Evaluation of an Ambient Artificial Intelligence Documentation Platform for Clinicians.

Area of Science:

• Healthcare technology
• Clinical informatics
• Artificial intelligence in medicine

Background:

• Electronic health record (EHR) systems contribute to clinician burnout.
• Ambient artificial intelligence (AI) documentation platforms offer a potential solution to reduce EHR-related workload.
• Improving clinician well-being is crucial for healthcare quality.

Purpose of the Study:

• To evaluate the impact of an ambient AI documentation platform on clinician experience and EHR workload.
• To assess changes in clinician well-being metrics before and after AI implementation.
• To identify variations in outcomes based on clinician demographics and specialty.

Main Methods:

• A pilot quality improvement study using a before-and-after design.
• Implementation of an ambient AI documentation platform in a large healthcare organization.
• Collection of EHR metrics and survey data from 100 clinicians over a 6-month period.
• Analysis using logistic regression and linear mixed-effect models.

Main Results:

• Ambient AI significantly reduced time spent in notes per appointment (P < .001).
• Clinician-reported workload, including mental demand and hurried pace, decreased significantly (P < .001).
• Overall job satisfaction improved, particularly among primary care clinicians (P < .001).
• Burnout rates showed a non-significant decrease.

Conclusions:

• Ambient AI documentation platforms can improve clinician experience by reducing documentation time and perceived workload.
• Outcomes may vary by clinician sex and specialty, warranting further investigation.
• Widespread adoption of ambient AI holds potential for mitigating EHR-related burnout.