HEALTH EXPENSE-RISK ASSESSMENT USING ADMINISTRATIVE DATA

护理管理方案已经出现，作为一个流行的反应，持续增长和集中的卫生保健支出。有效的护理管理对二级和三级预防的贡献部分依赖于有效识别“高风险”患者。能够识别可预测费用特征的前瞻性识别系统优于同期或回顾性系统，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的，但实际上尚未开发的前瞻性识别方法。Kaiser Permanente健康研究中心（CHR）、健康研究中心（Center for Health Studies）和其他地方的合作者已经创建了一个来自美国6家hmo的大型管理数据集。该数据集的创建是为了开发前瞻性健康风险评估模型，主要用于调整医疗保险支付，以适应投保人的健康状况。这些数据也提供了一个独特的机会来研究风险评估模型，作为初步的基于人群的筛选，以筛选那些未来产生大量医疗支出（即成为“高成本”）的风险相对较高的参保人。我们断言，早期识别这些登登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，向保健计划决策者（例如护理管理方案主任）提供有用的信息，以便尽可能有效地减轻其人口的疾病负担。《先驱报》的具体目标是：1。比较全球风险评估模型（GRAM）调整临床组（acg，以前称为门诊护理组）诊断成本组（dcg）慢性病评分的预测未来“高成本”状态的能力和先付模式。2. 评估GRAM的“高成本”预测性能：在不同的注册人群规模（例如，50,000,100,000）中，在特定的人群亚组（例如，老年人，儿童和老年家属）中，针对高成本状态的多个风险类别（例如，极端，中等，低）。3. 评估GRAM预测绩效的时间稳定性（例如，1995年风险因素预测1997年费用的时间距离）。我们希望HERALD(1)的结果可以作为未来探索风险评估模型的基础，如GRAM，通过二级诊断、功能健康状况和行为风险因素的数据来增强；(2)为未来的GRAM“增值”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.