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

Experiences of Creating Computable Knowledge Tutorials Using HL7 Clinical Quality Language

Computable knowledge artefact development is challenging and often culminates in the development of unique single usage solutions. Libraries of computable knowledge artefacts have the possibility to enhance the Learning Health System in order to improve the benefits of innovation and the decision making of clinicians. This paper aims to discuss the process of creating the use cases and the tutorial material that would enable students to both understand how the interaction between the dataset and the outcome occurs as well as how HL7 Clinical Quality Language can be used to create artefacts of re-usable code

What is the general population’s perception of smart motorways in the UK?

Motorway users have various opinions about the types of smart motorways. Motor-way utilization can be affected if road users have a negative perception towards certain types of smart motorway, particularly on the topic of safety. There are three types of smart motorways in the UK. These are Controlled, Dynamic and All-Lane Running. This study focuses on the comparison of ALR and DHS smart motorways as ALR smart motorways are aiming to replace and improve upon DHS smart motorways. The aim of this project is to understand how the general population perceive smart motorways in the UK. This aim will be achieved through answering a series of these research questions: (1) How does existing knowledge of smart motorways effect perception of smart motorways; (2) How does age effect perception of smart motorways; (3) How does car ownership affect perception of smart motorways? Data were collected using an online survey disseminated to the UK vehicle and non-vehicle drivers via social media and advertisements. Descriptive statistics and cluster analysis were used to analyse the dataset and find similarity clusters. The primary research shows that ~57% of the survey respondents had never heard of or did not know the meaning of the 3 different types of smart motorway and only ~13% of respondents fully understand the different types. Car owners in both cluster analysis models show substantial variation in the results of the comfort / smart motorway choice variables. Greater knowledge and awareness about smart motorways is required to improve the perception of smart motorways. It would seem that this is particularly true for all-lane running smart motorways which are both the newest and most physically different type of smart motorway with their removal of the hard shoulder

One Health in a Digital World: Technology, Data, Information and Knowledge

Objectives: To describe the origins and growth of the One Health concept and its recent application in One Digital Health. Methods: Bibliometric review and critical discussion of emergent themes derived from co-occurrence of MeSH keywords. Results: The fundamental interrelationship between human health, animal health and the wider environment has been recognized since ancient times. One Health as a distinct term originated in 2004 and has been a rapidly growing concept of interest in the biomedical literature since 2017. One Digital Health has quickly established itself as a unifying construct that highlights the critical role of technology, data, information and knowledge to facilitate the interdisciplinary collaboration that One Health requires. The principal application domains of One Digital Health to date are in FAIR data integration and analysis, disease surveillance, antimicrobial stewardship and environmental monitoring. Conclusions: One Health and One Digital Health offer powerful lenses to examine and address crises in our living world. We propose thinking in terms of Learning One Health Systems that can dynamically capture, integrate, analyse and monitor application of data across the biosphere