Advanced search


Search results        Search results      Copy URL to E-Mail


Evaluating the Efficacy of AI Note-Taking Software in Documenting Osteopathic Manipulative Medicine Treatment Encounters

Journal: The AAO Journal Date: 2025/06, 35(2):Pages: 26-27. doi: Subito , type of study: observational study

Full text    (https://aaoj.kglmeridian.com/view/journals/aaoj/35/2/article-p13.xml)

Keywords:

AI [1790]
artificial intelligence [4]
documentation [8]
observational study [209]
osteopathic physicians [203]
patient encounter [2]
therapeutic process [49]

Abstract:

Introduction: Recent research shows that physicians spend nearly half of their workday interacting with electronic health records (EHR), with a significant portion of this time occurring during patient visits. This contributes to a substantial documentation burden both during and outside of encounters. While artificial intelligence (AI) softwares are increasingly being used to generate clinical notes, allowing physicians to focus on patient interactions, there is limited research on their effectiveness in documenting osteopathic manipulative medicine (OMM) encounters. Hypothesis: Not all AI softwares are created equally. This study aims to identify whether a certain program is more accurate than others at capturing and documenting an OMM-specific patient encounter—both the diagnosis and treatment of somatic dysfunction. Research Design and Methods: Four AI softwares were used to record seven patient encounters that involved OMT. Student doctors and physicians then reviewed the generated notes and compared one software’s note to another for each encounter, and generated a score based on the follow criteria: consistency of the primary diagnosis with the physician's diagnosis, accuracy of documentation of somatic dysfunction diagnosis and treatment, inclusion of appropriate ICD-10 codes, note formulation speed, and ease of use. Results: The AI softwares were ranked based on their total scores, from highest to lowest: Freed achieved the highest score at 81.43%, followed by Heidi with 79.64%, Nabla with 66.67%, and Swifty with 26.19%. Conclusion: These results can guide osteopathic physicians in selecting the most effective AI software for documenting OMM encounters. Our goal is to reduce documentation burdens, alleviate physician fatigue and burnout, and ultimately improve patient outcomes while focusing on osteopathic medical care.


Search results      Copy URL to E-Mail

 
 
 






  • ImpressumLegal noticeDatenschutz


ostlib.de/data_rysutqwbxdagjmkzfhnp



Supported by

OSTLIB recommends