Abstract 4143727: AI Integration Decreased Rural Documentation Burden by 40% in Medicare's Chronic Care Management Setting

Efforts to ameliorate rural healthcare burnout stemming from overwork have taken many approaches, including the increased usage of Artificial Intelligence (AI) to aid in generating electronic medical records (EMR). This new application of AI technology demands characterization of the possible benefits and efficacy of its new use. Using a commercially available AI software (Freed AI) designed for EMR production, we conducted a study to determine the quality of EMR generated by AI. Methods: This trial analyzed 248 patient-provider interactions within a rural setting of Medicare’s Chronic Care Management (CCM program), recorded by Freed AI and a human scribe. Three blinded readers with a clinical background were given 2 notes from the same patient-encounter. One note was written by a provider and one was generated by Freed AI. Each reader performed a binary, independent assessment of each note compared with the note generated by the human control. Readers were trained on definitions of the categories clarity, accuracy, completeness, and relevance. The time required per encounter was also recorded and analyzed. Discussion: As seen in Table 1, AI performed better than a human-generated EMR in the fields of clarity and relevance, suggesting effective formatting and a well calibrated understanding of what information is medically relevant. No significant difference in completeness was observed, suggesting AI was able to record as much valuable information as traditional charting. AI performed below the human standard in the category of accuracy. For example, AI sometimes misspelled names or misunderstood complex situations being discussed because it was not able to infer certain information which a human, using outside knowledge and past experiences, could. The reduction in time spent while utalizing AI demonstrates a highly significant decrease in workload and allows providers freedom to allocate more time interacting with each patient and hopefully reducing physician burnout.

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Publication Details

Journal
Circulation
Published
2024-11-12
DOI
https://doi.org/10.1161/circ.150.suppl_1.4143727
Primary Topic
Healthcare Systems and Public Health
Type
article
Field-Weighted Citation Impact
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article

Abstract 4143727: AI Integration Decreased Rural Documentation Burden by 40% in Medicare's Chronic Care Management Setting

Caroline Wheeler, Chris H. Miller, Aniruddha Dey, Samuel A. Miller et al.
Circulation
Healthcare Systems and Public Health
article

Abstract 4143727: AI Integration Decreased Rural Documentation Burden by 40% in Medicare's Chronic Care Management Setting

Caroline Wheeler, Chris H. Miller, Aniruddha Dey, Samuel A. Miller, Ghaith Al Tibi, Garrett Jimmerson, Nicolas Chronos, Jered Miller
article en

Abstract

Efforts to ameliorate rural healthcare burnout stemming from overwork have taken many approaches, including the increased usage of Artificial Intelligence (AI) to aid in generating electronic medical records (EMR). This new application of AI technology demands characterization of the possible benefits and efficacy of its new use. Using a commercially available AI software (Freed AI) designed for EMR production, we conducted a study to determine the quality of EMR generated by AI. Methods: This trial analyzed 248 patient-provider interactions within a rural setting of Medicare’s Chronic Care Management (CCM program), recorded by Freed AI and a human scribe. Three blinded readers with a clinical background were given 2 notes from the same patient-encounter. One note was written by a provider and one was generated by Freed AI. Each reader performed a binary, independent assessment of each note compared with the note generated by the human control. Readers were trained on definitions of the categories clarity, accuracy, completeness, and relevance. The time required per encounter was also recorded and analyzed. Discussion: As seen in Table 1, AI performed better than a human-generated EMR in the fields of clarity and relevance, suggesting effective formatting and a well calibrated understanding of what information is medically relevant. No significant difference in completeness was observed, suggesting AI was able to record as much valuable information as traditional charting. AI performed below the human standard in the category of accuracy. For example, AI sometimes misspelled names or misunderstood complex situations being discussed because it was not able to infer certain information which a human, using outside knowledge and past experiences, could. The reduction in time spent while utalizing AI demonstrates a highly significant decrease in workload and allows providers freedom to allocate more time interacting with each patient and hopefully reducing physician burnout.

CirculationVol. 150(Suppl_1)
Albert Einstein College of Medicine (US), University of Georgia (US), Eaton (United States) (US)
Quality Education
Openalex Percentile: Top 69%
Healthcare Systems and Public Health
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