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Assessing the Impact of an Artificial Intelligence Ambient Documentation Program on Provider Press Ganey Patient Satisfaction Scores: A Pilot Study
ABSTRACT
Background:
Patient experience is a critical aspect for any healthcare institution. Leveraging Artificial Intelligence (AI) to improve healthcare delivery has rapidly become an institutional priority across the nation. This study analyzes Press Ganey (PG) patient experience scores for providers before and after the implementation of an ambient AI documentation system (Dragon Ambient Experience (DAX) - Nuance).
Objective:
This objective of this study is to assess the impact of the implementation of an ambient AI documentation system (DAX) on Press Ganey patient experience scores.
Methods:
A retrospective study was conducted to evaluate the relationship between provider use of DAX and PG patient satisfaction scores from January 2023 to December 2024. Three domains were analyzed: 1. Overall assessment of experience, 2. Concern the care provider showed for your questions or worries, and 3. Likelihood of recommending this care provider to others.
Results:
49 providers across 9 departments participated in the DAX pilot. Aggregate scores for individual questions increased between 0.9 to 1.9 points. Care provider concern for a patient’s question/worries increased the most (1.9 points, P = .013), overall assessment of experience (1.3 points, P = .091), and likelihood of recommending this provider (0.9 points, P = .328).
Conclusions:
This pilot study aimed to investigate the relationship between provider use of DAX and PG patient experience scores in the outpatient setting at a large academic medical center. Increases in PG scores after implementing DAX were found across all PG questions assessed. As technology and AI continues to improve and become more widespread, these results are encouraging. Providers may consider leveraging AI notetaking software as a way to enhance their communication and interactions with patients.
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Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.