Rector Thomas S, Wickstrom Steven L, Shah Mona, Thomas Greeenlee N, Rheault Paula, Rogowski Jeannette, Freedman Vicki, Adams John, Escarce José J
Center for Chronic Disease Outcomes Research, VA Medical Center, Minneapolis, MN 55417, USA.
Health Serv Res. 2004 Dec;39(6 Pt 1):1839-57. doi: 10.1111/j.1475-6773.2004.00321.x.
To examine the effects of varying diagnostic and pharmaceutical criteria on the performance of claims-based algorithms for identifying beneficiaries with hypertension, heart failure, chronic lung disease, arthritis, glaucoma, and diabetes.
Secondary 1999-2000 data from two Medicare+Choice health plans.
Retrospective analysis of algorithm specificity and sensitivity.
Physician, facility, and pharmacy claims data were extracted from electronic records for a sample of 3,633 continuously enrolled beneficiaries who responded to an independent survey that included questions about chronic diseases.
Compared to an algorithm that required a single medical claim in a one-year period that listed the diagnosis, either requiring that the diagnosis be listed on two separate claims or that the diagnosis to be listed on one claim for a face-to-face encounter with a health care provider significantly increased specificity for the conditions studied by 0.03 to 0.11. Specificity of algorithms was significantly improved by 0.03 to 0.17 when both a medical claim with a diagnosis and a pharmacy claim for a medication commonly used to treat the condition were required. Sensitivity improved significantly by 0.01 to 0.20 when the algorithm relied on a medical claim with a diagnosis or a pharmacy claim, and by 0.05 to 0.17 when two years rather than one year of claims data were analyzed. Algorithms that had specificity more than 0.95 were found for all six conditions. Sensitivity above 0.90 was not achieved all conditions.
Varying claims criteria improved the performance of case-finding algorithms for six chronic conditions. Highly specific, and sometimes sensitive, algorithms for identifying members of health plans with several chronic conditions can be developed using claims data.
研究不同的诊断和用药标准对基于索赔数据的算法识别高血压、心力衰竭、慢性肺病、关节炎、青光眼和糖尿病受益人的性能影响。
来自两个医疗保险 + 选择健康计划的1999 - 2000年二级数据。
对算法特异性和敏感性的回顾性分析。
从电子记录中提取医生、医疗机构和药房的索赔数据，样本为3633名持续参保的受益人，他们回应了一项包含慢性病问题的独立调查。
与要求在一年内有一条列出诊断的单一医疗索赔的算法相比，要求诊断列在两条单独的索赔上，或者列在与医疗服务提供者面对面就诊的一条索赔上，可使所研究疾病的特异性显著提高0.03至0.11。当同时要求有诊断的医疗索赔和用于治疗该疾病的常用药物的药房索赔时，算法的特异性显著提高0.03至0.17。当算法依赖于有诊断的医疗索赔或药房索赔时，敏感性显著提高0.01至0.20；当分析两年而非一年的索赔数据时，敏感性提高0.05至0.17。所有六种疾病都找到了特异性超过0.95的算法。并非所有疾病都能达到0.90以上的敏感性。
不同的索赔标准改善了六种慢性病病例发现算法的性能。利用索赔数据可以开发出高度特异且有时敏感的算法，用于识别患有多种慢性病的健康计划成员。
