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Background: The rise in opioid prescribing in primary care represents a significant international public health challenge, associated with increased psychosocial problems, hospitalisations, and mortality. We evaluated the effects of a comparative feedback intervention with persuasive messaging and action planning on opioid prescribing in primary care.Methods and Findings: A quasi-experimental controlled interrupted time series analysis used anonymised, aggregated practice data from electronic health records and prescribing data from publicly available sources. The study included 316 intervention and 130 control primary care practices in the Yorkshire and Humber region, UK, serving 2.2 million and 1 million residents, respectively. We observed the number of adult patients prescribed opioid medication by practice between July 2013 and December 2017. We excluded adults with coded cancer or drug dependency. The intervention, the Campaign to Reduce Opioid Prescribing (CROP), entailed bimonthly, comparative, and practice-individualised feedback reports to practices, with persuasive messaging and suggested actions over 1 year. Outcomes comprised the number of adults per 1,000 adults per month prescribed any opioid (main outcome), prescribed strong opioids, prescribed opioids in high-risk groups, prescribed other analgesics, and referred to musculoskeletal services. The number of adults prescribed any opioid rose pre-intervention in both intervention and control practices, by 0.18 (95% CI 0.11, 0.25) and 0.36 (95% CI 0.27, 0.46) per 1,000 adults per month, respectively. During the intervention period, prescribing per 1,000 adults fell in intervention practices (change -0.11; 95% CI -0.30, -0.08) and continued rising in control practices (change 0.54; 95% CI 0.29, 0.78), with a difference of -0.65 per 1,000 patients (95% CI -0.96, -0.34), corresponding to 15,000 fewer patients prescribed opioids. These trends continued post-intervention, although at slower rates. Prescribing of strong opioids, total opioid prescriptions, and prescribing in high-risk patient groups also generally fell. Prescribing of other analgesics fell whilst musculoskeletal referrals did not rise. Effects were attenuated after feedback ceased. Study limitations include being limited to 1 region in the UK, possible coding errors in routine data, being unable to fully account for concurrent interventions, and uncertainties over how general practices actually used the feedback reports and whether reductions in prescribing were always clinically appropriate.Conclusions: Repeated comparative feedback offers a promising and relatively efficient population-level approach to reduce opioid prescribing in primary care, including prescribing of strong opioids and prescribing in high-risk patient groups. Such feedback may also prompt clinicians to reconsider prescribing other medicines associated with chronic pain, without causing a rise in referrals to musculoskeletal clinics. Feedback may need to be sustained for maximum effect. [ABSTRACT FROM AUTHOR]

Background: The rise in opioid prescribing in primary care represents a significant public health challenge, associated with increased psychosocial problems, hospitalisations, and mortality. An evidence-based bimonthly feedback intervention to reduce opioid prescribing was developed and implemented, targeting 316 general practices in West Yorkshire over 1 year.Aim: To understand how general practice staff received and responded to the feedback intervention.Design and Setting: Qualitative process evaluation involving semi-structured interviews, guided by Normalisation Process Theory (NPT), of primary care healthcare professionals targeted by feedback.Method: Participants were purposively recruited according to baseline opioid prescribing levels and degree of change following feedback. Interview data were coded to NPT constructs, and thematically analysed.Results: Interviews were conducted with 21 staff from 20 practices. Reducing opioid prescribing was recognised as a priority. While high achievers had clear structures for quality improvement, feedback encouraged some less structured practices to embed changes. The non-prescriptive nature of the feedback reports allowed practices to develop strategies consistent with their own ways of working and existing resources. Practice concerns were allayed by the credibility of the reports and positive experiences of reducing opioid prescribing. The scale, frequency, and duration of feedback may have ensured a good overall level of practice population reach.Conclusion: The intervention engaged general practice staff in change by targeting an issue of emerging concern, and allowing adaption to different ways of working. Practice efforts to reduce opioid prescribing were reinforced by regular feedback, credible comparative data showing progress, and shared experiences of patient benefit. [ABSTRACT FROM AUTHOR]

