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从医生记录中识别出的医院再入院和社会风险因素。

Hospital Readmission and Social Risk Factors Identified from Physician Notes.

机构信息

Division of Health Policy, University of Pennsylvania, Philadelphia, PA.

CMC Philadelphia VA Medical Center, Philadelphia, PA.

出版信息

Health Serv Res. 2018 Apr;53(2):1110-1136. doi: 10.1111/1475-6773.12670. Epub 2017 Mar 13.

Abstract

OBJECTIVE

To evaluate the prevalence of seven social factors using physician notes as compared to claims and structured electronic health records (EHRs) data and the resulting association with 30-day readmissions.

STUDY SETTING

A multihospital academic health system in southeastern Massachusetts.

STUDY DESIGN

An observational study of 49,319 patients with cardiovascular disease admitted from January 1, 2011, to December 31, 2013, using multivariable logistic regression to adjust for patient characteristics.

DATA COLLECTION/EXTRACTION METHODS: All-payer claims, EHR data, and physician notes extracted from a centralized clinical registry.

PRINCIPAL FINDINGS

All seven social characteristics were identified at the highest rates in physician notes. For example, we identified 14,872 patient admissions with poor social support in physician notes, increasing the prevalence from 0.4 percent using ICD-9 codes and structured EHR data to 16.0 percent. Compared to an 18.6 percent baseline readmission rate, risk-adjusted analysis showed higher readmission risk for patients with housing instability (readmission rate 24.5 percent; p < .001), depression (20.6 percent; p < .001), drug abuse (20.2 percent; p = .01), and poor social support (20.0 percent; p = .01).

CONCLUSIONS

The seven social risk factors studied are substantially more prevalent than represented in administrative data. Automated methods for analyzing physician notes may enable better identification of patients with social needs.

摘要

目的

通过与索赔和结构化电子健康记录 (EHR) 数据相比,使用医生记录评估七种社会因素的流行情况,并将其与 30 天再入院率相关联。

研究设置

马萨诸塞州东南部的多医院学术医疗系统。

研究设计

一项观察性研究,纳入 2011 年 1 月 1 日至 2013 年 12 月 31 日期间患有心血管疾病的 49319 名患者,使用多变量逻辑回归调整患者特征。

数据收集/提取方法:从集中式临床注册中心提取所有支付方索赔、EHR 数据和医生记录。

主要发现

所有七种社会特征在医生记录中的识别率最高。例如,我们在医生记录中识别出 14872 例社会支持较差的患者入院,将基于 ICD-9 代码和结构化 EHR 数据的流行率从 0.4%提高到 16.0%。与 18.6%的基线再入院率相比,风险调整分析显示,住房不稳定(再入院率 24.5%;p<0.001)、抑郁(20.6%;p<0.001)、药物滥用(20.2%;p=0.01)和社会支持差(20.0%;p=0.01)的患者再入院风险更高。

结论

研究中七种社会风险因素的流行程度远远高于管理数据所代表的程度。分析医生记录的自动化方法可能能够更好地识别有社会需求的患者。

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