Mazurenko Olena, Harle Christopher A, Musey Paul I, Schleyer Titus K, Sanner Lindsey M, Vest Joshua R
Department of Health Policy & Management, Richard M. Fairbanks School of Public Health-Indiana University, Indianapolis, Indiana, 46202, United States.
Center for Health Services Research, Regenstrief Institute, Indianapolis, Indiana, 46202, United States.
JAMIA Open. 2025 Jul 4;8(4):ooaf060. doi: 10.1093/jamiaopen/ooaf060. eCollection 2025 Aug.
To improve the identification of patients with health-related social needs (HRSNs) in the emergency department (ED), we developed and integrated a risk prediction score into an existing Fast Healthcare Interoperability Resources (FHIR)-based clinical decision support (CDS).
We conducted 2 phases of individual semi-structured qualitative interviews with ED clinicians to identify HRSN risk score design preferences for CDS integration. Following this, we used patient HRSN screening survey, health information exchange (HIE), and clinical data to run logistic regressions, developing an HRSN risk score aligned with ED clinician preferences.
Emergency department clinicians preferred HRSN risk scores displayed via visual cues like color-coding with different ranges (low, medium, and high) with higher model sensitivity to avoid missing patients with HRSNs. The overall performance of the risk prediction model was modest. Risk scores for food insecurity, transportation barriers, and financial strain were more sensitive, aligning with users' preference for inclusivity and accurately identifying patients likely to screen positive for these HRSNs.
The design and risk score model choices, such as visual displays with additional data, higher sensitivity thresholds, and use of different thresholds for fairness, may support effective CDS use by ED clinicians.
Using HIE data and an external CDS is a feasible route for including patient HRSNs information in the ED. We relied on clinician preferences for incorporation into the existing CDS and were attentive to performance fairness. While the predictive performance of our risk score is modest, providing risk scores in this manner may potentially improve the identification of patients' HRSNs in the ED.
为了提高急诊科(ED)中具有健康相关社会需求(HRSNs)患者的识别率,我们开发了一种风险预测评分,并将其整合到现有的基于快速医疗保健互操作性资源(FHIR)的临床决策支持(CDS)中。
我们对急诊科临床医生进行了两个阶段的个人半结构化定性访谈,以确定用于CDS整合的HRSN风险评分设计偏好。在此之后,我们使用患者HRSN筛查调查、健康信息交换(HIE)和临床数据进行逻辑回归分析,开发出与急诊科临床医生偏好一致的HRSN风险评分。
急诊科临床医生更喜欢通过视觉提示(如不同范围的颜色编码,低、中、高)显示的HRSN风险评分,且模型具有更高的敏感性,以避免遗漏有HRSNs的患者。风险预测模型的整体表现一般。粮食不安全、交通障碍和经济压力的风险评分更敏感,符合用户对包容性的偏好,并能准确识别可能筛查出这些HRSNs呈阳性的患者。
设计和风险评分模型的选择,如带有附加数据的视觉显示、更高的敏感性阈值以及为公平性使用不同的阈值,可能支持急诊科临床医生有效地使用CDS。
使用HIE数据和外部CDS是在急诊科纳入患者HRSNs信息的可行途径。我们依据临床医生的偏好将其纳入现有的CDS,并关注性能公平性。虽然我们的风险评分预测性能一般,但以这种方式提供风险评分可能会潜在地改善急诊科对患者HRSNs的识别。