Effect of AI-Assisted Clinical Documentation on Nurse Productivity, Professional Experience, and Documentation Quality: A Randomized Repeated Crossover Trial in Primary Care Telehealth

Background: No randomized controlled trial evidence currently exists on the effectiveness of ambient artificial intelligence (AI)-assisted clinical documentation.

Objective: To evaluate the effect of AI-assisted documentation on nurse productivity, professional experience, and documentation quality in nurse-led telehealth (telephone and chat) contacts in Finnish primary care (Wellbeing Services County of Kanta-Häme, OmaHäme).

Methods: In this randomized, open-label, repeated crossover trial, approximately 64 nurses are allocated 1:1 to an ABAB or BABA sequence of four two-week periods (A = AI-assisted documentation, B = standard manual documentation) over eight weeks. The primary outcome is the number of patient contacts handled per nurse, analyzed with a generalized linear mixed-effects model for count data with nurse as a random effect; the treatment effect is expressed as an incidence rate ratio (IRR). A Monte Carlo simulation-based power analysis indicated 85% power to detect an IRR of 1.15 at a two-sided alpha of 0.05. Secondary outcomes include self-reported work-time savings, nurse experience and satisfaction, and patient satisfaction. Discrepancies between AI-generated draft notes, and final signed notes are analyzed to characterize the frequency, type, and clinical criticality of AI errors and omissions.

The trial is investigator-initiated (OmaHäme, HUS Helsinki University Hospital, University of Helsinki) and funded by the Strategic Research Council (GAINS project). The technology provider (Tandem Health) supplies the technical solution and participates in study design and manuscript preparation; responsibility for the study design, data analysis, and conclusions rests with the academic study group.