Preliminary findings regarding the association between patient demographics and ED experience scores across a regional health system: A cross sectional study using natural language processing of patient comments

IF 3.7 2区 医学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS
Diane Kuhn , Nicholas E. Harrison , Paul I. Musey Jr , David J. Crandall , Peter S. Pang , Julie L. Welch , Christopher A Harle
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引用次数: 0

Abstract

Objective

Existing literature shows associations between patient demographics and reported experiences of care, but this relationship is poorly understood. Our objective was to use natural language processing of patient comments to gain insight into associations between patient demographics and experiences of care.

Methods

This is a cross-sectional study of 14,848 unique emergency department (ED) patient visits from 1/1/2020 to 12/31/2020. Patients discharged from one of 16 ED sites in a regional health system who filled out a patient experience survey with comments were included. This study had two outcome variables: (1) positive vs. non-positive (negative/neutral) comment sentiment, and (2) promoter vs. non-promoter status (based on NRCHealth’s Net Promoter Score; likelihood to recommend of 9 or 10 are considered “promoters”, while scores of 8 or below are “non-promoters”). We used natural language processing to sort patient comments into topics and sentiments. Logistic regression with mediation analysis was used to estimate the associations between patient demographics and the following: (1) comments about compassion vs. other topics, (2) positive comments, and (3) patient experience, defined as likelihood to recommend.

Results

Comments about care and compassion (51 % of total comments) had highly positive sentiment (97 %), compared to mixed sentiment for other topics. Older, male, and Asian patients were more likely to comment on compassion and most likely to make positive comments. Our mediation analysis suggests that the demographic association with positive patient comments and net promoter scores was mediated by their focus on care and compassion as a primary comment theme for their visit. Notably, the overall percentage of patients providing comments was only 1.8 %, raising concerns about whether data currently used for hospital and physician feedback has adequate validity to yield meaningful insights.

Conclusions

The increased likelihood of specific patient sub-groups to comment on compassionate care may explain previously reported differences in experience by patient demographics.
关于区域卫生系统中患者人口统计学与ED经验评分之间关系的初步发现:一项使用自然语言处理患者评论的横断面研究。
目的:现有文献显示,患者的人口统计学特征与所报告的护理体验之间存在关联,但人们对这种关系的了解却很少。我们的目的是利用对患者评论的自然语言处理,深入了解患者人口统计学特征与护理体验之间的关系:这是一项横断面研究,研究对象是 2020 年 1 月 1 日至 2020 年 12 月 31 日期间 14848 名急诊科(ED)患者。研究对象包括从一个地区医疗系统的 16 个急诊室之一出院并填写了患者体验调查表的患者。本研究有两个结果变量:(1) 正面与非正面(负面/中性)评论情绪;(2) 促进者与非促进者状态(基于 NRCHealth 的净促进者得分;推荐可能性为 9 或 10 的被视为 "促进者",而得分为 8 或以下的为 "非促进者")。我们使用自然语言处理技术将患者评论按主题和情感分类。我们使用带有中介分析的逻辑回归来估算患者人口统计学特征与以下内容之间的关联:(1) 关于同情心的评论与其他主题的评论,(2) 正面评论,以及 (3) 患者体验,即推荐的可能性:结果:关于护理和同情的评论(占评论总数的 51%)具有高度积极的情感(97%),而关于其他主题的评论则喜忧参半。年龄较大、男性和亚裔患者更有可能就同情心发表评论,也最有可能做出积极评价。我们的中介分析表明,患者对护理和同情的关注是他们就诊的主要评论主题,这对患者正面评论和净促进者得分的人口统计学关联起到了中介作用。值得注意的是,提供评论的患者总体比例仅为 1.8%,这让人担心目前用于医院和医生反馈的数据是否具有足够的有效性,从而产生有意义的见解:结论:特定患者亚群对仁爱护理发表评论的可能性增加,这可能是之前报道的不同患者人口统计学体验差异的原因。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
International Journal of Medical Informatics
International Journal of Medical Informatics 医学-计算机:信息系统
CiteScore
8.90
自引率
4.10%
发文量
217
审稿时长
42 days
期刊介绍: International Journal of Medical Informatics provides an international medium for dissemination of original results and interpretative reviews concerning the field of medical informatics. The Journal emphasizes the evaluation of systems in healthcare settings. The scope of journal covers: Information systems, including national or international registration systems, hospital information systems, departmental and/or physician''s office systems, document handling systems, electronic medical record systems, standardization, systems integration etc.; Computer-aided medical decision support systems using heuristic, algorithmic and/or statistical methods as exemplified in decision theory, protocol development, artificial intelligence, etc. Educational computer based programs pertaining to medical informatics or medicine in general; Organizational, economic, social, clinical impact, ethical and cost-benefit aspects of IT applications in health care.
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