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Introduction: Persons after stroke experience limitations in activities of daily living even in the chronic phase. Many patients who had a stroke report mobility limitations with loss of social roles such as reduced gait-related participation. International best-practice recommendations for patients who had a stroke include interprofessional diagnostics as a core element for goal setting and intervention planning to improve social participation. Interprofessional diagnostics has not yet been implemented in Germany., Methods and Analysis: The aim is to develop an interprofessional diagnostic toolkit. This will be done in a multi-step process: first, an integrative review is conducted to synthesise the literature. Second, the experiences regarding diagnostics and walking outside is captured in focus groups with persons after stroke, relatives and health professionals. Third, a toolkit for the interprofessional diagnostic process of gait-related-participation will be developed based on the results of the previous steps in a future workshop. Fourth, the results of each work package will be integrated into the iterative development process for evaluation and implementation. All steps will be performed in accordance with the respective reporting guidelines., Ethics and Dissemination: This study has been approved by the ethics committee at the Ludwig Maximilians University (LMU), Germany and is overseen by LMU-Medical Institutional Review Board. Written informed consent will be obtained from all participants. Results will be disseminated through knowledge exchange with stakeholders and in peer-reviewed journal publications, scientific conferences, formal and informal reports. Stakeholders, patients and providers will be involved in most steps of the development from the beginning, which will facilitate later implementation at a larger scale., Trial Registration Number: German Register Clinical Trials/Deutsches Register Klinischer Studien DRKS00032389., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Introduction: General practitioners often criticise clinical trials for their poor applicability in primary care, which may at least partially explain why their engagement in primary care research remains limited. In order to enhance primary care research, the German government has funded six regional practice based research networks (PBRNs). Within the Bavarian PBRN (BayFoNet), two cluster-randomised pilot trials will be conducted. This paper presents the protocol of the process evaluation accompanying both trials, which aims to explore relevance, feasibility, acceptability and credibility of clinical research in primary care from the perspectives of BayFoNet researchers, general practitioners, and patients., Methods and Analysis: The BayFoNet will be established by recruiting general practices (GPs) as prospective research collaborators in two cluster randomised pilot trials. Research teams will provide training in good clinical practice, and support practices in patient recruitment, data collection and documentation. Our process evaluation explores barriers and facilitators in the set up of the BayFoNet PBRN and both cluster randomised pilot trials, under the application of the consolidated framework for implementation research and the theoretical domains framework. In a mixed-methods concept, we will use qualitative and quantitative approaches to evaluate both pilot cluster-randomised trials as well as the BayFoNet itself: focus groups with researchers, semi-structured interviews with general practitioners and questionnaires for patients participating in the pilot cluster-randomised trials at three different time points., Ethics and Dissemination: Research ethical approval for this study was granted by the Ethics Committee of the Medical Department, Ludwig-Maximilians-University Munich (AZ 21-1135). Results will be published in international peer-reviewed journals and summaries will be provided to the funders of the study as well as other PBRNs, GP teams and patients., Trial Registration Numbers: DRKS00028805, NCT05667207., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Introduction: People in need of care or support are severely affected by the COVID-19 pandemic. We lack valid data of long-term assessments. We present a register study to detect the physical and psychosocial impact of the COVID-19 pandemic on people in need of care or support in Bavaria, Germany. To describe the persons' life conditions comprehensively, we assess the perspectives and needs of the respective care teams too. Results will serve as evidence-based source to manage the pandemic and long-term prevention strategies., Methods and Analysis: The 'Bavarian ambulatory COVID-19 Monitor' is a multicentre registry including a purposive sample of up to 1000 patient-participants across three study sites in Bavaria. The study group consists of 600 people in need of care with a positive SARS-CoV-2 PCR test. Control group 1 comprises 200 people in need of care with a negative SARS-CoV-2 PCR test, while control group 2 comprises 200 people with a positive SARS-CoV-2 PCR test but are not in need of care. We assess the clinical course of infection, psychosocial aspects and care needs using validated measures. Follow-up is every 6 months for up to 3 years. Additionally, we assess up to 400 people linked to these patient-participants (caregivers, general practitioners (GPs)) for their health and needs. Main analyses are stratified by level of care I-V (I=minor/V=most severe impairment of independence), inpatient/outpatient care setting, sex and age. We use descriptive and inferential statistics to analyse cross-sectional data and changes over time. In qualitative interviews with 60 stakeholders (people in need of care, caregivers, GPs, politicians), we explore interface problems of different