ArticleJAMIA open2019
Testing and improving the acceptability of a web-based platform for collective intelligence to improve diagnostic accuracy in primary care clinics.
Article in JAMIA open, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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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
4 citing papers in PubMed.
- 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
- Experimental evidence for structured information-sharing networks reducing medical errors.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Comparison of Diagnostic Recommendations from Individual Physicians versus the Collective Intelligence of Multiple Physicians in Ambulatory Cases Referred for Specialist Consultation.Medical decision making : an international journal of the Society for Medical Decision Making · 2022Article
- Self-Diagnosis through AI-enabled Chatbot-based Symptom Checkers: User Experiences and Design Considerations.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
objectivesUsable tools to support individual primary care clinicians in their diagnostic processes could help to reduce preventable harm from diagnostic errors. We conducted a formative study with primary care providers to identify key requisites to optimize the acceptability of 1 online collective intelligence platform (Human Diagnosis Project; Human Dx). MATERIALS AND
methodsWe conducted semistructured interviews with practicing primary care clinicians in a sample of the US community-based clinics to examine the acceptability and early usability of the collective intelligence online platform using standardized clinical cases and real-world clinical cases from the participants' own practice. We used an integrated inductive-deductive qualitative analysis approach to analyze the interview transcripts. RESULTS AND DISCUSSION: Perceived usefulness, perceived accuracy, quality assurance, trust, and ease of use emerged as essential domains of acceptability required for providers to use a collective intelligence tool in clinical practice. Participants conveyed that the collective opinion should: (1) contribute to their clinical reasoning, (2) boost their confidence, (3) be generated in a timely manner, and (4) be relevant to their clinical settings and use cases. Trust in the technology platform and the clinical accuracy of its collective intelligence output emerged as an incontrovertible requirement for user acceptance and engagement.
conclusionWe documented key requisites to building a collective intelligence technology platform that is trustworthy, useful, and acceptable to target end users for assistance in the diagnostic process. These key lessons may be applicable to other provider-facing decision support platforms.
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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.