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Al-Sahab B, Leviton A, Loddenkemper T, Paneth N, Zhang B. Biases in Electronic Health Records Data for Generating Real-World Evidence: An Overview. JOURNAL OF HEALTHCARE INFORMATICS RESEARCH 2024; 8:121-139. [PMID: 38273982 PMCID: PMC10805748 DOI: 10.1007/s41666-023-00153-2] [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: 06/30/2023] [Revised: 09/05/2023] [Accepted: 11/07/2023] [Indexed: 01/27/2024]
Abstract
Electronic Health Records (EHR) are increasingly being perceived as a unique source of data for clinical research as they provide unprecedentedly large volumes of real-time data from real-world settings. In this review of the secondary uses of EHR, we identify the anticipated breadth of opportunities, pointing out the data deficiencies and potential biases that are likely to limit the search for true causal relationships. This paper provides a comprehensive overview of the types of biases that arise along the pathways that generate real-world evidence and the sources of these biases. We distinguish between two levels in the production of EHR data where biases are likely to arise: (i) at the healthcare system level, where the principal source of bias resides in access to, and provision of, medical care, and in the acquisition and documentation of medical and administrative data; and (ii) at the research level, where biases arise from the processes of extracting, analyzing, and interpreting these data. Due to the plethora of biases, mainly in the form of selection and information bias, we conclude with advising extreme caution about making causal inferences based on secondary uses of EHRs.
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Affiliation(s)
- Ban Al-Sahab
- Department of Family Medicine, College of Human Medicine, Michigan State University, B100 Clinical Center, 788 Service Road, East Lansing, MI USA
| | - Alan Leviton
- Department of Neurology, Harvard Medical School, Boston, MA USA
- Department of Neurology, Boston Children’s Hospital, Boston, MA USA
| | - Tobias Loddenkemper
- Department of Neurology, Harvard Medical School, Boston, MA USA
- Department of Neurology, Boston Children’s Hospital, Boston, MA USA
| | - Nigel Paneth
- Department of Epidemiology and Biostatistics, College of Human Medicine, Michigan State University, East Lansing, MI USA
- Department of Pediatrics and Human Development, College of Human Medicine, Michigan State University, East Lansing, MI USA
| | - Bo Zhang
- Department of Neurology, Boston Children’s Hospital, Boston, MA USA
- Biostatistics and Research Design, Institutional Centers of Clinical and Translational Research, Boston Children’s Hospital, Boston, MA USA
- Harvard Medical School, Boston, MA USA
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Li E, Lounsbury O, Clarke J, Ashrafian H, Darzi A, Neves AL. Perceptions of chief clinical information officers on the state of electronic health records systems interoperability in NHS England: a qualitative interview study. BMC Med Inform Decis Mak 2023; 23:158. [PMID: 37573388 PMCID: PMC10423420 DOI: 10.1186/s12911-023-02255-8] [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: 12/12/2022] [Accepted: 08/02/2023] [Indexed: 08/14/2023] Open
Abstract
BACKGROUND In the era of electronic health records (EHR), the ability to share clinical data is a key facilitator of healthcare delivery. Since the introduction of EHRs, this aspect has been extensively studied from the perspective of healthcare providers. Less often explored are the day-to-day challenges surrounding the procurement, deployment, maintenance, and use of interoperable EHR systems, from the perspective of healthcare administrators, such as chief clinical information officers (CCIOs). OBJECTIVE Our study aims to capture the perceptions of CCIOs on the current state of EHR interoperability in the NHS, its impact on patient safety, the perceived facilitators and barriers to improving EHR interoperability, and what the future of EHR development in the NHS may entail. METHODS Semi-structured interviews were conducted between November 2020 - October 2021. Convenience sampling was employed to recruit NHS England CCIOs. Interviews were digitally recorded and transcribed verbatim. A thematic analysis was performed by two independent researchers to identify emerging themes. RESULTS Fifteen CCIOs participated in the study. Participants reported that limited EHR interoperability contributed to the inability to easily access and transfer data into a unified source, thus resulting in data fragmentation. The resulting lack of clarity on patients' health status negatively impacts patient safety through suboptimal care coordination, duplication of efforts, and more defensive practice. Facilitators to improving interoperability included the recognition of the need by clinicians, patient expectations, and the inherent centralised nature of the NHS. Barriers included systems usability difficulties, and institutional, data management, and financial-related challenges. Looking ahead, participants acknowledged that realising that vision across the NHS would require a renewed focus on mandating data standards, user-centred design, greater patient involvement, and encouraging inter-organisational collaboration. CONCLUSION Tackling poor interoperability will require solutions both at the technical level and in the wider policy context. This will involve demanding interoperability functionalities from the outset in procurement contracts, fostering greater inter-organisation cooperation on implementation strategies, and encouraging systems vendors to prioritise interoperability in their products. Only by comprehensively addressing these challenges would the full potential promised by the use of fully interoperable EHRs be realised.
