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      Barriers and facilitators to use of a digital clinical decision support tool: a cohort study combining clickstream and survey data

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          Abstract

          Objectives

          This research aimed to understand the barriers and facilitators clinicians face in using a digital clinical decision support tool—UpToDate—around the globe.

          Design

          We used a mixed-methods cohort study design that enrolled 1681 clinicians (physicians, surgeons or physician assistants) who applied for free access to UpToDate through our established donation programme during a 9-week study enrolment period. Eligibility included working outside of the USA for a limited-resource public or non-profit health facility, serving vulnerable populations, having at least intermittent internet access, completing the application in English; and not being otherwise able to afford the subscription.

          Intervention

          After consenting to study participation, clinicians received a 1-year subscription to UpToDate. They completed a series of surveys over the year, and we collected clickstream data tracking their use of the tool.

          Primary and secondary outcome measures

          (1) The variation in use by demographic; (2) the prevalence of barriers and facilitators of use; and (3) the relationship between barriers, facilitators and use.

          Results

          Of 1681 study enrollees, 69% were men and 71% were between 25 and 35 years old, with the plurality practicing general medicine and the majority in sub-Saharan Africa or Southeast Asia. Of the 11 barriers we assessed, fitting the tool into the workflow was a statistically significant barrier, making clinicians 50% less likely to use it. Of the 10 facilitators we assessed, a supportive professional context and utility were significant drivers of use.

          Conclusions

          There are several clear barriers and facilitators to promoting the use of digital clinical decision support tools in practice. We recommend tools like UpToDate be implemented with complementary services. These include generating a supportive professional context, helping clinicians realise the tools’ use and working with health systems to better integrate digital, clinical decision support tools into workflows.

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          Most cited references14

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          Research electronic data capture (REDCap)--a metadata-driven methodology and workflow process for providing translational research informatics support.

          Research electronic data capture (REDCap) is a novel workflow methodology and software solution designed for rapid development and deployment of electronic data capture tools to support clinical and translational research. We present: (1) a brief description of the REDCap metadata-driven software toolset; (2) detail concerning the capture and use of study-related metadata from scientific research teams; (3) measures of impact for REDCap; (4) details concerning a consortium network of domestic and international institutions collaborating on the project; and (5) strengths and limitations of the REDCap system. REDCap is currently supporting 286 translational research projects in a growing collaborative network including 27 active partner institutions.
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            Validation of a New General Self-Efficacy Scale

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              Diagnostic error in internal medicine.

              The goal of this study was to determine the relative contribution of system-related and cognitive components to diagnostic error and to develop a comprehensive working taxonomy. One hundred cases of diagnostic error involving internists were identified through autopsy discrepancies, quality assurance activities, and voluntary reports. Each case was evaluated to identify system-related and cognitive factors underlying error using record reviews and, if possible, provider interviews. Ninety cases involved injury, including 33 deaths. The underlying contributions to error fell into 3 natural categories: "no fault," system-related, and cognitive. Seven cases reflected no-fault errors alone. In the remaining 93 cases, we identified 548 different system-related or cognitive factors (5.9 per case). System-related factors contributed to the diagnostic error in 65% of the cases and cognitive factors in 74%. The most common system-related factors involved problems with policies and procedures, inefficient processes, teamwork, and communication. The most common cognitive problems involved faulty synthesis. Premature closure, ie, the failure to continue considering reasonable alternatives after an initial diagnosis was reached, was the single most common cause. Other common causes included faulty context generation, misjudging the salience of findings, faulty perception, and errors arising from the use of heuristics. Faulty or inadequate knowledge was uncommon. Diagnostic error is commonly multifactorial in origin, typically involving both system-related and cognitive factors. The results identify the dominant problems that should be targeted for additional research and early reduction; they also further the development of a comprehensive taxonomy for classifying diagnostic errors.
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                Author and article information

                Journal
                BMJ Open
                BMJ Open
                bmjopen
                bmjopen
                BMJ Open
                BMJ Publishing Group (BMA House, Tavistock Square, London, WC1H 9JR )
                2044-6055
                2022
                21 November 2022
                : 12
                : 11
                : e064952
                Affiliations
                [1 ]departmentAriadne Labs , Harvard T.H. Chan School of Public Health and Brigham and Women's Hospital , Boston, MA, USA
                [2 ]departmentDivision of Global Health Equity , Brigham and Women's Hospital , Boston, MA, USA
                [3 ]departmentGlobal Health and Social Medicine , Harvard Medical School , Boston, MA, USA
                Author notes
                [Correspondence to ] Julie Rosenberg; jrosenberg@ 123456ariadnelabs.org
                Author information
                http://orcid.org/0000-0003-0961-0692
                Article
                bmjopen-2022-064952
                10.1136/bmjopen-2022-064952
                9680158
                36410838
                bd511f84-30df-432b-8906-a503b31d4d43
                © Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

                This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See:  http://creativecommons.org/licenses/by-nc/4.0/.

                History
                : 25 May 2022
                : 27 October 2022
                Funding
                Funded by: Patrick J. McGovern Foundation;
                Award ID: N/A
                Funded by: Goldsmith Foundation;
                Award ID: N/A
                Funded by: Paul G Allen Family Foundation;
                Award ID: N/A
                Funded by: Wolters Kluwer UpToDate;
                Award ID: N/A
                Categories
                Health Informatics
                1506
                1702
                Original research
                Custom metadata
                unlocked

                Medicine
                information technology,health & safety,change management,international health services,quality in health care,medical education & training

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