AI-Powered Voice Documentation: A Nurse-Led Pilot Program

BACKGROUND: Documentation burden consumes an average of 31% of a nurse's 12-hour shift. This challenge necessitates nurse-led innovations to streamline charting and reclaim valuable time at the bedside. PURPOSE: This pilot program aimed to examine a nurse-led implementation of an artificial intelligence (AI)-powered voice documentation mobile application and evaluate outcomes related to documentation timeliness, application usage, patient experience, staff satisfaction, and incidental overtime. METHODS: The six-month pilot was conducted on a 48-bed adult medical-surgical unit at a large academic medical center. It included 90 RNs and 33 certified nursing assistants. The mobile application enabled nursing staff to document directly into the electronic health record using voice commands. The project was guided by Kotter's eight-step change management model. RESULTS: Documentation timeliness improved across all targeted tasks. Patient experience improved across all six Press Ganey domains, with percentile increases ranging from 7 to 41 points. Staff satisfaction survey results showed a majority agreed that the technology helped reduce documentation time. Usage grew from 14,231 documentation entries in January 2025 to over 35,000 by June. Incidental overtime decreased from an average of 97 to 48.5 hours per month. CONCLUSION: This pilot demonstrated that nurse-led implementation of an AI-powered voice documentation application can improve charting efficiency, patient experience, and staff outcomes. When nurses contribute to each stage of technology design and implementation, innovation thrives.

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Publication Details

Journal
AJN American Journal of Nursing
Published
2026-07-23
DOI
https://doi.org/10.1097/ajn.0000000000000349
Primary Topic
Electronic Health Records Systems
Type
article
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article

AI-Powered Voice Documentation: A Nurse-Led Pilot Program

Golda Morales, Ryan Trias, Bernice Coleman, Scott Mondejar et al.
AJN American Journal of Nursing
Electronic Health Records Systems
article

AI-Powered Voice Documentation: A Nurse-Led Pilot Program

Golda Morales, Ryan Trias, Bernice Coleman, Scott Mondejar, Peachy Hain, Cynthia Bowers
article en

Abstract

BACKGROUND: Documentation burden consumes an average of 31% of a nurse's 12-hour shift. This challenge necessitates nurse-led innovations to streamline charting and reclaim valuable time at the bedside. PURPOSE: This pilot program aimed to examine a nurse-led implementation of an artificial intelligence (AI)-powered voice documentation mobile application and evaluate outcomes related to documentation timeliness, application usage, patient experience, staff satisfaction, and incidental overtime. METHODS: The six-month pilot was conducted on a 48-bed adult medical-surgical unit at a large academic medical center. It included 90 RNs and 33 certified nursing assistants. The mobile application enabled nursing staff to document directly into the electronic health record using voice commands. The project was guided by Kotter's eight-step change management model. RESULTS: Documentation timeliness improved across all targeted tasks. Patient experience improved across all six Press Ganey domains, with percentile increases ranging from 7 to 41 points. Staff satisfaction survey results showed a majority agreed that the technology helped reduce documentation time. Usage grew from 14,231 documentation entries in January 2025 to over 35,000 by June. Incidental overtime decreased from an average of 97 to 48.5 hours per month. CONCLUSION: This pilot demonstrated that nurse-led implementation of an AI-powered voice documentation application can improve charting efficiency, patient experience, and staff outcomes. When nurses contribute to each stage of technology design and implementation, innovation thrives.

AJN American Journal of NursingVol. 126(8)
University of Nursing Yangon (MM)
Industry, innovation and infrastructure
Openalex Percentile: Top 3%
Electronic Health Records Systems
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AI-Powered Voice Documentation: A Nurse-Led Pilot Program — Golda Morales, Ryan Trias, et al. · AJN American Journal of Nursing (2026) | TGRS Research Map | TGRS