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The use of primary care databases has been integral in pharmacoepidemiological studies and pharmacovigilance. Primary care databases derive from electronic health records and offer a comprehensive description of aggregate patient data, from demography to medication history, and good sample sizes. Studies using these databases improve our understanding of prescribing characteristics and associated risk factors to facilitate better patient care, but there are limitations. We describe eight key scenarios where study data outcomes can be affected by absent prescriptions in UK primary care databases: (1) out-of-hours, urgent care and acute care prescriptions; (2) specialist-only prescriptions; (3) alternative community prescribing, such as pharmacy, family planning clinic or sexual health clinic medication prescriptions; (4) newly licensed medication prescriptions; (5) medications that do not require prescriptions; (6) hospital inpatient and outpatient prescriptions; (7) handwritten prescriptions; and (8) private pharmacy and private doctor prescriptions. The significance of each scenario is dependent on the type of medication under investigation, nature of the study and expected outcome measures. We recommend that all researchers using primary care databases be aware of the potential for missing prescribing data and be sensitive to how this can vary substantially between items, drug classes, patient groups and over time. Close liaison with practising primary care clinicians in the UK is often essential to ensure awareness of nuances in clinical practice. [ABSTRACT FROM AUTHOR]

Background: Quality improvement (QI) initiatives are increasingly used to improve the quality of care and reduce prescribing errors. The Royal College of General Practitioners (RCGP) and Clinical Practice Research Datalink (CPRD) QI initiative uses routinely collected electronic primary care data to provide bespoke practice-level reports on prescribing safety. The aim of this study was to explore how the QI reports were used, barriers and facilitators to use, long-term culture change and perceived impact on patient care and practices systems as a result of receiving the reports.Methods: A qualitative study using purposive sampling of practices contributing to the CPRD, semi-structured interviews and inductive thematic analysis. We interviewed general practitioners, pharmacists, practice managers and research nurses.Results: We conducted 18 interviews, and organised themes summarising the use of QI reports in practice: receiving the report, facilitators and barriers to acting upon the reports, acting upon the report, and how the reports contribute to a quality culture. Effective dissemination of reports, and a positive attitude to audit and the perceived relevance of the clinical topic facilitated use. Lack of time and failure to see or act upon the reports meant they were not used. Factors influencing use of the reports included the structure of the report, ease of identifying cases, and perceptions about coding accuracy. GPs and pharmacists used the reports to conduct case reviews and directly contact patients to discuss unsafe prescribing and patient medication preferences. Finally, the reports contributed to the development of a quality culture within practices through promoting audit activity and acting as a reminder of good prescribing behaviours, promoting future patient safety initiatives, contributing to continuing professional development and improving local networks.Conclusions: This study found the reports facilitated individual case review leading to an enhanced sense of quality culture in practices where they were utilised. Our findings demonstrate that the reports were generally considered useful and have been used to support patient safety and clinical practice in specific cases. [ABSTRACT FROM AUTHOR]

Electronic healthcare record data have been used to study risk factors of disease, treatment effectiveness and safety, and to inform healthcare service planning. There has been increasing interest in utilizing these data for new purposes such as for machine learning to develop predictive algorithms to aid diagnostic and treatment decisions. Synthetic data could potentially be an alternative to real‐world data for these purposes as well as reveal any biases in the data used for algorithm development. This article discusses the key requirements of synthetic data for multiple purposes and proposes an approach to generate and evaluate synthetic data focused on, but not limited to, cross‐sectional healthcare data. To our knowledge, this is the first article to propose a framework to generate and evaluate synthetic healthcare data with the aim of simultaneously preserving the complexities of ground truth data in the synthetic data while also ensuring privacy. We include findings and new insights from synthetic datasets modeled on both the Indian liver patient dataset and UK primary care dataset to demonstrate the application of this framework under different scenarios. [ABSTRACT FROM AUTHOR]

This study measured the exposure to different categories of medicinal products discussed by the European Union (EU) Pharmacovigilance Risk Assessment Committee from September to November 2018 in four electronic primary care health databases: IQVIA Medical Research Data‐UK, IQVIA Medical Research Data‐France, IQVIA Medical Research Data‐Germany, and Clinical Practice Research Datalink Aurum, in the entire lifespan of each database until August 31, 2018. The assessment of 83 centrally authorized products and 45 nationally authorized products showed that coverage was better for products marketed for longer duration and worse for orphan drugs. The ability to detect associations against hypothetical comparators was better for more common events and for larger effect sizes. Coverage of advanced therapies was worse for those typically administered in a specialized rather than primary care setting. This study shows that to enable better informed regulatory decisions there is a need to access complementary data sources, particularly capturing secondary care prescribing. [ABSTRACT FROM AUTHOR]

This paper describes the evaluation of a pilot e-supervision programme, with a focus on feasibility. The findings suggest that e-supervision in mental health using the World Health Organization (WHO) Mental Health Gap Action Programme (mhGAP) Intervention Guide and case-based discussions is valued by participants and can improve the knowledge, confidence and beliefs of primary care doctors in low- and middle-income countries. [ABSTRACT FROM AUTHOR]

