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      Barriers and facilitators to the adoption of electronic clinical decision support systems: a qualitative interview study with UK general practitioners

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          Abstract

          Background

          Well-established electronic data capture in UK general practice means that algorithms, developed on patient data, can be used for automated clinical decision support systems (CDSSs). These can predict patient risk, help with prescribing safety, improve diagnosis and prompt clinicians to record extra data. However, there is persistent evidence of low uptake of CDSSs in the clinic. We interviewed UK General Practitioners (GPs) to understand what features of CDSSs, and the contexts of their use, facilitate or present barriers to their use.

          Methods

          We interviewed 11 practicing GPs in London and South England using a semi-structured interview schedule and discussed a hypothetical CDSS that could detect early signs of dementia. We applied thematic analysis to the anonymised interview transcripts.

          Results

          We identified three overarching themes: trust in individual CDSSs; usability of individual CDSSs; and usability of CDSSs in the broader practice context, to which nine subthemes contributed. Trust was affected by CDSS provenance, perceived threat to autonomy and clear management guidance. Usability was influenced by sensitivity to the patient context, CDSS flexibility, ease of control, and non-intrusiveness. CDSSs were more likely to be used by GPs if they did not contribute to alert proliferation and subsequent fatigue, or if GPs were provided with training in their use.

          Conclusions

          Building on these findings we make a number of recommendations for CDSS developers to consider when bringing a new CDSS into GP patient records systems. These include co-producing CDSS with GPs to improve fit within clinic workflow and wider practice systems, ensuring a high level of accuracy and a clear clinical pathway, and providing CDSS training for practice staff. These recommendations may reduce the proliferation of unhelpful alerts that can result in important decision-support being ignored.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s12911-021-01557-z.

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          Most cited references 38

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          Using thematic analysis in psychology

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            Consolidated criteria for reporting qualitative research (COREQ): a 32-item checklist for interviews and focus groups.

             A Tong,  P SAINSBURY,  J. Craig (2007)
            Qualitative research explores complex phenomena encountered by clinicians, health care providers, policy makers and consumers. Although partial checklists are available, no consolidated reporting framework exists for any type of qualitative design. To develop a checklist for explicit and comprehensive reporting of qualitative studies (in depth interviews and focus groups). We performed a comprehensive search in Cochrane and Campbell Protocols, Medline, CINAHL, systematic reviews of qualitative studies, author or reviewer guidelines of major medical journals and reference lists of relevant publications for existing checklists used to assess qualitative studies. Seventy-six items from 22 checklists were compiled into a comprehensive list. All items were grouped into three domains: (i) research team and reflexivity, (ii) study design and (iii) data analysis and reporting. Duplicate items and those that were ambiguous, too broadly defined and impractical to assess were removed. Items most frequently included in the checklists related to sampling method, setting for data collection, method of data collection, respondent validation of findings, method of recording data, description of the derivation of themes and inclusion of supporting quotations. We grouped all items into three domains: (i) research team and reflexivity, (ii) study design and (iii) data analysis and reporting. The criteria included in COREQ, a 32-item checklist, can help researchers to report important aspects of the research team, study methods, context of the study, findings, analysis and interpretations.
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              Ten commandments for effective clinical decision support: making the practice of evidence-based medicine a reality.

              While evidence-based medicine has increasingly broad-based support in health care, it remains difficult to get physicians to actually practice it. Across most domains in medicine, practice has lagged behind knowledge by at least several years. The authors believe that the key tools for closing this gap will be information systems that provide decision support to users at the time they make decisions, which should result in improved quality of care. Furthermore, providers make many errors, and clinical decision support can be useful for finding and preventing such errors. Over the last eight years the authors have implemented and studied the impact of decision support across a broad array of domains and have found a number of common elements important to success. The goal of this report is to discuss these lessons learned in the interest of informing the efforts of others working to make the practice of evidence-based medicine a reality.
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                Author and article information

                Contributors
                e.m.ford@bsms.ac.uk
                Journal
                BMC Med Inform Decis Mak
                BMC Med Inform Decis Mak
                BMC Medical Informatics and Decision Making
                BioMed Central (London )
                1472-6947
                21 June 2021
                21 June 2021
                2021
                : 21
                Affiliations
                [1 ]GRID grid.414601.6, ISNI 0000 0000 8853 076X, Department of Primary Care and Public Health, , Brighton and Sussex Medical School, ; Watson Building, Village Way, Falmer, Brighton, BN1 9PH UK
                [2 ]GRID grid.12477.37, ISNI 0000000121073784, School of Sport and Health Sciences, , University of Brighton, ; Brighton, UK
                [3 ]GRID grid.88379.3d, ISNI 0000 0001 2324 0507, Psychosocial Department, Centre for Researching and Embedding Human Rights (CREHR), , Birkbeck College, ; London, UK
                [4 ]GRID grid.13097.3c, ISNI 0000 0001 2322 6764, School of Population Health and Environmental Sciences, , King’s College London, ; London, UK
                [5 ]GRID grid.7445.2, ISNI 0000 0001 2113 8111, Dyson School of Design Engineering, , Imperial College London, ; London, UK
                Article
                1557
                10.1186/s12911-021-01557-z
                8215812
                34154580
                © The Author(s) 2021

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

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                © The Author(s) 2021

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