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Introduction: To understand when knowledge objects in a computable biomedical knowledge library are likely to be subject to regulation as a medical device in the United Kingdom. Methods: A briefing paper was circulated to a multi‐disciplinary group of 25 including regulators, lawyers and others with insights into device regulation. A 1‐day workshop was convened to discuss questions relating to our aim. A discussion paper was drafted by lead authors and circulated to other authors for their comments and contributions. Results: This article reports on those deliberations and describes how UK device regulators are likely to treat the different kinds of knowledge objects that may be stored in computable biomedical knowledge libraries. While our focus is the likely approach of UK regulators, our analogies and analysis will also be relevant to the approaches taken by regulators elsewhere. We include a table examining the implications for each of the four knowledge levels described by Boxwala in 2011 and propose an additional level. Conclusions: If a knowledge object is described as directly executable for a medical purpose to provide decision support, it will generally be in scope of UK regulation as “software as a medical device.” However, if the knowledge object consists of an algorithm, a ruleset, pseudocode or some other representation that is not directly executable and whose developers make no claim that it can be used for a medical purpose, it is not likely to be subject to regulation. We expect similar reasoning to be applied by regulators in other countries.

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 modellin