Enhancement of claims data to improve risk adjustment of hospital mortality
Enhancement of claims data to improve risk adjustment of hospital mortality
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DOI：
10.1001/jama.297.1.71
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发表时间：
2007-01-03
影响因子：
120.7
通讯作者：
Gonzales, Junius
中科院分区：
文献类型：
--
作者：
Pine, Michael;Jordan, Harmon S.;Gonzales, Junius
风险调整后的医院绩效比较通常是公开报告、绩效付费计划和质量改进计划的重要组成部分。在这些分析中使用的风险调整方程必须包含足够的临床细节，以确保准确测量医院质量。目的探讨在行政理赔数据中加入入院编号和实验室数字数据对风险调整后的医院死亡率的影响。设计、设置和患者2000年7月至2003年6月住院患者死亡率风险调整方程的比较，该方法是通过将越来越难以获得的临床数据依次添加到宾夕法尼亚州188家医院的管理数据库中得出的。患者因急性心肌梗死、充血性心力衰竭、脑血管意外、胃肠道出血、肺炎住院或行腹主动脉瘤修复术、冠状动脉搭桥手术、开颅手术。主要结局指标——统计数据作为替代风险调整模型（5种情况和3种程序中的每一种的行政、入院、实验室和临床）的歧视性力量的度量。结果给药模型的平均（SD） c统计量为0.79（0.02）。加入入院代码和入院时收集的数值实验室数据，大大改善了风险调整方程（平均[SD] c统计量分别为0.84[0.01]和0.86[0.01]）。通过增加更复杂和昂贵的临床数据收集，如生命体征、血培养结果、关键临床结果和从患者病历中提取的综合评分，获得了适度的额外改善（平均[SD] c统计量为0.88[0.01]）。结论本研究支持在管理数据库中增加入场代码和数值实验室值的价值。对难以获得的关键临床结果的二次抽象对风险调整方程的预测能力增加很少。
Context Comparisons of risk-adjusted hospital performance often are important components of public reports, pay-for-performance programs, and quality improvement initiatives. Risk-adjustment equations used in these analyses must contain sufficient clinical detail to ensure accurate measurements of hospital quality.Objective To assess the effect on risk-adjusted hospital mortality rates of adding present on admission codes and numerical laboratory data to administrative claims data.Design, Setting, and Patients Comparison of risk-adjustment equations for inpatient mortality from July 2000 through June 2003 derived by sequentially adding increasingly difficult-to-obtain clinical data to an administrative database of 188 Pennsylvania hospitals. Patients were hospitalized for acute myocardial infarction, congestive heart failure, cerebrovascular accident, gastrointestinal tract hemorrhage, or pneumonia or underwent an abdominal aortic aneurysm repair, coronary artery bypass graft surgery, or craniotomy.Main Outcome Measures C statistics as a measure of the discriminatory power of alternative risk-adjustment models ( administrative, present on admission, laboratory, and clinical for each of the 5 conditions and 3 procedures).Results The mean (SD) c statistic for the administrative model was 0.79 (0.02). Adding present on admission codes and numerical laboratory data collected at the time of admission resulted in substantially improved risk-adjustment equations ( mean [ SD] c statistic of 0.84 [0.01] and 0.86 [ 0.01], respectively). Modest additional improvements were obtained by adding more complex and expensive to collect clinical data such as vital signs, blood culture results, key clinical findings, and composite scores abstracted from patients' medical records ( mean [ SD] c statistic of 0.88 [ 0.01]).Conclusions This study supports the value of adding present on admission codes and numerical laboratory values to administrative databases. Secondary abstraction of difficult-to-obtain key clinical findings adds little to the predictive power of risk-adjustment equations.