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There is uncertainty about how best to diagnose asthma, especially in primary care where mis-diagnosis is common. To address this, we developed a clinical decision support system (CDSS) for asthma diagnosis in children and young people (aged 5-25 years). This study explored the feasibility and acceptability of the CDSS in UK primary care. We recruited general practices from England and Scotland. The CDSS was available for use during routine consultations for six months. We analysed CDSS usage and, toward the end of the study, undertook qualitative interviews with clinicians who had used the CDSS. Within the 10 practices who completed the study, the CDSS was used by 75 out of 94 clinicians. 11 clinicians from 8 practices were interviewed. The CDSS was acceptable to participants who particularly commented on the ease of use and auto-population of information from the patient record. Barriers to use included the inability to record findings directly into the patient notes and a sense that, whilst possibly useful for trainees and junior colleagues, the CDSS would not necessarily lead to a change in their own practice. The CDSS was generally well received by primary care clinicians, though participants felt it would be most useful for trainees and less experienced colleagues., Competing Interests: Competing interests: C.S. reports unconditional honoraria via a Limited company for consultancy and delivery of education from AstraZeneca, Bedfont, Boehringer Inglehieim, Cheisi, GSK, Teva, and Trudell; policy lead PCRS and Co-chair of Early and Accurate Diagnosis working group of Taskforce for Lung Health. D.P. has advisory board membership with AstraZeneca, Boehringer Ingelheim, Chiesi, GlaxoSmithKline, Novartis, Viatris, Teva Pharmaceuticals; consultancy agreements with AstraZeneca, Boehringer Ingelheim, Chiesi, GlaxoSmithKline, Novartis, Viatris, Teva Pharmaceuticals; grants and unrestricted funding for investigator-initiated studies (conducted through Observational and Pragmatic Research Institute Pte Ltd.) from AstraZeneca, Chiesi, Viatris, Novartis, Regeneron Pharmaceuticals, Sanofi Genzyme, and UK National Health Service; payment for lectures/speaking engagements from AstraZeneca, Boehringer Ingelheim, Chiesi, Cipla, Inside Practice, GlaxoSmithKline, Medscape, Viatris, Novartis, Regeneron Pharmaceuticals and Sanofi Genzyme, Teva Pharmaceuticals; payment for travel/accommodation/meeting expenses from AstraZeneca, Boehringer Ingelheim, Novartis, Medscape, Teva Pharmaceuticals.; owns 74% of the social enterprise Optimum Patient Care Ltd. (Australia and UK) and 92.61% of Observational and Pragmatic Research Institute Pte Ltd. (Singapore); 5% shareholding in Timestamp which develops adherence monitoring technology; is peer reviewer for grant committees of the UK Efficacy and Mechanism Evaluation programme, and Health Technology Assessment; and was an expert witness for GlaxoSmithKline. H.P. has received fees for non-promotional talks in sponsored symposia from Teva and Sandoz Pharmaceuticals. The remaining authors declare no competing interests., (© 2024. The Author(s).)

Remote digital postoperative wound monitoring provides an opportunity to strengthen postoperative community care and minimise the burden of surgical-site infection (SSI). This study aimed to pilot a remote digital postoperative wound monitoring service and evaluate the readiness for implementation in routine clinical practice. This was a single-arm pilot implementational study of remote digital postoperative wound monitoring across two tertiary care hospitals in the UK (IDEAL stage 2b, clinicaltrials.gov: NCT05069103). Adults undergoing abdominal surgery were recruited and received a smartphone-delivered wound assessment tool for 30-days postoperatively. Patients received 30-day postoperative follow-up, including the Telehealth Usability Questionnaire (TUQ). A thematic mixed-methods approach was used, according to the WHO framework for monitoring and evaluating digital health interventions. 200 patients were enroled, of whom 115 (57.5%) underwent emergency surgical procedures. Overall, the 30-day SSI rate was 16.5% (n = 33/200), with 72.7% (n = 24) diagnosed post-discharge. Usage of the intervention was 83.0% (n = 166/200), with subsequently 74.1% (n = 123/166) TUQ completion. There were no issues reported with feasibility of the technology, with the reliability (3.87, 95% CI: 3.73-4.00) and quality of the interface rated highly (4.18, 95%: 4.06-4.30). Patient acceptance was similarly high with regards to ease of use (4.51, 95% CI: 4.41-4.62), satisfaction (4.27, 95% CI: 4.13-4.41), and usefulness (4.07, 95% CI: 3.92-4.23). Despite the desire for more frequent and personalised interactions, the majority viewed the intervention as providing meaningful benefit over routine postoperative care. Remote digital postoperative wound monitoring successfully demonstrated readiness for implementation with regards to the technology, usability, and healthcare process improvement., (© 2023. The Author(s).)

