Ambient AI for Reducing Nursing Staff Documentation Time
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- The protocol lists: Artificial Intelligence.
- Who it may be relevant to
- Registry conditions: Nursing Documentation Burden. Basic parameters: from 18 years · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- United States
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
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Official title
An EHR-Embedded Pragmatic Stepped-Wedge Clinical Trial of Ambient Artificial Intelligence to Reduce Nursing Staff Documentation Time
Overview
The goal of this clinical trial is to learn whether using Ambient Artificial Intelligence for nursing staff documentation in an inpatient setting will reduce the time spent in flowsheet documentation and enhance nurse staffing wellbeing. Participants will use Ambient Listening AI software to draft documentation in discrete fields.
Detailed description
This pragmatic trial is being conducted to test the effectiveness of Artificial Intelligence Driven Ambient Listening software on reducing time in flowsheet documentation among Registered Nurses (RNs) and nursing assistants (NAs) in an inpatient setting. The Ambient AI software uses Automated Speech Recognition technology with Large Language Models to automatically generate discrete flowsheet values from nurse-patient conversations in real-time. The clinical trial is an examination of the impact and usability of this implementation on the workload and well-being of nurses involved in the rollout of the software pragmatic Electronic Health Record embedded design integrated into clinical workflows and the health system IT infrastructure.
Interventions
- Other Artificial Intelligence
Ambient AI software intervention is implemented into the nursing staff workflow. The software incorporates Automated Speech Recognition technology with Large Language Models to generate clinical documentation in real-time
Primary outcome measures
- Change in Active Time Spent in Flowsheets per shift hour [Time frame: Baseline to 22 weeks]
Secondary outcome measures (7)
- Change in Active Time Spent in Flowsheets per patient per shift [Time frame: Baseline to 22 weeks]
- Change in the number of clicks or taps in Flowsheets [Time frame: Baseline to 22 weeks]
- Change in Amount of Overtime Charting [Time frame: Baseline to 22 weeks]
- Change in Clinician Worklife Survey (Mini-Z) Score [Time frame: Baseline to 22 weeks]
- Change in Mini-Z Subscale Scores: Supportive Work Environment [Time frame: Baseline to 22 weeks]
- Change in Mini-Z (3.0) Subscale Scores: EHR Stress [Time frame: Baseline to 22 weeks]
- Change System Usability Scale (SUS) [Time frame: 11 weeks to 22 weeks]
Eligibility criteria
Inclusion criteria
- Willingness to engage and use ambient technology
- English speaking
- All Registered Nurses and Nursing Assistants with the study inpatient units
- Attest to completing all required training
Exclusion criteria
- Planned leave more than 6 weeks during study timeframe
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: Yes
Study design
- Allocation
- Randomized
- Model
- Parallel assignment
- Masking
- Open label
- Primary purpose
- Health services research
Study locations
United States · 1 center
- UW Health - East Madison Hospital — Madison
Publications
- Afshar M, Baumann MR, Resnik F, Hintzke J, Sullivan AG, Wills G, Lemmon K, Dambach J, Ann Mrotek L, Quinn M, Abramson K, Kleinschmidt P, Brazelton TB, Leaf MA, Twedt H, Kunstman D, Patterson B, Liao F, Rasmussen S, Burnside ES, Goswami C, Gordon J. A Pragmatic Randomized Controlled Trial of Ambient Artificial Intelligence to Improve Health Practitioner Well-Being. NEJM AI. 2025 Dec;2(12):10.1056/a PMID 41625485
- Afshar M, Resnik F, Baumann MR, Hintzke J, Lemmon K, Sullivan AG, Shah T, Stordalen A, Oberst M, Dambach J, Mrotek LA, Quinn M, Abramson K, Kleinschmidt P, Brazelton T, Twedt H, Kunstman D, Wills G, Long J, Patterson BW, Liao FJ, Rasmussen S, Burnside E, Goswami C, Gordon JE. A Novel Playbook for Pragmatic Trial Operations to Monitor and Evaluate Ambient Artificial Intelligence in Clinical Practic PMID 40959192
Identifiers
NCT: NCT07456241 · 2026-0311 · UWMSN | Nursing | Admin · Protocol Version 5/15/26