ArticleJMIR AI2026
Ambient AI Documentation and Patient Satisfaction in Outpatient Care: Retrospective Pilot Study.
Article in JMIR AI, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Harmonizing Multi-Institutional Clinical Documentation Using Natural Language Processing in Neurofibromatosis Type 1.Neurology. Clinical practice · 2026Observational
- Selecting, Scaling, and Measuring the Value of Ambient AI in a Nonacademic Health System: Multiphase Pilot Study.JMIR medical informatics · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Patient experience is a critical consideration for any health care institution. Leveraging artificial intelligence (AI) to improve health care delivery has rapidly become an institutional priority across the United States. Ambient AI documentation systems such as Dragon Ambient eXperience (DAX) may influence patient perception of health care provider communication and overall experience. Objective: The objective of this study was to assess the impact of the implementation of an ambient AI documentation system (DAX) on Press Ganey (PG) patient experience scores. Methods: A retrospective study was conducted to evaluate the relationship between provider use of DAX (N=49) and PG patient satisfaction scores from January 2023 to December 2024. Three domains were analyzed: (1) overall assessment of the experience, (2) concern the care provider showed for patients' questions or worries, and (3) likelihood of recommending the care provider to others. Mean pretest-posttest score differences and P values were calculated. Results: A total of 49 health care providers across 9 departments participated in the DAX pilot. Aggregate scores for individual items increased between 0.9 and 1.9 points. Care provider concern for a patient's questions or worries increased the most (1.9 points; P=.01), followed by overall assessment of the experience (1.3 points; P=.09) and likelihood of recommending the provider (0.9 points; P=.33). Subgroup analysis showed a larger increase in concern scores among providers using DAX <50% of the time (3.2-point increase; P=.03). 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 observed across all PG items assessed. As technology and AI continue to improve and become more widespread, these results are encouraging. Health care providers may consider leveraging AI note-taking software as a way to enhance their communication and interactions with patients.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.