Retooling of Paper-based Outcome Measures to Electronic Format: Comparison of the NY State Public Risk Model and EHR-derived Risk Models for CABG Mortality.
Retooling of Paper-based Outcome Measures to Electronic Format: Comparison of the NY State Public Risk Model and EHR-derived Risk Models for CABG Mortality.
复制标题
将纸质结果测量重组为电子格式：纽约州公共风险模型与 EHR 衍生的 CABG 死亡率风险模型的比较。
DOI：
10.1097/mlr.0000000000001104
复制
发表时间：
2019
期刊：
影响因子：
3
通讯作者：
Schmaltz,StephenP
中科院分区：
文献类型：
--
作者：
Hannan,EdwardL;Barrett,StaceyC;Samadashvili,Zaza;Schmaltz,StephenP
背景：风险调整是至关重要的比较质量的护理和医疗保健结果的提供者。电子健康记录（EHRs）有可能消除需要昂贵和耗时的手动数据抽象的病人的结果和风险因素的必要risk adjustment.Methods：领先的电子健康记录供应商和医院焦点小组被要求审查风险因素在纽约州（NYS）冠状动脉旁路移植术（CABG）手术死亡率和再入院的统计模型，并评估EHR数据采集的可行性。风险模型的基础上只登记数据元素，可以捕获的EHR（一个容易获得的数据和一个更困难获得的数据）的开发和比较与纽约州模型不同years.Results：只有6个数据元素可以从EHR中提取，离群医院差异很大，但不是再入院死亡率。在患者水平，拟合和预测能力指标表明EHR模型劣于NYS CABG手术风险模型[例如，c-统计量为0.76 vs. 0.71（P< 0.001）和0.76 vs. 0.74（P= 0.009）的死亡率在2010年]，虽然纽约和EHR模型之间的预测概率的相关性很高，结论：使用EHR数据元素的简化风险模型不能捕获纽约州CABG手术风险模型中的大多数风险因素，许多离群医院的再入院率不同，患者水平的拟合度较差。1国家质量论坛（NQF）已经批准了700多项质量措施，其中许多措施正在酝酿之中。2对于其中许多措施而言，收集和报告数据是一个复杂、耗时的手工过程。2采用电子健康记录（EHR）一直被视为消除这一医疗保健测量主要障碍的关键。3随着EHR的发展和复杂性的增长，4-8它越来越成为测量和测量开发人员的焦点。9–15
Background:Risk adjustment is critical in the comparison of quality of care and health care outcomes for providers. Electronic health records (EHRs) have the potential to eliminate the need for costly and time-consuming manual data abstraction of patient outcomes and risk factors necessary for risk adjustment.Methods:Leading EHR vendors and hospital focus groups were asked to review risk factors in the New York State (NYS) coronary artery bypass graft (CABG) surgery statistical models for mortality and readmission and assess feasibility of EHR data capture. Risk models based only on registry data elements that can be captured by EHRs (one for easily obtained data and one for data obtained with more difficulty) were developed and compared with the NYS models for different years.Results:Only 6 data elements could be extracted from the EHR, and outlier hospitals differed substantially for readmission but not for mortality. At the patient level, measures of fit and predictive ability indicated that the EHR models are inferior to the NYS CABG surgery risk model [eg, c-statistics of 0.76 vs. 0.71 (P< 0.001) and 0.76 vs. 0.74 (P= 0.009) for mortality in 2010], although the correlation of the predicted probabilities between the NYS and EHR models was high, ranging from 0.96 to 0.98.Conclusions:A simplified risk model using EHR data elements could not capture most of the risk factors in the NYS CABG surgery risk models, many outlier hospitals were different for readmissions, and patient-level measures of fit were inferior.BACKGROUNDHealth care quality measurement is an important accountability factor that is used for assessing provider quality and making provider payments. 1 The National Quality Forum (NQF) has endorsed> 700 quality measures, with many increase in the pipeline. 2 For many of these measures, collecting and reporting data are a complex, time-consuming, manual process. 2 The adoption of electronic health records (EHRs) has long been viewed as the key to eliminate this major barrier to health care measurement. 3 As the development and sophistication of the EHR grows, 4–8 it has increasingly become a focus of measurement and measure developers. 9–15