Department of Mental Health, Johns Hopkins Bloomberg School of Public Health.
Maryland Health Services Cost Review Commission, Baltimore, MD.
Med Care. 2023 Jul 1;61(7):484-489. doi: 10.1097/MLR.0000000000001863. Epub 2023 May 24.
Social factors are a key determinant of hospital readmission. We describe the development of the country's first statewide policy providing hospitals with financial incentives to reduce readmission disparities.
To describe the development and evaluation of a novel program that measures hospital-level disparity in readmission and rewards hospitals for improvement.
Observational study using inpatient claims.
Baseline data included 454,372 all-cause inpatient discharges in 2018 and 2019. Of the included discharges, 34.01% involved Black patients, 40.44% involved female patients, 33.1% involved patients covered by Medicaid, and 11.76% involved patients who were readmitted. Mean age was 55.18.
The key measure was the percentage change over time within the hospital in readmission disparity. Readmission disparity was measured using a multilevel model that gauged the association between social factors and readmission risk at a given hospital. Three social factors (Race, Medicaid coverage, and Area Deprivation Index) were combined into an index reflecting exposure to social adversity.
Of the State's 45 acute-care hospitals, 26 exhibited improved disparity performance in 2019.
The program is limited to inpatients within a single state; the analysis does not provide evidence on the causal relationship between the intervention and readmission disparities.
This represents the first large-scale effort in the US to link disparities to hospital payment. Because the methodology relies on claims data, it could easily be adopted elsewhere. The incentives are directed to within-hospital disparities, thus mitigating concerns about penalizing hospitals with patients with greater social exposure. This methodology could be used to measure disparity in other outcomes.
社会因素是医院再入院的关键决定因素。我们描述了制定全国首个为医院提供经济激励以减少再入院差异的全州政策的过程。
描述一种衡量医院再入院差异并对改善情况进行奖励的新型方案的开发和评估。
使用住院患者索赔进行的观察性研究。
纳入了 2018 年和 2019 年共 454372 例全因住院患者的基线数据。纳入的出院患者中，34.01%为黑人患者，40.44%为女性患者，33.1%为医疗补助覆盖患者，11.76%为再入院患者。平均年龄为 55.18 岁。
关键指标是医院内再入院差异随时间的百分比变化。再入院差异通过多水平模型进行测量，该模型衡量了给定医院的社会因素与再入院风险之间的关联。将三个社会因素（种族、医疗补助覆盖范围和区域贫困指数）组合成一个反映社会逆境暴露程度的指数。
在该州的 45 家急症医院中，有 26 家在 2019 年改善了差异表现。
该方案仅限于单一州的住院患者；该分析并未提供干预与再入院差异之间因果关系的证据。
这是美国首次将差异与医院支付挂钩的大规模努力。由于该方法依赖于索赔数据，因此很容易在其他地方采用。激励措施针对院内差异，从而减轻了对惩罚有更多社会暴露患者的医院的担忧。这种方法可用于衡量其他结果的差异。
