLUSIONS As the importance of health data analysis increases, so does the need for effective privacy protection methods. While existing guidelines provide a basis for de-identifying datasets, achieving a balance between high privacy and utility is a complex task that requires understanding the data's intended use and involving input from data users. This approach could help find a suitable compromise between data privacy and utility.
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Affiliation(s)
- Eunyoung Im
- College of Nursing, Seoul National University, Seoul, South Korea
- Center for World-leading Human-care Nurse Leaders for the Future by Brain Korea 21 (BK 21) four project, College of Nursing, Seoul National University, Seoul, South Korea
| | - Hyeoneui Kim
- College of Nursing, Seoul National University, Seoul, South Korea.
- Center for World-leading Human-care Nurse Leaders for the Future by Brain Korea 21 (BK 21) four project, College of Nursing, Seoul National University, Seoul, South Korea.
- The Research Institute of Nursing Science, Seoul National University, Seoul, South Korea.
| | - Hyungbok Lee
- College of Nursing, Seoul National University, Seoul, South Korea
- Seoul National University Hospital, Seoul, South Korea
| | - Xiaoqian Jiang
- School of Biomedical Informatics, UTHealth, Houston, TX, USA
| | - Ju Han Kim
- Seoul National University Hospital, Seoul, South Korea
- College of Medicine, Seoul National University, Seoul, South Korea
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Youssef A, Ng MY, Long J, Hernandez-Boussard T, Shah N, Miner A, Larson D, Langlotz CP. Organizational Factors in Clinical Data Sharing for Artificial Intelligence in Health Care. JAMA Netw Open 2023; 6:e2348422. [PMID: 38113040 PMCID: PMC10731479 DOI: 10.1001/jamanetworkopen.2023.48422] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Accepted: 11/03/2023] [Indexed: 12/21/2023] Open
Abstract
Importance Limited sharing of data sets that accurately represent disease and patient diversity limits the generalizability of artificial intelligence (AI) algorithms in health care. Objective To explore the factors associated with organizational motivation to share health data for AI development. Design, Setting, and Participants This qualitative study investigated organizational readiness for sharing health data across the academic, governmental, nonprofit, and private sectors. Using a multiple case studies approach, 27 semistructured interviews were conducted with leaders in data-sharing roles from August 29, 2022, to January 9, 2023. The interviews were conducted in the English language using a video conferencing platform. Using a purposive and nonprobabilistic sampling strategy, 78 individuals across 52 unique organizations were identified. Of these, 35 participants were enrolled. Participant recruitment concluded after 27 interviews, as theoretical saturation was reached and no additional themes emerged. Main Outcome and Measure Concepts defining organizational readiness for data sharing and the association between data-sharing factors and organizational behavior were mapped through iterative qualitative analysis to establish a framework defining organizational readiness for sharing clinical data for AI development. Results Interviews included 27 leaders from 18 organizations (academia: 10, government: 7, nonprofit: 8, and private: 2). Organizational readiness for data sharing centered around 2 main constructs: motivation and capabilities. Motivation related to the alignment of an organization's values with data-sharing priorities and was associated with its engagement in data-sharing efforts. However, organizational motivation could be modulated by extrinsic incentives for financial or reputational gains. Organizational capabilities comprised infrastructure, people, expertise, and access to data. Cross-sector collaboration was a key strategy to mitigate barriers to access health data. Conclusions and Relevance This qualitative study identified sector-specific factors that may affect the data-sharing behaviors of health organizations. External incentives may bolster cross-sector collaborations by helping overcome barriers to accessing health data for AI development. The findings suggest that tailored incentives may boost organizational motivation and facilitate sustainable flow of health data for AI development.
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Affiliation(s)
- Alaa Youssef
- Department of Radiology, Stanford University School of Medicine, Stanford, California
- Department of Medicine, Biomedical Informatics Research, Stanford University School of Medicine, California
| | - Madelena Y. Ng
- Department of Medicine, Biomedical Informatics Research, Stanford University School of Medicine, California
| | - Jin Long
- Department of Pediatrics, Stanford University School of Medicine, Stanford, California
| | - Tina Hernandez-Boussard
- Department of Medicine, Biomedical Informatics Research, Stanford University School of Medicine, California
- Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, California
| | - Nigam Shah
- Department of Medicine, Biomedical Informatics Research, Stanford University School of Medicine, California
- Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, California
| | - Adam Miner
- Department of Psychiatry, Stanford University School of Medicine, Stanford, California
| | - David Larson
- Department of Radiology, Stanford University School of Medicine, Stanford, California
| | - Curtis P. Langlotz
- Department of Radiology, Stanford University School of Medicine, Stanford, California
- Department of Medicine, Biomedical Informatics Research, Stanford University School of Medicine, California
- Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, California
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Varhol RJ, Norman R, Randall S, Man Ying Lee C, Trevenen L, Boyd JH, Robinson S. Public preference on sharing health data to inform research, health policy and clinical practice in Australia: A stated preference experiment. PLoS One 2023; 18:e0290528. [PMID: 37972118 PMCID: PMC10653479 DOI: 10.1371/journal.pone.0290528] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Accepted: 08/10/2023] [Indexed: 11/19/2023] Open
Abstract
OBJECTIVE To investigate public willingness to share sensitive health information for research, health policy and clinical practice. METHODS A total of 1,003 Australian respondents answered an online, attribute-driven, survey in which participants were asked to accept or reject hypothetical choice sets based on a willingness to share their health data for research and frontline-medical support as part of an integrated health system. The survey consisted of 5 attributes: Stakeholder access for analysis (Analysing group); Type of information collected; Purpose of data collection; Information governance; and Anticipated benefit; the results of which were analysed using logistic regression. RESULTS When asked about their preference for sharing their health data, respondents had no preference between data collection for the purposes of clinical practice, health policy or research, with a slight preference for having government organisations manage, govern and curate the integrated datasets from which the analysis was being conducted. The least preferred option was for personal health records to be integrated with insurance records or for their data collected by privately owned corporate organisations. Individuals preferred their data to be analysed by a public healthcare provider or government staff and expressed a dislike for any private company involvement. CONCLUSIONS The findings from this study suggest that Australian consumers prefer to share their health data when there is government oversight, and have concerns about sharing their anonymised health data for clinical practice, health policy or research purposes unless clarity is provided pertaining to its intended purpose, limitations of use and restrictions to access. Similar findings have been observed in the limited set of existing international studies utilising a stated preference approach. Evident from this study, and supported by national and international research, is that the establishment and preservation of a social license for data linkage in health research will require routine public engagement as a result of continuously evolving technological advancements and fluctuating risk tolerance. Without more work to understand and address stakeholder concerns, consumers risk being reluctant to participate in data-sharing and linkage programmes.
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Affiliation(s)
- Richard J. Varhol
- School of Population Health, Curtin University, Perth, Western Australia, Australia
| | - Richard Norman
- School of Population Health, Curtin University, Perth, Western Australia, Australia
| | - Sean Randall
- Deakin Health Economics, Institute for Health Transformation, Deakin University, Melbourne, Victoria, Australia
| | - Crystal Man Ying Lee
- School of Population Health, Curtin University, Perth, Western Australia, Australia
| | - Luke Trevenen
- School of Population Health, Curtin University, Perth, Western Australia, Australia
| | - James H. Boyd
- School of Psychology and Public Health, La Trobe University, Melbourne, Australia
| | - Suzanne Robinson
- School of Population Health, Curtin University, Perth, Western Australia, Australia
- Deakin Health Economics, Institute for Health Transformation, Deakin University, Melbourne, Victoria, Australia
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Baum L, Johns M, Poikela M, Möller R, Ananthasubramaniam B, Prasser F. Data integration and analysis for circadian medicine. Acta Physiol (Oxf) 2023; 237:e13951. [PMID: 36790321 DOI: 10.1111/apha.13951] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Revised: 02/04/2023] [Accepted: 02/12/2023] [Indexed: 02/16/2023]
Abstract
Data integration, data sharing, and standardized analyses are important enablers for data-driven medical research. Circadian medicine is an emerging field with a particularly high need for coordinated and systematic collaboration between researchers from different disciplines. Datasets in circadian medicine are multimodal, ranging from molecular circadian profiles and clinical parameters to physiological measurements and data obtained from (wearable) sensors or reported by patients. Uniquely, data spanning both the time dimension and the spatial dimension (across tissues) are needed to obtain a holistic view of the circadian system. The study of human rhythms in the context of circadian medicine has to confront the heterogeneity of clock properties within and across subjects and our inability to repeatedly obtain relevant biosamples from one subject. This requires informatics solutions for integrating and visualizing relevant data types at various temporal resolutions ranging from milliseconds and seconds to minutes and several hours. Associated challenges range from a lack of standards that can be used to represent all required data in a common interoperable form, to challenges related to data storage, to the need to perform transformations for integrated visualizations, and to privacy issues. The downstream analysis of circadian rhythms requires specialized approaches for the identification, characterization, and discrimination of rhythms. We conclude that circadian medicine research provides an ideal environment for developing innovative methods to address challenges related to the collection, integration, visualization, and analysis of multimodal multidimensional biomedical data.