Background: There are widely recognised variations in the delivery and outcomes of healthcare but an incomplete understanding of their causes. There is a growing interest in using routinely collected ‘big data’ in the evaluation of healthcare. We developed a set of evidence-based ‘high impact’ quality indicators (QIs) for primary care and examined variations in achievement of these indicators using routinely collected data in the United Kingdom (UK). Methods: Cross-sectional analysis of routinely collected, electronic primary care data from a sample of general practices in West Yorkshire, UK (n = 89). The QIs covered aspects of care (including processes and intermediate clinical outcomes) in relation to diabetes, hypertension, atrial fibrillation, myocardial infarction, chronic kidney disease (CKD) and ‘risky’ prescribing combinations. Regression models explored the impact of practice and patient characteristics. Clustering within practice was accounted for by including a random intercept for practice. Results: Median practice achievement of the QIs ranged from 43.2% (diabetes control) to 72.2% (blood pressure control in CKD). Considerable between-practice variation existed for all indicators: the difference between the highest and lowest performing practices was 26.3 percentage points for risky prescribing and 100 percentage points for anticoagulation in atrial fibrillation. Odds ratios associated with the random effects for practices emphasised this; there was a greater than ten-fold difference in the likelihood of achieving the hypertension indicator between the lowest and highest performing practices. Patient characteristics, in particular age, gender and comorbidity, were consistently but modestly associated with indicator achievement. Statistically significant practice characteristics were identified less frequently in adjusted models. Conclusions: Despite various policy and improvement initiatives, there are enduring inappropriate variations in the delivery of evidence-based care. Much of this variation is not explained by routinely collected patient or practice variables, and is likely to be attributable to differences in clinical and organisational behaviour. [ABSTRACT FROM AUTHOR]

Background: There are recognised gaps between evidence and practice in general practice, a setting posing particular implementation challenges. We earlier screened clinical guideline recommendations to derive a set of 'high-impact' indicators based upon criteria including potential for significant patient benefit, scope for improved practice and amenability to measurement using routinely collected data. Here, we explore health professionals' perceived determinants of adherence to these indicators, examining the degree to which determinants were indicator-specific or potentially generalisable across indicators.Methods: We interviewed 60 general practitioners, practice nurses and practice managers in West Yorkshire, the UK, about adherence to four indicators: avoidance of risky prescribing; treatment targets in type 2 diabetes; blood pressure targets in treated hypertension; and anticoagulation in atrial fibrillation. Interview questions drew upon the Theoretical Domains Framework (TDF). Data were analysed using framework analysis.Results: Professional role and identity and environmental context and resources featured prominently across all indicators whilst the importance of other domains, for example, beliefs about consequences, social influences and knowledge varied across indicators. We identified five meta-themes representing more general organisational and contextual factors common to all indicators.Conclusions: The TDF helped elicit a wide range of reported determinants of adherence to 'high-impact' indicators in primary care. It was more difficult to pinpoint which determinants, if targeted by an implementation strategy, would maximise change. The meta-themes broadly underline the need to align the design of interventions targeting general practices with higher level supports and broader contextual considerations. However, our findings suggest that it is feasible to develop interventions to promote the uptake of different evidence-based indicators which share common features whilst also including content-specific adaptations. [ABSTRACT FROM AUTHOR]

Background: Recruitment of representative samples in primary care research is essential to ensure high-quality, generalizable results. This is particularly important for research using routinely recorded patient data to examine the delivery of care. Yet little is known about how different recruitment strategies influence the characteristics of the practices included in research.Objective: We describe three approaches for recruiting practices to data-sharing studies, examining differences in recruitment levels and practice representativeness.Methods: We examined three studies that included varying populations of practices from West Yorkshire, UK. All used anonymized patient data to explore aspects of clinical practice. Recruitment strategies were 'opt-in', 'mixed opt-in and opt-out' and 'opt-out'. We compared aggregated practice data between recruited and not-recruited practices for practice list size, deprivation, chronic disease management, patient experience and rates of unplanned hospital admission.Results: The opt-out strategy had the highest recruitment (80%), followed by mixed (70%) and opt-in (58%). Practices opting-in were larger (median 7153 versus 4722 patients, P = 0.03) than practices that declined to opt-in. Practices recruited by mixed approach were larger (median 7091 versus 5857 patients, P = 0.04) and had differences in the clinical quality measure (58.4% versus 53.9% of diabetic patients with HbA1c ≤ 59 mmol/mol, P < 0.01). We found no differences between practices recruited and not recruited using the opt-out strategy for any demographic or quality of care measures.Conclusion: Opt-out recruitment appears to be a relatively efficient approach to ensuring participation of typical general practices. Researchers should, with appropriate ethical safeguards, consider opt-out recruitment of practices for studies involving anonymized patient data sharing. [ABSTRACT FROM AUTHOR]

