Nanyang Business School, Nanyang Technological University, Singapore, Singapore.
City University of Hong Kong, Hong Kong, Hong Kong.
J Med Internet Res. 2021 Oct 19;23(10):e26486. doi: 10.2196/26486.
Prior literature suggests that psychosocial factors adversely impact health and health care utilization outcomes. However, psychosocial factors are typically not captured by the structured data in electronic medical records (EMRs) but are rather recorded as free text in different types of clinical notes.
We here propose a text-mining approach to analyze EMRs to identify older adults with key psychosocial factors that predict adverse health care utilization outcomes, measured by 30-day readmission. The psychological factors were appended to the LACE (Length of stay, Acuity of the admission, Comorbidity of the patient, and Emergency department use) Index for Readmission to improve the prediction of readmission risk.
We performed a retrospective analysis using EMR notes of 43,216 hospitalization encounters in a hospital from January 1, 2017 to February 28, 2019. The mean age of the cohort was 67.51 years (SD 15.87), the mean length of stay was 5.57 days (SD 10.41), and the mean intensive care unit stay was 5% (SD 22%). We employed text-mining techniques to extract psychosocial topics that are representative of these patients and tested the utility of these topics in predicting 30-day hospital readmission beyond the predictive value of the LACE Index for Readmission.
The added text-mined factors improved the area under the receiver operating characteristic curve of the readmission prediction by 8.46% for geriatric patients, 6.99% for the general hospital population, and 6.64% for frequent admitters. Medical social workers and case managers captured more of the psychosocial text topics than physicians.
The results of this study demonstrate the feasibility of extracting psychosocial factors from EMR clinical notes and the value of these notes in improving readmission risk prediction. Psychosocial profiles of patients can be curated and quantified from text mining clinical notes and these profiles can be successfully applied to artificial intelligence models to improve readmission risk prediction.
先前的文献表明，心理社会因素会对健康和医疗保健利用结果产生不利影响。然而，心理社会因素通常不会被电子病历（EMR）中的结构化数据捕获，而是作为不同类型临床记录中的自由文本记录。
我们在此提出一种文本挖掘方法来分析 EMR，以确定具有关键心理社会因素的老年人，这些因素预测不良的医疗保健利用结果，以 30 天再入院率来衡量。将心理因素附加到 LACE（住院时间、入院紧急程度、患者合并症和急诊使用）再入院指数中，以提高再入院风险预测能力。
我们对 2017 年 1 月 1 日至 2019 年 2 月 28 日期间一家医院的 43216 次住院记录进行了回顾性分析。队列的平均年龄为 67.51 岁（标准差 15.87），平均住院时间为 5.57 天（标准差 10.41），平均重症监护病房入住率为 5%（标准差 22%）。我们采用文本挖掘技术提取代表这些患者的心理社会主题，并测试这些主题在预测 30 天内医院再入院方面的效用，其预测能力超过 LACE 再入院指数。
对于老年患者，添加的文本挖掘因素将再入院预测的接收者操作特征曲线下面积提高了 8.46%；对于综合医院人群，提高了 6.99%；对于经常入院者，提高了 6.64%。与医生相比，医务社工和个案管理员更能捕捉到更多的心理社会文本主题。
这项研究的结果表明，从 EMR 临床记录中提取心理社会因素是可行的，并且这些记录在提高再入院风险预测方面具有价值。可以从文本挖掘临床记录中提取和量化患者的心理社会概况，并成功将这些概况应用于人工智能模型，以提高再入院风险预测。