Misdiagnosis (over-diagnosis and under-diagnosis) of asthma is common. Under-diagnosis can lead to avoidable morbidity and mortality, while over-diagnosis exposes patients to unnecessary side effects of treatment(s) and results in unnecessary healthcare expenditure. We explored diagnostic approaches and challenges faced by general practitioners (GPs) and practice nurses when making a diagnosis of asthma. Fifteen healthcare professionals (10 GPs and 5 nurses) of both sexes, different ages and varying years of experience who worked in NHS Lothian, Scotland were interviewed using in-depth, semi-structured qualitative interviews. Transcripts were analysed using a thematic approach. Clinical judgement of the probability of asthma was fundamental in the diagnostic process. Participants used heuristic approaches to assess the clinical probability of asthma and then decide what tests to do, selecting peak expiratory flow measurements, spirometry and/or a trial of treatment as appropriate for each patient. Challenges in the diagnostic process included time pressures, the variable nature of asthma, overlapping clinical features of asthma with other conditions such as respiratory viral illnesses in children and chronic obstructive pulmonary disease (COPD) in adults. To improve diagnostic decision-making, participants suggested regular educational opportunities and better diagnostic tools. In the future, standardising the clinical assessment made by healthcare practitioners should be supported by improved access to diagnostic services for additional investigation(s) and clarification of diagnostic uncertainty.

Diagnosing asthma is challenging. Misdiagnosis can lead to untreated symptoms, incorrect treatment and avoidable deaths. The best combination of clinical features and tests to achieve a diagnosis of asthma is unclear. As asthma is usually diagnosed in non-specialist settings, a clinical prediction model to aid the assessment of the probability of asthma in primary care may improve diagnostic accuracy. We aimed to identify and describe existing prediction models to support the diagnosis of asthma in children and adults in primary care. We searched Medline, Embase, CINAHL, TRIP and US National Guidelines Clearinghouse databases from 1 January 1990 to 23 November 17. We included prediction models designed for use in primary care or equivalent settings to aid the diagnostic decision-making of clinicians assessing patients with symptoms suggesting asthma. Two reviewers independently screened titles, abstracts and full texts for eligibility, extracted data and assessed risk of bias. From 13,798 records, 53 full-text articles were reviewed. We included seven modelling studies; all were at high risk of bias. Model performance varied, and the area under the receiving operating characteristic curve ranged from 0.61 to 0.82. Patient-reported wheeze, symptom variability and history of allergy or allergic rhinitis were associated with asthma. In conclusion, clinical prediction models may support the diagnosis of asthma in primary care, but existing models are at high risk of bias and thus unreliable for informing practice. Future studies should adhere to recognised standards, conduct model validation and include a broader range of clinical data to derive a prediction model of value for clinicians.

Substantial over-diagnosis and under-diagnosis of asthma in adults and children has recently been reported. As asthma is mostly diagnosed in non-specialist settings, a clinical prediction model (CPM) to aid the diagnosis of asthma in primary care may help improve diagnostic accuracy. We aim to systematically identify, describe, compare, and synthesise existing CPMs designed to support the diagnosis of asthma in children and adults presenting with symptoms suggestive of the disease, in primary care settings or equivalent populations. We will systematically search Medline, Embase and CINAHL from 1 January 1990 to present. Any CPM derived for use in a primary care population will be included. Equivalent populations in countries without a developed primary care service will also be included. The probability of asthma diagnosis will be the primary outcome. We will include CPMs designed for use in clinical practice to aid the diagnostic decision making of a healthcare professional during the assessment of an individual with symptoms suggestive of asthma. We will include derivation studies, and external model validation studies. Two reviewers will independently screen titles/abstracts and full texts for eligibility and extract data from included papers. The CHARMS checklist (or PROBAST if available) will be used to assess risk of bias within each study. Results will be summarised by narrative synthesis with meta-analyses completed if possible. This systematic review will provide comprehensive information about existing CPMs for the diagnosis of asthma in primary care and will inform the development of a future diagnostic model.

Despite an overwhelming evidence base, supported self-management of asthma is poorly implemented into routine practice. Strategies for implementation must address organisational routines, as well as provide resources for patients and training to improve professionals' skills. We aimed to explore the priority that primary care practices attach to asthma self-management, to describe their existing asthma management routines, and to generate innovative implementation strategies. We recruited 33 participants (23 general practitioners; seven nurses; three administrative staff) from 14 general practices. The 12 interviews and three focus groups were transcribed, coded and analysed thematically. Supported self-management was largely a nurse-led task within clinic-based annual reviews. Barriers included poor attendance at asthma clinics, lack of time, demarcation of roles, limited access to a range of tailored resources, and competing agendas in consultation, often due to multimorbidity. Suggestions for initiatives to improve the provision of supported self-management included emphasising the evidence for benefit (to influence prioritisation), improving teamwork (including team-based education), organisational strategies (including remote consulting) which need to fit within existing practice routines. Technology offers some potential solutions (e.g., improved templates, 'app'-based plans), but must be integrated with the practice information technology systems. Building on these insights, we will now develop a theoretically-based implementation strategy that will address patient, professional, and organisational buy-in, provide team-based education and offer a range of practical options and tools, which can be adapted and integrated within existing routines of individual practices.OVERCOMING THE ORGANISATIONAL BARRIERS TO IMPLEMENTING ASTHMA SELF-MANAGEMENT: Understanding the routines of primary care practices can suggest strategies to implement supported self-management in general practice. Supported self-management of asthma including provision of individual action plans improves patient health and reduces the burden on healthcare services, but is not well implemented in routine practice. As part of a large-scale programme to implement self-management into UK general practice, Hilary Pinnock at the University of Edinburgh and co-workers conducted interviews and focus groups with 33 participants from 14 general practices to explore the organisational routines that hinder or enable professionals to provide support asthma self-management. Poor attendance at asthma clinics, demarcation of roles, lack of time and limited access to tailored resources were identified as specific barriers. Improvements suggested included improved teamwork between doctors and other medical healthcare professionals, comprehensive training, and improvements to IT systems.