Time Savings Through an AI Speech Assistant for Nursing Documentation: A Pre–Post Time-Motion Study in German Long-Term Care (Preprint)

BACKGROUND Nurses in long-term care spend up to one-third of their working time on documentation, contributing to administrative burden and limited time for direct care. Artificial Intelligence (AI) speech assistants have shown potential to accelerate documentation, but longitudinal evidence from real-world long-term care settings remains scarce. OBJECTIVE This study aimed to evaluate whether implementing a domain-specific, mobile AI speech assistant reduces documentation time in German long-term care under routine conditions. Secondary objectives included examining usability, perceived documentation effort, interruptions, and workplace satisfaction. METHODS A pre–post time-motion study with full-shift observation was conducted. Continuous, event-based observations were performed before (t0) and after (t1) implementation of the mobile speech assistant voize. The primary outcome was total documentation time per morning shift. In addition to observations, questionnaires were administered to assess perceived documentation effort, interruptions, satisfaction, and workplace-related satisfaction. Data were analyzed using ANCOVA-of-change models with multiple imputation and Holm–Bonferroni correction across secondary endpoints. RESULTS Fifty-two registered nurses from 14 long-term care facilities participated (mean age 42.4 years; 81% female). Across 770 observed hours, total documentation time per morning shift decreased significantly by an adjusted mean of 14.8 minutes, corresponding to a 27% reduction relative to baseline and a large effect size (Cohen’s d = 0.96). Self-reported documentation time and interruptions also declined significantly, while satisfaction with the documentation system increased. Usability was rated as good. CONCLUSIONS This study provides real-world evidence that an AI-based speech assistant can reduce documentation workload in long-term care. Integrating a mobile, domain-specific speech system into daily workflows substantially decreased documentation time and improved perceived efficiency. Beyond measurable time savings, such technology has the potential to alleviate workload, free time for resident care, and enhance working conditions. These findings are also relevant for policy discussions on addressing the nursing workforce shortage, showing that well-integrated, speech-enabled documentation systems can support more sustainable long-term care environments. CLINICALTRIAL German Clinical Trials Register (DRKS00035512)