A synthetic dataset of patient appointments, referrals, and journeys to a fictional service in the North East of England. The code can be adjusted to incorporate any area on mainland Great Britain. NI or the islands can be integrated too, however the structure of postcode, GP and OSA public data is different, and data input handlers will need to be adjusted.

The behaviour of the patients (visiting their nearby GP followed by attending a

specialist clinic), appointments (clinic appointments within 7day-6weeks of the referral (gp appointment)), and facilities (one major facility taking the load, along with minor facilities) is meant to mirror the real data used under Pilot 2 of the Track & Know Project.

Real postcodes, from Royal Mail, are used to generate the appointment population, real facilities are used based on the British Lung Foundations study of Obstructive Sleep Apnoea, and real GP's are used based on public data from the NHS.

To support respiratory research, a synthetic asthma dataset was generated based on a real-world data, originally documenting 381 patients with physician-confirmed asthma who were admitted to secondary care at a single centre in 2019. The dataset is highly detailed, covering demographics, structured physiological data, medication records, and clinical outcomes. The synthetic version extends to 561 patients admitted over a year, offering insights into patient patterns, risk factors, and treatment strategies.

The dataset was created using the Synthetic Data Vault package, specifically employing the GAN synthesizer. Real data was first read and pre-processed, ensuring datetime columns were correctly parsed and identifiers were handled as strings. Metadata was defined to capture the schema, specifying field types and primary keys. This metadata guided the synthesizer in understanding the structure of the data. The GAN synthesizer was then fitted to the real data, learning the distributions and dependencies within. After fitting, the synthesizer generated synthetic data that mirrors the statistical properties and relationships of the original dataset.

Geography: The West Midlands has a population of 6 million & includes a diverse ethnic & socio-economic mix. UHB is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & > 120 ITU bed capacity. UHB runs a fully electronic healthcare record (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”.

Data set availability: Data access is available via the PIONEER Hub for projects which will benefit the public or patients. This can be by developing a new understanding of disease, by providing insights into how to improve care, or by developing new models, tools, treatments, or care processes. Data access can be provided to NHS, academic, commercial, policy and third sector organisations. Applications from SMEs are welcome. There is a single data access process, with public oversight provided by our public review committee, the Data Trust Committee. Contact pioneer@uhb.nhs.uk or visit www.pioneerdatahub.co.uk for more details.

Available supplementary data: Real world data. Matched controls; ambulance and community data. Unstructured data (images). We can provide the dataset in OMOP and other common data models and can provide real-world data upon request.

Available supplementary support: Analytics, model build, validation & refinement; A.I. support. Data partner support for ETL (extract, transform & load) processes. Bespoke and “off the shelf” Trusted Research Environment build and run. Consultancy with clinical, patient & end-user and purchaser access/ support. Support for regulatory requirements. Cohort discovery. Data-driven trials and “fast screen” services to assess population size.

OMOP dataset: Hospital COVID patients: severity, acuity, therapies, outcomes Dataset number 2.0

Coronavirus disease 2019 (COVID-19) was identified in January 2020. Currently, there have been more than 6 million cases & more than 1.5 million deaths worldwide. Some individuals experience severe manifestations of infection, including viral pneumonia, adult respiratory distress syndrome (ARDS) & death. There is a pressing need for tools to stratify patients, to identify those at greatest risk. Acuity scores are composite scores which help identify patients who are more unwell to support & prioritise clinical care. There are no validated acuity scores for COVID-19 & it is unclear whether standard tools are accurate enough to provide this support. This secondary care COVID OMOP dataset contains granular demographic, morbidity, serial acuity and outcome data to inform risk prediction tools in COVID-19.

PIONEER geography The West Midlands (WM) has a population of 5.9 million & includes a diverse ethnic & socio-economic mix. There is a higher than average percentage of minority ethnic groups. WM has a large number of elderly residents but is the youngest population in the UK. Each day >100,000 people are treated in hospital, see their GP or are cared for by the NHS. The West Midlands was one of the hardest hit regions for COVID admissions in both wave 1 & 2.

EHR. University Hospitals Birmingham NHS Foundation Trust (UHB) is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & 100 ITU beds. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”. UHB has cared for >5000 COVID admissions to date. This is a subset of data in OMOP format.