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Affiliation(s)
- Edmond Li
- Institute of Global Health Innovation, National Institute for Health and Care Research (NIHR) Imperial Patient Safety Translational Research Centre, Imperial College London, London, UK.
| | | | - Jonathan Clarke
- Institute of Global Health Innovation, National Institute for Health and Care Research (NIHR) Imperial Patient Safety Translational Research Centre, Imperial College London, London, UK
- Centre for Mathematics of Precision Healthcare, Department of Mathematics, Imperial College London, London, UK
| | - Hutan Ashrafian
- Institute of Global Health Innovation, National Institute for Health and Care Research (NIHR) Imperial Patient Safety Translational Research Centre, Imperial College London, London, UK
| | - Ara Darzi
- Institute of Global Health Innovation, National Institute for Health and Care Research (NIHR) Imperial Patient Safety Translational Research Centre, Imperial College London, London, UK
| | - Ana Luisa Neves
- Institute of Global Health Innovation, National Institute for Health and Care Research (NIHR) Imperial Patient Safety Translational Research Centre, Imperial College London, London, UK
- Department of Primary Care and Public Health, Imperial College London, London, UK
- Department of Community Medicine, Health Information and Decision, Center for Health Technology and Services Research, University of Porto, Porto, Portugal
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Lovis C. Unlocking the Power of Artificial Intelligence and Big Data in Medicine. J Med Internet Res 2019; 21:e16607. [PMID: 31702565 PMCID: PMC6874800 DOI: 10.2196/16607] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2019] [Revised: 10/18/2019] [Accepted: 10/20/2019] [Indexed: 12/17/2022] Open
Abstract
Data-driven science and its corollaries in machine learning and the wider field of artificial intelligence have the potential to drive important changes in medicine. However, medicine is not a science like any other: It is deeply and tightly bound with a large and wide network of legal, ethical, regulatory, economical, and societal dependencies. As a consequence, the scientific and technological progresses in handling information and its further processing and cross-linking for decision support and predictive systems must be accompanied by parallel changes in the global environment, with numerous stakeholders, including citizen and society. What can be seen at the first glance as a barrier and a mechanism slowing down the progression of data science must, however, be considered an important asset. Only global adoption can transform the potential of big data and artificial intelligence into an effective breakthroughs in handling health and medicine. This requires science and society, scientists and citizens, to progress together.
