Impact of Ambient Artificial Intelligence Notes on Provider Burnout

Jason MIsurac, Lindsey A Knake, James M Blum
{"title":"Impact of Ambient Artificial Intelligence Notes on Provider Burnout","authors":"Jason MIsurac, Lindsey A Knake, James M Blum","doi":"10.1101/2024.07.18.24310656","DOIUrl":null,"url":null,"abstract":"Background: Healthcare provider burnout is a critical issue with significant implications for individual well-being, patient care, and healthcare system efficiency. Addressing burnout is essential for improving both provider well-being and the quality of patient care. Ambient artificial intelligence (AI) offers a novel approach to mitigating burnout by reducing the documentation burden through advanced speech recognition and natural language processing technologies that summarize the patient encounter into a clinical note to be reviewed by clinicians.\nObjective: To assess provider burnout and professional fulfilment associated with Ambient AI technology during a pilot study, assessed using the Stanford Professional Fulfillment Index (PFI). Methods: A pre-post observational study was conducted at University of Iowa Health Care with 38 volunteer physicians and advanced practice providers. Participants used a commercial ambient AI tool, over a 5-week trial in ambulatory environments. The AI tool transcribed patient-clinician conversations and generated preliminary clinical notes for review and entry into the electronic medical record. Burnout and professional fulfillment were assessed using the Stanford PFI at baseline and post-intervention. Results: Pre-test and post-test surveys were completed by 35/38 participants (92% survey completion rate). Results showed a significant reduction in burnout scores, with the median burnout score improving from 4.16 to 3.16 (p=0.005), with validated Stanford PFI cutoff for overall burnout 3.33. Burnout rates decreased from 69% to 43%. There was a notable improvement in interpersonal disengagement scores (3.6 vs. 2.5, p<0.001), although work exhaustion scores did not significantly change. Professional fulfillment showed a modest, non-significant increase (6.1 vs. 6.5, p=0.10). Conclusions: Ambient AI significantly reduces healthcare provider burnout and modestly enhances professional fulfillment. By alleviating documentation burdens, ambient AI improves operational efficiency and provider well-being. These findings suggest that broader implementation of ambient AI could be a strategic intervention to combat burnout in healthcare settings.","PeriodicalId":501454,"journal":{"name":"medRxiv - Health Informatics","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"medRxiv - Health Informatics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1101/2024.07.18.24310656","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Background: Healthcare provider burnout is a critical issue with significant implications for individual well-being, patient care, and healthcare system efficiency. Addressing burnout is essential for improving both provider well-being and the quality of patient care. Ambient artificial intelligence (AI) offers a novel approach to mitigating burnout by reducing the documentation burden through advanced speech recognition and natural language processing technologies that summarize the patient encounter into a clinical note to be reviewed by clinicians. Objective: To assess provider burnout and professional fulfilment associated with Ambient AI technology during a pilot study, assessed using the Stanford Professional Fulfillment Index (PFI). Methods: A pre-post observational study was conducted at University of Iowa Health Care with 38 volunteer physicians and advanced practice providers. Participants used a commercial ambient AI tool, over a 5-week trial in ambulatory environments. The AI tool transcribed patient-clinician conversations and generated preliminary clinical notes for review and entry into the electronic medical record. Burnout and professional fulfillment were assessed using the Stanford PFI at baseline and post-intervention. Results: Pre-test and post-test surveys were completed by 35/38 participants (92% survey completion rate). Results showed a significant reduction in burnout scores, with the median burnout score improving from 4.16 to 3.16 (p=0.005), with validated Stanford PFI cutoff for overall burnout 3.33. Burnout rates decreased from 69% to 43%. There was a notable improvement in interpersonal disengagement scores (3.6 vs. 2.5, p<0.001), although work exhaustion scores did not significantly change. Professional fulfillment showed a modest, non-significant increase (6.1 vs. 6.5, p=0.10). Conclusions: Ambient AI significantly reduces healthcare provider burnout and modestly enhances professional fulfillment. By alleviating documentation burdens, ambient AI improves operational efficiency and provider well-being. These findings suggest that broader implementation of ambient AI could be a strategic intervention to combat burnout in healthcare settings.
环境人工智能笔记对医务人员职业倦怠的影响
背景:医疗服务提供者的职业倦怠是一个关键问题,对个人福祉、患者护理和医疗系统效率都有重大影响。解决职业倦怠问题对于提高医疗服务提供者的福利和患者护理质量至关重要。环境人工智能(AI)通过先进的语音识别和自然语言处理技术,将患者就诊情况总结为临床笔记供临床医生审阅,从而减轻了记录负担,为减轻职业倦怠提供了一种新方法:目的:在一项试点研究中,评估与 Ambient AI 技术相关的医疗服务提供者的职业倦怠和职业满足感,并使用斯坦福职业满足感指数 (PFI) 进行评估。方法爱荷华大学医疗保健中心对 38 名志愿医生和高级医疗服务提供者进行了一项前后观察研究。参与者在门诊环境中使用了一款商用环境人工智能工具,试用期为 5 周。该人工智能工具转录了病人与医生的对话,并生成了初步的临床笔记,以供审查并输入电子病历。在基线和干预后,使用斯坦福 PFI 对职业倦怠和职业成就感进行了评估。结果35/38 名参与者完成了测试前和测试后的调查(调查完成率为 92%)。结果显示,倦怠感得分明显降低,倦怠感得分中位数从 4.16 降至 3.16(p=0.005),经验证的斯坦福 PFI 整体倦怠感临界值为 3.33。倦怠率从 69% 降至 43%。人际关系疏离得分有明显改善(3.6 对 2.5,p<0.001),但工作枯竭得分没有显著变化。职业成就感则略有提高,但不明显(6.1 对 6.5,p=0.10)。结论环境人工智能大大降低了医疗服务提供者的职业倦怠,并适度提高了职业成就感。通过减轻文档负担,环境人工智能提高了运营效率和医疗服务提供者的幸福感。这些研究结果表明,在医疗机构中更广泛地实施环境人工智能可以成为消除职业倦怠的战略性干预措施。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信