Scope: All COVID swab confirmed hospitalised patients to UHB from January – August 2020. The dataset includes highly granular patient demographics & co-morbidities taken from ICD-10 & SNOMED-CT codes. Serial, structured data pertaining to care process (timings, staff grades, specialty review, wards), presenting complaint, acuity, all physiology readings (pulse, blood pressure, respiratory rate, oxygen saturations), all blood results, microbiology, all prescribed & administered treatments (fluids, antibiotics, inotropes, vasopressors, organ support), all outcomes.

Available supplementary data: Health data preceding & following admission event. Matched “non-COVID” controls; ambulance, 111, 999 data, synthetic data. Further OMOP data available as an additional service.

Available supplementary support: Analytics, Model build, validation & refinement; A.I.; Data partner support for ETL (extract, transform & load) process, Clinical expertise, Patient & end-user access, Purchaser access, Regulatory requirements, Data-driven trials, “fast screen” services.

This highly granular synthetic dataset created as an asset for the HDR UK Medicines programme includes information on 680 cancer patients over a period of three years. Includes simulated patient-related data, such as demographics & co-morbidities extracted from ICD-10 and SNOMED-CT codes. Serial, structured data pertaining to acute care process (readmissions, survival), primary diagnosis, presenting complaint, physiology readings, blood results (infection, inflammatory markers) and acuity markers such as AVPU Scale, NEWS2 score, imaging reports, prescribed & administered treatments including fluids, blood products, procedures, information on outpatient admissions and survival outcomes following one-year post discharge.

The data was generated using a generative adversarial network model (CTGAN). A flat real data table was created by consolidating essential information from various key relational tables (medications, demographics). A synthetic version of the flat table was generated using a customized script based on the SDV package (N. Patki, 2016), that replicated the real distribution and logic relationships.

Geography: The West Midlands (WM) has a population of 6 million & includes a diverse ethnic & socio-economic mix. UHB is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & > 120 ITU bed capacity. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”.

Data set availability: Data access is available via the PIONEER Hub for projects which will benefit the public or patients. This can be by developing a new understanding of disease, by providing insights into how to improve care, or by developing new models, tools, treatments, or care processes. Data access can be provided to NHS, academic, commercial, policy and third sector organisations. Applications from SMEs are welcome. There is a single data access process, with public oversight provided by our public review committee, the Data Trust Committee. Contact pioneer@uhb.nhs.uk or visit www.pioneerdatahub.co.uk for more details.

Available supplementary data: Matched controls; ambulance and community data. Unstructured data (images). We can provide the dataset in OMOP and other common data models and provide the real-data via application.

Available supplementary support: Analytics, model build, validation & refinement; A.I. support. Data partner support for ETL (extract, transform & load) processes. Bespoke and “off the shelf” Trusted Research Environment (TRE) build and run. Consultancy with clinical, patient & end-user and purchaser access/ support. Support for regulatory requirements. Cohort discovery. Data-driven trials and “fast screen” services to assess population size.

This collection comprises of interview and focus group data gathered in 2024-2025 as part of a project aimed at investigating how synthetic data can support secure data access and improve research workflows, particularly from the perspective of data-owning organisations.

The interviews included 4 case studies of UK-based organisations who had piloted work generating and disseminating synthetic datasets, including the Ministry of Justice, NHS England, the project team working in partnership with the Department for Education, and Office for National Statistics. It also includes 2 focus groups with Trusted Repository Environment (TRE) representatives who had published or were considering publishing synthetic data.

The motivation for this collection stemmed from the growing interest in synthetic data as a tool to enhance access to sensitive data and reduce pressure on Trusted Research Environments (TREs). The study explored organisational engagement with two types of synthetic data: synthetic data generated from real data, and “data-free” synthetic data created using metadata only.

The aims of the case studies and focus groups were to assess current practices, explore motivations and barriers to adoption, understand cost and governance models, and gather perspectives on scaling and outsourcing synthetic data production. Conditional logic was used to tailor the survey to organisations actively producing, planning, or not engaging with synthetic data.

The interviews covered 5 key themes: organisational background; Infrastructure, operational costs, and resourcing; challenges of sharing synthetic data; benefits and use cases of synthetic data; and organisational policy and procedures.

The data offers exploratory insights into how UK organisations are approaching synthetic data in practice and can inform future research, infrastructure development, and policy guidance in this evolving area.

The findings have informed recommendations to support the responsible and efficient scaling of synthetic data production across sectors.

