This paper is in the following e-collection/theme issue:

RCTs - Protocols/Proposals (eHealth) (541) Safety and Error Prevention in Health (204)

Published on in Vol 8, No 8 (2019): August

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/13151, first published .
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

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

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

Authors of this article:

Valy Fontil1, 2 Author Orcid Image ;   Elaine C Khoong1 Author Orcid Image ;   Mekhala Hoskote1, 2 Author Orcid Image ;   Kate Radcliffe1, 2 Author Orcid Image ;   Neda Ratanawongsa1, 2 Author Orcid Image ;   Courtney Rees Lyles1, 2 Author Orcid Image ;   Urmimala Sarkar1, 2 Author Orcid Image
Article Authors Cited by (11) Tweetations (16) Metrics

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  2. Khoong E, Fontil V, Rivadeneira N, Hoskote M, Nundy S, Lyles C, Sarkar U. Impact of digitally acquired peer diagnostic input on diagnostic confidence in outpatient cases: A pragmatic randomized trial. Journal of the American Medical Informatics Association 2021;28(3):632 View
  3. Rogers J, Lee J, Zhou Z, Cheung Y, Hripcsak G, Weng C. Contemporary use of real-world data for clinical trial conduct in the United States: a scoping review. Journal of the American Medical Informatics Association 2021;28(1):144 View
  4. Ronzio L, Campagner A, Cabitza F, Gensini G. Unity Is Intelligence: A Collective Intelligence Experiment on ECG Reading to Improve Diagnostic Performance in Cardiology. Journal of Intelligence 2021;9(2):17 View
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  6. Kurvers R, Nuzzolese A, Russo A, Barabucci G, Herzog S, Trianni V. Automating hybrid collective intelligence in open-ended medical diagnostics. Proceedings of the National Academy of Sciences 2023;120(34) View
  7. Henderson K, Reihm J, Koshal K, Wijangco J, Miller N, Sara N, Doyle M, Mallory A, Sheridan J, Guo C, Oommen L, Feinstein A, Mangurian C, Lazar A, Bove R. Pragmatic phase II clinical trial to improve depression care in a real-world diverse MS cohort from an academic MS centre in Northern California: MS CATCH study protocol. BMJ Open 2024;14(2):e077432 View
  8. Henderson K, Reihm J, Koshal K, Wijangco J, Sara N, Miller N, Doyle M, Mallory A, Sheridan J, Guo C, Oommen L, Rankin K, Sanders S, Feinstein A, Mangurian C, Bove R. 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 2024;8:e52809 View
  9. McGinley M, Carlson J, Reihm J, Plow M, Roser M, Sisodia N, Cohen J, Misra-Hebert A, Lazar A, Bove R. Virtual versus usual in-office care for multiple sclerosis: The VIRTUAL-MS multi-site randomized clinical trial study protocol. Contemporary Clinical Trials 2024;142:107544 View
  10. Becker M, Matt C. How individuals perceive and process diagnostic device errors. Journal of Decision Systems 2024:1 View
  11. Tetik G, Türkeli S, Pinar S, Tarim M. Health information systems with technology acceptance model approach: A systematic review. International Journal of Medical Informatics 2024;190:105556 View