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Affiliation(s)
- Lena Baum
- Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Marco Johns
- Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Maija Poikela
- Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Ralf Möller
- Institute of Information Systems, University of Lübeck, Lübeck, Germany
| | | | - Fabian Prasser
- Berlin Institute of Health at Charité-Universitätsmedizin Berlin, Berlin, Germany
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Gupta R, Iyengar R, Sharma M, Cannuscio CC, Merchant RM, Asch DA, Mitra N, Grande D. Consumer Views on Privacy Protections and Sharing of Personal Digital Health Information. JAMA Netw Open 2023; 6:e231305. [PMID: 36862410 PMCID: PMC9982693 DOI: 10.1001/jamanetworkopen.2023.1305] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/03/2023] Open
Abstract
IMPORTANCE Digital health information has many potential health applications, but privacy is a growing concern among consumers and policy makers. Consent alone is increasingly seen as inadequate to safeguard privacy. OBJECTIVE To determine whether different privacy protections are associated with consumers' willingness to share their digital health information for research, marketing, or clinical uses. DESIGN, SETTING, AND PARTICIPANTS This 2020 national survey with an embedded conjoint experiment recruited US adults from a nationally representative sample with oversampling of Black and Hispanic individuals. Willingness to share digital information across 192 different scenarios reflecting the product of 4 possible privacy protections, 3 uses of information, 2 users of information, and 2 sources of digital information was evaluated. Each participant was randomly assigned 9 scenarios. The survey was administrated between July 10 and July 31, 2020, in Spanish and English. Analysis for this study was conducted between May 2021 and July 2022. MAIN OUTCOMES AND MEASURES Participants rated each conjoint profile on a 5-point Likert scale measuring their willingness to share their personal digital information (with 5 indicating the most willingness to share). Results are reported as adjusted mean differences. RESULTS Of the 6284 potential participants, 3539 (56%) responded to the conjoint scenarios. A total of 1858 participants (53%) were female, 758 (21%) identified as Black, 833 (24%) identified as Hispanic, 1149 (33%) had an annual income less than $50 000, and 1274 (36%) were 60 years or older. Participants were more willing to share health information with the presence of each individual privacy protection, including consent (difference, 0.32; 95% CI, 0.29-0.35; P < .001), followed by data deletion (difference, 0.16; 95% CI, 0.13-0.18; P < .001), oversight (difference, 0.13; 95% CI, 0.10-0.15; P < .001), and transparency of data collected (difference, 0.08; 95% CI, 0.05-0.10; P < .001). The relative importance (importance weight on a 0%-100% scale) was greatest for the purpose of use (29.9%) but when considered collectively, the 4 privacy protections together were the most important (51.5%) factor in the conjoint experiment. When the 4 privacy protections were considered separately, consent was the most important (23.9%). CONCLUSIONS AND RELEVANCE In this survey study of a nationally representative sample of US adults, consumers' willingness to share personal digital health information for health purposes was associated with the presence of specific privacy protections beyond consent alone. Additional protections, including data transparency, oversight, and data deletion may strengthen consumer confidence in sharing their personal digital health information.
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Affiliation(s)
- Ravi Gupta
- Johns Hopkins University School of Medicine, Baltimore, Maryland
- Hopkins Business of Health Initiative, Johns Hopkins University, Baltimore, Maryland
- Center for Health Services and Outcomes Research, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | | | - Meghana Sharma
- Perelman School of Medicine, Division of General Internal Medicine, Department of Medicine, University of Pennsylvania, Philadelphia
| | - Carolyn C. Cannuscio
- Perelman School of Medicine, Department of Family Medicine and Community Health, University of Pennsylvania, Philadelphia
- Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia
| | - Raina M. Merchant
- Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia
- Perelman School of Medicine, Department of Emergency Medicine, University of Pennsylvania, Philadelphia
| | - David A. Asch
- Wharton School, University of Pennsylvania, Philadelphia
- Perelman School of Medicine, Division of General Internal Medicine, Department of Medicine, University of Pennsylvania, Philadelphia
- Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia
| | - Nandita Mitra
- Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia
- Perelman School of Medicine, Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia
| | - David Grande
- Perelman School of Medicine, Division of General Internal Medicine, Department of Medicine, University of Pennsylvania, Philadelphia
- Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia
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Thoma B, Karwowska A, Samson L, Labine N, Waters H, Giuliani M, Chan TM, Atkinson A, Constantin E, Hall AK, Gomez-Garibello C, Fowler N, Tourian L, Frank J, Anderson R, Snell L, Van Melle E. Emerging concepts in the CanMEDS physician competency framework. CANADIAN MEDICAL EDUCATION JOURNAL 2023; 14:4-12. [PMID: 36998506 PMCID: PMC10042782 DOI: 10.36834/cmej.75591] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Background The CanMEDS physician competency framework will be updated in 2025. The revision occurs during a time of disruption and transformation to society, healthcare, and medical education caused by the COVID-19 pandemic and growing acknowledgement of the impacts of colonialism, systemic discrimination, climate change, and emerging technologies on healthcare and training. To inform this revision, we sought to identify emerging concepts in the literature related to physician competencies. Methods Emerging concepts were defined as ideas discussed in the literature related to the roles and competencies of physicians that are absent or underrepresented in the 2015 CanMEDS framework. We conducted a literature scan, title and abstract review, and thematic analysis to identify emerging concepts. Metadata for all articles published in five medical education journals between October 1, 2018 and October 1, 2021 were extracted. Fifteen authors performed a title and abstract review to identify and label underrepresented concepts. Two authors thematically analyzed the results to identify emerging concepts. A member check was conducted. Results 1017 of 4973 (20.5%) of the included articles discussed an emerging concept. The thematic analysis identified ten themes: Equity, Diversity, Inclusion, and Social Justice; Anti-racism; Physician Humanism; Data-Informed Medicine; Complex Adaptive Systems; Clinical Learning Environment; Virtual Care; Clinical Reasoning; Adaptive Expertise; and Planetary Health. All themes were endorsed by the authorship team as emerging concepts. Conclusion This literature scan identified ten emerging concepts to inform the 2025 revision of the CanMEDS physician competency framework. Open publication of this work will promote greater transparency in the revision process and support an ongoing dialogue on physician competence. Writing groups have been recruited to elaborate on each of the emerging concepts and how they could be further incorporated into CanMEDS 2025.