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Affiliation(s)
- Christian Lovis
- Division of Medical Information Sciences, University Hospitals of Geneva, Geneva, Switzerland
- Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland
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Bartlett G, Macgibbon B, Rubinowicz A, Nease C, Dawes M, Tamblyn R. The Importance of Relevance: Willingness to Share eHealth Data for Family Medicine Research. Front Public Health 2018; 6:255. [PMID: 30234095 PMCID: PMC6131658 DOI: 10.3389/fpubh.2018.00255] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2018] [Accepted: 08/16/2018] [Indexed: 11/13/2022] Open
Abstract
Objective: To determine the proportion of family medicine patients unwilling to allow their eHealth data to be used for research purposes, and evaluate how patient characteristics and the relevance of research impact that decision. Design: Cross-sectional questionnaire. Setting: Acute care respiratory clinic or an outpatient family medicine clinic in Montreal, Quebec. Participants: Four hundred seventy-four waiting room patients recruited via convenience sampling. Main Outcome Measures: A self-administered questionnaire collected data on age, gender, employment status, education, mother tongue and perceived health status. The main outcome of was self-reported relevance of three research scenarios and willingness or refusal to share their anonymized data. Responses were compared for family practice vs. specialty care patients. Results: The questionnaire was completed by 229 family medicine respondents and 245 outpatient respondents. Almost a quarter of all respondents felt the research was not relevant. Family medicine patients (15.7%) were unwilling to allow their data to be used for at least one scenario vs. 9.4% in the outpatient clinic. Lack of relevance (OR 11.55; 95% CI 5.12-26.09) and being in family practice (OR 2.13; 95% CI 1.06-4.27) increased the likelihood of refusal to share data for research. Conclusion: Family medicine patients were somewhat less willing to share eHealth data, but the overall refusal rate indicates a need to better engage patients in understanding the significance of full access to eHealth data for the purposes of research. Personal relevance of the research had a strong impact on the responses arguing for better efforts to make research more pertinent to patients.
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Affiliation(s)
| | - Brenda Macgibbon
- Mathematics, Université du Québec à Montréal, Montreal, QC, Canada
| | | | - Cecilia Nease
- Joan C. Edwards School of Medicine, Marshall University, Huntington, WV, United States
| | - Martin Dawes
- Family Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Robyn Tamblyn
- Epidemiology and Biostatistics, McGill University, Montreal, QC, Canada
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Georgiou A, Magrabi F, Hyppönen H, Wong ZSY, Nykänen P, Scott PJ, Ammenwerth E, Rigby M. The Safe and Effective Use of Shared Data Underpinned by Stakeholder Engagement and Evaluation Practice. Yearb Med Inform 2018; 27:25-28. [PMID: 29681039 PMCID: PMC6115216 DOI: 10.1055/s-0038-1641194] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
Abstract
Objectives:
The paper draws attention to: i) key considerations involving the confidentiality, privacy, and security of shared data; and ii) the requirements needed to build collaborative arrangements encompassing all stakeholders with the goal of ensuring safe, secure, and quality use of shared data.
Method:
A narrative review of existing research and policy approaches along with expert perspectives drawn from the International Medical Informatics Association (IMIA) Working Group on Technology Assessment and Quality Development in Health Care and the European Federation for Medical Informatics (EFMI) Working Group for Assessment of Health Information Systems.
Results:
The technological ability to merge, link, re-use, and exchange data has outpaced the establishment of policies, procedures, and processes to monitor the ethics and legality of shared use of data. Questions remain about how to guarantee the security of shared data, and how to establish and maintain public trust across large-scale shared data enterprises. This paper identifies the importance of data governance frameworks (incorporating engagement with all stakeholders) to underpin the management of the ethics and legality of shared data use. The paper also provides some key considerations for the establishment of national approaches and measures to monitor compliance with best practice.
Conclusion:
Data sharing endeavours can help to underpin new collaborative models of health care which provide shared information, engagement, and accountability amongst all stakeholders. We believe that commitment to rigorous evaluation and stakeholder engagement will be critical to delivering health data benefits and the establishment of collaborative models of health care into the future.
