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

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

Architectural Paint Research and the Archaeology of Buildings

Architectural Paint Research (APR) is the archaeological study of interior and exterior applied decoration. Over time, applied layers of paint and other decorative finishes build-up on the surface of a built structure, encapsulating microscopic deposits of material evidence. This evidence can be used to inform the phase dating of a structure, or illuminate the historic function of a space. It can challenge preconceived ideas of how specific areas were decorated, and track the changes in aesthetics over time. It can identify when architects’ ideologies have been balanced by practical considerations. It can provide an insight into the intangible and ephemeral atmosphere that decoration gives to a room. Finally, it can examine the dirt trapped between layers of decoration and thus categorize the physical environmental conditions that surrounded a building at varying points in its history. Although used in the commercial heritage and conservation sectors, Architectural Paint Research is almost completely unknown to building archaeologists. This article aims to introduce APR to a new audience, and argues that is an invaluable tool in the archaeological interpretation of buildings

Consolidated Health Economic Evaluation Reporting Standards for Interventions That Use Artificial Intelligence (CHEERS-AI)

Objectives: Economic evaluations (EEs) are commonly used by decision makers to understand the value of health interventions. The Consolidated Health Economic Evaluation Reporting Standards (CHEERS 2022) provide reporting guidelines for EEs. Healthcare systems will increasingly see new interventions that use artificial intelligence (AI) to perform their function. We developed Consolidated Health Economic Evaluation Reporting Standards for Interventions that use AI (CHEERS-AI) to ensure EEs of AI-based health interventions are reported in a transparent and reproducible manner. Methods: Potential CHEERS-AI reporting items were informed by 2 published systematic literature reviews of EEs and a contemporary update. A Delphi study was conducted using 3 survey rounds to elicit multidisciplinary expert views on 26 potential items, through a 9-point Likert rating scale and qualitative comments. An online consensus meeting was held to finalize outstanding reporting items. A digital health patient group reviewed the final checklist from a patient perspective. Results: A total of 58 participants responded to survey round 1, 42, and 31 of whom responded to rounds 2 and 3, respectively. Nine participants joined the consensus meeting. Ultimately, 38 reporting items were included in CHEERS-AI. They comprised the 28 original CHEERS 2022 items, plus 10 new AI-specific reporting items. Additionally, 8 of the original CHEERS 2022 items were elaborated on to ensure AI-specific nuance is reported. Conclusions: CHEERS-AI should be used when reporting an EE of an intervention that uses AI to perform its function. CHEERS-AI will help decision makers and reviewers to understand important AI-specific details of an intervention, and any implications for the EE methods used and cost-effectiveness conclusions