Journal article
Autonomous artificial intelligence increases real-world specialist clinic productivity in a cluster-randomized trial
NPJ digital medicine, Vol.6(1), 184
10/04/2023
DOI: 10.1038/s41746-023-00931-7
PMCID: PMC10550906
PMID: 37794054
Abstract
Autonomous artificial intelligence (AI) promises to increase healthcare productivity, but real-world evidence is lacking. We developed a clinic productivity model to generate testable hypotheses and study design for a preregistered cluster-randomized clinical trial, in which we tested the hypothesis that a previously validated US FDA-authorized AI for diabetic eye exams increases clinic productivity (number of completed care encounters per hour per specialist physician) among patients with diabetes. Here we report that 105 clinic days are cluster randomized to either intervention (using AI diagnosis; 51 days; 494 patients) or control (not using AI diagnosis; 54 days; 499 patients). The prespecified primary endpoint is met: AI leads to 40% higher productivity (1.59 encounters/hour, 95% confidence interval [CI]: 1.37–1.80) than control (1.14 encounters/hour, 95% CI: 1.02–1.25),
p
< 0.00; the secondary endpoint (productivity in all patients) is also met. Autonomous AI increases healthcare system productivity, which could potentially increase access and reduce health disparities. ClinicalTrials.gov NCT05182580.
Details
- Title: Subtitle
- Autonomous artificial intelligence increases real-world specialist clinic productivity in a cluster-randomized trial
- Creators
- Michael D. Abramoff - University of IowaNoelle Whitestone - Orbis International, New York, New York USAJennifer L. Patnaik - University of Colorado DenverEmily Rich - Queen's University BelfastMunir Ahmed - Orbis Bangladesh, Dhaka, BangladeshLutful Husain - Orbis Bangladesh, Dhaka, BangladeshMohammad Yeadul Hassan - Orbis Bangladesh, Dhaka, BangladeshMd. Sajidul Huq Tanjil - Child Health Research FoundationDena Weitzman - Behavioral Diagnostics (United States)Tinglong Dai - Johns Hopkins UniversityBrandie D. Wagner - Colorado School of Public HealthDavid H. Cherwek - Orbis International, New York, New York USANathan Congdon - Queen's University BelfastKhairul Islam - Child Health Research Foundation
- Resource Type
- Journal article
- Publication Details
- NPJ digital medicine, Vol.6(1), 184
- DOI
- 10.1038/s41746-023-00931-7
- PMID
- 37794054
- PMCID
- PMC10550906
- NLM abbreviation
- NPJ Digit Med
- ISSN
- 2398-6352
- eISSN
- 2398-6352
- Publisher
- Nature Publishing Group UK
- Grant note
- ;
- Language
- English
- Date published
- 10/04/2023
- Academic Unit
- Electrical and Computer Engineering; Fraternal Order of Eagles Diabetes Research Center; Ophthalmology and Visual Sciences
- Record Identifier
- 9984474569802771
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