Stella Sarah A, Hanratty Rebecca, Davidson Arthur J, Podewils Laura J, Elliott Laura, Keith Amy, Everhart Rachel
Department of Medicine, Denver Health, Denver, CO, USA.
Department of Medicine, University of Colorado Anschutz Medical Campus School of Medicine, Aurora, CO, USA.
J Gen Intern Med. 2024 Dec;39(16):3113-3119. doi: 10.1007/s11606-024-08909-1. Epub 2024 Sep 16.
Identification of persons experiencing homelessness (PEH) within healthcare systems is critical to facilitate patient and population-level interventions to address health inequities.
We created an enhanced electronic health record (EHR) registry to improve identification of PEH within a safety net healthcare system.
We compared patients identified as experiencing homelessness in 2021, stratified by method of identification (i.e., through registration data sources versus through new EHR registry criteria).
Sociodemographic and clinical characteristics, healthcare utilization, engagement with homeless service providers, and mortality.
In total, 10,896 patients met the registry definition of a PEH; 30% more than identified through standard registration processes; 78% were identified through only one data source. Compared with those identified only through registration data, PEH identified through new registry criteria were more likely to be female (42% vs. 25%, p < 0.001), Hispanic/Latinx or Black/African American (30% versus 25% and 25% vs. 18%, p < 0.0001), and Medicaid or Medicare beneficiaries (74% vs. 67% and 16% vs.10%, respectively, p < 0.0001). New data sources also identified a higher proportion of patients: at extremes of age (16% < 18 years and 9% ≥ 65 years vs. 2% and 5%, respectively, p < 0.0001), with increased clinical risk (31% with CRG 6-9 vs. 18%, p < 0.0001), and with a mental health diagnosis (56% vs. 42%, p < 0.0001), and a lower proportion of patients with a substance use diagnosis (39% vs. 54%, p < 0.0001) or criminal justice involvement (8% vs. 15%, p < 0.0001). Newly identified patients were more likely to be engaged in primary care (OR 2.03, 95% CI 1.83-2.26) but less likely to be engaged with homeless service providers (OR 0.70, 95% CI 0.63-0.77).
Commonly utilized methods of identifying PEH within healthcare systems may underestimate the population and introduce reporting biases. Recognizing alternate identification methods may more comprehensively and inclusively identify PEH for intervention.
在医疗保健系统中识别无家可归者对于推动针对患者和人群层面的干预措施以解决健康不平等问题至关重要。
我们创建了一个强化电子健康记录(EHR)登记系统,以改善在安全网医疗保健系统中对无家可归者的识别。
我们比较了2021年被确定为无家可归的患者,按识别方法分层(即通过登记数据源与通过新的EHR登记标准)。
社会人口统计学和临床特征、医疗保健利用情况、与无家可归服务提供者的接触情况以及死亡率。
总共有10896名患者符合无家可归者的登记定义;比通过标准登记流程识别的患者多30%;78%是通过单一数据源识别的。与仅通过登记数据识别的患者相比,通过新登记标准识别的无家可归者更可能为女性(42%对25%,p<0.001)、西班牙裔/拉丁裔或黑人/非裔美国人(分别为30%对25%和25%对18%,p<0.0001)以及医疗补助或医疗保险受益人(分别为74%对67%和16%对10%,p<0.0001)。新数据源还识别出更高比例的患者:处于年龄极端情况(16%<18岁和9%≥65岁,分别对比2%和5%,p<0.0001)、临床风险增加(31%的患者临床风险组为6 - 9级,对比18%,p<0.0001)、有心理健康诊断(56%对比42%,p<0.0001),以及有物质使用诊断或涉及刑事司法的患者比例较低(39%对比54%,p<0.0001;8%对比15%,p<0.0001)。新识别出的患者更可能接受初级保健(比值比2.03,95%置信区间1.83 - 2.26),但与无家可归服务提供者接触的可能性较小(比值比0.70,95%置信区间0.63 - 0.77)。
在医疗保健系统中常用的识别无家可归者的方法可能会低估该人群数量并引入报告偏差。认识到替代识别方法可能会更全面、更包容地识别无家可归者以进行干预。