Codelists play a crucial role in ensuring accurate and standardized communication within healthcare. However, preparation of high-quality codelists is a rigorous and time-consuming process. The literature focuses on transparency of clinical codelists and overlooks the utility of automation. Here we present a Codelist Generation Framework that can automate generation of codelists with minimal input from clinical experts. We demonstrate the process using a specific project, DynAIRx, producing appropriate codelists and a framework allowing future projects to take advantage of automated codelist generation. Both the framework and codelist are publicly available. DynAIRx is an NIHR-funded project aiming to develop AIs to help optimise prescribing of medicines in patients with multiple long-term conditions. DynAIRx requires complex codelists to describe the trajectory of each patient, and the interaction between their conditions. We promptly generated ≈ 214 codelists for DynAIRx using the proposed framework and validated them with a panel of experts, significantly reducing the amount of time required by making effective use of automation. The framework reduced the clinician time required to validate codes, automatically shrunk codelists using trusted sources and added new codes for review against existing codelists. In the DynAIRx case study, a codelist of ≈ 14000 codes required only 7-9 hours of clinician’s time in the end (while existing methods takes months), and application of the automation framework reduced the workload by >80

Here we introduce graphPAF, a comprehensive R package designed for estimation, inference and display of population attributable fractions (PAF) and impact fractions. In addition to allowing inference for standard population attributable fractions and impact fractions, graphPAF facilitates display of attributable fractions over multiple risk factors using fan-plots and nomograms, calculations of attributable fractions for continuous exposures, inference for attributable fractions appropriate for specific risk factor → mediator → outcome pathways (pathway-specific attributable fractions) and Bayesian network-based calculations and inference for joint, sequential and average population attributable fractions in multi-risk factor scenarios. This article can be used as both a guide to the theory of attributable fraction estimation and a tutorial regarding how to use graphPAF in practical examples.

Background: Population ageing has led to an increase in multimorbidity and polypharmacy. Some medications may need to be stopped, but patient attitudes towards deprescribing are poorly understood. This study explores attitudes towards (de)prescribing in patients with multimorbidity in the UK primary care. Methods: Patients with multimorbidity were invited to complete the Revised Patients Attitudes Towards Deprescribing (rPATD) Questionnaire using the Evergreen Life Personal Health Record App (Manchester, UK). The responses were linked to electronic health records. Anonymised data were analysed in a trusted research environment (University of Liverpool) for group comparisons and using multivariable logistic regression to identify factors associated with satisfaction with current medications. Results: A total 1,019 patients participated in the study (n=365 aged <65, 30% males; n=654 ≥65, 57% males). Most patients were satisfied with their current medications (74% aged <65, 70% aged ≥65) but were willing to stop one or more of their regular medicines if their doctor said it was possible (82%, 68% accordingly). Polypharmacy, use of antihypertensive drugs, and antidepressants were associated with patient-reported burden in taking medicines. Frailty did not influence patient deprescribing attitudes. Patients who were satisfied with current medications had fewer medications. Independent predictors of satisfaction with current medications were higher total involvement and appropriateness scores, and lower total burden score. Conclusions: Most patients with multimorbidity would consider stopping some of their medications, even when they are generally satisfied with the treatments they received. Frailty status does not imply willingness to stop medications. Clinicians should discuss medication deprescribing for shared decision. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This study/project is funded by the National Institute for Health Research (NIHR) under its Programme Artificial Intelligence for Multiple and Long-Term Conditions (NIHR203986). The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care. IB is supported by NIHR as Senior Investigator award (NIHR205131).AW is partly funded by Health and Care Research Wales award (NHS-RTA-21-02) ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The Newcastle North Tyneside Research Ethics Committee (REC reference:22/NE/0088) granted ethical approval for the DynAIRx study. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data produced in the present study are available upon reasonable request to the authors

