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Background: The National Institute of Health and Social Care Research (NIHR) Health Informatics Collaborative (HIC) for Hearing Health has been established in the UK to curate routinely collected hearing health data to address research questions. This study defines priority research areas, outlines its aims, governance structure and demonstrates how hearing health data have been integrated into a common data model using pure tone audiometry (PTA) as a case study., Methods: After identifying key research aims in hearing health, the governance structure for the NIHR HIC for Hearing Health is described. The Observational Medical Outcomes Partnership (OMOP) was chosen as our common data model to provide a case study example., Results: The NIHR HIC Hearing Health theme have developed a data architecture outlying the flow of data from all of the various siloed electronic patient record systems to allow the effective linkage of data from electronic patient record systems to research systems. Using PTAs as an example, OMOPification of hearing health data successfully collated a rich breadth of datapoints across multiple centres., Conclusion: This study identified priority research areas where routinely collected hearing health data could be useful. It demonstrates integration and standardisation of such data into a common data model from multiple centres. By describing the process of data sharing across the HIC, we hope to invite more centres to contribute and utilise data to address research questions in hearing health. This national initiative has the power to transform UK hearing research and hearing care using routinely collected clinical data., (© 2024. The Author(s).)

Objective: The COVID-19 pandemic was associated with a reduction in the incidence of myocardial infarction (MI) diagnosis, in part because patients were less likely to present to hospital. Whether changes in clinical decision making with respect to the investigation and management of patients with suspected MI also contributed to this phenomenon is unknown., Methods: Multicentre retrospective cohort study in three UK centres contributing data to the National Institute for Health Research Health Informatics Collaborative. Patients presenting to the Emergency Department (ED) of these centres between 1st January 2020 and 1st September 2020 were included. Three time epochs within this period were defined based on the course of the first wave of the COVID-19 pandemic: pre-pandemic (epoch 1), lockdown (epoch 2), post-lockdown (epoch 3)., Results: During the study period, 10,670 unique patients attended the ED with chest pain or dyspnoea, of whom 6,928 were admitted. Despite fewer total ED attendances in epoch 2, patient presentations with dyspnoea were increased ( p  < 0.001), with greater likelihood of troponin testing in both chest pain ( p  = 0.001) and dyspnoea ( p  < 0.001). There was a dramatic reduction in elective and emergency cardiac procedures (both p  < 0.001), and greater overall mortality of patients ( p  < 0.001), compared to the pre-pandemic period. Positive COVID-19 and/or troponin test results were associated with increased mortality ( p  < 0.001), though the temporal risk profile differed., Conclusions: The first wave of the COVID-19 pandemic was associated with significant changes not just in presentation, but also the investigation, management, and outcomes of patients presenting with suspected myocardial injury or MI., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (© 2024 Chammas, Yuan, Little, Roadknight, Varnai, Chang, Sze, Davies, Tsui, Salih, Glampson, Papadimitriou, Mulla, Woods, O'Gallagher, Shah, Williams, Asselbergs, Mayer, Lee, Herbert, Johnson, Grant, Curzen, Shah, Perera, Patel, Channon, Kaura, Mayet, Eyre, Squire, Kharbanda, Lewis and Wijesurendra.)

Background: To determine the impact of the COVID-19 pandemic on the population with chronic Hepatitis B virus (HBV) infection under hospital follow-up in the UK, we quantified the coverage and frequency of measurements of biomarkers used for routine surveillance (alanine transferase [ALT] and HBV viral load)., Methods: We used anonymized electronic health record data from the National Institute for Health Research (NIHR) Health Informatics Collaborative (HIC) pipeline representing five UK National Health Service (NHS) Trusts., Results: We report significant reductions in surveillance of both biomarkers during the pandemic compared to pre-COVID-19 years, both in terms of the proportion of patients who had ≥1 measurement annually, and the mean number of measurements per patient., Conclusions: These results demonstrate the real-time utility of HIC data in monitoring health-care provision, and support interventions to provide catch-up services to minimise the impact of the pandemic. Further investigation is required to determine whether these disruptions will be associated with increased rates of adverse chronic HBV outcomes., Competing Interests: Competing interests: GC reports personal fees from Gilead and Merck Sharp & Dohme outside the submitted work. BG reports other from Imperial National Institute for Health Research (NIHR) Biomedical Research Centres (BRC), during the conduct of the study. EN reports grants from ViiV healthcare, grants from GlaxoSmithKline (GSK), grants from Gilead, outside the submitted work. Other authors have no conflict of interest., (Copyright: © 2023 Campbell C et al.)

Objective: Colorectal cancer is a common cause of death and morbidity. A significant amount of data are routinely collected during patient treatment, but they are not generally available for research. The National Institute for Health Research Health Informatics Collaborative in the UK is developing infrastructure to enable routinely collected data to be used for collaborative, cross-centre research. This paper presents an overview of the process for collating colorectal cancer data and explores the potential of using this data source., Methods: Clinical data were collected from three pilot Trusts, standardised and collated. Not all data were collected in a readily extractable format for research. Natural language processing (NLP) was used to extract relevant information from pseudonymised imaging and histopathology reports. Combining data from many sources allowed reconstruction of longitudinal histories for each patient that could be presented graphically., Results: Three pilot Trusts submitted data, covering 12 903 patients with a diagnosis of colorectal cancer since 2012, with NLP implemented for 4150 patients. Timelines showing individual patient longitudinal history can be grouped into common treatment patterns, visually presenting clusters and outliers for analysis. Difficulties and gaps in data sources have been identified and addressed., Discussion: Algorithms for analysing routinely collected data from a wide range of sites and sources have been developed and refined to provide a rich data set that will be used to better understand the natural history, treatment variation and optimal management of colorectal cancer., Conclusion: The data set has great potential to facilitate research into colorectal cancer., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2022. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)