Arina Tamborska

Background: The Secondary Care Medicines Data (SCMD) makes detailed data on medicines issued by National Health Service (NHS) hospital trusts in England openly available for the first time. These data provide an opportunity to understand medicine use patterns, highlight variation, and drive improvements in evidence-based practice, prescribing safety, and cost-effectiveness. However, this dataset requires extensive domain and technical knowledge to access, clean, transform, and link to other external sources before it can be used effectively. Objective: This study aimed to describe the development, implementation, and functionality of OpenPrescribing Hospitals and showcases example use cases. OpenPrescribing Hospitals is an open-access platform for analyzing hospital medicines use in England using the SCMD. Methods: We developed a data pipeline that imports the SCMD and links further medicinal product information from the dictionary of medicines and devices, organization information from the Organisation Reference Data, and medicines classifications and measurement information from the Anatomical Therapeutic Chemical and Defined Daily Dose index. Using these linked data, we calculated alternative measures of quantity to support appropriate comparison across the products reported. These data are presented through an open-access web platform with 3 main features: Submission History, Measures, and Analyse. We describe these features through illustrative use cases. We also summarize the web analytics collected between the platform launch in February 2025 and September 2026. Results: The platform supports open, accessible use of preprocessed hospital medicines data and supports analysis and comparisons across NHS trusts, Integrated Care Boards, NHS regions, ingredients, and therapeutic groups. The Submission History feature displays patterns that can indicate changes in the completeness and consistency of data submissions by NHS trusts. The Measures feature presents predefined measures and benchmarking based on national medicines recommendations. The Analyse feature supports detailed user-defined analyses. Illustrative examples show how incomplete submissions can affect apparent medicines use and how the platform can surface trends and variation in the issuing of selected medicines. As of September 2026, the platform attracts approximately 1000 unique visitors monthly and has been used to run more than 20,000 custom analyses. Conclusions: OpenPrescribing Hospitals reduces the technical barriers to analyzing hospital medicines data in England. The platform is openly accessible and accompanied by extensive documentation to support transparent and reproducible analyses. We encourage adoption of the platform by clinicians, academics, and policy advisors to support rapid local feedback against national medicines standards and to monitor the extent of and variation in the uptake of new medicines. This early report does not assess usability, clinical outcomes, prescribing changes, or cost savings. Further research is needed to evaluate data validity, user experience, and effects on medicine optimization.

NHS trusts in England vary in size, degree of specialisation and the severity of illnesses that they care for. Some trusts have emergency departments and offer acute care, while others focus on longer-stay rehabilitation, mental health or community-based follow-up care. Some provide a broad range of services, whereas others are specialised in a single clinical area. These factors determine the quantity and range of medicines a trust uses, so understanding the type of trust provides useful context when analysing medicines usage in secondary care. Here, we explain what types of trusts exist in more detail and how they can be identified.

Background: The migration status of the 9.8 million migrants living in England is not consistently recorded in primary care electronic health records (EHRs). Codelist approaches enable creation of cohorts of individuals who have had a predefined, optional migration-related code (for example, ‘refugee’) added to their EHR. Aim: To explore the use of migration-related Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) codes to inform future research using primary care data. Design & setting: We used our OpenCodeCounts tool to explore data published by NHS England on SNOMED CT code usage in English primary care. Method: We created migration-related codelists and described their use from 1 August 2011 to 31 July 2025. To understand code usage in the context of known information on migrants in England, we compared code usage with trends in migration-related statistics from the Home Office and the 2021 census. Results: There were 34.2 million uses of 1119 migration-related codes from 2011–2025. Migration-related coding increased over time, generally exceeding the increase observed for coding overall, with a sharp increase from 2020, particularly for country of birth and language. Language-related coding represented 65% of code usage, and where country of birth was recorded there was mixed agreement with the 2021 census. Coding of immigration legal statuses was low and overwhelmingly about asylum or refugee status. Conclusion: Utilising OpenCodeCounts, we demonstrate the feasibility of using migration-related SNOMED CT codelists within primary care EHRs and highlight some of the potential biases that cohorts created based on these codelists may have to inform future research.