IntroductionStructured medication reviews (SMRs), introduced in the United Kingdom (UK) in 2020, aim to enhance shared decision-making in medication optimisation, particularly for patients with multimorbidity and polypharmacy. Despite its potential, there is limited empirical evidence on the implementation of SMRs, and the challenges faced in the process. This study is part of a larger DynAIRx (Artificial Intelligence for dynamic prescribing optimisation and care integration in multimorbidity) project which aims to introduce Artificial Intelligence (AI) to SMRs and develop machine learning models and visualisation tools for patients with multimorbidity. Here, we explore how SMRs are currently undertaken and what barriers are experienced by those involved in them.MethodsQualitative focus groups and semi-structured interviews took place between 2022-2023. Six focus groups were conducted with doctors, pharmacists and clinical pharmacologists (n = 21), and three patient focus groups with patients with multimorbidity (n = 13). Five semi-structured interviews were held with 2 pharmacists, 1 trainee doctor, 1 policy-maker and 1 psychiatrist. Transcripts were analysed using thematic analysis.ResultsTwo key themes limiting the effectiveness of SMRs in clinical practice were identified: 'Medication Reviews in Practice' and 'Medication-related Challenges'. Participants noted limitations to the efficient and effectiveness of SMRs in practice including the scarcity of digital tools for identifying and prioritising patients for SMRs; organisational and patient-related challenges in inviting patients for SMRs and ensuring they attend; the time-intensive nature of SMRs, the need for multiple appointments and shared decision-making; the impact of the healthcare context on SMR delivery; poor communication and data sharing issues between primary and secondary care; difficulties in managing mental health medications and specific challenges associated with anticholinergic medication.ConclusionSMRs are complex, time consuming and medication optimisation may require multiple follow-up appointments to enable a comprehensive review. There is a need for a prescribing support system to identify, prioritise and reduce the time needed to understand the patient journey when dealing with large volumes of disparate clinical information in electronic health records. However, monitoring the effects of medication optimisation changes with a feedback loop can be challenging to establish and maintain using current electronic health record systems.

Context: The DynAIRx project aims to develop artificial intelligence (AI) tools to support medication reviews for patients with multimorbidity (people with ≥2 chronic conditions), targeting those at greatest risk of medicine-related harm. Challenges faced by healthcare professionals (HCPs) managing multimorbid patients include poor integration of health records across providers and few tools to assist in stratifying patients requiring medication reviews. Objective: To explore how medication reviews are currently being undertaken and how they might be augmented by AI. Study design and analysis: 5 semistructured interviews and 6 focus groups with HCPs (n=26); 2 focus groups with patients (n=10). These were transcribed verbatim and analysed using inductive thematic analysis. Setting: England. Focus groups were conducted via MicroSoft Teams with pharmacists, general practitioners, secondary care clinicians, and policy makers. Patient focus groups were conducted face-to-face. Population studied: The DynAIRx tool will target potentially problematic polypharmacy in 3 key multimorbidity groups: people with mental and physical health problems; those with ≥4 chronic conditions or taking ≥10 drugs; and older patients with frailty. Outcome measures: Report on barriers and facilitators to effective medicine reviews and potential implications for the implementation of new decision support tools. Results: Availability of staff, access to patient information, organisational contracts and patient demographics influenced uptake of medication reviews. Time was a major limiting factor due to the overwhelming density of information in electronic health records, especially for complex patients. Building continuity in medication reviews and adopting a team-based approach to dealing with complex multimorbid patients was emphasised. HCPs welcomed user-friendly digital tools with an intuitive interface that could be used to reduce "detective work" and enable shared decision making with patients. A timeline including diagnoses linked to medicines by indications and previous investigations was viewed as an early potential solution to reduce laborious searching. HCPs were generally positive about using AI tools to aid risk stratification of patients needing medication reviews but emphasised ease of use as important. Conclusions: These findings and those of an observational time-and-motion study will inform development of the DynAIRx prototype. HCPs seem receptive to such a tool.

