Hawai'i Pacific Health, 55 Merchant St, Honolulu, HI 96813. Email:
University of Hawai'i at Mānoa, Honolulu, Hawai'i.
Prev Chronic Dis. 2019 Feb 7;16:E16. doi: 10.5888/pcd16.180189.
The effect of social factors on health care outcomes is widely recognized. Health care systems are encouraged to add social and behavioral measures to electronic health records (EHRs), but limited research demonstrates how to leverage this information. We assessed 2 social factors collected from EHRs - social isolation and homelessness - in predicting 30-day potentially preventable readmissions (PPRs) to hospital.
EHR data were collected from May 2015 through April 2017 from inpatients at 2 urban hospitals on O'ahu, Hawai'i (N = 21,274). We performed multivariable logistic regression models predicting 30-day PPR by living alone versus living with others and by documented homelessness versus no documented homelessness, controlling for relevant factors, including age group, race/ethnicity, sex, and comorbid conditions.
Among the 21,274 index hospitalizations, 16.5% (3,504) were people living alone and 11.2% (2,385) were homeless; 4.2% (899) hospitalizations had a 30-day PPR. In bivariate analysis, living alone did not significantly affect likelihood of a 30-day PPR (16.6% [3,376 hospitalizations] without PPR vs 14.4% [128 hospitalizations] with PPR; P = .09). However, documented homelessness did show a significant effect on the likelihood of 30-day PPR in the bivariate analysis (11.1% [2,259 hospitalizations] without PPR vs 14.1% [126 hospitalizations] with PPR; P = .006). In multivariable models, neither living alone nor homelessness was significantly associated with PPR. Factors that were significantly associated with PPR were comorbid conditions, discharge disposition, and use of an assistive device.
Homelessness predicted PPR in descriptive analyses. Neither living alone nor homelessness predicted PPR once other factors were controlled. Instead, indicators of physical frailty (ie, use of an assistive device) and medical complexity (eg, hospitalizations that required assistive care post-discharge, people with a high number of comorbid conditions) were significant. Future research should focus on refining, collecting, and applying social factor data obtained through acute care EHRs.
社会因素对医疗保健结果的影响已得到广泛认可。医疗保健系统被鼓励在电子健康记录（EHR）中添加社会和行为措施，但有限的研究表明如何利用这些信息。我们评估了从电子健康记录中收集到的 2 个社会因素-社会隔离和无家可归-在预测 30 天内可能可预防的再入院（PPR）到医院。
从 2015 年 5 月至 2017 年 4 月，从夏威夷欧胡岛的 2 家城市医院的住院患者中收集电子健康记录数据（N=21274）。我们通过多变量逻辑回归模型预测 30 天内的 PPR，通过独居与与他人同住和有记录的无家可归与无记录的无家可归来预测，控制了相关因素，包括年龄组、种族/族裔、性别和合并症。
在 21274 次住院治疗中，16.5%（3504 人）为独居者，11.2%（2385 人）为无家可归者；4.2%（899 人）的住院患者发生 30 天内 PPR。在单变量分析中，独居并不显著影响 30 天 PPR 的可能性（无 PPR 为 16.6%[3376 次住院]，有 PPR 为 14.4%[128 次住院]；P=0.09）。然而，有记录的无家可归在单变量分析中确实显示出与 30 天 PPR 可能性显著相关（无 PPR 为 11.1%[2259 次住院]，有 PPR 为 14.1%[126 次住院]；P=0.006）。在多变量模型中，独居或无家可归均与 PPR 无显著相关性。与 PPR 显著相关的因素是合并症、出院处置和使用辅助设备。
在描述性分析中，无家可归预测 PPR。在控制其他因素后，独居或无家可归均不能预测 PPR。相反，身体虚弱的指标（即使用辅助设备）和医疗复杂性（例如，需要辅助护理的出院后住院，患有多种合并症的人）是显著的。未来的研究应侧重于完善、收集和应用通过急性护理电子健康记录获得的社会因素数据。
