Abstract
BackgroundDiagnostic error in ambulatory care, a frequent cause of preventable harm, may be mitigated using the collective intelligence of multiple clinicians. The National Academy of Medicine has identified enhanced clinician collaboration and digital tools as a means to improve the diagnostic process.ObjectiveThis study aims to assess the efficacy of a collective intelligence output to improve diagnostic confidence and accuracy in ambulatory care cases (from primary care and urgent care clinic visits) with diagnostic uncertainty.MethodsThis is a pragmatic randomized controlled trial of using collective intelligence in cases with diagnostic uncertainty from clinicians at primary care and urgent care clinics in 2 health care systems in San Francisco. Real-life cases, identified for having an element of diagnostic uncertainty, will be entered into a collective intelligence digital platform to acquire collective intelligence from at least 5 clinician contributors on the platform. Cases will be randomized to an intervention group (where clinicians will view the collective intelligence output) or control (where clinicians will not view the collective intelligence output). Clinicians will complete a postvisit questionnaire that assesses their diagnostic confidence for each case; in the intervention cases, clinicians will complete the questionnaire after reviewing the collective intelligence output for the case. Using logistic regression accounting for clinician clustering, we will compare the primary outcome of diagnostic confidence and the secondary outcome of time with diagnosis (the time it takes for a clinician to reach a diagnosis), for intervention versus control cases. We will also assess the usability and satisfaction with the digital tool using measures adapted from the Technology Acceptance Model and Net Promoter Score.ResultsWe have recruited 32 out of our recruitment goal of 33 participants. This study is funded until May 2020 and is approved by the University of California San Francisco Institutional Review Board until January 2020. We have completed data collection as of June 2019 and will complete our proposed analysis by December 2019.ConclusionsThis study will determine if the use of a digital platform for collective intelligence is acceptable, useful, and efficacious in improving diagnostic confidence and accuracy in outpatient cases with diagnostic uncertainty. If shown to be valuable in improving clinicians’ diagnostic process, this type of digital tool may be one of the first innovations used for reducing diagnostic errors in outpatient care. The findings of this study may provide a path forward for improving the diagnostic process.International Registered Report Identifier (IRRID)DERR1-10.2196/13151
Highlights
BackgroundDiagnostic errors in primary care affect an estimated 1 in 20 US adults every year [1]
This study will determine if the use of a digital platform for collective intelligence is acceptable, useful, and efficacious in improving diagnostic confidence and accuracy in outpatient cases with diagnostic uncertainty
If shown to be valuable in improving clinicians’ diagnostic process, this type of digital tool may be one of the first innovations used for reducing diagnostic errors in outpatient care
Summary
Diagnostic errors (defined as missed, delayed, or wrong diagnoses) in primary care affect an estimated 1 in 20 US adults every year [1] About half of these errors can lead to serious preventable harm, but few interventions have been developed and tested to reduce diagnostic errors in real-world ambulatory care settings such as primary care or urgent care clinics [1,2,3,4]. In its recent report on diagnostic error, the National Academy of Medicine suggested that health systems employ 2 key strategies that are essential to reducing diagnostic error in the ambulatory care setting: (1) enhance interclinician collaboration and (2) develop and utilize health IT innovations in the diagnostic process [6]. The National Academy of Medicine has identified enhanced clinician collaboration and digital tools as a means to improve the diagnostic process
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