Matthew C. Baker (
Philip M. Alberti is the senior director of health equity research and policy, Association of American Medical Colleges.
Health Aff (Millwood). 2021 Apr;40(4):645-654. doi: 10.1377/hlthaff.2020.01742.
This study assessed the impact of individual social risk factor variables and social determinants of health (SDOH) measures on hospital readmission rates and penalties used in the Centers for Medicare and Medicaid Services (CMS) Hospital Readmissions Reduction Program (HRRP). Using 2012-16 hospital discharge data from New York City, we projected HRRP penalties by augmenting CMS's readmission model for heart attack, heart failure, and pneumonia with SDOH scores constructed at each of four geographic levels and a measure of individual-level social risk. Including additional SDOH scores in the model, especially those constructed with the most granular geographic data, along with social risk factor variables substantially affects projected penalties for hospitals treating the highest proportion of patients with high SDOH scores. Improved performance occurred even after we included peer-group stratification in the HRRP model pursuant to the 21st Century Cures Act. Small improvements in model accuracy were associated with substantial shifts in projected performance. Our results suggest that CMS's continued omission of relevant patient and geographic data from the HRRP readmission model misallocates penalties attributable to SDOH and social risk factor effects to hospitals with the largest share of high-risk patients.
本研究评估了个体社会风险因素变量和健康社会决定因素(SDOH)措施对医疗保险和医疗补助服务中心(CMS)医院再入院率降低计划(HRRP)中再入院率和处罚的影响。利用来自纽约市的 2012-16 年医院出院数据,我们通过在 CMS 的心脏病发作、心力衰竭和肺炎再入院模型中增加 SDOH 评分,构建了四个地理层面的每个层面的 SDOH 评分和个体层面的社会风险指标,预测了 HRRP 的处罚。在模型中包含更多的 SDOH 评分,特别是使用最细粒度的地理数据构建的评分,以及社会风险因素变量,这会极大地影响治疗 SDOH 评分较高的患者比例最高的医院的预测处罚。即使我们根据《21 世纪治愈法案》在 HRRP 模型中纳入了同行群体分层,模型的性能也得到了显著提高。模型准确性的微小提高与预测性能的显著变化相关。我们的研究结果表明,CMS 继续将与患者和地理位置相关的数据从 HRRP 再入院模型中排除,导致 SDOH 和社会风险因素影响的处罚错误地分配给高危患者比例最大的医院。