BackgroundThe National Health Service (NHS) aspires to be a world leader of Artificial Intelligence (AI) in healthcare, however, there are several barriers facing translation and implementation. A key enabler of AI within the NHS is the education and engagement of doctors, however evidence suggests that there is an overall lack of awareness of and engagement with AI.Research aimThis qualitative study explores the experiences and views of doctor developers working with AI within the NHS exploring; their role within medical AI discourse, their views on the implementation of AI more widely and how they consider the engagement of doctors with AI technologies may increase in the future.MethodsThis study involved eleven semi-structured, one-to-one interviews conducted with doctors working with AI in English healthcare. Data was subjected to thematic analysis.ResultsThe findings demonstrate that there is an unstructured pathway for doctors to enter the field of AI. The doctors described the various challenges they had experienced during their career, with many arising from the differing demands of operating in a commercial and technological environment. The perceived awareness and engagement among frontline doctors was low, with two prominent barriers being the hype surrounding AI and a lack of protected time. The engagement of doctors is vital for both the development and adoption of AI.ConclusionsAI offers big potential within the medical field but is still in its infancy. For the NHS to leverage the benefits of AI, it must educate and empower current and future doctors. This can be achieved through; informative education within the medical undergraduate curriculum, protecting time for current doctors to develop understanding and providing flexible opportunities for NHS doctors to explore this field.

BackgroundThe National Health Service (NHS) aspires to be a world leader of Artificial Intelligence (AI) in healthcare, however, there are several barriers facing translation and implementation. A key enabler of AI within the NHS is the education and engagement of doctors, however evidence suggests that there is an overall lack of awareness of and engagement with AI.Research aimThis qualitative study explores the experiences and views of doctor developers working with AI within the NHS exploring; their role within medical AI discourse, their views on the implementation of AI more widely and how they consider the engagement of doctors with AI technologies may increase in the future.MethodsThis study involved eleven semi-structured, one-to-one interviews conducted with doctors working with AI in English healthcare. Data was subjected to thematic analysis.ResultsThe findings demonstrate that there is an unstructured pathway for doctors to enter the field of AI. The doctors described the various challenges they had experienced during their career, with many arising from the differing demands of operating in a commercial and technological environment. The perceived awareness and engagement among frontline doctors was low, with two prominent barriers being the hype surrounding AI and a lack of protected time. The engagement of doctors is vital for both the development and adoption of AI.ConclusionsAI offers big potential within the medical field but is still in its infancy. For the NHS to leverage the benefits of AI, it must educate and empower current and future doctors. This can be achieved through; informative education within the medical undergraduate curriculum, protecting time for current doctors to develop understanding and providing flexible opportunities for NHS doctors to explore this field.

A highly granular dataset of 16,052 Same day emergency care (SDEC) spells with a focus on care pathways. It includes demography, co-morbidities, presenting symptoms, serial physiology, investigations, and outcomes.

Description (3000 Characters) – Current 2540 (with spaces)

Emergency care services face increasing pressure. NHS England (NHSE) has prioritised pathways for patients which avoid admission, including Same Day Emergency Care (SDEC) services. The NHS Long Term Plan recommends SDEC assessment for one third of medical attendances.

​Care quality indicators (CQI) include times from arrival to assessment by senior clinical teams. Performance measured against these CQI are impacted by other factors, such as delays in referrals, awaiting investigation results.​ 

PIONEER has curated a highly granular dataset of 16,052 Same day emergency care (SDEC) spells, including not only detailed patient level information, but data about the wider clinical environment on the day of admission.

Geography: The West Midlands (WM) has a population of 6 million & includes a diverse ethnic & socio-economic mix. UHB is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & > 120 ITU bed capacity. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”.

Data set availability: Data access is available via the PIONEER Hub for projects which will benefit the public or patients. This can be by developing a new understanding of disease, by providing insights into how to improve care, or by developing new models, tools, treatments, or care processes. Data access can be provided to NHS, academic, commercial, policy and third sector organisations. Applications from SMEs are welcome. There is a single data access process, with public oversight provided by our public review committee, the Data Trust Committee. Contact pioneer@uhb.nhs.uk or visit www.pioneerdatahub.co.uk for more details.

Available supplementary data: Matched controls; ambulance and community data. Unstructured data (images). We can provide the dataset in OMOP and other common data models and can build synthetic data to meet bespoke requirements.