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Affiliation(s)
- Brent Thoma
- University of Saskatchewan, Saskatchewan, Canada
- Royal College of Physicians and Surgeons of Canada, Ontario, Canada
| | - Anna Karwowska
- University of Ottawa, Ontario, Canada
- Association of Faculties of Medicine of Canada, Ontario, Canada
| | - Louise Samson
- Université de Montréal, Quebec, Canada
- Collège des médecins du Québec, Quebec, Canada
| | | | | | | | | | - Adelle Atkinson
- Royal College of Physicians and Surgeons of Canada, Ontario, Canada
- University of Toronto, Ontario, Canada
| | | | - Andrew K Hall
- Royal College of Physicians and Surgeons of Canada, Ontario, Canada
- University of Ottawa, Ontario, Canada
| | | | - Nancy Fowler
- McMaster University, Ontario, Canada
- College of Family Physicians of Canada, Ontario, Canada
| | | | | | - Rob Anderson
- Royal College of Physicians and Surgeons of Canada, Ontario, Canada
- NOSM University, Ontario, Canada
| | - Linda Snell
- Royal College of Physicians and Surgeons of Canada, Ontario, Canada
- McGill University, Quebec, Canada
| | - Elaine Van Melle
- Royal College of Physicians and Surgeons of Canada, Ontario, Canada
- Queen’s University, Ontario, Canada
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Soellner M, Koenigstorfer J. Motive perception pathways to the release of personal information to healthcare organizations. BMC Med Inform Decis Mak 2022; 22:240. [PMID: 36100876 PMCID: PMC9468521 DOI: 10.1186/s12911-022-01986-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Accepted: 09/07/2022] [Indexed: 11/23/2022] Open
Abstract
Background The goal of the study is to assess the downstream effects of who requests personal information from individuals for artificial intelligence-(AI) based healthcare research purposes—be it a pharmaceutical company (as an example of a for-profit organization) or a university hospital (as an example of a not-for-profit organization)—as well as their boundary conditions on individuals’ likelihood to release personal information about their health. For the latter, the study considers two dimensions: the tendency to self-disclose (which is aimed to be high so that AI applications can reach their full potential) and the tendency to falsify (which is aimed to be low so that AI applications are based on both valid and reliable data). Methods Across three experimental studies with Amazon Mechanical Turk workers from the U.S. (n = 204, n = 330, and n = 328, respectively), Covid-19 was used as the healthcare research context. Results University hospitals (vs. pharmaceutical companies) score higher on altruism and lower on egoism. Individuals were more willing to disclose data if they perceived that the requesting organization acts based on altruistic motives (i.e., the motives function as gate openers). Individuals were more likely to protect their data by intending to provide false information when they perceived egoistic motives to be the main driver for the organization requesting their data (i.e., the motives function as a privacy protection tool). Two moderators, namely message appeal (Study 2) and message endorser credibility (Study 3) influence the two indirect pathways of the release of personal information. Conclusion The findings add to Communication Privacy Management Theory as well as Attribution Theory by suggesting motive-based pathways to the release of correct personal health data. Compared to not-for-profit organizations, for-profit organizations are particularly recommended to match their message appeal with the organizations’ purposes (to provide personal benefit) and to use high-credibility endorsers in order to reduce inherent disadvantages in motive perceptions. Supplementary Information The online version contains supplementary material available at 10.1186/s12911-022-01986-4.
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Healthcare Providers’ Knowledge of Value-Based Care in Germany: An Adapted, Mixed-Methods Approach. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19148466. [PMID: 35886327 PMCID: PMC9322307 DOI: 10.3390/ijerph19148466] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 07/02/2022] [Accepted: 07/05/2022] [Indexed: 12/04/2022]
Abstract
Background: Value-Based Care (VBC) is being discussed to provide better outcomes to patients, with an aim to reimburse healthcare providers (HCPs) based on the quality of care they deliver. Little is known about German HCPs’ knowledge of VBC. This study aims to investigate the knowledge of HCPs of VBC and to identify potential needs for further education toward implementation of VBC in Germany. Methods: For evidence generation, we performed a literature search and conducted an online survey among HCPs at 89 hospitals across Germany. The questionnaire was based on published evidence and co-developed with an expert panel using a mixed methods approach. Results: We found HCPs to believe that VBC is more applicable in surgery than internal medicine and that well-defined cycles of care are essential for its application. HCPs believe that VBC can reduce health care costs significantly. However, they also assume that implementing VBC will be challenging. Conclusions: The concept in general is well perceived, however, HCPs do not want to participate in any financial risk sharing. Installing an authority/independent agency that measures achieved value, digital transformation, and that improves the transition between the inpatient and the outpatient sectors are top interests of HCPs.
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Subbiah IM, Grewal US. Development of a Regulatory Framework Governing Health Care Interactions on Social Media Platforms. JCO Oncol Pract 2022; 18:529-532. [PMID: 35357900 DOI: 10.1200/op.21.00879] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Affiliation(s)
- Ishwaria M Subbiah
- Division of Cancer Medicine, University of Texas MD Anderson Cancer Center, Houston, TX
| | - Udhayvir S Grewal
- Division of Internal Medicine, Louisiana State University Health Sciences Center, Shreveport, LA
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Rabello GM, Pêgo-Fernandes PM, Jatene FB. Are we preparing for the digital healthcare era? SAO PAULO MED J 2022; 140:161-162. [PMID: 35293940 PMCID: PMC9610250 DOI: 10.1590/1516-3180.2022.140225112021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
Affiliation(s)
- Guilherme Machado Rabello
- Engineer, Escola Politécnica da Universidade de São Paulo. Innovation Manager of InovaInCor, Instituto do Coracao, Hospital das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de Sao Paulo, Sao Paulo, SP, BR
| | - Paulo Manuel Pêgo-Fernandes
- MD, PhD. Full Professor, Thoracic Surgery Program, Instituto do Coracao, Hospital das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de Sao Paulo, Sao Paulo, SP, BR; Director, Scientific Department, Associação Paulista de Medicina (APM), São Paulo (SP), Brazil
| | - Fabio Biscegli Jatene
- MD, PhD. Full Professor, Cardiovascular Surgery Division, Coordinator of InovaInCor, Instituto do Coracao, Hospital das Clinicas HCFMUSP, Faculdade de Medicina, Universidade de Sao Paulo, Sao Paulo, SP, BR
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Adedinsewo DA, Pollak AW, Phillips SD, Smith TL, Svatikova A, Hayes SN, Mulvagh SL, Norris C, Roger VL, Noseworthy PA, Yao X, Carter RE. Cardiovascular Disease Screening in Women: Leveraging Artificial Intelligence and Digital Tools. Circ Res 2022; 130:673-690. [PMID: 35175849 PMCID: PMC8889564 DOI: 10.1161/circresaha.121.319876] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Cardiovascular disease remains the leading cause of death in women. Given accumulating evidence on sex- and gender-based differences in cardiovascular disease development and outcomes, the need for more effective approaches to screening for risk factors and phenotypes in women is ever urgent. Public health surveillance and health care delivery systems now continuously generate massive amounts of data that could be leveraged to enable both screening of cardiovascular risk and implementation of tailored preventive interventions across a woman's life span. However, health care providers, clinical guidelines committees, and health policy experts are not yet sufficiently equipped to optimize the collection of data on women, use or interpret these data, or develop approaches to targeting interventions. Therefore, we provide a broad overview of the key opportunities for cardiovascular screening in women while highlighting the potential applications of artificial intelligence along with digital technologies and tools.
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Affiliation(s)
- Demilade A. Adedinsewo
- Department of Cardiovascular Medicine (D.A.A., A.W.P., S.D.P.), Mayo Clinic, Jacksonville, FL
| | - Amy W. Pollak
- Department of Cardiovascular Medicine (D.A.A., A.W.P., S.D.P.), Mayo Clinic, Jacksonville, FL
| | - Sabrina D. Phillips
- Department of Cardiovascular Medicine (D.A.A., A.W.P., S.D.P.), Mayo Clinic, Jacksonville, FL
| | - Taryn L. Smith
- Division of General Internal Medicine (T.L.S.), Mayo Clinic, Jacksonville, FL
| | - Anna Svatikova
- Department of Cardiovascular Diseases (A.S.), Mayo Clinic, Phoenix, AZ
| | - Sharonne N. Hayes
- Department of Cardiovascular Medicine (S.N.H., S.L.M., V.L.R., P.A.N.), Mayo Clinic, Rochester, MN
| | - Sharon L. Mulvagh
- Department of Cardiovascular Medicine (S.N.H., S.L.M., V.L.R., P.A.N.), Mayo Clinic, Rochester, MN
- Division of Cardiology, Dalhousie University, Halifax, Nova Scotia, Canada (S.L.M.)
| | - Colleen Norris
- Cardiovascular Health and Stroke Strategic Clinical Network, Edmonton, Canada (C.N.)
| | - Veronique L. Roger
- Department of Cardiovascular Medicine (S.N.H., S.L.M., V.L.R., P.A.N.), Mayo Clinic, Rochester, MN
- Department of Quantitative Health Sciences (V.L.R.), Mayo Clinic, Rochester, MN
- Epidemiology and Community Health Branch, National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, MD (V.L.R.)
| | - Peter A. Noseworthy
- Department of Cardiovascular Medicine (S.N.H., S.L.M., V.L.R., P.A.N.), Mayo Clinic, Rochester, MN
| | - Xiaoxi Yao
- Robert D. and Patricia E. Kern Center for the Science of Health Care Delivery (X.Y.), Mayo Clinic, Rochester, MN
| | - Rickey E. Carter
- Department of Quantitative Health Sciences (R.E.C.), Mayo Clinic, Jacksonville, FL
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Oliva A, Grassi S, Vetrugno G, Rossi R, Della Morte G, Pinchi V, Caputo M. Management of Medico-Legal Risks in Digital Health Era: A Scoping Review. Front Med (Lausanne) 2022; 8:821756. [PMID: 35087854 PMCID: PMC8787306 DOI: 10.3389/fmed.2021.821756] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Accepted: 12/20/2021] [Indexed: 12/11/2022] Open
Abstract
Artificial intelligence needs big data to develop reliable predictions. Therefore, storing and processing health data is essential for the new diagnostic and decisional technologies but, at the same time, represents a risk for privacy protection. This scoping review is aimed at underlying the medico-legal and ethical implications of the main artificial intelligence applications to healthcare, also focusing on the issues of the COVID-19 era. Starting from a summary of the United States (US) and European Union (EU) regulatory frameworks, the current medico-legal and ethical challenges are discussed in general terms before focusing on the specific issues regarding informed consent, medical malpractice/cognitive biases, automation and interconnectedness of medical devices, diagnostic algorithms and telemedicine. We aim at underlying that education of physicians on the management of this (new) kind of clinical risks can enhance compliance with regulations and avoid legal risks for the healthcare professionals and institutions.