INTRODUCTION:A population attributable fraction represents the relative change in disease prevalence that one might expect if a particular exposure was absent from the population. Often, one might be interested in what percentage of this effect acts through particular pathways. For instance, the effect of a sedentary lifestyle on stroke risk may be mediated by blood pressure, body mass index and several other intermediate risk factors. METHODS:We define a new metric, the pathway-specific population attributable fraction (PS-PAF), for mediating pathways of interest. PS-PAFs can be informally defined as the relative change in disease prevalence from an intervention that shifts the distribution of the mediator to its expected distribution if the risk factor were eliminated, and sometimes more simply as the relative change in disease prevalence if the mediating pathway were disabled. A potential outcomes framework is used for formal definitions and associated estimands are derived via relevant identifiability conditions. Computationally efficient estimators for PS-PAFs are derived based on these identifiability conditions. RESULTS:Calculations are demonstrated using INTERSTROKE-an international case-control study designed to quantify disease burden attributable to a number of known causal risk factors. The applied results suggest that mediating pathways from physical activity through blood pressure, blood lipids and body size explain comparable proportions of stroke disease burden, but a large proportion of the disease burden due to physical inactivity may be explained by alternative pathways. CONCLUSION:PS-PAFs measure disease burden attributable to differing mediating pathways and can generate insights into the dominant mechanisms by which a risk factor affects disease at a population level.

Background A well-functioning general practice sector that has a strong research component is recognised as a key foundation of any modern health system. General practitioners (GPs) are more likely to collaborate in research if they are part of an established research network. The primary aims of this study are to describe Ireland’s newest general practice-based research network and to analyse the perspectives of the network’s members on research engagement. Method A survey was sent to all GPs participating in the network in order to document practice characteristics so that this research network’s profile could be compared to other national profiles of Irish general practice. In depth interviews were then conducted and analysed thematically to explore the experiences and views of a selection of these GPs on research engagement. Results All 134 GPs responded to the survey. Practices have similar characteristics to the national profile in terms of location, size, computerisation, type of premises and out of hours arrangements. Twenty-two GPs were interviewed and the resulting data was categorised into subthemes and four related overarching themes: GPs described catalysts for research in their practices, the need for coherence in how research is understood in this context, systems failures, whereby the current health system design is prohibitive of GP participation and aspirations for a better future. Conclusion This study has demonstrated that the research network under examination is representative of current trends in Irish general practice. It has elucidated a better understanding of factors that need to be addressed in order to encourage more GPs to engage in the research process.

In 1995, Eide and Gefeller introduced the concepts of sequential and average attributable fractions as methods to partition the risk of disease among differing exposures. In particular, sequential attributable fractions are interpreted in terms of an incremental reduction in disease prevalence associated with removing a particular risk factor from the population, having removed other risk factors. Clearly, both concepts are causal entities, but are not usually estimated within a causal inference framework. We propose causal definitions of sequential and average attributable fractions using the potential outcomes framework. To estimate these quantities in practice, we model exposure-exposure and exposure-disease interrelationships using a causal Bayesian network, assuming no unmeasured latent confounders. This allows us to model not only the direct impact of removing a risk factor on disease, but also the indirect impact through the effect on the prevalence of causally downstream risk factors that are typically ignored when calculating sequential and average attributable fractions. The procedure for calculating sequential attributable fractions involves repeated applications of Pearl’s do-operator over a fitted Bayesian network, and simulation from the resulting joint probability distributions. The methods are applied to the INTERSTROKE study, which was designed to quantify disease burden attributable to the major risk factors for stroke. The resulting sequential and average attributable fractions are compared with results from a prior estimation approach which uses a single logistic model and which does not properly account for differing causal pathways. In contrast to estimation using a single regression model, the proposed approaches allow consistent estimation of sequential, joint and average attributable fractions under general causal structures.

Psychosocial intervention makes a vital contribution to dementia care. However, the lack of consensus about which outcome measures to use to evaluate effectiveness prevents meaningful comparisons between different studies and interventions. This study used an iterative collaborative, evidence-based approach to identify the best of currently available outcome measures for European psychosocial intervention research. This included consensus workshops, a web-based pan-European consultation and a systematic literature review and a rigorous evaluation against agreed criteria looking at utility across Europe, feasibility and psychometric properties. For people with dementia the measures covered the domains of quality of life, mood, global function, behaviour and daily living skills. Family carer domains included mood and burden, which incorporated coping with behaviour and quality of life. The only specific staff domain identified was morale, but this included satisfaction and coping with behaviour. In conclusion twenty-two measures across nine domains were recommended in order to improve the comparability of intervention studies in Europe. Areas were identified where improved outcome measures for psychosocial intervention research studies are required.