Availability of structured and unstructured clinical data for comparative effectiveness research and quality improvement: a multisite assessment.
Availability of structured and unstructured clinical data for comparative effectiveness research and quality improvement: a multisite assessment.
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DOI：
10.13063/2327-9214.1079
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发表时间：
2014-01-01
期刊：
影响因子：
--
通讯作者：
Tarczy-Hornoch, Peter
中科院分区：
文献类型：
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作者：
Capurro, Daniel;Yetisgen, Meliha;Tarczy-Hornoch, Peter
简介：学习型医疗保健系统的一个关键属性是能够收集和分析常规收集的临床数据，以便快速生成新的临床证据，并监测所提供的护理质量。为了实现这一愿景，临床数据必须易于提取并以计算机可读格式存储。我们在多个组织中进行了这项研究，以评估这些数据的可用性，特别是用于外科手术的比较有效性研究（CER）和质量改进（QI）。本研究是在已经建立的外科护理和结局评估计划（SCOAP）所需数据的背景下进行的，SCOAP是一个临床医生主导的性能基准，方法：我们选择了六家医院，由两个健康信息技术（HIT）组管理，并评估了自动提取完成SCOAP数据收集表格所需的数据的难易程度。每个数据元素被归类为容易，适度，或复杂的extraction.RESULTS：总体而言，所需的数据自动完成SCOAP形式的显着比例不存储在结构化的计算机可读格式，超过75%的所有数据元素被归类为中度复杂或复杂的提取。分布显着不同的医疗保健systems studied.CONCLUSIONS：虽然非常可取的，学习医疗保健系统不会自动出现从实施电子健康记录（EHRs）。需要改进临床数据的结构化捕获的创新方法，以便于使用常规收集的临床数据进行患者表型分析。
INTRODUCTION: A key attribute of a learning health care system is the ability to collect and analyze routinely collected clinical data in order to quickly generate new clinical evidence, and to monitor the quality of the care provided. To achieve this vision, clinical data must be easy to extract and stored in computer readable formats. We conducted this study across multiple organizations to assess the availability of such data specifically for comparative effectiveness research (CER) and quality improvement (QI) on surgical procedures.SETTING: This study was conducted in the context of the data needed for the already established Surgical Care and Outcomes Assessment Program (SCOAP), a clinician-led, performance benchmarking, and QI registry for surgical and interventional procedures in Washington State.METHODS: We selected six hospitals, managed by two Health Information Technology (HIT) groups, and assessed the ease of automated extraction of the data required to complete the SCOAP data collection forms. Each data element was classified as easy, moderate, or complex to extract.RESULTS: Overall, a significant proportion of the data required to automatically complete the SCOAP forms was not stored in structured computer-readable formats, with more than 75 percent of all data elements being classified as moderately complex or complex to extract. The distribution differed significantly between the health care systems studied.CONCLUSIONS: Although highly desirable, a learning health care system does not automatically emerge from the implementation of electronic health records (EHRs). Innovative methods to improve the structured capture of clinical data are needed to facilitate the use of routinely collected clinical data for patient phenotyping.