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Affiliation(s)
- Antonio Oliva
- Legal Medicine, Department of Health Surveillance and Bioethics, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Simone Grassi
- Legal Medicine, Department of Health Surveillance and Bioethics, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Giuseppe Vetrugno
- Legal Medicine, Department of Health Surveillance and Bioethics, Università Cattolica del Sacro Cuore, Rome, Italy.,Risk Management Unit, Fondazione Policlinico A. Gemelli Istituto di Ricovero e Cura a Carattere Scientifico, Rome, Italy
| | - Riccardo Rossi
- Legal Medicine, Department of Health Surveillance and Bioethics, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Gabriele Della Morte
- International Law, Institute of International Studies, Università Cattolica del Sacro Cuore, Milan, Italy
| | - Vilma Pinchi
- Department of Health Sciences, Section of Forensic Medical Sciences, University of Florence, Florence, Italy
| | - Matteo Caputo
- Criminal Law, Department of Juridical Science, Università Cattolica del Sacro Cuore, Milan, Italy
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13
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14
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Tahri Sqalli M, Al-Thani D. Evolution of Wearable Devices in Health Coaching: Challenges and Opportunities. Front Digit Health 2021; 2:545646. [PMID: 34713031 PMCID: PMC8521831 DOI: 10.3389/fdgth.2020.545646] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2020] [Accepted: 09/15/2020] [Indexed: 11/13/2022] Open
Abstract
Wearable devices hold an enormous potential in contributing to an improved global health. The availability, non-invasiveness, and affordability of those systems make them promising candidates to transform the standard of care for health coaching. These wearable devices are now considered as versatile coaching systems. Patients who wish to improve their health and well-being refer to wearables for tracking and quantifying their improvement. The timeliness of the “wearable device as a health coaching enabler” field of research will inevitably know a prominent growth in the upcoming years. This growth is expected to stem from both the computing and the medical fields. In this perspective article, we list the potential challenges as well as the opportunities of this newly born field from an interdisciplinary perspective. We mainly focus on both the computing and healthcare perspectives. We also chart guidelines for the healthcare research community that is willing to get involved in the computing field to harness the benefits of wearable devices.
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Affiliation(s)
- Mohammed Tahri Sqalli
- Information and Computing Technology Division, College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar
| | - Dena Al-Thani
- Information and Computing Technology Division, College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar
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15
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Aggarwal R, Farag S, Martin G, Ashrafian H, Darzi A. Patient Perceptions on Data Sharing and Applying Artificial Intelligence to Health Care Data: Cross-sectional Survey. J Med Internet Res 2021; 23:e26162. [PMID: 34236994 PMCID: PMC8430862 DOI: 10.2196/26162] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Revised: 04/04/2021] [Accepted: 07/05/2021] [Indexed: 12/25/2022] Open
Abstract
Background Considerable research is being conducted as to how artificial intelligence (AI) can be effectively applied to health care. However, for the successful implementation of AI, large amounts of health data are required for training and testing algorithms. As such, there is a need to understand the perspectives and viewpoints of patients regarding the use of their health data in AI research. Objective We surveyed a large sample of patients for identifying current awareness regarding health data research, and for obtaining their opinions and views on data sharing for AI research purposes, and on the use of AI technology on health care data. Methods A cross-sectional survey with patients was conducted at a large multisite teaching hospital in the United Kingdom. Data were collected on patient and public views about sharing health data for research and the use of AI on health data. Results A total of 408 participants completed the survey. The respondents had generally low levels of prior knowledge about AI. Most were comfortable with sharing health data with the National Health Service (NHS) (318/408, 77.9%) or universities (268/408, 65.7%), but far fewer with commercial organizations such as technology companies (108/408, 26.4%). The majority endorsed AI research on health care data (357/408, 87.4%) and health care imaging (353/408, 86.4%) in a university setting, provided that concerns about privacy, reidentification of anonymized health care data, and consent processes were addressed. Conclusions There were significant variations in the patient perceptions, levels of support, and understanding of health data research and AI. Greater public engagement levels and debates are necessary to ensure the acceptability of AI research and its successful integration into clinical practice in future.
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Affiliation(s)
- Ravi Aggarwal
- Institute of Global Health Innovation, Imperial College London, London, United Kingdom
| | - Soma Farag
- Institute of Global Health Innovation, Imperial College London, London, United Kingdom
| | - Guy Martin
- Institute of Global Health Innovation, Imperial College London, London, United Kingdom
| | - Hutan Ashrafian
- Institute of Global Health Innovation, Imperial College London, London, United Kingdom
| | - Ara Darzi
- Institute of Global Health Innovation, Imperial College London, London, United Kingdom
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16
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Grande D, Luna Marti X, Merchant RM, Asch DA, Dolan A, Sharma M, Cannuscio CC. Consumer Views on Health Applications of Consumer Digital Data and Health Privacy Among US Adults: Qualitative Interview Study. J Med Internet Res 2021; 23:e29395. [PMID: 34106074 PMCID: PMC8262668 DOI: 10.2196/29395] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2021] [Revised: 05/10/2021] [Accepted: 05/16/2021] [Indexed: 11/15/2022] Open
Abstract
BACKGROUND In 2020, the number of internet users surpassed 4.6 billion. Individuals who create and share digital data can leave a trail of information about their habits and preferences that collectively generate a digital footprint. Studies have shown that digital footprints can reveal important information regarding an individual's health status, ranging from diet and exercise to depression. Uses of digital applications have accelerated during the COVID-19 pandemic where public health organizations have utilized technology to reduce the burden of transmission, ultimately leading to policy discussions about digital health privacy. Though US consumers report feeling concerned about the way their personal data is used, they continue to use digital technologies. OBJECTIVE This study aimed to understand the extent to which consumers recognize possible health applications of their digital data and identify their most salient concerns around digital health privacy. METHODS We conducted semistructured interviews with a diverse national sample of US adults from November 2018 to January 2019. Participants were recruited from the Ipsos KnowledgePanel, a nationally representative panel. Participants were asked to reflect on their own use of digital technology, rate various sources of digital information, and consider several hypothetical scenarios with varying sources and health-related applications of personal digital information. RESULTS The final cohort included a diverse national sample of 45 US consumers. Participants were generally unaware what consumer digital data might reveal about their health. They also revealed limited knowledge of current data collection and aggregation practices. When responding to specific scenarios with health-related applications of data, they had difficulty weighing the benefits and harms but expressed a desire for privacy protection. They saw benefits in using digital data to improve health, but wanted limits to health programs' use of consumer digital data. CONCLUSIONS Current privacy restrictions on health-related data are premised on the notion that these data are derived only from medical encounters. Given that an increasing amount of health-related data is derived from digital footprints in consumer settings, our findings suggest the need for greater transparency of data collection and uses, and broader health privacy protections.