Available supplementary support: Analytics, model build, validation & refinement; A.I. support. Data partner support for ETL (extract, transform & load) processes. Bespoke and “off the shelf” Trusted Research Environment (TRE) build and run. Consultancy with clinical, patient & end-user and purchaser access/ support. Support for regulatory requirements. Cohort discovery. Data-driven trials and “fast screen” services to assess population size.

Objectives: The medical community is in agreement that artificial intelligence (AI) will have a radical impact on patient care in the near future. The purpose of this study is to assess the awareness of AI technologies among health professionals and to investigate their perceptions toward AI applications in medicine.Design: A web-based Google Forms survey was distributed via the Royal Free London NHS Foundation Trust e-newsletter.Setting: Only staff working at the NHS Foundation Trust received an invitation to complete the online questionnaire.Participants: 98 healthcare professionals out of 7,538 (response rate 1.3%; CI 95%; margin of error 9.64%) completed the survey, including medical doctors, nurses, therapists, managers, and others.Primary outcome: To investigate the prior knowledge of health professionals on the subject of AI as well as their attitudes and worries about its current and future applications.Results: 64% of respondents reported never coming across applications of AI in their work and 87% did not know the difference between machine learning and deep learning, although 50% knew at least one of the two terms. Furthermore, only 5% stated using speech recognition or transcription applications on a daily basis, while 63% never utilize them. 80% of participants believed there may be serious privacy issues associated with the use of AI and 40% considered AI to be potentially even more dangerous than nuclear weapons. However, 79% also believed AI could be useful or extremely useful in their field of work and only 10% were worried AI will replace them at their job.Conclusions: Despite agreeing on the usefulness of AI in the medical field, most health professionals lack a full understanding of the principles of AI and are worried about potential consequences of its widespread use in clinical practice. The cooperation of healthcare workers is crucial for the integration of AI into clinical practice and without it the NHS may miss out on an exceptionally rewarding opportunity. This highlights the need for better education and clear regulatory frameworks.

Community Acquired Pneumonia (CAP) is the leading cause of infectious death and the third leading cause of death globally. Disease severity and outcomes are highly variable, dependent on host factors (such as age, smoking history, frailty and comorbidities), microbial factors (the causative organism) and what treatments are given. Clinical decision pathways are complex and despite guidelines, there is significant national variability in how guidelines are adhered to and patient outcomes.

For clinicians treating pneumonia in the hospital setting, care of these patients can be challenging. Key decisions include the type of antibiotics (oral or intravenous), the appropriate place of care (home, hospital or intensive care), and when it is appropriate to stop antibiotics. Decision support tools to help inform clinical management would be highly valuable to the clinical community.

This dataset is synthetic, formed from statistical modelling using real patient data, and represents a population with significant diversity in terms of patient demography, socio-economic status, CAP severity, treatments and outcomes. It can be used to develop code for deployment on real data, train data analysts and increase familiarity with this disease and its management.

PIONEER geography: The West Midlands (WM) has a population of 5.9 million & includes a diverse ethnic & socio-economic mix.

EHR. UHB is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & an expanded 250 ITU bed capacity during COVID. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”. This synthetic dataset has been modelled to reflect data collected from this EHR.

Scope: A synthetic dataset which has been statistically modelled on all hospitalised patients admitted to UHB with Community Acquired Pneumonia. The dataset includes highly granular patient demographics & co-morbidities taken from ICD-10 & SNOMED-CT codes. Serial, structured data pertaining to process of care including timings, admissions, escalation of care to ITU, discharge outcomes, physiology readings (heart rate, blood pressure, AVPU score and others), blood results and drug prescribing and administration.

Available supplementary data: Matched synthetic controls; ambulance, OMOP data, real patient CAP data. Available supplementary support: Analytics, Model build, validation & refinement; A.I.; Data partner support for ETL (extract, transform & load) process, Clinical expertise, Patient & end-user access, Purchaser access, Regulatory requirements, Data-driven trials, “fast screen” services.

The time spent on administrative tasks in the NHS, or national health service, is projected to have the most time saved with the use of AI. The NHS expected an ** percent reduction in time spent on administrative tasks, a considerable improvement in efficiency.

