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临床诊断原型学习健康系统的要求与验证

Requirements and validation of a prototype learning health system for clinical diagnosis.

作者信息

Corrigan Derek, Munnelly Gary, Kazienko Przemysław, Kajdanowicz Tomasz, Soler Jean-Karl, Mahmoud Samhar, Porat Talya, Kostopoulou Olga, Curcin Vasa, Delaney Brendan

机构信息

Royal College of Surgeons in Ireland Dublin Ireland.

Trinity College Dublin Dublin Ireland.

出版信息

Learn Health Syst. 2017 May 31;1(4):e10026. doi: 10.1002/lrh2.10026. eCollection 2017 Oct.

Abstract

INTRODUCTION

Diagnostic error is a major threat to patient safety in the context of family practice. The patient safety implications are severe for both patient and clinician. Traditional approaches to diagnostic decision support have lacked broad acceptance for a number of well-documented reasons: poor integration with electronic health records and clinician workflow, static evidence that lacks transparency and trust, and use of proprietary technical standards hindering wider interoperability. The learning health system (LHS) provides a suitable infrastructure for development of a new breed of learning decision support tools. These tools exploit the potential for appropriate use of the growing volumes of aggregated sources of electronic health records.

METHODS

We describe the experiences of the TRANSFoRm project developing a diagnostic decision support infrastructure consistent with the wider goals of the LHS. We describe an architecture that is model driven, service oriented, constructed using open standards, and supports evidence derived from electronic sources of patient data. We describe the architecture and implementation of 2 critical aspects for a successful LHS: the model representation and translation of clinical evidence into effective practice and the generation of curated clinical evidence that can be used to populate those models, thus closing the LHS loop.

RESULTS/CONCLUSIONS: Six core design requirements for implementing a diagnostic LHS are identified and successfully implemented as part of this research work. A number of significant technical and policy challenges are identified for the LHS community to consider, and these are discussed in the context of evaluating this work: medico-legal responsibility for generated diagnostic evidence, developing trust in the LHS (particularly important from the perspective of decision support), and constraints imposed by clinical terminologies on evidence generation.

摘要

引言

在家庭医疗环境中,诊断错误是对患者安全的重大威胁。这对患者和临床医生的患者安全影响都很严重。传统的诊断决策支持方法由于一些有充分记录的原因而未被广泛接受:与电子健康记录和临床医生工作流程整合不佳、缺乏透明度和可信度的静态证据,以及使用专有技术标准阻碍了更广泛的互操作性。学习型健康系统(LHS)为开发新型学习决策支持工具提供了合适的基础设施。这些工具利用了适当使用不断增加的电子健康记录汇总源的潜力。

方法

我们描述了TRANSFoRm项目开发与LHS更广泛目标一致的诊断决策支持基础设施的经验。我们描述了一种由模型驱动、面向服务、使用开放标准构建并支持从患者数据电子源得出的证据的架构。我们描述了成功的LHS的两个关键方面的架构和实施:临床证据的模型表示和转化为有效实践,以及生成可用于填充这些模型的精选临床证据,从而闭合LHS循环。

结果/结论:确定了实施诊断性LHS的六项核心设计要求,并作为本研究工作的一部分成功实施。为LHS社区确定了一些重大的技术和政策挑战以供考虑,并在评估这项工作时进行了讨论:生成的诊断证据的医疗法律责任、建立对LHS的信任(从决策支持角度特别重要)以及临床术语对证据生成的限制。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1944/6508515/582c80c1d4ad/LRH2-1-e10026-g001.jpg

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