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Affiliation(s)
- David Grande
- Division of General Internal Medicine, University of Pennsylvania, Philadelphia, PA, United States
- Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, PA, United States
| | - Xochitl Luna Marti
- Division of General Internal Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Raina M Merchant
- Department of Emergency Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - David A Asch
- Division of General Internal Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Abby Dolan
- Department of Emergency Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Meghana Sharma
- Division of General Internal Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Carolyn C Cannuscio
- Department of Family Medicine and Community Health, University of Pennsylvania, Philadelphia, PA, United States
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17
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Spinazze P, Aardoom J, Chavannes N, Kasteleyn M. The Computer Will See You Now: Overcoming Barriers to Adoption of Computer-Assisted History Taking (CAHT) in Primary Care. J Med Internet Res 2021; 23:e19306. [PMID: 33625360 PMCID: PMC7946588 DOI: 10.2196/19306] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2020] [Revised: 12/23/2020] [Accepted: 01/24/2021] [Indexed: 01/10/2023] Open
Abstract
Patient health information is increasingly collected through multiple modalities, including electronic health records, wearables, and connected devices. Computer-assisted history taking could provide an additional channel to collect highly relevant, comprehensive, and accurate patient information while reducing the burden on clinicians and face-to-face consultation time. Considering restrictions to consultation time and the associated negative health outcomes, patient-provided health data outside of consultation can prove invaluable in health care delivery. Over the years, research has highlighted the numerous benefits of computer-assisted history taking; however, the limitations have proved an obstacle to adoption. In this viewpoint, we review these limitations under 4 main categories (accessibility, affordability, accuracy, and acceptability) and discuss how advances in technology, computing power, and ubiquity of personal devices offer solutions to overcoming these.
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Affiliation(s)
- Pier Spinazze
- Global Digital Health Unit, Department of Primary Care and Public Health, School of Public Health, Imperial College London, London, United Kingdom
| | - Jiska Aardoom
- Department of Public Health and Primary Care, Leiden University Medical Center, Leiden, Netherlands
| | - Niels Chavannes
- Department of Public Health and Primary Care, Leiden University Medical Center, Leiden, Netherlands
| | - Marise Kasteleyn
- Department of Public Health and Primary Care, Leiden University Medical Center, Leiden, Netherlands
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18
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Rothstein MA. Big Data, Surveillance Capitalism, and Precision Medicine: Challenges for Privacy. THE JOURNAL OF LAW, MEDICINE & ETHICS : A JOURNAL OF THE AMERICAN SOCIETY OF LAW, MEDICINE & ETHICS 2021; 49:666-676. [PMID: 35006048 DOI: 10.1017/jme.2021.91] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Surveillance capitalism companies, such as Google and Facebook, have substantially increased the amount of information collected, analyzed, and monetized, including health information increasingly used in precision medicine research, thereby presenting great challenges for health privacy.
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19
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Jin F, Yao C, Yan X, Dong C, Lai J, Li L, Wang B, Tan Y, Zhu S. Gap between real-world data and clinical research within hospitals in China: a qualitative study. BMJ Open 2020; 10:e038375. [PMID: 33376160 PMCID: PMC7778758 DOI: 10.1136/bmjopen-2020-038375] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
OBJECTIVE To investigate the gap between real-world data and clinical research initiated by doctors in China, explore the potential reasons for this gap and collect different stakeholders' suggestions. DESIGN This qualitative study involved three types of hospital personnel based on three interview outlines. The data analysis was performed using the constructivist grounded theory analysis process. SETTING Six tertiary hospitals (three general hospitals and three specialised hospitals) in Beijing, China, were included. PARTICIPANTS In total, 42 doctors from 12 departments, 5 information technology managers and 4 clinical managers were interviewed through stratified purposive sampling. RESULTS Electronic medical record data cannot be directly downloaded into clinical research files, which is a major problem in China. The lack of data interoperability, unstructured electronic medical record data and concerns regarding data security create a gap between real-world data and research data. Updating hospital information systems, promoting data standards and establishing an independent clinical research platform may be feasible suggestions for solving the current problems. CONCLUSIONS Determining the causes of gaps and targeted solutions could contribute to the development of clinical research in China. This research suggests that updating the hospital information system, promoting data standards and establishing a clinical research platform could promote the use of real-world data in the future.
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Affiliation(s)
- Feifei Jin
- Department of Biostatistics, Peking University First Hospital, Beijing, Beijing, China
| | - Chen Yao
- Department of Biostatistics, Peking University First Hospital, Beijing, Beijing, China
- Peking University Clinical Research Institute, Beijing, Beijing, China
| | - Xiaoyan Yan
- Peking University Clinical Research Institute, Beijing, Beijing, China
| | - Chongya Dong
- Department of Biostatistics, Peking University First Hospital, Beijing, Beijing, China
| | - Junkai Lai
- Department of Biostatistics, Peking University First Hospital, Beijing, Beijing, China
| | - Li Li
- First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin, Tianjin, China
| | - Bin Wang
- Department of Biostatistics, Peking University First Hospital, Beijing, Beijing, China
| | - Yao Tan
- Department of Biostatistics, Peking University First Hospital, Beijing, Beijing, China
| | - Sainan Zhu
- Department of Biostatistics, Peking University First Hospital, Beijing, Beijing, China
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20
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Affiliation(s)
- Neil Shah
- Division of Cardiovascular Medicine, Hospital of the University of Pennsylvania
| | - Srinath Adusumalli
- Division of Cardiovascular Medicine, Hospital of the University of Pennsylvania.,Penn Medicine Nudge Unit, Penn Medicine Center for Healthcare Innovation.,Office of the Chief Medical Information Officer, University of Pennsylvania Health System
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21
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Kirk CP, Rifkin LS. I'll trade you diamonds for toilet paper: Consumer reacting, coping and adapting behaviors in the COVID-19 pandemic. JOURNAL OF BUSINESS RESEARCH 2020; 117:124-131. [PMID: 32834208 PMCID: PMC7241317 DOI: 10.1016/j.jbusres.2020.05.028] [Citation(s) in RCA: 116] [Impact Index Per Article: 29.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/13/2020] [Accepted: 05/14/2020] [Indexed: 05/04/2023]
Abstract
In this research, we document some of the many unusual consumer behavior patterns that came to dominate the early days of the COVID-19 pandemic. We offer insights based on theory to help explain and predict these behaviors and associated outcomes in order to inform future research and marketing practice. Taking an environmentally-imposed constraints point of view, we examine behaviors during each of three phases: reacting (e.g., hoarding and rejecting), coping (e.g. maintaining social connectedness, do-it-yourself behaviors, changing views of brands) and longer-term adapting (e.g. potentially transformative changes in consumption and individual and social identity). We discuss implications for marketing researchers and practice.
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Affiliation(s)
- Colleen P Kirk
- Manhattan College, 4513 Manhattan College Pkwy, The Bronx, NY 10471, United States
- New York Institute of Technology, 1855 Broadway, New York, NY 10023, United States
| | - Laura S Rifkin
- Brooklyn College, 2900 Bedford Avenue, Brooklyn, NY 11210, United States
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22
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Jongsma KR, van den Heuvel JFM, Rake J, Bredenoord AL, Bekker MN. User Experiences With and Recommendations for Mobile Health Technology for Hypertensive Disorders of Pregnancy: Mixed Methods Study. JMIR Mhealth Uhealth 2020; 8:e17271. [PMID: 32749225 PMCID: PMC7435610 DOI: 10.2196/17271] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2019] [Revised: 05/05/2020] [Accepted: 05/13/2020] [Indexed: 01/13/2023] Open
Abstract
Background Hypertensive disorders of pregnancy (HDP) are a primary cause of adverse maternal and neonatal outcomes worldwide. For women at risk of hypertensive complications, guidelines recommend frequent surveillance of blood pressure and signs of preeclampsia. Clinic visits range from every 2 weeks to several times a week. Given the wide ubiquity of smartphones and computers in most countries and a growing attention for self-management, digital technologies, including mobile health (mHealth), constitute a promising component of monitoring (self-measured) blood pressure during pregnancy. Currently, little is known about the experiences of women using such platforms and how mHealth can be aligned with their needs and preferences. Objective The objectives were twofold: (1) to explore the experiences of Dutch women who had an increased risk of HDP with a blended care approach (mHealth combined with face-to-face care) for remote self-monitoring of blood pressure and preeclampsia symptoms and (2) to formulate recommendations for the use and integration of mHealth in clinical care. Methods Alongside a prospective blended care study (SAFE@home study) that monitors pregnant women at increased risk of HPD with mHealth technology, a mixed methods study was conducted, including questionnaires (n=52) and interviews (n=11). Results were analyzed thematically. Results Of the 4 themes, 2 themes were related to the technologies themselves (expectations, usability), and 2 themes were related to the interaction and use of mHealth (autonomy and responsibilities of patients, responsibilities of health care professionals). First, the digital platform met the expectations of patients, which contributed to user satisfaction. Second, the platform was considered user-friendly, and patients favored different moments and frequencies for measuring their blood pressure. Third, patient autonomy was mentioned in terms of increased insight about their own condition and being able to influence clinical decision making. Fourth, clinical expertise of health care professionals was considered essential to interpret the data, which translates to subsequent responsibilities for clinical management. Data from the questionnaires and interviews corresponded. Conclusions Blended care using an mHealth tool to monitor blood pressure in pregnancy was positively evaluated by its users. Insights from participants led to 7 recommendations for designing and implementing similar interventions and to enhance future, morally sound use of digital technologies in clinical care.