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Affiliation(s)
- Andrew Georgiou
- Macquarie University, Australian Institute of Health Innovation, Sydney, Australia
| | - Farah Magrabi
- Macquarie University, Australian Institute of Health Innovation, Sydney, Australia
| | - Hannele Hyppönen
- National Institute for Health and Welfare, Information Department, Helsinki, Finland
| | | | - Pirkko Nykänen
- University of Tampere, Faculty of Natural Sciences, Tampere, Finland
| | - Philip J Scott
- University of Portsmouth, Centre for Healthcare Modelling and Informatics, Portsmouth, United Kingdom
| | - Elske Ammenwerth
- UMIT, University for Health Sciences, Medical Informatics and Technology, Institute of Medical Informatics, Hall in Tyrol, Austria
| | - Michael Rigby
- Keele University, School of Social Science and Public Policy, Keele, United Kingdom
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Ong TC, Kahn MG, Kwan BM, Yamashita T, Brandt E, Hosokawa P, Uhrich C, Schilling LM. Dynamic-ETL: a hybrid approach for health data extraction, transformation and loading. BMC Med Inform Decis Mak 2017; 17:134. [PMID: 28903729 PMCID: PMC5598056 DOI: 10.1186/s12911-017-0532-3] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2016] [Accepted: 08/31/2017] [Indexed: 11/24/2022] Open
Abstract
Background Electronic health records (EHRs) contain detailed clinical data stored in proprietary formats with non-standard codes and structures. Participating in multi-site clinical research networks requires EHR data to be restructured and transformed into a common format and standard terminologies, and optimally linked to other data sources. The expertise and scalable solutions needed to transform data to conform to network requirements are beyond the scope of many health care organizations and there is a need for practical tools that lower the barriers of data contribution to clinical research networks. Methods We designed and implemented a health data transformation and loading approach, which we refer to as Dynamic ETL (Extraction, Transformation and Loading) (D-ETL), that automates part of the process through use of scalable, reusable and customizable code, while retaining manual aspects of the process that requires knowledge of complex coding syntax. This approach provides the flexibility required for the ETL of heterogeneous data, variations in semantic expertise, and transparency of transformation logic that are essential to implement ETL conventions across clinical research sharing networks. Processing workflows are directed by the ETL specifications guideline, developed by ETL designers with extensive knowledge of the structure and semantics of health data (i.e., “health data domain experts”) and target common data model. Results D-ETL was implemented to perform ETL operations that load data from various sources with different database schema structures into the Observational Medical Outcome Partnership (OMOP) common data model. The results showed that ETL rule composition methods and the D-ETL engine offer a scalable solution for health data transformation via automatic query generation to harmonize source datasets. Conclusions D-ETL supports a flexible and transparent process to transform and load health data into a target data model. This approach offers a solution that lowers technical barriers that prevent data partners from participating in research data networks, and therefore, promotes the advancement of comparative effectiveness research using secondary electronic health data. Electronic supplementary material The online version of this article (10.1186/s12911-017-0532-3) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Toan C Ong
- Departments of Pediatrics, University of Colorado Anschutz Medical Campus, School of Medicine, Building AO1 Room L15-1414, 12631 East 17th Avenue, Mail Stop F563, Aurora, CO, 80045, USA.
| | - Michael G Kahn
- Departments of Pediatrics, University of Colorado Anschutz Medical Campus, School of Medicine, Building AO1 Room L15-1414, 12631 East 17th Avenue, Mail Stop F563, Aurora, CO, 80045, USA.,Colorado Clinical and Translational Sciences Institute, University of Colorado Anschutz Medical Campus, School of Medicine, Aurora, CO, USA
| | - Bethany M Kwan
- Departments of Family Medicine, University of Colorado Anschutz Medical Campus, School of Medicine, Aurora, CO, USA
| | - Traci Yamashita
- Departments of Medicine, University of Colorado Anschutz Medical Campus, School of Medicine, Aurora, CO, USA
| | | | - Patrick Hosokawa
- Departments of Family Medicine, University of Colorado Anschutz Medical Campus, School of Medicine, Aurora, CO, USA
| | | | - Lisa M Schilling
- Departments of Medicine, University of Colorado Anschutz Medical Campus, School of Medicine, Aurora, CO, USA
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