SummaryBackground-Metformin might reduce insulin requirement and improve glycaemia in patients with type 1 diabetes, but whether it has cardiovascular benefits is unknown. We aimed to investigate whether metformin treatment (added to titrated insulin therapy) reduced atherosclerosis, as measured by progression of common carotid artery intima-media thickness (cIMT), in adults with type 1 diabetes at increased risk for cardiovascular disease.

ObjectivesStudy objectives were to investigate the prevalence and causes of prescribing errors amongst foundation doctors (i.e. junior doctors in their first (F1) or second (F2) year of post-graduate training), describe their knowledge and experience of prescribing errors, and explore their self-efficacy (i.e. confidence) in prescribing.MethodA three-part mixed-methods design was used, comprising: prospective observational study; semi-structured interviews and cross-sectional survey. All doctors prescribing in eight purposively selected hospitals in Scotland participated. All foundation doctors throughout Scotland participated in the survey. The number of prescribing errors per patient, doctor, ward and hospital, perceived causes of errors and a measure of doctors' self-efficacy were established.Results4710 patient charts and 44,726 prescribed medicines were reviewed. There were 3364 errors, affecting 1700 (36.1%) charts (overall error rate: 7.5%; F1:7.4%; F2:8.6%; consultants:6.3%). Higher error rates were associated with : teaching hospitals (p<0.001), surgical (p = <0.001) or mixed wards (0.008) rather thanmedical ward, higher patient turnover wards (p<0.001), a greater number of prescribed medicines (p<0.001) and the months December and June (p<0.001). One hundred errors were discussed in 40 interviews. Error causation was multi-factorial; work environment and team factors were particularly noted. Of 548 completed questionnaires (national response rate of 35.4%), 508 (92.7% of respondents) reported errors, most of which (328 (64.6%) did not reach the patient. Pressure from other staff, workload and interruptions were cited as the main causes of errors. Foundation year 2 doctors reported greater confidence than year 1 doctors in deciding the most appropriate medication regimen.ConclusionsPrescribing errors are frequent and of complex causation. Foundation doctors made more errors than other doctors, but undertook the majority of prescribing, making them a key target for intervention. Contributing causes included work environment, team, task, individual and patient factors. Further work is needed to develop and assess interventions that address these.

BackgroundPrescribing errors are a major source of morbidity and mortality and represent a significant patient safety concern. Evidence suggests that trainee doctors are responsible for most prescribing errors. Understanding the factors that influence prescribing behavior may lead to effective interventions to reduce errors. Existing investigations of prescribing errors have been based on Human Error Theory but not on other relevant behavioral theories. The aim of this study was to apply a broad theory-based approach using the Theoretical Domains Framework (TDF) to investigate prescribing in the hospital context among a sample of trainee doctors.MethodSemistructured interviews, based on 12 theoretical domains, were conducted with 22 trainee doctors to explore views, opinions, and experiences of prescribing and prescribing errors. Content analysis was conducted, followed by applying relevance criteria and a novel stage of critical appraisal, to identify which theoretical domains could be targeted in interventions to improve prescribing.ResultsSeven theoretical domains met the criteria of relevance: “social professional role and identity,” “environmental context and resources,” “social influences,” “knowledge,” “skills,” “memory, attention, and decision making,” and “behavioral regulation.” From critical appraisal of the interview data, “beliefs about consequences” and “beliefs about capabilities” were also identified as potentially important domains. Interrelationships between domains were evident. Additionally, the data supported theoretical elaboration of the domain behavioral regulation.ConclusionsIn this investigation of hospital-based prescribing, participants’ attributions about causes of errors were used to identify domains that could be targeted in interventions to improve prescribing. In a departure from previous TDF practice, critical appraisal was used to identify additional domains that should also be targeted, despite participants’ perceptions that they were not relevant to prescribing errors. These were beliefs about consequences and beliefs about capabilities. Specifically, in the light of the documented high error rate, beliefs that prescribing errors were not likely to have consequences for patients and that trainee doctors are capable of prescribing without error should also be targeted in an intervention. This study is the first to suggest critical appraisal for domain identification and to use interview data to propose theoretical elaborations and interrelationships between domains.

Introduction

AimTo create and validate a simple clinical score to estimate the probability of admission at the time of triage.MethodsThis was a multicentre, retrospective, cross-sectional study of triage records for all unscheduled adult attendances in North Glasgow over 2 years. Clinical variables that had significant associations with admission on logistic regression were entered into a mixed-effects multiple logistic model. This provided weightings for the score, which was then simplified and tested on a separate validation group by receiving operator characteristic (ROC) analysis and goodness-of-fit tests.Results215 231 presentations were used for model derivation and 107 615 for validation. Variables in the final model showing clinically and statistically significant associations with admission were: triage category, age, National Early Warning Score (NEWS), arrival by ambulance, referral source and admission within the last year. The resulting 6-variable score showed excellent admission/discharge discrimination (area under ROC curve 0.8774, 95% CI 0.8752 to 0.8796). Higher scores also predicted early returns for those who were discharged: the odds of subsequent admission within 28 days doubled for every 7-point increase (log odds=+0.0933 per point, p<0.0001).ConclusionsThis simple, 6-variable score accurately estimates the probability of admission purely from triage information. Most patients could accurately be assigned to ‘admission likely’, ‘admission unlikely’, ‘admission very unlikely’ etc., by setting appropriate cut-offs. This could have uses in patient streaming, bed management and decision support. It also has the potential to control for demographics when comparing performance over time or between departments.