Psychiatric crisis care is under great pressure, with the number of psychiatric presentations to emergency departments increasing and inpatient wards operating with occupancy rates above recommended levels. Internationally, hospital-based short-stay crisis units (named Psychiatric Decision Units; (PDU) in the UK) have been introduced to address these challenges, but the current evidence for their effectiveness is limited. We estimated the effects of PDUs in four geographic locations in England, linked to three National Health Service (NHS) mental health trusts and six NHS acute hospital trusts. Using national data sets to create synthetic controls from areas without PDUs (following the generalised synthetic control method), we estimated trust-wide changes to the primary outcomes of psychiatric inpatient admissions and psychiatric presentations to emergency departments (ED), compared to the synthetic controls, alongside secondary outcomes. We used meta-analysis to robustly combine outcomes. We analysed NHS hospital activity data for adults aged between 18 and 75 years covering 24 months preceding and following the introduction of each PDU (November 2012 to January 2021). We found no significant impacts of PDUs on primary outcomes, except at Sheffield Teaching Hospitals NHS Foundation Trust with 1.5 fewer psychiatric presentations to ED per 10,000 trust population per month (relative difference: 24.9%, p = 0.034) than the synthetic control. We found mixed effects of the opening of PDUs on secondary outcomes. Meta-analyses indicated a significantly lower mean length of stay for psychiatric admissions (-6.4 days, p 

This wholly synthetic dataset is based on real anonymised primary care patient data extracted from the CPRD Aurum database. Researchers will not be able to access the real anonymised patient data extract which were used as the basis for the synthetic dataset generation to preserve patient privacy.

The dataset focuses on patients presenting to primary care with symptoms indicative of COVID-19 (confirmed/suspected COVID-19) and control patients with negative COVID-19 test results. The dataset includes data on sociodemographic and clinical risk factors. The ‘ground truth’ CPRD Aurum data extract used as the basis for generating this synthetic dataset included data till 13/04/2021 on patients with either suspected or confirmed COVID-19 as ascertained from the primary care record. The ground truth data extract was subject to data pre-processing and as such, the synthetic dataset based on this, does not reflect the structure of the source CPRD Aurum database.

The development of this synthetic dataset was funded by NHS X using the synthetic data generation and evaluation framework developed by CPRD under a grant from the Regulators’ Pioneer Fund launched by The Department for Business, Energy and Industrial Strategy (BEIS) and managed by Innovate UK. The methodology used to generate and evaluate this synthetic dataset is outlined in Wang et al. 2019 (DOI Bookmark:10.1109/CBMS.2019.00036).

Background:

A PIONEER synthetic dataset of 20,000 ethnically diverse hypertrophic cardiomyopathy patients created using CT-GAN generative AI. Data includes clinical & biological phenotyping, co-morbidities, investigations (ECG, ECHO), procedures & outcomes.

Well-created synthetic data establishes a governance risk-free environment for algorithm development & experimentation. This includes evaluating new treatment models, care management systems, clinical decision support, and more. Synthetic data is of particular use in rare diseases, where real data may be in short supply, or to replicate disease in less common patient demographics (e.g. ethnicities).

Familial hypertrophic cardiomyopathy (HCM) is a rare genetic condition characterised by thickening (hypertrophy) of the cardiac muscle, usually of the interventricular septum. Arrhythmias can be life threatening and HCM is associated with an increased risk of sudden death. Some affected individuals develop potentially fatal heart failure, which may require heart transplantation. Approximately 130,000 people have HCM in the UK, but there is a significant burden of undiagnosed disease and diagnostic delay.

Geography: The West Midlands (WM) has a population of 6 million & includes a diverse ethnic & socio-economic mix. UHB is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & > 120 ITU bed capacity. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”.

Data set availability: Data access is available via the PIONEER Hub for projects which will benefit the public or patients. This can be by developing a new understanding of disease, by providing insights into how to improve care, or by developing new models, tools, treatments, or care processes. Data access can be provided to NHS, academic, commercial, policy and third sector organisations. Applications from SMEs are welcome. There is a single data access process, with public oversight provided by our public review committee, the Data Trust Committee. Contact pioneer@uhb.nhs.uk or visit www.pioneerdatahub.co.uk for more details.

Available supplementary data: Matched controls; ambulance and community data. Unstructured data (images). We can provide the dataset in OMOP and other common data models and can provide real world data to meet bespoke requirements.

Available supplementary support: Analytics, model build, validation & refinement; A.I. support. Data partner support for ETL (extract, transform & load) processes. Bespoke and “off the shelf” Trusted Research Environment (TRE) build and run. Consultancy with clinical, patient & end-user and purchaser access/ support. Support for regulatory requirements. Cohort discovery. Data-driven trials and “fast screen” services to assess population size.

