ArticleJMIR research protocols2019
Evaluation of a Health Information Technology-Enabled Collective Intelligence Platform to Improve Diagnosis in Primary Care and Urgent Care Settings: Protocol for a Pragmatic Randomized Controlled Trial.
Article in JMIR research protocols, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Reporting Quality of AI Intervention in Randomized Controlled Trials in Primary Care: Systematic Review and Meta-Epidemiological Study.Journal of medical Internet research · 2025Pooled it
- Impact of digitally acquired peer diagnostic input on diagnostic confidence in outpatient cases: A pragmatic randomized trial.Journal of the American Medical Informatics Association : JAMIA · 2021Trial
- A Closed-Loop Digital Health Tool to Improve Depression Care in Multiple Sclerosis: Iterative Design and Cross-Sectional Pilot Randomized Controlled Trial and its Impact on Depression Care.JMIR formative research · 2024Article
- Article
- Automating hybrid collective intelligence in open-ended medical diagnostics.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Monitoring Diagnostic Safety Risks in Emergency Departments: Protocol for a Machine Learning Study.JMIR research protocols · 2021Article
- Unity Is Intelligence: A Collective Intelligence Experiment on ECG Reading to Improve Diagnostic Performance in Cardiology.Journal of Intelligence · 2021Article
- Contemporary use of real-world data for clinical trial conduct in the United States: a scoping review.Journal of the American Medical Informatics Association : JAMIA · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.