PhD Candidate in Health Policy, Harvard University, Cambridge, Massachusetts, USA
Center for Health Policy and Center for Primary Care & Outcomes Research, Stanford University, Stanford, California, USA.
BMJ Health Care Inform. 2021 Sep;28(1). doi: 10.1136/bmjhci-2021-100414.
To identify undercompensated groups in plan payment risk adjustment that are defined by multiple attributes with a systematic new approach, improving on the arbitrary and inconsistent nature of existing evaluations.
Extending the concept of variable importance for single attributes, we construct a measure of 'group importance' in the random forests algorithm to identify groups with multiple attributes that are undercompensated by current risk adjustment formulas. Using 2016-2018 IBM MarketScan and 2015-2018 Medicare claims and enrolment data, we evaluate two risk adjustment scenarios: the risk adjustment formula used in the individual health insurance Marketplaces and the risk adjustment formula used in Medicare.
A number of previously unidentified groups with multiple chronic conditions are undercompensated in the Marketplaces risk adjustment formula, while groups without chronic conditions tend to be overcompensated in the Marketplaces. The magnitude of undercompensation when defining groups with multiple attributes is many times larger than with single attributes. No complex groups were found to be consistently undercompensated or overcompensated in the Medicare risk adjustment formula.
Our method is effective at identifying complex undercompensated groups in health plan payment risk adjustment where undercompensation creates incentives for insurers to discriminate against these groups. This work provides policy-makers with new information on potential targets of discrimination in the healthcare system and a path towards more equitable health coverage.
通过一种系统的新方法，识别支付计划风险调整中被多个属性定义的补偿不足群体，改进现有评估方法的任意性和不一致性。
扩展单一属性的变量重要性概念，我们在随机森林算法中构建了一种“群体重要性”度量方法，以识别当前风险调整公式补偿不足的多属性群体。利用 2016-2018 年 IBM MarketScan 和 2015-2018 年医疗保险索赔和参保数据，我们评估了两种风险调整情景：医疗保险市场中使用的风险调整公式和医疗保险中使用的风险调整公式。
在医疗保险市场风险调整公式中，一些以前未被识别的具有多种慢性病的群体被补偿不足，而没有慢性病的群体在医疗保险市场中往往被过度补偿。当用多个属性定义群体时，补偿不足的程度比用单一属性时要大得多。在医疗保险风险调整公式中，没有发现复杂群体被一致地补偿不足或过度补偿。
我们的方法能够有效地识别医疗保险支付风险调整中复杂的补偿不足群体，补偿不足会导致保险公司对这些群体产生歧视。这项工作为政策制定者提供了医疗保健系统中潜在歧视目标的新信息，以及实现更公平的医疗保险覆盖的途径。