Universities had the highest use of generative AI within their profession and the highest awareness of AI usage in 2024, or ** and ** percent. This is unsurprising as the higher education fields have more capacity for AI usage compared to professions such as the NHS and Emergency Services. That is to say that AI helps in limited cases with driving ambulances, aside from road navigation, as compared to essay writing and document handling in the universities.

When it comes to mass spectrometry data analysis for identification of peptide pairs linked by N-hydroxysuccinimide (NHS) ester cross-linkers, search engines bifurcate in their setting of cross-linkable sites. Some restrict NHS ester cross-linkable sites to lysine (K) and protein N-terminus, referred to as K only for short, whereas others additionally include serine (S), threonine (T), and tyrosine (Y) by default. Here, by setting amino acids with chemically inert side chains such as glycine (G), valine (V), and leucine (L) as cross-linkable sites, which serves as a negative control, we show that software-identified STY-cross-links are only as reliable as GVL-cross-links. This is true across different NHS ester cross-linkers including DSS, DSSO, and DSBU, and across different search engines including MeroX, xiSearch, and pLink. Using a published data set originated from synthetic peptides, we demonstrate that STY-cross-links indeed have a high false discovery rate. Further analysis revealed that depending on the data and the search engine used to analyze the data, up to 65% of the STY-cross-links identified are actually K–K cross-links of the same peptide pairs, up to 61% are actually K-mono-links, and the rest tend to contain short peptides at high risk of false identification.

The size of the Insulin Drugs & Delivery Devices Market in UK market was valued at USD XXX Million in 2023 and is projected to reach USD XXX Million by 2032, with an expected CAGR of 1.90% during the forecast period.Insulin drugs and delivery device are major controlling agents in the management of diabetes, which is a chronic condition characterized by high blood sugar levels. Drugs with insulin are from either natural sources or synthetic origin and mimic the body's normal insulin hormone, that regulates blood sugar. Such drugs are administered to patients with type 1 diabetes and to patients with type 2 diabetes who require insulin therapy.There are also insulin delivery devices, intended to ensure proper administration of insulin and facilitate the process. Traditional options include syringes and pens. The most advanced devices that provide continuous release of insulin are insulin pumps, working close to natural body processes, thus being very beneficial for patients with type 1 diabetes, as they allow greater flexibility in the diet and tighter blood sugar control.Given this high prevalence of diabetes, the UK insulin drugs and delivery devices market becomes quite significant.The National Health Service provides accessible access to such medications and devices. Market drivers include an ageing population, an increase in diabetes incidence, and the technology advancements in the insulin delivery system. UK insulin drugs and delivery devices market will continue growing during those phases of changes in the healthcare landscape driven by the requirement for good diabetes management and improved patient outcomes. Recent developments include: May 2022: United Kingdom National Patient Safety Alert issued the alert about insulin leakage from the Roche Accu-Chek Insight insulin pump when paired with NovoRapid PumpCart insulin cartridges. UK regulator has taken safety action to reduce risks associated with Roche Accu-Chek Insight Insulin Pumps by issuing a national safety alert and outlining recommendations for patients., April 2022: NHS did the world's first test into a ‘sci-fi-like’ artificial pancreas. Almost 1,000 adults and children with type 1 diabetes have been given a potentially life-altering ‘artificial pancreas’ by the NHS in England as part of the first nationwide test into the effectiveness of this technology in the world.. Key drivers for this market are: , The Rise in Global Prevalence of Cases of Obesity due to Modern Sedentary Lifestyles; Rise in Awareness and Disposable Income in Developed Economies. Potential restraints include: , Highly Cost of Branded Products in Emerging Countries; Severe Adverse Associated with Medication Including Seizures, Suicidal Attempts and Even Death; Adoption of Traditional Yoga and Herbal Products. Notable trends are: Rising diabetes prevalence.