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Affiliation(s)
- Karin Rolanda Jongsma
- Department of Medical Humanities, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
| | | | - Jasmijn Rake
- Obstetrics and Gynaecology, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
| | - Annelien L Bredenoord
- Department of Medical Humanities, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
| | - Mireille N Bekker
- Obstetrics and Gynaecology, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
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23
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Ghafur S, Van Dael J, Leis M, Darzi A, Sheikh A. Public perceptions on data sharing: key insights from the UK and the USA. LANCET DIGITAL HEALTH 2020; 2:e444-e446. [PMID: 32838250 PMCID: PMC7380931 DOI: 10.1016/s2589-7500(20)30161-8] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Affiliation(s)
- Saira Ghafur
- Institute of Global Health Innovation, Imperial College London, St Mary's Hospital, London W2 1NY, UK
| | - Jackie Van Dael
- Institute of Global Health Innovation, Imperial College London, St Mary's Hospital, London W2 1NY, UK
| | - Melanie Leis
- Institute of Global Health Innovation, Imperial College London, St Mary's Hospital, London W2 1NY, UK
| | - Ara Darzi
- Institute of Global Health Innovation, Imperial College London, St Mary's Hospital, London W2 1NY, UK
| | - Aziz Sheikh
- Usher Institute, University of Edinburgh, Edinburgh, UK
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24
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Affiliation(s)
- Jayoung Kim
- Departments of Surgery, Cedars-Sinai Medical Center, Los Angeles, CA, USA.,Department of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Bruce Gewertz
- Departments of Surgery, Cedars-Sinai Medical Center, Los Angeles, CA, USA
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25
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Flanagin A, Bauchner H, Fontanarosa PB. Patient and Study Participant Rights to Privacy in Journal Publication. JAMA 2020; 323:2147-2150. [PMID: 32484519 DOI: 10.1001/jama.2020.3590] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
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26
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Mascitti M, Campisi G. Dental Public Health Landscape: Challenges, Technological Innovation and Opportunities in the 21st Century and COVID-19 Pandemic. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2020; 17:ijerph17103636. [PMID: 32455777 PMCID: PMC7277855 DOI: 10.3390/ijerph17103636] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 05/18/2020] [Accepted: 05/20/2020] [Indexed: 12/20/2022]
Abstract
In response to the 2008 economic and financial crisis and to its effects on healthcare systems, dental care has become unaffordable for many people, and a huge number of patients worldwide are avoiding or skipping necessary dental treatments [...].
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Affiliation(s)
- Marco Mascitti
- Department of Clinical Specialistic and Dental Sciences, Marche Polytechnic University, 60126 Ancona, Italy
- Correspondence: (M.M.); (G.C.); Tel.: +39-071-2206226 (M.M.); +39-091-6552236 (G.C.)
| | - Giuseppina Campisi
- Department of Surgical, Oncological and Oral Sciences, University of Palermo, 90127 Palermo, Italy
- Correspondence: (M.M.); (G.C.); Tel.: +39-071-2206226 (M.M.); +39-091-6552236 (G.C.)
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27
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Yasaka TM, Lehrich BM, Sahyouni R. Peer-to-Peer Contact Tracing: Development of a Privacy-Preserving Smartphone App. JMIR Mhealth Uhealth 2020; 8:e18936. [PMID: 32240973 PMCID: PMC7144575 DOI: 10.2196/18936] [Citation(s) in RCA: 97] [Impact Index Per Article: 24.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2020] [Revised: 03/31/2020] [Accepted: 04/01/2020] [Indexed: 11/13/2022] Open
Abstract
BACKGROUND The novel coronavirus disease 2019 (COVID-19) pandemic is an urgent public health crisis, with epidemiologic models predicting severe consequences, including high death rates, if the virus is permitted to run its course without any intervention or response. Contact tracing using smartphone technology is a powerful tool that may be employed to limit disease transmission during an epidemic or pandemic; yet, contact tracing apps present significant privacy concerns regarding the collection of personal data such as location. OBJECTIVE The aim of this study is to develop an effective contact tracing smartphone app that respects user privacy by not collecting location information or other personal data. METHODS We propose the use of an anonymized graph of interpersonal interactions to conduct a novel form of contact tracing and have developed a proof-of-concept smartphone app that implements this approach. Additionally, we developed a computer simulation model that demonstrates the impact of our proposal on epidemic or pandemic outbreak trajectories across multiple rates of adoption. RESULTS Our proof-of-concept smartphone app allows users to create "checkpoints" for contact tracing, check their risk level based on their past interactions, and anonymously self-report a positive status to their peer network. Our simulation results suggest that higher adoption rates of such an app may result in a better controlled epidemic or pandemic outbreak. CONCLUSIONS Our proposed smartphone-based contact tracing method presents a novel solution that preserves privacy while demonstrating the potential to suppress an epidemic or pandemic outbreak. This app could potentially be applied to the current COVID-19 pandemic as well as other epidemics or pandemics in the future to achieve a middle ground between drastic isolation measures and unmitigated disease spread.
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Affiliation(s)
- Tyler M Yasaka
- Department of Otolaryngology - Head and Neck Surgery, University of California, Irvine, Irvine, CA, United States
| | - Brandon M Lehrich
- Department of Biomedical Engineering, University of California, Irvine, Irvine, CA, United States
| | - Ronald Sahyouni
- Department of Biomedical Engineering, University of California, Irvine, Irvine, CA, United States.,Medical Scientist Training Program, University of California, Irvine, Irvine, CA, United States
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28
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Consumer preference to utilise a mobile health app: A stated preference experiment. PLoS One 2020; 15:e0229546. [PMID: 32084250 PMCID: PMC7034842 DOI: 10.1371/journal.pone.0229546] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2019] [Accepted: 02/08/2020] [Indexed: 01/19/2023] Open
Abstract
BACKGROUND One prominent barrier faced by healthcare consumers when accessing health services is a common requirement to complete repetitive, inefficient paper-based documentation at multiple registration sites. Digital innovation has a potential role to reduce the burden in this area, through the collection and sharing of data between healthcare providers. While there is growing evidence for digital innovations to potentially improve the effectiveness and efficiency of health systems, there is less information on the willingness of healthcare consumers to embrace and utilise technology to provide data. AIM The study aims to improve understanding of consumers' preference for utilising a digital health administration mobile app. METHODS The online study used a stated preference experiment design to explore aspects of consumers' preference for a mobile health administration app and its impact on the likelihood of using the app. The survey was answered by a representative sample (by age and gender) of Australian adults, and sociodemographic factors were also recorded for analysis. Each participant answered eight choice sets in which a hypothetical app (defined by a set of dimensions and levels) was presented and the respondent was asked if they would be willing to provide data using that app. Analysis was conducted using bivariate logistic regression. RESULTS For the average respondent, the two most important dimensions were the time it took to register on the app and the electronic governance arrangements around their personal information. Willingness to use any app was found to differ based on respondent characteristics: people with higher education, and women, were relatively more willing to utilise the mobile health app. CONCLUSION This study investigated consumers' willingness to utilise a digital health administration mobile app. The identification of key characteristics of more acceptable apps provide valuable insight and recommendations for developers of similar digital health administration technologies. This would increase the likelihood of achieving successful acceptance and utilisation by consumers. The results from this study provide evidence-based recommendations for future research and policy development, planning and implementation of digital health administration mobile applications in Australia.
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Abstract
BACKGROUND Sharing de-identified individual-level health research data is widely promoted and has many potential benefits. However there are also some potential harms, such as misuse of data and breach of participant confidentiality. One way to promote the benefits of sharing while ameliorating its potential harms is through the adoption of a managed access approach where data requests are channeled through a Data Access Committee (DAC), rather than making data openly available without restrictions. A DAC, whether a formal or informal group of individuals, has the responsibility of reviewing and assessing data access requests. Many individual groups, consortiums, institutional and independent DACs have been established but there is currently no widely accepted framework for their organization and function. MAIN TEXT We propose that DACs, should have the role of both promotion of data sharing and protection of data subjects, their communities, data producers, their institutions and the scientific enterprise. We suggest that data access should be granted by DACs as long as the data reuse has potential social value and provided there is low risk of foreseeable harms. To promote data sharing and to motivate data producers, DACs should encourage secondary uses that are consistent with the interests of data producers and their own institutions. Given the suggested roles of DACs, there should be transparent, simple and clear application procedures for data access. The approach to review of applications should be proportionate to the potential risks involved. DACs should be established within institutional and legal frameworks with clear lines of accountability, terms of reference and membership. We suggest that DACs should not be modelled after research ethics committees (RECs) because their functions and goals of review are different from those of RECs. DAC reviews should be guided by the principles of public health ethics instead of research ethics. CONCLUSIONS In this paper we have suggested a framework under which DACs should operate, how they should be organised, and how to constitute them.
