Meenan R T, O'Keeffe-Rosetti C, Hornbrook M C, Bachman D J, Goodman M J, Fishman P A, Hurtado A V
Center for Health Research, Kaiser Permanente, Northwest Division, Portland, OR 97227-1110, USA.
Med Care. 1999 Aug;37(8):815-23. doi: 10.1097/00005650-199908000-00011.
This study compares the ability of 3 risk-assessment models to distinguish high and low expense-risk status within a managed care population. Models are the Global Risk-Assessment Model (GRAM) developed at the Kaiser Permanente Center for Health Research; a logistic version of GRAM; and a prior-expense model. GRAM was originally developed for use in adjusting Medicare payments to health plans.
Our sample of 98,985 cases was drawn from random samples of memberships of 3 staff/group health plans. Risk factor data were from 1992 and expenses were measured for 1993. Models produced distributions of individual-level annual expense forecasts (or predicted probabilities of high expense-risk status for logistic) for comparison to actual values. Prespecified "high-cost" thresholds were set within each distribution to analyze the models' ability to distinguish high and low expense-risk status. Forecast stability was analyzed through bootstrapping.
GRAM discriminates better overall than its comparators (although the models are similar for policy-relevant thresholds). All models forecast the highest-cost cases relatively well. GRAM forecasts high expense-risk status better than its comparators within chronic and serious disease categories that are amenable to early intervention but also generates relatively more false positives within these categories.
This study demonstrates the potential of risk-assessment models to inform care management decisions by efficiently screening managed care populations for high expense-risk. Such models can act as preliminary screens for plans that can refine model forecasts with detailed surveys. Future research should involve multiple-year data sets to explore the temporal stability of forecasts.
本研究比较了3种风险评估模型在管理式医疗人群中区分高费用风险状态和低费用风险状态的能力。这些模型分别是凯撒永久健康研究中心开发的全球风险评估模型（GRAM）；GRAM的逻辑回归版本；以及一个既往费用模型。GRAM最初是为调整医疗保险向健康计划的支付而开发的。
我们的98985例样本取自3个员工/团体健康计划会员的随机样本。风险因素数据来自1992年，费用数据测量的是1993年的情况。各模型生成了个体层面年度费用预测的分布（或逻辑回归模型中高费用风险状态的预测概率），以便与实际值进行比较。在每个分布中设定了预先指定的“高成本”阈值，以分析模型区分高费用风险状态和低费用风险状态的能力。通过自抽样法分析预测稳定性。
GRAM在总体上比其比较模型的区分能力更强（尽管在与政策相关的阈值方面各模型相似）。所有模型对成本最高的病例预测相对较好。在适合早期干预的慢性和严重疾病类别中，GRAM预测高费用风险状态的能力优于其比较模型，但在这些类别中也产生了相对较多的假阳性结果。
本研究证明了风险评估模型通过有效筛选管理式医疗人群中的高费用风险，为护理管理决策提供信息的潜力。此类模型可作为初步筛选工具，供那些能够通过详细调查完善模型预测的计划使用。未来的研究应涉及多年数据集，以探索预测的时间稳定性。
