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

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Scott, Philip and Heigl, Michaela and McCay, Charles and Shepperdson, Polly and Lima‐Walton, Elia and Andrikopoulou, Elisavet and Brunnhuber, Klara and Cornelius, Gary and Faulding, Susan and McAlister, Ben and Rowark, Shaun and South, Matthew and Thomas, Mark R. and Whatling, Justin and Williams, John and Wyatt, Jeremy C. and Greaves, Felix (2023) Modelling clinical narrative as computable knowledge : the NICE computable implementation guidance project. Learning Health Systems, 7 (4). e10394. ISSN 2379-6146 (https://doi.org/10.1002/lrh2.10394)

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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.

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