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医生须知:如何分析基于索赔的报告卡。

Caveat doctor: how to analyze claims-based report cards.

作者信息

Dans P E

机构信息

Johns Hopkins University, Baltimore, MD, USA.

出版信息

Jt Comm J Qual Improv. 1998 Jan;24(1):21-30. doi: 10.1016/s1070-3241(16)30356-x.

DOI:10.1016/s1070-3241(16)30356-x
PMID:9494871
Abstract

"Report cards" based on claims (billing) data are being widely used to evaluate the quality of care given by providers, even though they often lack sufficient clinical detail to render definitive judgments. Furthermore, their accuracy, especially for outpatient care, is quite variable. Nevertheless, claims data will continue to be used until better clinical information becomes widely available. To determine the suitability of automated claims data for measuring clinical performance, careful attention should be paid to the integrity of the data. Providers profiled by claims-based report cards should ask four questions about the source, robustness, management, and analysis of the data: 1. What are the key characteristics of the data set used to construct the profile? These include the insurer's name, coverage type, time period, geographic area, and number of patients, claims lines, and providers. 2. What clinical conditions and events are being measured and how well? In short, are the patients' conditions and their clinical encounters reasonably well characterized? 3. Is the information about the patients and providers accurate and up to date? 4. Once the insurer receives the medical claim, are data elements deleted or altered in ways that might affect their accuracy and completeness? Ensuring data integrity is not sufficient; the analysis of the data must be scrutinized. Potential pitfalls in analyzing claims data arise in choosing clinically meaningful measures, recognizing important differences in patients and their providers, and making fair comparisons against appropriate benchmarks. Monitoring patient care outcomes is no longer voluntary. By routinely constructing and augmenting profiles using outpatient claims data, provider groups become proactive rather than reactive in evaluating their patients' care.

摘要

基于索赔(计费)数据的“成绩单”正被广泛用于评估医疗服务提供者所提供的医疗质量,尽管这些数据往往缺乏足够的临床细节来做出明确的判断。此外,它们的准确性,尤其是对于门诊医疗而言,差异很大。然而,在更好的临床信息广泛可用之前,索赔数据仍将继续被使用。为了确定自动化索赔数据用于衡量临床绩效的适用性,应仔细关注数据的完整性。基于索赔的成绩单所描述的医疗服务提供者应就数据的来源、稳健性、管理和分析提出四个问题:1. 用于构建该描述的数据集的关键特征是什么?这些包括保险公司名称、保险类型、时间段、地理区域以及患者数量、索赔项目数量和医疗服务提供者数量。2. 正在衡量哪些临床病症和事件,以及衡量得如何?简而言之,患者的病情及其临床遭遇是否得到了合理的描述?3. 关于患者和医疗服务提供者的信息是否准确且最新?4. 一旦保险公司收到医疗索赔,数据元素是否以可能影响其准确性和完整性的方式被删除或更改?确保数据完整性是不够的;对数据的分析也必须受到审查。在分析索赔数据时,潜在的陷阱出现在选择具有临床意义的衡量标准、识别患者及其医疗服务提供者之间的重要差异以及与适当的基准进行公平比较等方面。监测患者护理结果已不再是可选项。通过定期使用门诊索赔数据构建和扩充描述,医疗服务提供者群体在评估其患者护理时变得更加主动而非被动。

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