functional logics, of everyday and professional perspectives., Ethics and Dissemination: The Institutional Review Board of the University Hospital LMU Munich (#20-860) and the study sites (Universities of Wurzburg and Erlangen) approved the protocol. We disseminate the results by peer-reviewed publications, international conferences, governmental reports, etc., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Objectives: Our study aimed to assess the frequency of potentially inappropriate medication (PIM) use (according to three PIM lists) and to examine the association between PIM use and cognitive function among participants in the MultiCare cohort., Design: MultiCare is conducted as a longitudinal, multicentre, observational cohort study., Setting: The MultiCare study is located in eight different study centres in Germany., Participants: 3189 patients (59.3% female)., Primary and Secondary Outcome Measures: The study had a cross-sectional design using baseline data from the German MultiCare study. Prescribed and over-the-counter drugs were classified using FORTA (Fit fOR The Aged), PRISCUS (Latin for 'time-honoured') and EU(7)-PIM lists. A mixed-effect multivariate linear regression was performed to calculate the association between PIM use patients' cognitive function (measured with (LDST))., Results: Patients (3189) used 2152 FORTA PIM (mean 0.9±1.03 per patient), 936 PRISCUS PIM (0.3±0.58) and 4311 EU(7)-PIM (1.4±1.29). The most common FORTA PIM was phenprocoumon (13.8%); the most prevalent PRISCUS PIM was amitriptyline (2.8%); the most common EU(7)-PIM was omeprazole (14.0%). The lists rate PIM differently, with an overall overlap of 6.6%. Increasing use of PIM is significantly associated with reduced cognitive function that was detected with a correlation coefficient of -0.60 for FORTA PIM (p=0.002), -0.72 for PRISCUS PIM (p=0.025) and -0.44 for EU(7)-PIM (p=0.005)., Conclusion: We identified PIM using FORTA, PRISCUS and EU(7)-PIM lists differently and found that PIM use is associated with cognitive impairment according to LDST, whereby the FORTA list best explained cognitive decline for the German population. These findings are consistent with a negative impact of PIM use on multimorbid elderly patient outcomes., Trial Registration Number: ISRCTN89818205., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Objectives: The quality and safety of drug therapy in primary care are global concerns. The Pharmacist and Data-Driven Quality Improvement in Primary Care (P-DQIP) intervention aims to improve prescribing safety via an informatics tool, which facilitates proactive management of drug therapy risks (DTRs) by health-board employed pharmacists with established roles in general practices. Study objectives were (1) to identify and prioritise factors that could influence P-DQIP implementation from the perspective of practice pharmacists and (2) to identify potentially effective, acceptable and feasible strategies to support P-DQIP implementation., Design: Semistructured face-to-face interviews using a Theoretical Domains Framework informed topic guide. The framework method was used for data analysis. Identified implementation factors were prioritised for intervention based on research team consensus. Candidate intervention functions, behavioural change techniques (BCTs) and policies targeting these were identified from the behavioural change wheel. The final intervention content and modes of delivery were agreed with local senior pharmacists., Setting: General practices from three Health and Social Care Partnerships in National Health Service (NHS) Tayside., Participants: 14 NHS employed practice pharmacists., Results: Identified implementation factors were linked to thirteen theoretical domains (all except intentions) and six (skill, memory/attention/decision making, behavioural regulation, reinforcement, environmental context/resources, social influences) were prioritised. Three intervention functions (training, enablement and environmental restructuring) were relevant and were served by two policy categories (guidelines, communication/marketing) and eight BCTs (instructions on how to perform a behaviour, problem solving, action planning, prompt/cues, goal setting, self-monitoring, feedback and restructuring the social environment). Intervention components encompass an informatics tool, written educational material, a workshop for pharmacists, promotional activities and small financial incentives., Conclusions: This study explored pharmacists' perceptions of implementation factors which could influence management of DTRs in general practices to inform implementation of P-DQIP, which will initially be implemented in one Scottish health board with parallel evaluation of effectiveness and implementation., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Objectives: Healthcare is a complex system, so quality improvement will commonly lead to unintended consequences which are rarely evaluated. In previous qualitative work, we proposed a framework for considering the range of these potential consequences, in terms of their desirability and the extent to which they were predictable or expected during planning. This paper elaborates on the previous findings, using consensus methods to examine what consequences should be identified, why and how to prioritise, evaluate and interpret all identified consequences, and what stakeholders should be involved throughout this process., Design: Two-round modified Delphi consensus study., Setting and Participants: Both rounds were completed by 60 panellists from an academic, clinical or management background and experience in designing, implementing or evaluating quality improvement programmes., Results: Panellists agreed that trade-offs (expected undesirable consequences) and unpleasant surprises (unexpected undesirable consequences) should be actively