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Affiliation(s)
- Phaik Yeong Cheah
- Mahidol Oxford Tropical Medicine Research Unit (MORU), Faculty of Tropical Medicine, Mahidol University, 420/6 Rajvithi Road, Bangkok, Thailand
- Centre for Tropical Medicine and Global Health, Nuffield Department of Clinical Medicine, University of Oxford, Old Road Campus, Roosevelt Drive, Oxford, UK
- The Ethox Centre, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Jan Piasecki
- Department of Philosophy and Bioethics, Faculty of Health Sciences, Jagiellonian University Medical College, ul. Michalowskiego 12, Krakow, Poland
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30
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Bauer M, Glenn T, Geddes J, Gitlin M, Grof P, Kessing LV, Monteith S, Faurholt-Jepsen M, Severus E, Whybrow PC. Smartphones in mental health: a critical review of background issues, current status and future concerns. Int J Bipolar Disord 2020; 8:2. [PMID: 31919635 PMCID: PMC6952480 DOI: 10.1186/s40345-019-0164-x] [Citation(s) in RCA: 55] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/26/2019] [Accepted: 10/24/2019] [Indexed: 02/06/2023] Open
Abstract
There has been increasing interest in the use of smartphone applications (apps) and other consumer technology in mental health care for a number of years. However, the vision of data from apps seamlessly returned to, and integrated in, the electronic medical record (EMR) to assist both psychiatrists and patients has not been widely achieved, due in part to complex issues involved in the use of smartphone and other consumer technology in psychiatry. These issues include consumer technology usage, clinical utility, commercialization, and evolving consumer technology. Technological, legal and commercial issues, as well as medical issues, will determine the role of consumer technology in psychiatry. Recommendations for a more productive direction for the use of consumer technology in psychiatry are provided.
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Affiliation(s)
- Michael Bauer
- Department of Psychiatry and Psychotherapy, University Hospital Carl Gustav Carus, Medical Faculty, Technische Universität Dresden, Fetscherstr. 74, 01307, Dresden, Germany.
| | - Tasha Glenn
- ChronoRecord Association, Fullerton, CA, USA
| | - John Geddes
- Department of Psychiatry, University of Oxford, Warneford Hospital, Oxford, UK
| | - Michael Gitlin
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles (UCLA), Los Angeles, CA, USA
| | - Paul Grof
- Mood Disorders Center of Ottawa, Ottawa, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Lars V Kessing
- Copenhagen Affective Disorder Research Center (CADIC), Psychiatric Center Copenhagen, Rigshospitalet, Copenhagen, Denmark
| | - Scott Monteith
- Michigan State University College of Human Medicine, Traverse City Campus, Traverse City, MI, USA
| | - Maria Faurholt-Jepsen
- Copenhagen Affective Disorder Research Center (CADIC), Psychiatric Center Copenhagen, Rigshospitalet, Copenhagen, Denmark
| | - Emanuel Severus
- Department of Psychiatry and Psychotherapy, University Hospital Carl Gustav Carus, Medical Faculty, Technische Universität Dresden, Fetscherstr. 74, 01307, Dresden, Germany
| | - Peter C Whybrow
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute for Neuroscience and Human Behavior, University of California Los Angeles (UCLA), Los Angeles, CA, USA
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31
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Pham Q, Shaw J, Morita PP, Seto E, Stinson JN, Cafazzo JA. The Service of Research Analytics to Optimize Digital Health Evidence Generation: Multilevel Case Study. J Med Internet Res 2019; 21:e14849. [PMID: 31710296 PMCID: PMC6878108 DOI: 10.2196/14849] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2019] [Revised: 08/05/2019] [Accepted: 09/02/2019] [Indexed: 01/19/2023] Open
Abstract
Background The widespread adoption of digital health interventions for chronic disease self-management has catalyzed a paradigm shift in the selection of methodologies used to evidence them. Recently, the application of digital health research analytics has emerged as an efficient approach to evaluate these data-rich interventions. However, there is a growing mismatch between the promising evidence base emerging from analytics mediated trials and the complexity of introducing these novel research methods into evaluative practice. Objective This study aimed to generate transferable insights into the process of implementing research analytics to evaluate digital health interventions. We sought to answer the following two research questions: (1) how should the service of research analytics be designed to optimize digital health evidence generation? and (2) what are the challenges and opportunities to scale, spread, and sustain this service in evaluative practice? Methods We conducted a qualitative multilevel embedded single case study of implementing research analytics in evaluative practice that comprised a review of the policy and regulatory climate in Ontario (macro level), a field study of introducing a digital health analytics platform into evaluative practice (meso level), and interviews with digital health innovators on their perceptions of analytics and evaluation (microlevel). Results The practice of research analytics is an efficient and effective means of supporting digital health evidence generation. The introduction of a research analytics platform to evaluate effective engagement with digital health interventions into a busy research lab was ultimately accepted by research staff, became routinized in their evaluative practice, and optimized their existing mechanisms of log data analysis and interpretation. The capacity for research analytics to optimize digital health evaluations is highest when there is (1) a collaborative working relationship between research client and analytics service provider, (2) a data-driven research agenda, (3) a robust data infrastructure with clear documentation of analytic tags, (4) in-house software development expertise, and (5) a collective tolerance for methodological change. Conclusions Scientific methods and practices that can facilitate the agile trials needed to iterate and improve digital health interventions warrant continued implementation. The service of research analytics may help to accelerate the pace of digital health evidence generation and build a data-rich research infrastructure that enables continuous learning and evaluation.
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Affiliation(s)
- Quynh Pham
- Institute of Health Policy, Management, and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada.,Centre for Global eHealth Innovation, Techna Institute, University Health Network, Toronto, ON, Canada
| | - James Shaw
- Institute of Health Policy, Management, and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada.,Women's College Hospital, Institute for Health System Solutions and Virtual Care, Toronto, ON, Canada
| | - Plinio P Morita
- Institute of Health Policy, Management, and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada.,School of Public Health and Health Systems, Faculty of Applied Health Sciences, University of Waterloo, Waterloo, ON, Canada
| | - Emily Seto
- Institute of Health Policy, Management, and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada.,Centre for Global eHealth Innovation, Techna Institute, University Health Network, Toronto, ON, Canada
| | - Jennifer N Stinson
- Institute of Health Policy, Management, and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada.,Department of Anesthesia and Pain Medicine, The Hospital for Sick Children, Toronto, ON, Canada.,Lawrence S Bloomberg Faculty of Nursing, University of Toronto, Toronto, ON, Canada.,Child Health Evaluative Sciences Research Institute, The Hospital for Sick Children, Toronto, ON, Canada
| | - Joseph A Cafazzo
- Institute of Health Policy, Management, and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada.,Centre for Global eHealth Innovation, Techna Institute, University Health Network, Toronto, ON, Canada.,Institute of Biomaterials and Biomedical Engineering, Faculty of Applied Science and Engineering, University of Toronto, Toronto, ON, Canada
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Kim HH, Kim B, Joo S, Shin SY, Cha HS, Park YR. Why Do Data Users Say Health Care Data Are Difficult to Use? A Cross-Sectional Survey Study. J Med Internet Res 2019; 21:e14126. [PMID: 31389335 PMCID: PMC6701164 DOI: 10.2196/14126] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2019] [Revised: 06/27/2019] [Accepted: 06/29/2019] [Indexed: 12/27/2022] Open
Abstract
Background There has been significant effort in attempting to use health care data. However, laws that protect patients’ privacy have restricted data use because health care data contain sensitive information. Thus, discussions on privacy laws now focus on the active use of health care data beyond protection. However, current literature does not clarify the obstacles that make data usage and deidentification processes difficult or elaborate on users’ needs for data linking from practical perspectives. Objective The objective of this study is to investigate (1) the current status of data use in each medical area, (2) institutional efforts and difficulties in deidentification processes, and (3) users’ data linking needs. Methods We conducted a cross-sectional online survey. To recruit people who have used health care data, we publicized the promotion campaign and sent official documents to an academic society encouraging participation in the online survey. Results In total, 128 participants responded to the online survey; 10 participants were excluded for either inconsistent responses or lack of demand for health care data. Finally, 118 participants’ responses were analyzed. The majority of participants worked in general hospitals or universities (62/118, 52.5% and 51/118, 43.2%, respectively, multiple-choice answers). More than half of participants responded that they have a need for clinical data (82/118, 69.5%) and public data (76/118, 64.4%). Furthermore, 85.6% (101/118) of respondents conducted deidentification measures when using data, and they considered rigid social culture as an obstacle for deidentification (28/101, 27.7%). In addition, they required data linking (98/118, 83.1%), and they noted deregulation and data standardization to allow access to health care data linking (33/98, 33.7% and 38/98, 38.8%, respectively). There were no significant differences in the proportion of responded data needs and linking in groups that used health care data for either public purposes or commercial purposes. Conclusions This study provides a cross-sectional view from a practical, user-oriented perspective on the kinds of data users want to utilize, efforts and difficulties in deidentification processes, and the needs for data linking. Most users want to use clinical and public data, and most participants conduct deidentification processes and express a desire to conduct data linking. Our study confirmed that they noted regulation as a primary obstacle whether their purpose is commercial or public. A legal system based on both data utilization and data protection needs is required.
