Ash A, Schwartz M, Payne S M, Restuccia J D
Department of General Internal Medicine, Boston University School of Medicine, MA.
Med Care. 1990 Nov;28(11):1025-39.
Medical record review is increasing in importance as the need to identify and monitor utilization and quality of care problems grow. To conserve resources, reviews are usually performed on a subset of cases. If judgment is used to identify subgroups for review, this raises the following questions: How should subgroups be determined, particularly since the locus of problems can change over time? What standard of comparison should be used in interpreting rates of problems found in subgroups? How can population problem rates be estimated from observed subgroup rates? How can the bias be avoided that arises because reviewers know that selected cases are suspected of having problems? How can changes in problem rates over time be interpreted when evaluating intervention programs? Simple random sampling, an alternative to subgroup review, overcomes the problems implied by these questions but is inefficient. The Self-Adapting Focused Review System (SAFRS), introduced and described here, provides an adaptive approach to record selection that is based upon model-weighted probability sampling. It retains the desirable inferential properties of random sampling while allowing reviews to be concentrated on cases currently thought most likely to be problematic. Model development and evaluation are illustrated using hospital data to predict inappropriate admissions.
随着识别和监测医疗服务利用情况及质量问题的需求不断增加，病历审查的重要性日益凸显。为了节省资源，审查通常针对部分病例进行。如果依靠判断来确定审查的亚组，就会引发以下问题：应如何确定亚组，尤其是鉴于问题的发生部位可能随时间变化？在解释亚组中发现的问题发生率时应采用何种比较标准？如何根据观察到的亚组发生率估计总体问题发生率？如何避免因审查人员知道所选病例被怀疑存在问题而产生的偏差？在评估干预项目时，如何解释问题发生率随时间的变化？简单随机抽样作为亚组审查的替代方法，克服了这些问题所隐含的困难，但效率低下。本文介绍并阐述的自适应重点审查系统（SAFRS）提供了一种基于模型加权概率抽样的记录选择自适应方法。它保留了随机抽样所需的推断特性，同时允许将审查集中在当前认为最有可能存在问题的病例上。利用医院数据预测不适当入院情况来说明模型的开发和评估。