Background: There are recognised gaps between evidence and practice in general practice, a setting which provides particular challenges for implementation. We earlier screened clinical guideline recommendations to derive a set of 'high impact' indicators based upon criteria including potential for significant patient benefit, scope for improved practice and amenability to measurement using routinely collected data. We aim to evaluate the effectiveness and cost-effectiveness of a multifaceted, adaptable intervention package to implement four targeted, high impact recommendations in general practice.Methods/design: The research programme Action to Support Practice Implement Research Evidence (ASPIRE) includes a pair of pragmatic cluster-randomised trials which use a balanced incomplete block design. Clusters are general practices in West Yorkshire, United Kingdom (UK), recruited using an 'opt-out' recruitment process. The intervention package adapted to each recommendation includes combinations of audit and feedback, educational outreach visits and computerised prompts with embedded behaviour change techniques selected on the basis of identified needs and barriers to change. In trial 1, practices are randomised to adapted interventions targeting either diabetes control or risky prescribing and those in trial 2 to adapted interventions targeting either blood pressure control in patients at risk of cardiovascular events or anticoagulation in atrial fibrillation. The respective primary endpoints comprise achievement of all recommended target levels of haemoglobin A1c (HbA1c), blood pressure and cholesterol in patients with type 2 diabetes, a composite indicator of risky prescribing, achievement of recommended blood pressure targets for specific patient groups and anticoagulation prescribing in patients with atrial fibrillation. We are also randomising practices to a fifth, non-intervention control group to further assess Hawthorne effects. Outcomes will be assessed using routinely collected data extracted 1 year after randomisation. Economic modelling will estimate intervention cost-effectiveness. A process evaluation involving eight non-trial practices will examine intervention delivery, mechanisms of action and unintended consequences.Discussion: ASPIRE will provide 'real-world' evidence about the effects, cost-effectiveness and delivery of adapted intervention packages targeting high impact recommendations. By implementing our adaptable intervention package across four distinct clinical topics, and using 'opt-out' recruitment, our findings will provide evidence of wider generalisability.Trial Registration: ISRCTN91989345. [ABSTRACT FROM AUTHOR]

Background: Quality indicators (QIs) are an important tool for improving clinical practice and are increasingly being developed from evidence-based guideline recommendations. We aimed to identify, select and apply guideline recommendations to develop a set of QIs to measure the implementation of evidence-based practice using routinely recorded clinical data in United Kingdom (UK) primary care. Methods: We reviewed existing national clinical guidelines and QIs and used a four-stage consensus development process to derive a set of 'high impact' QIs relevant to primary care based upon explicit prioritisation criteria. We then field tested the QIs using remotely extracted, anonymised patient records from 89 randomly sampled primary care practices in the Yorkshire region of England. Results: Out of 2365 recommendations and QIs originally reviewed, we derived a set of 18 QIs (5 single, 13 composites -- comprising 2-9 individual recommendations) for field testing. QIs predominantly addressed chronic disease management, in particular diabetes, cardiovascular and renal disease, and included both processes and outcomes of care. Field testing proved to be critical for further refinement and final selection. Conclusions: We have demonstrated a rigorous and transparent methodology to develop a set of high impact, evidence-based QIs for primary care from clinical guideline recommendations. While the development process was successful in developing a limited set of QIs, it remains challenging to derive robust new QIs from clinical guidelines in the absence of established systems for routine, structured recording of clinical care. [ABSTRACT FROM AUTHOR]

Background: Interpreting evaluations of complex interventions can be difficult without sufficient description of key intervention content. We aimed to develop an implementation package for primary care which could be delivered using typically available resources and could be adapted to target determinants of behaviour for each of four quality indicators: diabetes control, blood pressure control, anticoagulation for atrial fibrillation and risky prescribing. We describe the development and prospective verification of behaviour change techniques (BCTs) embedded within the adaptable implementation packages.Methods: We used an over-lapping multi-staged process. We identified evidence-based, candidate delivery mechanisms-mainly audit and feedback, educational outreach and computerised prompts and reminders. We drew upon interviews with primary care professionals using the Theoretical Domains Framework to explore likely determinants of adherence to quality indicators. We linked determinants to candidate BCTs. With input from stakeholder panels, we prioritised likely determinants and intervention content prior to piloting the implementation packages. Our content analysis assessed the extent to which embedded BCTs could be identified within the packages and compared them across the delivery mechanisms and four quality indicators.Results: Each implementation package included at least 27 out of 30 potentially applicable BCTs representing 15 of 16 BCT categories. Whilst 23 BCTs were shared across all four implementation packages (e.g. BCTs relating to feedback and comparing behaviour), some BCTs were unique to certain delivery mechanisms (e.g. 'graded tasks' and 'problem solving' for educational outreach). BCTs addressing the determinants 'environmental context' and 'social and professional roles' (e.g. 'restructuring the social and 'physical environment' and 'adding objects to the environment') were indicator specific. We found it challenging to operationalise BCTs targeting 'environmental context', 'social influences' and 'social and professional roles' within our chosen delivery mechanisms.Conclusion: We have demonstrated a transparent process for selecting, operationalising and verifying the BCT content in implementation packages adapted to target four quality indicators in primary care. There was considerable overlap in BCTs identified across the four indicators suggesting core BCTs can be embedded and verified within delivery mechanisms commonly available to primary care. Whilst feedback reports can include a wide range of BCTs, computerised prompts can deliver BCTs at the time of decision making, and educational outreach can allow for flexibility and individual tailoring in delivery. [ABSTRACT FROM AUTHOR]