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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.

Background Quality of life is paramount for patients and clinicians, but existing measures of health were not developed for routine use. Objectives This paper describes the development and testing of a new generic tool for measuring health related quality of life (HRQoL) with direct comparison to the SF-12 Health Survey. Methods The new tool (howRu) has four items (discomfort, distress, disability and dependence), rated using four levels (none, a little, quite a lot and extreme), providing 256 possible states (44); it has an aggregate scoring scheme with a range from 0 (worst) to 12 (best). Psychometric properties were examined in a telephone survey, which also recorded SF-12. Results The howRu script is shorter than SF-12 (45 words vs 294 words) and has better readability statistics. 2751 subjects, all with long-term conditions (average age 62, female 62.8%), completed the survey; 21.7% were at the ceiling (no reported problems on any dimension); 0.9% at the floor. Inter-item correlations, Cronbach's alpha and principal factor analysis suggest that a single summary score is appropriate. Correlations between the physical and mental components of both howRu and SF-12 were as expected. Across all patients the howRu score was correlated with PCS-12 (r=0.74), MCS-12 (r=0.49) and the sum of PCS- 12 and MCS-12 (r=0.81). Subjects were classified by howRu score, primary condition, the number of conditions suffered, age group, duration of illness and area of residence. Across all six classifications, the correlation of themean howRu score with the mean PCS-12 for each class was r=0.91, with MCS-12, r=0.45 and with the sum of PCS-12 and MCS-12, r=0.97. Conclusions howRu is a new short generic measure of HRQoL, with good psychometric properties. It generates similar aggregate results to SF-12. It could provide a quick and easy way for practitioners to monitor the health of patients with long-term conditions.