Autonomous artificial intelligence increases real-world specialist clinic productivity in a cluster-randomized trial

Michael D Abramoff, Noelle Whitestone, Jennifer L Patnaik, Emily Rich, Munir Ahmed, Lutful Husain, Mohammad Yeadul Hassan, Md Sajidul Huq Tanjil, Dena Weitzman, Tinglong Dai, Brandie D Wagner, David H Cherwek, Nathan Congdon, Khairul Islam

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)
24 Downloads (Pure)

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 
Original languageEnglish
Article number184
JournalNPJ digital medicine
Volume6
DOIs
Publication statusPublished - 04 Oct 2023

Fingerprint

Dive into the research topics of 'Autonomous artificial intelligence increases real-world specialist clinic productivity in a cluster-randomized trial'. Together they form a unique fingerprint.

Cite this