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Affiliation(s)
- Ho Heon Kim
- Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Bora Kim
- Emphasis Information Technology Inc, Seoul, Republic of Korea
| | - Segyeong Joo
- Department of Biomedical Engineering, University of Ulsan College of Medicine, Seoul, Republic of Korea.,Biomedical Engineering Research Center, Asan Institute for Life Sciences, Asan Medical Center, Seoul, Republic of Korea
| | - Soo-Yong Shin
- Department of Digital Health, Samsung Advanced Institute for Health Sciences & Technology, SungKyunKwan University, Seoul, Republic of Korea.,Big Data Research Center, Samsung Medical Center, Seoul, Republic of Korea
| | - Hyo Soung Cha
- Cancer Big Data Center, National Cancer Center, Gyeonggi-do, Republic of Korea
| | - Yu Rang Park
- Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Seoul, Republic of Korea
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Price WN, Kaminski ME, Minssen T, Spector-Bagdady K. Shadow health records meet new data privacy laws. Science 2019; 363:448-450. [PMID: 30705168 DOI: 10.1126/science.aav5133] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
Affiliation(s)
- W Nicholson Price
- University of Michigan Law School, Ann Arbor, MI, USA. .,Centre for Advanced Studies in Biomedical Innovation Law, Copenhagen, Denmark
| | - Margot E Kaminski
- University of Colorado Law School, Boulder, CO, USA.,Silicon Flatirons Center, University of Colorado, Boulder, CO, USA
| | - Timo Minssen
- Centre for Advanced Studies in Biomedical Innovation Law, Copenhagen, Denmark.,University of Copenhagen Faculty of Law, Copenhagen, Denmark
| | - Kayte Spector-Bagdady
- Department of Obstetrics and Gynecology, University of Michigan Medical School, Ann Arbor, MI, USA.,Center for Bioethics and Social Sciences in Medicine, University of Michigan Medical School, Ann Arbor, MI, USA
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Shaw J, Rudzicz F, Jamieson T, Goldfarb A. Artificial Intelligence and the Implementation Challenge. J Med Internet Res 2019; 21:e13659. [PMID: 31293245 PMCID: PMC6652121 DOI: 10.2196/13659] [Citation(s) in RCA: 97] [Impact Index Per Article: 19.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2019] [Revised: 05/16/2019] [Accepted: 05/31/2019] [Indexed: 12/13/2022] Open
Abstract
Background Applications of artificial intelligence (AI) in health care have garnered much attention in recent years, but the implementation issues posed by AI have not been substantially addressed. Objective In this paper, we have focused on machine learning (ML) as a form of AI and have provided a framework for thinking about use cases of ML in health care. We have structured our discussion of challenges in the implementation of ML in comparison with other technologies using the framework of Nonadoption, Abandonment, and Challenges to the Scale-Up, Spread, and Sustainability of Health and Care Technologies (NASSS). Methods After providing an overview of AI technology, we describe use cases of ML as falling into the categories of decision support and automation. We suggest these use cases apply to clinical, operational, and epidemiological tasks and that the primary function of ML in health care in the near term will be decision support. We then outline unique implementation issues posed by ML initiatives in the categories addressed by the NASSS framework, specifically including meaningful decision support, explainability, privacy, consent, algorithmic bias, security, scalability, the role of corporations, and the changing nature of health care work. Results Ultimately, we suggest that the future of ML in health care remains positive but uncertain, as support from patients, the public, and a wide range of health care stakeholders is necessary to enable its meaningful implementation. Conclusions If the implementation science community is to facilitate the adoption of ML in ways that stand to generate widespread benefits, the issues raised in this paper will require substantial attention in the coming years.
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Affiliation(s)
- James Shaw
- Women's College Hospital, Institute for Health System Solutions and Virtual Care, Toronto, ON, Canada.,Joint Centre for Bioethics, University of Toronto, Toronto, ON, Canada
| | - Frank Rudzicz
- International Centre for Surgical Safety, Li Ka Shing Knowledge Institute, St Michael's Hospital, Toronto, ON, Canada
| | - Trevor Jamieson
- Women's College Hospital, Institute for Health System Solutions and Virtual Care, Toronto, ON, Canada.,St Michael's Hospital, Toronto, ON, Canada
| | - Avi Goldfarb
- Rotman School of Management, University of Toronto, Toronto, ON, Canada
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Affiliation(s)
- Claudia Pagliari
- eHealth Research Group, Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Edinburgh, UK
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Blease C, Kaptchuk TJ, Bernstein MH, Mandl KD, Halamka JD, DesRoches CM. Artificial Intelligence and the Future of Primary Care: Exploratory Qualitative Study of UK General Practitioners' Views. J Med Internet Res 2019; 21:e12802. [PMID: 30892270 PMCID: PMC6446158 DOI: 10.2196/12802] [Citation(s) in RCA: 89] [Impact Index Per Article: 17.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2018] [Revised: 12/16/2018] [Accepted: 12/17/2018] [Indexed: 12/31/2022] Open
Abstract
Background The potential for machine learning to disrupt the medical profession is the subject of ongoing debate within biomedical informatics and related fields. Objective This study aimed to explore general practitioners’ (GPs’) opinions about the potential impact of future technology on key tasks in primary care. Methods In June 2018, we conducted a Web-based survey of 720 UK GPs’ opinions about the likelihood of future technology to fully replace GPs in performing 6 key primary care tasks, and, if respondents considered replacement for a particular task likely, to estimate how soon the technological capacity might emerge. This study involved qualitative descriptive analysis of written responses (“comments”) to an open-ended question in the survey. Results Comments were classified into 3 major categories in relation to primary care: (1) limitations of future technology, (2) potential benefits of future technology, and (3) social and ethical concerns. Perceived limitations included the beliefs that communication and empathy are exclusively human competencies; many GPs also considered clinical reasoning and the ability to provide value-based care as necessitating physicians’ judgments. Perceived benefits of technology included expectations about improved efficiencies, in particular with respect to the reduction of administrative burdens on physicians. Social and ethical concerns encompassed multiple, divergent themes including the need to train more doctors to overcome workforce shortfalls and misgivings about the acceptability of future technology to patients. However, some GPs believed that the failure to adopt technological innovations could incur harms to both patients and physicians. Conclusions This study presents timely information on physicians’ views about the scope of artificial intelligence (AI) in primary care. Overwhelmingly, GPs considered the potential of AI to be limited. These views differ from the predictions of biomedical informaticians. More extensive, stand-alone qualitative work would provide a more in-depth understanding of GPs’ views.
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Affiliation(s)
- Charlotte Blease
- General Medicine and Primary Care, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States.,School of Psychology, University College Dublin, Dublin, Ireland
| | - Ted J Kaptchuk
- General Medicine and Primary Care, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States
| | | | - Kenneth D Mandl
- Computational Health Informatics Program, Boston Children's Hospital, Harvard Medical School, Boston, MA, United States.,Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States.,Department of Pediatrics, Harvard Medical School, Boston, MA, United States
| | - John D Halamka
- Beth Israel Deaconess Medical Center, Boston, MA, United States.,Brigham and Women's Hospital, Boston, MA, United States
| | - Catherine M DesRoches
- Open Notes, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States
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