Department of Population and Quantitative Health Sciences, University of Massachusetts Medical School, Worcester, MA.
Med Care. 2021 Apr 1;59(4):362-367. doi: 10.1097/MLR.0000000000001515.
Better patient management can reduce emergency department (ED) use. Performance measures should reward plans for reducing utilization by predictably high-use patients, rather than rewarding plans that shun them.
The objective of this study was to develop a quality measure for ED use for people diagnosed with serious mental illness or substance use disorder, accounting for both medical and social determinants of health (SDH) risks.
Regression modeling to predict ED use rates using diagnosis-based and SDH-augmented models, to compare accuracy overall and for vulnerable populations.
MassHealth, Massachusetts' Medicaid and Children's Health Insurance Program.
MassHealth members ages 18-64, continuously enrolled for the calendar year 2016, with a diagnosis of serious mental illness or substance use disorder.
Diagnosis-based model predictors are diagnoses from medical encounters, age, and sex. Additional SDH predictors describe housing problems, behavioral health issues, disability, and neighborhood-level stress.
We predicted ED use rates: (1) using age/sex and distinguishing between single or dual diagnoses; (2) adding summarized medical risk (DxCG); and (3) further adding social risk (SDH).
Among 144,981 study subjects, 57% were women, 25% dually diagnosed, 67% White/non-Hispanic, 18% unstably housed, and 37% disabled. Utilization was higher by 77% for those dually diagnosed, 50% for members with housing problems, and 18% for members living in the highest-stress neighborhoods. SDH modeling predicted best for these high-use populations and was most accurate for plans with complex patients.
To set appropriate benchmarks for comparing health plans, quality measures for ED visits should be adjusted for both medical and social risks.
更好的患者管理可以减少急诊部(ED)的使用。绩效措施应奖励那些能够降低高使用患者利用率的计划,而不是奖励那些回避这些患者的计划。
本研究的目的是开发一种用于诊断为严重精神疾病或物质使用障碍的患者的 ED 使用的质量衡量标准,该衡量标准考虑了医疗和社会决定健康因素(SDH)的风险。
回归建模,使用基于诊断和增强 SDH 的模型预测 ED 使用率,比较整体准确性和脆弱人群的准确性。
马萨诸塞州的医疗补助和儿童健康保险计划 MassHealth。
马萨诸塞州医疗补助计划年龄在 18-64 岁之间、在 2016 年日历年内持续参保、患有严重精神疾病或物质使用障碍的成员。
基于诊断的模型预测因素是医疗就诊中的诊断、年龄和性别。其他 SDH 预测因素描述了住房问题、行为健康问题、残疾和邻里压力。
我们预测了 ED 使用率:(1)使用年龄/性别,并区分单一或双重诊断;(2)增加医疗风险汇总(DxCG);(3)进一步增加社会风险(SDH)。
在 144981 名研究对象中,57%为女性,25%为双重诊断,67%为白人/非西班牙裔,18%住房不稳定,37%残疾。双重诊断患者的利用率增加了 77%,住房问题患者的利用率增加了 50%,居住在压力最大社区的患者的利用率增加了 18%。SDH 模型对这些高使用率人群的预测效果最佳,对复杂患者的计划最为准确。
为了为比较医疗保健计划设定适当的基准,ED 就诊的质量衡量标准应同时调整医疗和社会风险。