considered. Measurement of harmful consequences for patients, and those with high workload or financial impact was prioritised, and their evaluation could also involve the use of qualitative methods. Clinical teams were agreed as important to involve at all stages, from identifying potential consequences, prioritising which of those to systematically evaluate, undertaking appropriate evaluation and interpreting the findings. Patients were necessary in identifying consequences, managers in identifying and prioritising, and improvement advisors in interpreting the data., Conclusion: There was consensus that a balanced approach to considering all the consequences of improvement can be achieved by carefully considering predictable trade-offs from the outset and deliberately pausing after implementation to identify any unexpected surprises and make an informed decision as to whether quantitative or qualitative evaluation is needed and feasible. Stakeholders' roles in in the process of identifying, prioritising, evaluating and interpreting potential consequences should be explicitly addressed within planning and revisited during and after implementation., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Objectives: The cluster randomised trial of the Data-driven Quality Improvement in Primary Care (DQIP) intervention showed that education, informatics and financial incentives for general medical practices to review patients with ongoing high-risk prescribing of non-steroidal anti-inflammatory drugs and antiplatelets reduced the primary end point of high-risk prescribing by 37%, where both ongoing and new high-risk prescribing were significantly reduced. This quantitative process evaluation examined practice factors associated with (1) participation in the DQIP trial, (2) review activity (extent and nature of documented reviews) and (3) practice level effectiveness (relative reductions in the primary end point)., Setting/participants: Invited practices recruited (n=33) and not recruited (n=32) to the DQIP trial in Scotland, UK., Outcome Measures: (1) Characteristics of recruited versus non-recruited practices. Associations of (2) practice characteristics and 'adoption' (self-reported implementation work done by practices) with documented review activity and (3) of practice characteristics, DQIP adoption and review activity with effectiveness., Results: (1) Recruited practices had lower performance in the quality and outcomes framework than those declining participation. (2) Not being an approved general practitioner training practice and higher self-reported adoption were significantly associated with higher review activity. (3) Effectiveness ranged from a relative increase in high-risk prescribing of 24.1% to a relative reduction of 77.2%. High-risk prescribing and DQIP adoption (but not documented review activity) were significantly associated with greater effectiveness in the final multivariate model, explaining 64.0% of variation in effectiveness., Conclusions: Intervention implementation and effectiveness of the DQIP intervention varied substantially between practices. Although the DQIP intervention primarily targeted review of ongoing high-risk prescribing, the finding that self-reported DQIP adoption was a stronger predictor of effectiveness than documented review activity supports that reducing initiation and/or re-initiation of high-risk prescribing is key to its effectiveness., Trial Registration Number: NCT01425502; Post-results., Competing Interests: Competing interests: None declared., (© Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.)

Objective: To explore how different practices responded to the Data-driven Quality Improvement in Primary Care (DQIP) intervention in terms of their adoption of the work, reorganisation to deliver the intended change in care to patients, and whether implementation was sustained over time., Design: Mixed-methods parallel process evaluation of a cluster trial, reporting the comparative case study of purposively selected practices., Setting: Ten (30%) primary care practices participating in the trial from Scotland, UK., Results: Four practices were sampled because they had large rapid reductions in targeted prescribing. They all had internal agreement that the topic mattered, made early plans to implement including assigning responsibility for work and regularly evaluated progress. However, how they internally organised the work varied. Six practices were sampled because they had initial implementation failure. Implementation failure occurred at different stages depending on practice context, including internal disagreement about whether the work was worthwhile, and intention but lack of capacity to implement or sustain implementation due to unfilled posts or sickness. Practice context was not fixed, and most practices with initial failed implementation adapted to deliver at least some elements. All interviewed participants valued the intervention because it was an innovative way to address on an important aspect of safety (although one of the non-interviewed general practitioners in one practice disagreed with this). Participants felt that reviewing existing prescribing did influence their future initiation of targeted drugs, but raised concerns about sustainability., Conclusions: Variation in implementation and effectiveness was associated with differences in how practices valued, engaged with and sustained the work required. Initial implementation failure varied with practice context, but was not static, with most practices at least partially implementing by the end of the trial. Practices organised their delivery of changed care to patients in ways which suited their context, emphasising the importance of flexibility in any future widespread implementation., Trial Registration Number: NCT01425502., (Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.)

