Modelling clinical narrative as computable knowledge: The NICE computable implementation guidance project

IF 2.6 Q2 HEALTH POLICY & SERVICES
Philip Scott, Michaela Heigl, Charles McCay, Polly Shepperdson, Elia Lima-Walton, Elisavet Andrikopoulou, Klara Brunnhuber, Gary Cornelius, Susan Faulding, Ben McAlister, Shaun Rowark, Matthew South, Mark R. Thomas, Justin Whatling, John Williams, Jeremy C. Wyatt, Felix Greaves
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引用次数: 1

Abstract

Introduction

Translating narrative clinical guidelines to computable knowledge is a long-standing challenge that has seen a diverse range of approaches. The UK National Institute for Health and Care Excellence (NICE) Content Advisory Board (CAB) aims ultimately to (1) guide clinical decision support and other software developers to increase traceability, fidelity and consistency in supporting clinical use of NICE recommendations, (2) guide local practice audit and intervention to reduce unwarranted variation, (3) provide feedback to NICE on how future recommendations should be developed.

Objectives

The first phase of work was to explore a range of technical approaches to transition NICE toward the production of natively digital content.

Methods

Following an initial ‘collaborathon’ in November 2022, the NICE Computable Implementation Guidance project (NCIG) was established. We held a series of workstream calls approximately fortnightly, focusing on (1) user stories and trigger events, (2) information model and definitions, (3) horizon-scanning and output format. A second collaborathon was held in March 2023 to consolidate progress across the workstreams and agree residual actions to complete.

Results

While we initially focussed on technical implementation standards, we decided that an intermediate logical model was a more achievable first step in the journey from narrative to fully computable representation. NCIG adopted the WHO Digital Adaptation Kit (DAK) as a technology-agnostic method to model user scenarios, personae, processes and workflow, core data elements and decision-support logic. Further work will address indicators, such as prescribing compliance, and implementation in document templates for primary care patient record systems.

Conclusions

The project has shown that the WHO DAK, with some modification, is a promising approach to build technology-neutral logical specifications of NICE recommendations. Implementation of concurrent computable modelling by multidisciplinary teams during guideline development poses methodological and cultural questions that are complex but tractable given suitable will and leadership.

Abstract Image

将临床叙述建模为可计算知识:NICE可计算实施指导项目。
引言:将叙述性临床指南转化为可计算知识是一个长期存在的挑战,已经出现了多种方法。英国国家健康与护理卓越研究所(NICE)内容咨询委员会(CAB)的最终目标是(1)指导临床决策支持和其他软件开发人员提高支持NICE建议临床使用的可追溯性、保真度和一致性,(2)指导当地实践审计和干预,以减少不必要的变化,(3)就如何制定未来的建议向NICE提供反馈。目标:第一阶段的工作是探索一系列技术方法,将NICE转变为本地数字内容的生产。方法:在2022年11月的首次“合作”之后,NICE可计算实施指导项目(NCIG)成立。我们大约每两周举行一次工作流电话会议,重点讨论(1)用户故事和触发事件,(2)信息模型和定义,(3)地平线扫描和输出格式。第二次合作于2023年3月举行,以巩固各工作流的进展,并商定要完成的剩余行动。结果:虽然我们最初专注于技术实现标准,但我们决定,在从叙述到完全可计算表示的旅程中,中间逻辑模型是更容易实现的第一步。NCIG采用世界卫生组织数字适应工具包(DAK)作为一种技术认知方法,对用户场景、人物角色、流程和工作流程、核心数据元素和决策支持逻辑进行建模。进一步的工作将涉及指标,如处方合规性,以及初级保健患者记录系统文件模板的实施。结论:该项目表明,世界卫生组织DAK经过一些修改,是一种很有前途的方法,可用于建立NICE建议的技术-常规逻辑规范。在准则制定过程中,多学科团队实施并行可计算建模提出了方法和文化问题,这些问题很复杂,但只要有适当的意愿和领导力,就可以处理。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Learning Health Systems
Learning Health Systems HEALTH POLICY & SERVICES-
CiteScore
5.60
自引率
22.60%
发文量
55
审稿时长
20 weeks
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