从概念和代码到医疗保健质量测量:理解他汀类药物治疗临床质量测量值集词汇的变化。

Raja A Cholan, Nicole G Weiskopf, Doug Rhoton, Bhavaya Sachdeva, Nicholas V Colin, Shelby J Martin, David A Dorr
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引用次数: 3

摘要

目的:了解不同概念-值集映射对护理质量测量的影响。背景:临床质量测量(CQMs)旨在衡量所提供医疗服务的质量,并有助于促进循证治疗。大多数cqm由来自词汇表(或“值集”)的分组代码组成,这些代码表示定义度量规范的唯一标识符(即,对象标识符)、概念(即,值集名称)和概念定义(即,代码组)。在他汀类药物治疗CQM的开发过程中,两个独特的值集由独立的测量开发人员为相同的全局概念创建。方法:首先确定同一CQM的两种值集规格之间的差异。然后,我们在质量度量计算注册表中实现各种版本,以了解差异如何影响风险的计算流行率和度量性能。结果:全球表现率仅相差0.8%,但关键疾病患者的表现率高达2.3倍,“心肌梗死”患者的表现率相差7.5%,“缺血性血管疾病”患者的表现率相差3.5%。结论:CQM开发人员所做的关于在值集词汇表中包含或排除哪些概念和代码组的决策可能导致护理质量度量的不准确性。一种解决方案是,开发人员可以为这些决策提供基本原理。需要背书来鼓励系统供应商、付款人、信息学家和临床医生在创建更集成的术语集方面进行合作。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

From Concepts and Codes to Healthcare Quality Measurement: Understanding Variations in Value Set Vocabularies for a Statin Therapy Clinical Quality Measure.

From Concepts and Codes to Healthcare Quality Measurement: Understanding Variations in Value Set Vocabularies for a Statin Therapy Clinical Quality Measure.

From Concepts and Codes to Healthcare Quality Measurement: Understanding Variations in Value Set Vocabularies for a Statin Therapy Clinical Quality Measure.

From Concepts and Codes to Healthcare Quality Measurement: Understanding Variations in Value Set Vocabularies for a Statin Therapy Clinical Quality Measure.

Objective: To understand the impact of distinct concept to value set mapping on the measurement of quality of care.

Background: Clinical quality measures (CQMs) intend to measure the quality of healthcare services provided, and to help promote evidence-based therapies. Most CQMs consist of grouped codes from vocabularies - or 'value sets' - that represent the unique identifiers (i.e., object identifiers), concepts (i.e., value set names), and concept definitions (i.e., code groups) that define a measure's specifications. In the development of a statin therapy CQM, two unique value sets were created by independent measure developers for the same global concepts.

Methods: We first identified differences between the two value set specifications of the same CQM. We then implemented the various versions in a quality measure calculation registry to understand how the differences affected calculated prevalence of risk and measure performance.

Results: Global performance rates only differed by 0.8%, but there were up to 2.3 times as many patients included with key conditions, and differing performance rates of 7.5% for patients with 'myocardial infarction' and 3.5% for those with 'ischemic vascular disease'.

Conclusion: The decisions CQM developers make about which concepts and code groups to include or exclude in value set vocabularies can lead to inaccuracies in the measurement of quality of care. One solution is that developers could provide rationale for these decisions. Endorsements are needed to encourage system vendors, payers, informaticians, and clinicians to collaborate in the creation of more integrated terminology sets.

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