Maciejewski Matthew L, Liu Chuan-Fen, Derleth Ann, McDonell Mary, Anderson Steve, Fihn Stephan D
Health Services Research and Development Northwest Center of Excellence, Veterans Affairs Puget Sound Health Care System, Seattle, WA, USA.
Health Serv Res. 2005 Jun;40(3):887-904. doi: 10.1111/j.1475-6773.2005.00390.x.
To evaluate the performance of different prospective risk adjustment models of outpatient, inpatient, and total expenditures of veterans who regularly use Veterans Affairs (VA) primary care.
We utilized administrative, survey and expenditure data on 14,449 VA patients enrolled in a randomized trial that gave providers regular patient health assessments.
This cohort study compared five administrative data-based, two self-report risk adjusters, and base year expenditures in prospective models.
VA outpatient care and nonacute inpatient care expenditures were based on unit expenditures and utilization, while VA expenditures for acute inpatient care were calculated from a Medicare-based inpatient cost function. Risk adjusters for this sample were constructed from diagnosis, medication and self-report data collected during a clinical trial. Model performance was compared using adjusted R2 and predictive ratios.
In all expenditure models, administrative-based measures performed better than self-reported measures, which performed better than age and gender. The Diagnosis Cost Groups (DCG) model explained total expenditure variation (R2=7.2 percent) better than other models. Prior outpatient expenditures predicted outpatient expenditures best by far (R2=42 percent). Models with multiple measures improved overall prediction, reduced over-prediction of low expenditure quintiles, and reduced under-prediction in the highest quintile of expenditures.
Prediction of VA total expenditures was poor because expenditure variation reflected utilization variation, but not patient severity. Base year expenditures were the best predictor of outpatient expenditures and nearly the best for total expenditures. Models that combined two or more risk adjusters predicted expenditures better than single-measure models, but are more difficult and expensive to apply.
评估针对经常使用退伍军人事务部（VA）初级保健服务的退伍军人的门诊、住院及总支出的不同前瞻性风险调整模型的性能。
我们利用了14449名参与一项随机试验的VA患者的行政、调查及支出数据，该试验为医疗服务提供者提供定期的患者健康评估。
这项队列研究比较了前瞻性模型中五个基于行政数据的、两个自我报告风险调整器以及基年支出。
VA门诊护理和非急性住院护理支出基于单位支出和使用情况，而急性住院护理的VA支出则根据基于医疗保险的住院成本函数计算。该样本的风险调整器由临床试验期间收集的诊断、用药和自我报告数据构建而成。使用调整后的R2和预测比率比较模型性能。
在所有支出模型中，基于行政的测量方法比自我报告的测量方法表现更好，而自我报告的测量方法又比年龄和性别表现更好。诊断成本组（DCG）模型比其他模型能更好地解释总支出变化（R2 = 7.2%）。既往门诊支出对门诊支出的预测效果迄今为止最佳（R2 = 42%）。采用多种测量方法的模型改善了总体预测，减少了低支出五分位数的过度预测，并减少了最高支出五分位数的预测不足。
VA总支出的预测效果不佳，因为支出变化反映的是使用情况变化，而非患者病情严重程度。基年支出是门诊支出的最佳预测指标，对总支出而言也几乎是最佳指标。结合两种或更多风险调整器的模型对支出的预测比单一测量模型更好，但应用起来更困难且成本更高。
