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用于质量改进的数据收集和报告的模型驱动方法。

Model-driven approach to data collection and reporting for quality improvement.

作者信息

Curcin Vasa, Woodcock Thomas, Poots Alan J, Majeed Azeem, Bell Derek

机构信息

Department of Primary Care and Public Health, Imperial College London, Reynolds Building, St. Dunstan's Road, London W6 8RP, United Kingdom.

NIHR CLAHRC for NWL Unit, Chelsea and Westminster Hospital, 369 Fulham Road, London SW10 9NH, United Kingdom.

出版信息

J Biomed Inform. 2014 Dec;52:151-62. doi: 10.1016/j.jbi.2014.04.014. Epub 2014 May 27.

Abstract

Continuous data collection and analysis have been shown essential to achieving improvement in healthcare. However, the data required for local improvement initiatives are often not readily available from hospital Electronic Health Record (EHR) systems or not routinely collected. Furthermore, improvement teams are often restricted in time and funding thus requiring inexpensive and rapid tools to support their work. Hence, the informatics challenge in healthcare local improvement initiatives consists of providing a mechanism for rapid modelling of the local domain by non-informatics experts, including performance metric definitions, and grounded in established improvement techniques. We investigate the feasibility of a model-driven software approach to address this challenge, whereby an improvement model designed by a team is used to automatically generate required electronic data collection instruments and reporting tools. To that goal, we have designed a generic Improvement Data Model (IDM) to capture the data items and quality measures relevant to the project, and constructed Web Improvement Support in Healthcare (WISH), a prototype tool that takes user-generated IDM models and creates a data schema, data collection web interfaces, and a set of live reports, based on Statistical Process Control (SPC) for use by improvement teams. The software has been successfully used in over 50 improvement projects, with more than 700 users. We present in detail the experiences of one of those initiatives, Chronic Obstructive Pulmonary Disease project in Northwest London hospitals. The specific challenges of improvement in healthcare are analysed and the benefits and limitations of the approach are discussed.

摘要

持续的数据收集和分析已被证明对实现医疗保健的改善至关重要。然而,地方改善计划所需的数据往往无法从医院电子健康记录(EHR)系统中轻易获取,或者未被常规收集。此外,改善团队的时间和资金通常有限,因此需要低成本且快速的工具来支持他们的工作。因此,医疗保健地方改善计划中的信息学挑战包括为非信息学专家提供一种对地方领域进行快速建模的机制,包括绩效指标定义,并以既定的改进技术为基础。我们研究了一种模型驱动的软件方法来应对这一挑战的可行性,即由一个团队设计的改进模型用于自动生成所需的电子数据收集工具和报告工具。为了实现这一目标,我们设计了一个通用的改进数据模型(IDM)来捕获与项目相关的数据项和质量指标,并构建了医疗保健网络改进支持(WISH),这是一个原型工具,它根据统计过程控制(SPC),采用用户生成的IDM模型并创建数据模式、数据收集网络界面和一组实时报告,供改进团队使用。该软件已在50多个改进项目中成功使用,用户超过700人。我们详细介绍了其中一个项目的经验,即伦敦西北部医院的慢性阻塞性肺疾病项目。分析了医疗保健改善的具体挑战,并讨论了该方法的优点和局限性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4a94/4266541/5b2e78ac427f/fx1.jpg

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