背景:具有未满足的社会需求和健康的社会决定因素(SDOH)挑战的患者继续面临疾病患病率增加的不成比例的风险,医疗保健使用,更高的医疗费用,更糟糕的结果。一些现有的预测模型已使用有关社会需求和SDOH挑战的可用数据来预测与健康相关的社会需求或对各种社会服务推荐的需求。尽管这些一次性的努力,到目前为止的工作表明,必须克服许多技术和组织挑战,然后才能在一个持续的,在大多数美国医疗保健组织内的大规模基础。
目的:我们旨在检索电子健康记录(EHR)中与识别有社会需要的人相关的可用信息,并开发社会风险评分,用于临床实践,以更好地识别有未来社会需要风险的患者。
方法:我们使用EHR数据(2016-2021年)和美国人口普查美国社区调查的数据进行了回顾性研究。我们开发了一个前瞻性模型,使用当前1年的风险因素来预测四个2年队列中未来2年的结果。感兴趣的预测因素包括人口统计学,以前的医疗保健使用,合并症,先前确定的社会需求,以及区域剥夺指数所反映的邻里特征。结果变量是一个二元指标,反映了有社会需求的患者存在的可能性。我们应用了广义估计方程方法,调整患者级别的风险因素,地理聚类数据的可能影响,以及每位患者多次就诊的效果。
结果:1,852,228名患者的研究人群包括中年人(平均年龄为53.76-55.95岁),白色(范围324,279/510,770,63.49%至290,688/488,666,64.79%),和女性患者(范围为314,741/510,770,61.62%至278,488/448,666,62.07%),来自具有高社会经济地位的社区(平均面积剥夺指数百分位数范围为28.76-30.31)。在研究队列中,8.28%(37,137/448,666)和11.55%(52,037/450,426)的患者在其EHR中记录了至少1种社会需求,安全问题和经济挑战(即,财政资源紧张,employment,和粮食不安全)是最常见的记录社会需求(87,152/1,852,228,4.71%和58,242/1,852,228,3.14%的患者,分别)。该模型在预测总体研究人群的预期社会需求方面的曲线下面积为0.702(95%CI0.699-0.705)。以前的社会需求(比值比3.285,95%CI3.237-3.335)和急诊科就诊(比值比1.659,95%CI1.634-1.684)是未来社会需求的最强预测因子。
结论:我们的模型为利用现有的EHR数据帮助识别具有高社会需求的患者提供了机会。我们提出的社会风险评分可以帮助确定从进一步的社会需求筛查和数据收集中受益最大的患者子集,以避免在目标人群中收集所有患者的潜在负担。
BACKGROUND: Patients with unmet social needs and social determinants of health (SDOH) challenges continue to face a disproportionate risk of increased prevalence of disease, health care use, higher health care costs, and worse outcomes. Some existing predictive models have used the available data on social needs and SDOH challenges to predict health-related social needs or the need for various social service referrals. Despite these one-off efforts, the work to date suggests that many technical and organizational challenges must be surmounted before SDOH-integrated solutions can be implemented on an ongoing, wide-scale basis within most US-based health care organizations.
OBJECTIVE: We aimed to retrieve available information in the electronic health record (EHR) relevant to the identification of persons with social needs and to develop a social risk score for use within clinical practice to better identify patients at risk of having future social needs.
METHODS: We conducted a retrospective study using EHR data (2016-2021) and data from the US Census American Community Survey. We developed a prospective model using current year-1 risk factors to predict future year-2 outcomes within four 2-year cohorts. Predictors of interest included demographics, previous health care use, comorbidity, previously identified social needs, and neighborhood characteristics as reflected by the area deprivation index. The outcome variable was a binary indicator reflecting the likelihood of the presence of a patient with social needs. We applied a generalized estimating equation approach, adjusting for patient-level risk factors, the possible effect of geographically clustered data, and the effect of multiple visits for each patient.
RESULTS: The study population of 1,852,228 patients included middle-aged (mean age range 53.76-55.95 years), White (range 324,279/510,770, 63.49% to 290,688/488,666, 64.79%), and female (range 314,741/510,770, 61.62% to 278,488/448,666, 62.07%) patients from neighborhoods with high socioeconomic status (mean area deprivation index percentile range 28.76-30.31). Between 8.28% (37,137/448,666) and 11.55% (52,037/450,426) of patients across the study cohorts had at least 1 social need documented in their EHR, with safety issues and economic challenges (ie, financial resource strain, employment, and food insecurity) being the most common documented social needs (87,152/1,852,228, 4.71% and 58,242/1,852,228, 3.14% of overall patients, respectively). The model had an area under the curve of 0.702 (95% CI 0.699-0.705) in predicting prospective social needs in the overall study population. Previous social needs (odds ratio 3.285, 95% CI 3.237-3.335) and emergency department visits (odds ratio 1.659, 95% CI 1.634-1.684) were the strongest predictors of future social needs.
CONCLUSIONS: Our model provides an opportunity to make use of available EHR data to help identify patients with high social needs. Our proposed social risk score could help identify the subset of patients who would most benefit from further social needs screening and data collection to avoid potentially more burdensome primary data collection on all patients in a target population of interest.