BACKGROUND The Secondary Care Medicines Data (SCMD) makes detailed data on medicines issued by National Health Service (NHS) hospital trusts in England openly available for the first time. These data provide an opportunity to understand medicine use patterns, highlight variation, and drive improvements in evidence-based practice, prescribing safety, and cost-effectiveness. However, this dataset requires extensive domain and technical knowledge to access, clean, transform, and link to other external sources before it can be used effectively. OBJECTIVE This study aimed to describe the development, implementation, and functionality of OpenPrescribing Hospitals and showcases example use cases. OpenPrescribing Hospitals is an open-access platform for analyzing hospital medicines use in England using the SCMD. METHODS We developed a data pipeline that imports the SCMD and links further medicinal product information from the dictionary of medicines and devices, organization information from the Organisation Reference Data, and medicines classifications and measurement information from the Anatomical Therapeutic Chemical and Defined Daily Dose index. Using these linked data, we calculated alternative measures of quantity to support appropriate comparison across the products reported. These data are presented through an open-access web platform with 3 main features: Submission History, Measures, and Analyse. We describe these features through illustrative use cases. We also summarize the web analytics collected between the platform launch in February 2025 and September 2026. RESULTS The platform supports open, accessible use of preprocessed hospital medicines data and supports analysis and comparisons across NHS trusts, Integrated Care Boards, NHS regions, ingredients, and therapeutic groups. The Submission History feature displays patterns that can indicate changes in the completeness and consistency of data submissions by NHS trusts. The Measures feature presents predefined measures and benchmarking based on national medicines recommendations. The Analyse feature supports detailed user-defined analyses. Illustrative examples show how incomplete submissions can affect apparent medicines use and how the platform can surface trends and variation in the issuing of selected medicines. As of September 2026, the platform attracts approximately 1000 unique visitors monthly and has been used to run more than 20,000 custom analyses. CONCLUSIONS OpenPrescribing Hospitals reduces the technical barriers to analyzing hospital medicines data in England. The platform is openly accessible and accompanied by extensive documentation to support transparent and reproducible analyses. We encourage adoption of the platform by clinicians, academics, and policy advisors to support rapid local feedback against national medicines standards and to monitor the extent of and variation in the uptake of new medicines. This early report does not assess usability, clinical outcomes, prescribing changes, or cost savings. Further research is needed to evaluate data validity, user experience, and effects on medicine optimization. CLINICALTRIAL

Primary care prescribing data for general practices in England has been openly published since 2011. The OpenPrescribing platform provides an open, transparent and easy-to-use interface to this data. OpenPrescribing is widely used by NHS professionals, the public and by industry and has supported analyses to identify cost-saving opportunities and unsafe prescribing in primary care in England. The Secondary Care Medicines Data, published since July 2021 makes analogous data for medicines issued in NHS trusts in England available for the first time. As with primary care prescribing data, this dataset requires extensive domain and technical knowledge to access, clean, transform and link to other external sources before it can be used effectively. We have developed the OpenPrescribing Hospitals platform to provide an intuitive interface to the dataset, built on top of a robust data pipeline that removes the barriers to the dataset’s use. This platform is openly accessible, with transparent methodology and aims to deliver the same benefits as OpenPrescribing. Here we describe why the platform is needed, its key features, and highlight potential use cases. We encourage adoption of the platform by clinicians, academics and policy advisors to support rapid local feedback against national medicines standards and to monitor the extent and variation of uptake of new medicines.