IntroductionThis study is a retrospective evaluation of the performance of deep learning models that were developed for the detection of COVID-19 from chest x-rays, undertaken with the goal of assessing the suitability of such systems as clinical decision support tools.MethodsModels were trained on the National COVID-19 Chest Imaging Database (NCCID), a UK-wide multi-centre dataset from 26 different NHS hospitals and evaluated on independent multi-national clinical datasets. The evaluation considers clinical and technical contributors to model error and potential model bias. Model predictions are examined for spurious feature correlations using techniques for explainable prediction.ResultsModels performed adequately on NHS populations, with performance comparable to radiologists, but generalised poorly to international populations. Models performed better in males than females, and performance varied across age groups. Alarmingly, models routinely failed when applied to complex clinical cases with confounding pathologies and when applied to radiologist defined “mild” cases.DiscussionThis comprehensive benchmarking study examines the pitfalls in current practices that have led to impractical model development. Key findings highlight the need for clinician involvement at all stages of model development, from data curation and label definition, to model evaluation, to ensure that all clinical factors and disease features are appropriately considered during model design. This is imperative to ensure automated approaches developed for disease detection are fit-for-purpose in a clinical setting.

The global prosthetic eye market, valued at $1924 million in 2024, is projected to experience robust growth, driven by a rising geriatric population susceptible to eye injuries and diseases, advancements in prosthetic eye technology leading to improved aesthetics and functionality, and increasing awareness about the availability of advanced prosthetic options. The 7.2% CAGR indicates a significant expansion over the forecast period (2025-2033). Market segmentation reveals a strong demand for both integrated and non-integrated implants across various applications, primarily hospitals and clinics. North America and Europe currently dominate the market, owing to established healthcare infrastructure and high disposable incomes. However, Asia Pacific is poised for significant growth due to increasing healthcare expenditure and a burgeoning population base. Technological advancements, such as the development of more lifelike and customizable prosthetic eyes, along with improved surgical techniques, will further fuel market expansion. Challenges remain, including high costs associated with advanced prosthetic eyes, limited awareness in developing regions, and the potential for complications during surgery. The competitive landscape features a mix of established players and emerging companies focused on innovation and technological advancements. The market's growth is expected to be further influenced by factors such as government initiatives aimed at improving access to prosthetic care, rising insurance coverage for prosthetic eye procedures, and collaborations between manufacturers and healthcare providers to improve patient outcomes. The continuous development of biocompatible materials and personalized prosthetic solutions will cater to the growing demand for aesthetically pleasing and functional artificial eyes. While geographical variations exist, the overall trend suggests a steadily increasing market size over the next decade, driven by a confluence of demographic, technological, and economic factors. The integration of advanced imaging and 3D printing technologies is likely to revolutionize the prosthetic eye manufacturing process, leading to a wider adoption of high-quality, customized solutions.

Background Acute compartment syndrome (ACS) is an emergency orthopaedic condition wherein a rapid rise in compartmental pressure compromises blood perfusion to the tissues leading to ischaemia and muscle necrosis. This serious condition is often misdiagnosed or associated with significant diagnostic delay, and can lead to limb amputations and death.

The most common causes of ACS are high impact trauma, especially fractures of the lower limbs which account for 40% of ACS cases. ACS is a challenge to diagnose and treat effectively, with differing clinical thresholds being utilised which can result in unnecessary osteotomy. The highly granular synthetic data for over 900 patients with ACS provide the following key parameters to support critical research into this condition:

1. Patient data (injury type, location, age, sex, pain levels, pre-injury status and comorbidities)
2. Physiological parameters (intracompartmental pressure, pH, tissue oxygenation, compartment hardness)
3. Muscle biomarkers (creatine kinase, myoglobin, lactate dehydrogenase)
4. Blood vessel damage biomarkers (glycocalyx shedding markers, endothelial permeability markers)

PIONEER geography: The West Midlands (WM) has a population of 5.9 million & includes a diverse ethnic & socio-economic mix. UHB is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & an expanded 250 ITU bed capacity during COVID. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”.

Scope: Enabling data-driven research and machine learning models towards improving the diagnosis of Acute compartment syndrome. Longitudinal & individually linked, so that the preceding & subsequent health journey can be mapped & healthcare utilisation prior to & after admission understood. The dataset includes highly granular patient demographics, physiological parameters, muscle biomarkers, blood biomarkers and co-morbidities taken from ICD-10 & SNOMED-CT codes. Serial, structured data pertaining to process of care (timings and admissions), presenting complaint, lab analysis results (eGFR, troponin, CRP, INR, ABG glucose), systolic and diastolic blood pressures, procedures and surgery details.

Available supplementary data: ACS cohort, Matched controls; ambulance, OMOP data. Available supplementary support: Analytics, Model build, validation & refinement; A.I.; Data partner support for ETL (extract, transform & load) process, Clinical expertise, Patient & end-user access, Purchaser access, Regulatory requirements, Data-driven trials, “fast screen” services.