Pivovarov Rimma, Albers David J, Hripcsak George, Sepulveda Jorge L, Elhadad Noémie
Department of Biomedical Informatics, Columbia University, New York, USA.
Department of Pathology & Cell Biology, Columbia University, New York, USA.
J Am Med Inform Assoc. 2014 Nov-Dec;21(6):1038-44. doi: 10.1136/amiajnl-2013-002592. Epub 2014 Jun 13.
The study of utilization patterns can quantify potential overuse of laboratory tests and find new ways to reduce healthcare costs. We demonstrate the use of distributional analytics for comparing electronic health record (EHR) laboratory test orders across time to diagnose and quantify overutilization.
We looked at hemoglobin A1c (HbA1c) testing across 119,000 patients and 15 years of hospital records. We examined the patterns of HbA1c ordering before and after the publication of the 2002 American Diabetes Association guidelines for HbA1c testing. We conducted analyses to answer three questions. What are the patterns of HbA1c ordering? Do HbA1c orders follow the guidelines with respect to frequency of measurement? If not, how and why do they depart from the guidelines?
The raw number of HbA1c orderings has steadily increased over time, with a specific increase in low-measurement orderings (<6.5%). There is a change in ordering pattern following the 2002 guideline (p<0.001). However, by comparing ordering distributions, we found that the changes do not reflect the guidelines and rather exhibit a new practice of rapid-repeat testing. The rapid-retesting phenomenon does not follow the 2009 guidelines for diabetes diagnosis either, illustrated by a stratified HbA1c value analysis.
Results suggest HbA1c test overutilization, and contributing factors include lack of care coordination, unexpected values prompting retesting, and point-of-care tests followed by confirmatory laboratory tests.
We present a method of comparing ordering distributions in an EHR across time as a useful diagnostic approach for identifying and assessing the trend of inappropriate use over time.
对检验利用模式的研究能够量化实验室检查潜在的过度使用情况，并找到降低医疗成本的新方法。我们展示了如何使用分布分析来比较电子健康记录（EHR）中不同时间的实验室检查医嘱，以诊断和量化过度使用情况。
我们查看了119,000名患者15年的医院记录中的糖化血红蛋白（HbA1c）检测情况。我们研究了2002年美国糖尿病协会HbA1c检测指南发布前后HbA1c医嘱的模式。我们进行了分析以回答三个问题。HbA1c医嘱的模式是怎样的？HbA1c医嘱在测量频率方面是否遵循指南？如果不遵循，它们是如何以及为何偏离指南的？
随着时间推移，HbA1c医嘱的原始数量稳步增加，低测量值（<6.5%）的医嘱有特定增加。2002年指南发布后医嘱模式发生了变化（p<0.001）。然而，通过比较医嘱分布，我们发现这些变化并未反映指南，而是呈现出一种快速重复检测的新做法。分层HbA1c值分析表明，快速重新检测现象也不符合2009年糖尿病诊断指南。
结果表明存在HbA1c检测过度使用的情况，促成因素包括缺乏护理协调、意外值促使重新检测以及即时检验后进行确认性实验室检测。
我们提出了一种跨时间比较电子健康记录中医嘱分布的方法，作为一种有用的诊断方法，用于识别和评估随时间推移不适当使用的趋势。