Background Healthcare services in England experience increased pressure during winter months due to seasonal infectious diseases, increased multimorbidity, and fluctuating demand. Understanding how characteristics of general practices, and their registered patient case-mix contribute to secondary care use—particularly for Ambulatory Care Sensitive Conditions (ACSCs)—is essential for planning and resource allocation. Primary and secondary care activity also significantly changed during the COVID-19 pandemic, and not all activity-types have returned to pre-pandemic levels in the years since, making it critical to examine trends across both pre-and post-pandemic periods. Methods OpenSAFELY-TPP was used to access linked electronic health record data, covering approximately 2,600 general practices (about 40% of all practices in England) and 26 million registered patients in England using TPP SystmOne software (2018-2025). Our analysis focused on weekly and aggregated rates of A&E attendances and hospital admissions during the flu and winter months (October to February), comparing patterns before and after the COVID-19 pandemic. Practice-level exposures included consultation rate per capita, practice size, region, and patient case-mix variables (e.g. age, sex, ethnicity, deprivation, multimorbidity). Outcomes included weekly rates of A&E attendances, total hospital admissions, and admissions for ACSCs. Analyses We will summarise variation in practice characteristics, registered patient sociodemographics, case-mix, and service use across time periods. Associations between exposures and outcomes will be examined using generalised linear models, with additional subgroup analyses by age distribution. Sensitivity analyses will assess alternative flu season definitions and account for holiday and extreme weather. Discussion This high-level descriptive study will provide valuable insights into variation in secondary care use across general practices and identify practice-level and case-mix factors that may contribute to winter pressures. The inclusion of both pre- and post-pandemic data will provide essential benchmarking data for future health system planning and further understanding of how the general practice context and patient case-mix affects hospital demand.

Background: The migration status of the 9.8 million migrants living in England is not systematically recorded in primary care electronic health records (EHRs). Codelist approaches enable us to create cohorts of individuals who have had a predefined, optional migration-related code (e.g. “refugee”) added to their EHR. Aims: We aimed to explore the use of migration-related SNOMED CT codes to inform future research using primary care data. Design and Setting: We used our Open Code Counts tool and R package to explore data published by NHS England on SNOMED CT code usage in English primary care. Method: We created migration-related codelists and described their use from 1st August 2011 to 31st July 2024. We compared code usage to trends in migration-related statistics from the Home Office and the 2021 Census. Results: There were 29.1 million uses of 1,114 migration-related codes from 2011 to 2024. Migration-related coding increased over time, exceeding the increase observed for coding overall, with a sharp increase from 2020, particularly for country-of-birth and language. Language coding represented 71% of code usage and where country of birth was recorded, there was mixed agreement with the census estimates. Coding of immigration legal statuses was low and overwhelmingly about asylum or refugee status. Conclusion: Rapid assessment of migration-related coding using Open Code Counts highlights that a non-English first language is the most strongly represented characteristic in migrant cohorts in English primary care EHRs, which should be considered when interpreting future research findings. How this fits in: This study offers population-wide insights into migration-related SNOMED CT coding in primary care in England from 2011 to 2024 using our new open-source tool, Open Code Counts. Here, we show that a first language that is not English is the most commonly recorded aspect of migration, which must be considered when interpreting results from studies that use this methodology for researching migrants’ health in primary care EHRs. We also show that migration-related coding has increased, particularly after the start of the pandemic and for country-of-birth and language codes. The increased use of these code types offers the opportunity for GP practices to better identify patients requiring language support and potential screening and service needs based on their country of birth. Summary sentence: Language is the most commonly coded aspect of migration in primary care, which must be considered when interpreting primary care data studies.

COVID–19 has been associated with a wide range of ongoing symptoms following recovery from the acute SARS-CoV-2 infection. Around one in three people with COVID-19 develop neurological symptoms with many reporting neuropathic pain and associated symptoms, including paraesthesia, numbness, and dysesthesia. Whilst the pathophysiology of long COVID-19-associated neuropathic pain remains unclear, it is likely to be multifactorial. Early identification, exclusion of common alternative causes, and a biopsychosocial approach to the management of the symptoms can help in relieving the burden of disease and improving the quality of life for patients.