Objectives Benzodiazepines have been associated with an increased incidence of infections, and mortality from sepsis, in the critically ill. Here, we determined the effect of community use of benzodiazepines on the occurrence of, and mortality following, pneumonia. Methods A nested case-control study using 29 697 controls and 4964 cases of community-acquired pneumonia (CAP) from The Health Improvement Network, a UK primary care patient database (2001-2002), investigated the association between benzodiazepines and pneumonia occurrence using conditional logistic regression. Cox regression was then used to determine the impact of benzodiazepines on mortality in the 4964 cases of CAP. Results are presented as adjusted OR, adjusted HR and 95% CI. Results Exposure to benzodiazepines was associated with an increased risk of pneumonia (OR 1.54, 95% CI 1.42 to 1.67). Individually diazepam, lorazepam and temazepam, but not chlordiazepoxide, were associated with an increased incidence of CAP. As a class, benzodiazepines were associated with increased 30-day (HR 1.22 (95% CI 1.06 to 1.39)) and long-term mortality (HR 1.32 (95% CI 1.19 to 1.47)) in patients with a prior diagnosis of CAP. Individually diazepam, chlordiazepoxide, lorazepam and temazepam affected long-term mortality in these patients. Conclusions Benzodiazepines were associated with an increased risk of, and mortality from, CAP. These hypothesis generating data suggest further research is required into the immune safety profile of benzodiazepines. [ABSTRACT FROM AUTHOR]

Background: Pneumonia is a common diagnosis in general practice in the United Kingdom and yet there is little known about the short- and long-term prognosis of people with a diagnosis of pneumonia in general practice. We investigated the short- and long-term survival of people with pneumonia diagnosed in general practice as compared to the general population for all ages. Methods: This was a general population-based cohort study. Data was obtained from a comprehensive general practice database called The Health Improvement Network (THIN) database which has computerized medical records from 300 general practice surgeries in the United Kingdom. We used Cox regression for our analyses. Results: For pneumonia cases the 30-day mortality was 18.5% and the 3-year mortality was 30.8%. The equivalent figures for the general population controls were 0.4% and 10.3% respectively. The adjusted hazard ratio (HR) for all-cause mortality (for total follow-up time) in pneumonia cases vs. general population was 4.64 (95% CI 4.35–4.95). For the first 30 days the risk of mortality in cases was 46 times more (adj. HR 45.90, 95% CI 36.80–55.20). Even in the period of follow-up 91 days after diagnosis cases were almost 20% more likely to die compared to general population (adj. HR 1.19, 95% CI 1.08–1.31). Conclusion: People in general practice who have a diagnosis of pneumonia have a markedly increased mortality in the short-term but some increase in mortality persists during longer-term follow-up. [ABSTRACT FROM PUBLISHER]

There is a growing demand for the uptake of modern artificial intelligence technologies within healthcare systems. Many of these technologies exploit historical patient health data to build powerful predictive models that can be used to improve diagnosis and understanding of disease. However, there are many issues concerning patient privacy that need to be accounted for in order to enable this data to be better harnessed by all sectors. One approach that could offer a method of circumventing privacy issues is the creation of realistic synthetic data sets that capture as many of the complexities of the original data set (distributions, non-linear relationships, and noise) but that does not actually include any real patient data. While previous research has explored models for generating synthetic data sets, here we explore the integration of resampling, probabilistic graphical modelling, latent variable identification, and outlier analysis for producing realistic synthetic data based on UK primary care patient data. In particular, we focus on handling missingness, complex interactions between variables, and the resulting sensitivity analysis statistics from machine learning classifiers, while quantifying the risks of patient re-identification from synthetic datapoints. We show that, through our approach of integrating outlier analysis with graphical modelling and resampling, we can achieve synthetic data sets that are not significantly different from original ground truth data in terms of feature distributions, feature dependencies, and sensitivity analysis statistics when inferring machine learning classifiers. What is more, the risk of generating synthetic data that is identical or very similar to real patients is shown to be low. [ABSTRACT FROM AUTHOR]

Background: Quality improvement (QI) is a priority for general practice, and GPs are expected to participate in and provide evidence of QI activity. There is growing interest in harnessing the potential of electronic health records (EHR) to improve patient care by supporting practices to find cases that could benefit from a medicines review. Aim: To develop scalable and reproducible prescribing safety reports using patient-level EHR data. Design and setting: UK general practices that contribute de-identified patient data to the Clinical Practice Research Datalink (CPRD). Method: A scoping phase used stakeholder consultations to identify primary care QI needs and potential indicators. QI reports containing real data were sent to 12 pilot practices that used Vision GP software and had expressed interest. The scale-up phase involved automating production and distribution of reports to all contributing practices that used both Vision and EMIS software systems. Benchmarking reports with patient-level case review lists for two prescribing safety indicators were sent to 457 practices in December 2017 following the initial scale-up (Figure 2). Results: Two indicators were selected from the Royal College of General Practitioners Patient Safety Toolkit following stakeholder consultations for the pilot phase involving 12 GP practices. Pilot phase interviews showed that reports were used to review individual patient care, implement wider QI actions in the practice, and for appraisal and revalidation. Conclusion: Electronic health record data can be used to provide standardised, reproducible reports that can be delivered at scale with minimal resource requirements. These can be used in a national QI initiative that impacts directly on patient care. [ABSTRACT FROM AUTHOR]