University of Pennsylvania, United States.
Independence Blue Cross, United States.
J Health Econ. 2019 Mar;64:68-79. doi: 10.1016/j.jhealeco.2019.02.002. Epub 2019 Feb 10.
This paper studies a commercial insurer-driven intervention to improve resource allocation. The insurer developed a claims-based algorithm to derive a member-level healthcare utilization risk score. Members with the highest scores were contacted by a care management team tasked with closing gaps in care. The number of members outreached was dictated by resource availability and not by severity, creating a set of arbitrary cutoff points, separating treated and untreated members with very similar predicted risk scores. Using a regression discontinuity approach, we find evidence that predictive analytics-driven interventions directed at high-risk individuals reduced emergency room and specialist visits, yet not hospitalizations.
本文研究了商业保险公司驱动的干预措施，以改善资源配置。该保险公司开发了一种基于索赔的算法，以得出会员级别的医疗保健利用风险评分。得分最高的会员会被一个负责缩小护理差距的护理管理团队联系。联系的会员人数取决于资源的可用性，而不是严重程度，这就产生了一组任意的截止点，将治疗和未治疗的会员分开，他们的预测风险评分非常相似。使用回归不连续性方法，我们发现证据表明，针对高风险个人的预测分析驱动的干预措施减少了急诊室和专科就诊次数，但没有减少住院治疗次数。
