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.

Using primary care data to assess comparative effectiveness and safety of apixaban and rivaroxaban in patients with nonvalvular atrial fibrillation in the UK: an observational cohort study.

OBJECTIVE: To compare real-world effectiveness and safety of direct oral anticoagulants (DOACs) in patients with nonvalvular atrial fibrillation (AFib) for prevention of stroke. STUDY DESIGN AND SETTING: A comparative cohort study in UK general practice data from The Health Improvement Network database. PARTICIPANTS AND INTERVENTIONS: Before matching, 5655 patients ≥18 years with nonvalvular AFib who initiated at least one DOAC between 1 July 2014 and 31 December 2020 were included. DOACs of interest included apixaban, rivaroxaban, edoxaban and dabigatran, with the primary comparison between apixaban and rivaroxaban. Initiators of DOACs were defined as new users with no record of prescription for any DOAC during 12 months before index date. PRIMARY AND SECONDARY OUTCOME MEASURES: The primary outcome was stroke (ischaemic or haemorrhagic). Secondary outcomes included the occurrence of all-cause mortality, myocardial infarction (MI), transient ischaemic attacks (TIA), major bleeding events and a composite angina/MI/stroke (AMS) endpoint. RESULTS: Compared with rivaroxaban, patients initiating apixaban showed similar rates of stroke (HR: 0.93; 95% CI 0.64 to 1.34), all-cause mortality (HR: 1.03; 95% CI 0.87 to 1.22), MI (HR: 0.95; 95% CI 0.54 to 1.68), TIA (HR: 1.03; 95% CI 0.61 to 1.72) and AMS (HR: 0.96; 95% CI 0.72 to 1.27). Apixaban initiators showed lower rates of major bleeding events (HR: 0.60; 95% CI 0.47 to 0.75). CONCLUSIONS: Among patients with nonvalvular AFib, apixaban was as effective as rivaroxaban in reducing rate of stroke and safer in terms of major bleeding episodes. This head-to-head comparison supports conclusions drawn from indirect comparisons of DOAC trials against warfarin and demonstrates the potential for real-world evidence to fill evidence gaps and reduce uncertainty in both health technology assessment decision-making and clinical guideline development.

Quality of Care in the United Kingdom after Removal of Financial Incentives.

BACKGROUND: The benefits of pay-for-performance schemes in improving the quality of care remain uncertain. There is little information on the effect of removing incentives from existing pay-for-performance schemes. METHODS: We conducted interrupted time-series analyses of electronic medical record (EMR) data from 2010 to 2017 for 12 quality-of-care indicators in the United Kingdom's Quality and Outcomes Framework for which financial incentives were removed in 2014 and 6 indicators for which incentives were maintained. We estimated the effects of removing incentives on changes in performance on quality-of-care measures. RESULTS: Complete longitudinal data were available for 2819 English primary care practices with more than 20 million registered patients. There were immediate reductions in documented quality of care for all 12 indicators in the first year after the removal of financial incentives. Reductions were greatest for indicators related to health advice, with a reduction of 62.3 percentage points (95% confidence interval [CI], -65.6 to -59.0) in EMR documentation of lifestyle counseling for patients with hypertension. Changes were smaller for indicators involving clinical actions that automatically update the EMR, such as laboratory testing, with a reduction of 10.7 percentage points (95% CI, -13.6 to -7.8) in control of cholesterol in patients with coronary heart disease and 12.1 percentage points (95% CI, -13.6 to -10.6) for thyroid-function testing in patients with hypothyroidism. There was little change in performance on the 6 quality measures for which incentives were maintained. CONCLUSIONS: Removal of financial incentives was associated with an immediate decline in performance on quality measures. In part, the decline probably reflected changes in EMR documentation, but declines on measures involving laboratory testing suggest that incentive removal also changed the care delivered.