Scotland has more than five million people within a single health system, using a single central Picture Archiving and Commication System (PACS) for radiography data. This enabled the project team to build a research resource exceeding a petabyte of imaging from 2010 onwards, with open-source tooling to collate and de-identify images on demand. Image metadata, treatment and diagnostic records can be used to define large cohorts of patients then make the data available remotely to researchers in a Trusted Research Environment (TRE). Original identifiable images are stored in one of three isolated zones with controlled data transfer to protect patient privacy and prevent inadvertent disclosure. Linkage to other records is performed in the second zone on de-identified images using encrypted patient identifiers. Automated screening with optical character recognition and natural language processing was implemented to identify and redact personally identifiable information before release to researchers in the third zone. A recent extension to this system has provided an ongoing feed of routine imaging, which is securely shared with regional counterparts to ensure the minimal possible additional load is placed on clinical PACS resources and avoid duplicate requests. The project launched in April 2022 and since then a variety of research projects have already used this environment and data representing millions of pounds of funding, some using large cohorts with historical data up to 14 years old, re-assessing historical scans with the benefit of subsequent diagnosis to investigate possible early warning signs of conditions including dementia and pre-cancerous lung nodules.

Keywords: MRI, Imaging Sequences, Ultrasound, Mammography, CT, Angiography, Conventional Radiography Published under a CC BY 4.0 license. See also the commentary by Whitman and Vining in this issue.

Background It is unclear what effect the pattern of health-care use before admission to hospital with COVID-19 (index admission) has on the long-term outcomes for patients. We sought to describe mortality and emergency readmission to hospital after discharge following the index admission (index discharge), and to assess associations between these outcomes and patterns of health-care use before such admissions.Methods We did a national, retrospective, complete cohort study by extracting data from several national databases and linking the databases for all adult patients admitted to hospital in Scotland with COVID-19. We used latent class trajectory modelling to identify distinct clusters of patients on the basis of their emergency admissions to hospital in the 2 years before the index admission. The primary outcomes were mortality and emergency readmission up to 1 year after index admission. We used multivariable regression models to explore associations between these outcomes and patient demographics, vaccination status, level of care received in hospital, and previous emergency hospital use.Findings Between March 1, 2020, and Oct 25, 2021, 33 580 patients were admitted to hospital with COVID-19 in Scotland. Overall, the Kaplan-Meier estimate of mortality within 1 year of index admission was 296% (95% CI 291-302). The cumulative incidence of emergency hospital readmission within 30 days of index discharge was 144% (95% CI 140-148), with the number increasing to 356% (349-363) patients at 1 year. Among the 33 580 patients, we identified four distinct patterns of previous emergency hospital use: no admissions (n=18 772 [559%]); minimal admissions (n=12 057 [359%]); recently high admissions (n=1931 [58%]), and persistently high admissions (n=820 [24%]). Patients with recently or persistently high admissions were older, more multimorbid, and more likely to have hospital-acquired COVID-19 than patients with no or minimal admissions. People in the minimal, recently high, and persistently high admissions groups had an increased risk of mortality and hospital readmission compared with those in the no admissions group. Compared with the no admissions group, mortality was highest in the recently high admissions group (post-hospital mortality HR 270 [95% CI 235-281]; p<00001) and the risk of readmission was highest in the persistently high admissions group (323 [289-361]; p<00001).Interpretation Long-term mortality and readmission rates for patients hospitalised with COVID-19 were high; within 1 year, one in three patients had died and a third had been readmitted as an emergency. Patterns of hospital use before index admission were strongly predictive of mortality and readmission risk, independent of age, pre-existing comorbidities, and COVID-19 vaccination status. This increasingly precise identification of individuals at high risk of poor outcomes from COVID-19 will enable targeted support.

ObjectivesTo research and develop tools and methods for building cohorts of images linked to longitudinal healthcare records from real-world clinical images from the whole Scottish population. To provide this capability for the Scottish Medical Imaging service (provided by the Scottish National Safe Haven) to support research and AI projects. ApproachClinical images, especially when linked to routinely collected health data, are extremely useful for many types of research and AI development. However, finding and using clinical images for research data is challenging because: 1) Existing software used to search for images are designed for clinical care rather than research making it easy to find images for a particular patient. They are not designed to search for all images with particular characteristics e.g. slice thickness/scanning protocol/contrast agent/patient medication. 2) Reuse of clinical images for research requires de-identification, yet identifiable data can be present in many areas of the associated image file. ResultsThe PICTURES (InterdisciPlInary Collaboration for efficienT and effective Use of clinical images in big data health care RESearch) 5-year programme has developed an architecture for building cohorts of images based upon research criteria and providing these in a di-identifiable form within a Safe Haven environment. There are 3 zones: An identifiable zone which stores the raw image data and a MongoDB database which captures the metadata A de-identified zone which provides a database and tools for cohort building which do not require imaging data expertise Several Project Private Zones (PPZs) where researchers can install custom software and access the de-identified images for their project The architecture supports cohort building based upon features within pixel data, image metadata and linking to longitudinal health care records. ConclusionPICTURES is currently enhancing the cohort building user interface used by the National Safe Haven and supporting exemplar projects. The SMI service is live and accepting requests for more information. The software is open source and we welcome the use of the platform by other Safe Havens/research groups.

Aim To enable a world-leading research dataset of routinely collected clinical images linked to other routinely collected data from the whole Scottish national population. This includes more than 30 million different radiological examinations from a population of 5.4 million and >2 PB of data collected since 2010. Methods Scotland has a central archive of radiological data used to directly provide clinical care to patients. We have developed an architecture and platform to securely extract a copy of those data, link it to other clinical or social datasets, remove personal data to protect privacy, and make the resulting data available to researchers in a controlled Safe Haven environment. Results An extensive software platform has been developed to host, extract, and link data from cohorts to answer research questions. The platform has been tested on 5 different test cases and is currently being further enhanced to support 3 exemplar research projects. Conclusions The data available are from a range of radiological modalities and scanner types and were collected under different environmental conditions. These real-world, heterogenous data are valuable for training algorithms to support clinical decision making, especially for deep learning where large data volumes are required. The resource is now available for international research access. The platform and data can support new health research using artificial intelligence and machine learning technologies, as well as enabling discovery science.