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

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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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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.
Original languageEnglish
Article numbere10394
Number of pages10
JournalLearning Health Systems
Early online date28 Sept 2023
Publication statusEarly online - 28 Sept 2023


  • computable knowledge
  • NHS
  • framework

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