THE SELF-ADAPTING FOCUSED REVIEW SYSTEM - PROBABILITY-SAMPLING OF MEDICAL RECORDS TO MONITOR UTILIZATION AND QUALITY OF CARE

被引:14
作者
ASH, A
SHWARTZ, M
PAYNE, SMC
RESTUCCIA, JD
机构
[1] BOSTON UNIV, SCH MANAGEMENT, DEPT OPERAT MANAGEMENT, 621 COMMONWEALTH AVE, BOSTON, MA 02215 USA
[2] BOSTON UNIV, SCH MED, DEPT GEN INTERNAL MED, HLTH CARE RES UNIT, BOSTON, MA 02118 USA
关键词
Medical record review; Probability sampling; Quality assurance; Utilization review;
D O I
10.1097/00005650-199011000-00005
中图分类号
R19 [保健组织与事业(卫生事业管理)];
学科分类号
摘要
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. © Lippincott-Raven Publishers.
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页码:1025 / 1039
页数:15
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