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Learning Health Systems
Article . 2023 . Peer-reviewed
License: CC BY NC ND
Data sources: Crossref
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Learning Health Systems
Article . 2023
Data sources: DOAJ
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Modelling clinical narrative as computable knowledge: The NICE computable implementation guidance project

Authors: Philip Scott; Michaela Heigl; Charles McCay; Polly Shepperdson; Elia Lima‐Walton; Elisavet Andrikopoulou; Klara Brunnhuber; +10 Authors

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

Abstract

AbstractIntroductionTranslating 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.ObjectivesThe first phase of work was to explore a range of technical approaches to transition NICE toward the production of natively digital content.MethodsFollowing 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.ResultsWhile 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.ConclusionsThe 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.

Country
United Kingdom
Keywords

Medicine (General), QA75 Electronic computers. Computer science, practice guideline, computable knowledge, decision modelling, R Medicine (General), 004, R5-920, QA76 Computer software, Electronic computers. Computer science, RA Public aspects of medicine, Public aspects of medicine, RA1-1270, Experience Report, clinical decision support systems

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
4
Top 10%
Average
Top 10%
Green
gold
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