Objectives: Primary care high-risk prescribing causes significant harm, but it is unclear if it is largely driven by individuals (a 'bad apple' problem) or by practices having higher or lower risk prescribing cultures (a 'spoiled barrel' problem). The study aimed to examine the extent of variation in high-risk prescribing between individual prescribers and between the practices they work in., Design, Setting and Participants: Multilevel logistic regression modelling of routine cross-sectional data from 38 Scottish general practices for 181,010 encounters between 398 general practitioners (GPs) and 26,539 patients particularly vulnerable to adverse drug events (ADEs) of non-steroidal anti-inflammatory drugs (NSAIDs) due to age, comorbidity or co-prescribing., Outcome Measure: Initiation of a new NSAID prescription in an encounter between GPs and eligible patients., Results: A new high-risk NSAID was initiated in 1953 encounters (1.1% of encounters, 7.4% of patients). Older patients, those with more vulnerabilities to NSAID ADEs and those with polypharmacy were less likely to have a high-risk NSAID initiated, consistent with GPs generally recognising the risk of NSAIDs in eligible patients. Male GPs were more likely to initiate a high-risk NSAID than female GPs (OR 1.73, 95% CI 1.39 to 2.16). After accounting for patient characteristics, 4.2% (95% CI 2.1 to 8.3) of the variation in high-risk NSAID prescribing was attributable to variation between practices, and 14.2% (95% CI 11.4 to 17.3) to variation between GPs. Three practices had statistically higher than average high-risk prescribing, but only 15.7% of GPs with higher than average high-risk prescribing and 18.5% of patients receiving such a prescription were in these practices., Conclusions: There was much more variation in high-risk prescribing between GPs than between practices, and only targeting practices with higher than average rates will miss most high-risk NSAID prescribing. Primary care prescribing safety improvement should ideally target all practices, but encourage practices to consider and act on variation between prescribers in the practice., (Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/)

Objectives: (A) To measure the extent to which different candidate outcome measures identified high-risk prescribing that is potentially changeable by the data-driven quality improvement in primary care (DQIP) intervention.(B) To explore the value of reviewing identified high-risk prescribing to clinicians.(C) To optimise the components of the DQIP intervention., Design: Mixed method study., Setting: General practices in two Scottish Health boards., Participants: 4 purposively sampled general practices of varying size and socioeconomic deprivation., Outcome Measures: Prescribing measures targeting (1) high-risk use of the non-steroidal anti-inflammatory drugs (NSAIDs) and antiplatelets; (2) 'Asthma control' and (3) 'Antithrombotics in atrial fibrillation (AF)'., Intervention: The prescribing measures were used to identify patients for review by general practices. The ability of the measures to identify potentially changeable high-risk prescribing was measured as the proportion of patients reviewed where practices identified a need for action. Field notes were recorded from meetings between researchers and staff and key staff participated in semistructured interviews exploring their experience of the piloted intervention processes., Results: Practices identified a need for action in 68%, 25% and 18% of patients reviewed for prescribing measures (1), (2) and (3), respectively. General practitioners valued being prompted to review patients, and perceived that (1) 'NSAID and antiplatelet' and (2) 'antithrombotics in AF' were the most important to act on. Barriers to initial and ongoing engagement and to sustaining improvements in prescribing were identified., Conclusions: 'NSAIDs and antiplatelets' measures were selected as the most suitable outcome measures for the DQIP trial, based on evidence of this prescribing being more easily changeable. In response to the barriers identified, the intervention was designed to include a financial incentive, additional ongoing feedback on progress and reprompting review of patients, whose high-risk prescribing was restarted after a decision to stop., Trial Registration Number: Clinicaltrials.gov NCT01425502.