HEALTH EXPENSE-RISK ASSESSMENT USING ADMINISTRATIVE DATA

随着医疗保健支出的持续增长和集中，护理管理计划已经成为一种流行的反应。有效的护理管理对二级和三级预防的贡献在一定程度上取决于有效地识别“高危”患者。能够识别可预测费用特征的预期识别系统比同期或追溯系统更可取，特别是在系统管理成本较低的情况下。使用HMO管理数据集中包含的人口统计和诊断信息的风险评估模型代表了一种合乎逻辑但几乎未被探索的前瞻性识别方法。凯撒永久健康研究中心(CHR)、健康研究中心和其他机构的合作者从美国各地的6家医疗保健组织创建了一个大型管理数据集。创建该数据集是为了开发预期的健康风险评估模型，主要用于调整医疗保险支付与参保人健康状况的健康计划。这些数据也提供了一个独特的机会来研究风险评估模型，作为基于人群的初步筛查，以相对较高的风险产生未来的大笔医疗支出，即成为“高成本”。我们认为，及早查明这些加入者可以促进有效的二级和三级预防措施。先驱报项目利用这一机会，为关心尽可能有效地减少其人口中的疾病负担的健康计划决策者(例如，护理管理计划主任)提供有用的信息。《先驱报》的具体目标是：1.比较全球风险评估模型(GRAM)调整后的临床组(ACGs，以前称为动态护理组)、诊断成本组(DCGS)慢性病评分逻辑模型和前期费用模型对未来高成本状态的预测能力。2.评估GRAM的“高成本”预测性能：在特定人口亚群(例如，老年人、儿童和老年受抚养人)内的不同参与者人口规模(例如，50,000,100,000)中，评估高成本状态的多个类别的风险(例如，极端、中等、低)。3.评估GRAM预测业绩的时间稳定性(例如，时间距离--预测1997年费用的风险因素)。我们期望《先驱报》的结果(1)将作为未来探索风险评估模型的基础，例如通过二次诊断、功能健康状况和行为风险因素的数据增强的GRAN；以及(2)为未来的GRAN测试提供信息，作为管理健康状况调查的初步筛选。

Care management programs have emerged as a popular response to the continued growth and concentration of health care expenditures. The contribution of effective care management to secondary and tertiary prevention relies in part on efficiently identifying "high-risk" patients. Prospective identification systems that can recognize characteristics of predictable expense are preferred to contemporaneous or retrospective systems, particularly if system administrative costs are low. Risk- assessment models that use demographic and diagnostic information contained in HMO administrative data sets represent a logical, yet virtually unexplored, prospective identification methodology. Collaborators at the Kaiser Permanente Center for Health Research (CHR), the Center for Health Studies, and elsewhere have created a large administrative data set from 6 HMOs across the U.S. This data set has been created to develop prospective health risk- assessment models for primary use in adjusting Medicare payments to health plans for enrollee health status. These data also present a unique opportunity to study risk-assessment models as preliminary population-based screens for enrollees at relatively higher risk of generating large future medical expenditures, i.e., becoming "high-cost." We assert that early identification of these enrollees can promote effective secondary and tertiary preventive measures. The HERALD project leverages this opportunity to provide useful information for health plan decisionmakers (e.g., care management program directors) concerned with reducing the illness burden in their populations as efficiently as possible. The specific aims of HERALD are to: 1. Compare the ability to forecast future "high-cost" status of the Global Risk-Assessment Model (GRAM) Adjusted Clinical Groups (ACGs, formerly called Ambulatory Care Groups) Diagnostic Cost Groups (DCGs) Chronic Disease Scores A logistic model; and A prior-expense model. 2. Evaluate the "high-cost" forecasting performance of GRAM: Across various enrollee population sizes (e.g., 50,000, 100,000) Within particular population subgroups (e.g., elderly, children, and older dependents) For multiple categories of risk of high-cost status (e.g., extreme, moderate, low). 3. Evaluate the temporal stability of GRAM's forecasting performance (e.g., distance in time - 1995 risk factors forecasting 1997 expense). We expect the results from HERALD (1) to serve as a base for future explorations of risk-assessment models such as GRAM enhanced by data on secondary diagnoses, functional health status, and behavioral risk factors; and (2) inform a future test of GRAM's "value-added" as a preliminary screen for an administered health status survey.