{"current_page":1,"data":[{"id":67,"created_at":"2024-10-08T12:01:49.000000Z","updated_at":"2026-07-28T14:52:43.000000Z","deleted_at":null,"active_date":"2026-03-31 10:13:00","non_gateway_datasets":[],"non_gateway_applicants":["Reecha Sofat"],"funders_and_sponsors":[],"other_approval_committees":[],"gateway_outputs_tools":[],"gateway_outputs_papers":[],"non_gateway_outputs":["https:\/\/doi.org\/10.1101\/2021.12.31.21268587","https:\/\/github.com\/BHFDSC\/CCU014_01","https:\/\/dx.doi.org\/10.2139\/ssrn.4544777","https:\/\/github.com\/BHFDSC\/CCU014_03","https:\/\/doi.org\/10.1038\/s41591-022-02158-7","https:\/\/doi.org\/10.1136\/bmjmed-2023-000760","https:\/\/dx.doi.org\/10.2139\/ssrn.4544777","https:\/\/github.com\/BHFDSC\/CCU014","https:\/\/doi.org\/10.1038\/s44360-026-00134-w"],"project_title":"CCU014: Assessing the impact of COVID-19 on clinical pathways using a medicines approach","project_id_text":"CCU014","organisation_name":"University of Liverpool","organisation_sector":"Academic Institute","lay_summary":"Cardiovascular disease (CVD, including heart attacks and strokes) remains one of the leading causes of death in the UK. There are a number of conditions that commonly increase an individual\u2019s risk of developing CVD. Some of these conditions, such as diabetes and having high circulating levels of cholesterol in the blood, can be controlled by using medicines.  However, these conditions need to be diagnosed before an individual can be prescribed the medicines to control them. Because of disruption from the COVID-19 pandemic resulting in changes in health care services and fewer face-to-face medical appointments, it is likely that the number of conditions being diagnosed has fallen. Therefore some individuals are not being prescribed the medicines to control the condition.  One way to investigate this problem is to look at what changes there have been in the prescriptions for these conditions. This involves looking at new and repeat prescriptions that have been issued by the GP, and also the amount of prescriptions dispensed by the pharmacy.  We already know that the COVID-19 pandemic caused a disruption to the usual pattern of prescribing of medicines for these conditions. For example, there was a significant increase in the number of repeat prescriptions issued in March 2020, presumably as doctors and patients ensured they had sufficient medication for the first lockdown. Subsequent patterns in the prescribing of medicines for these conditions during 2020 have not yet been adequately studied.  The number of GP appointments also fell during Spring-Summer 2020, presumably resulting in a reduced number of individuals being diagnosed with CVD. It is also presumed that there would be a reduction in the diagnosis in new patients of conditions that can increase their risk of developing CVD, and therefore a decrease in the amount of prescriptions for medicines to control these conditions.  For this project, we therefore propose to examine patterns in the prescription of medicines for these conditions. This will enable us to understand how the COVID-19 pandemic has had an impact on the control of CVD and its related conditions in the UK population. We will use this information to understand how many people are likely to be affected by cardiovascular disease in the future. It is hoped that this will enable more accurate planning for better patient care.  Amendment: The scope of this project has been extended to understand the impact of COVID-19 on a number of clinical pathways using medicines as an approach.  This additional work is funded through a funding call by Health Data Research UK and the Alan Turing Institute as part of the wider Data and Connectivity National Core Study.","technical_summary":"This project accessed the following datasets within the Trusted Research Environment(s) for CVD-COVID-UK \/ COVID-IMPACT: \n- ENGLAND: \n- Civil Registration - Deaths\n- COVID-19 SARI-Watch (formerly CHESS)\n- Covid-19 Second Generation Surveillance System\n- GPES Data for Pandemic Planning and Research (COVID-19)\n- Hospital Episode Statistics Accident and Emergency\n- Hospital Episode Statistics Admitted Patient Care\n- Hospital Episode Statistics Outpatients\n- Medicines dispensed in Primary Care (NHSBSA data)\n- NICOR \u2013 MINAP: Myocardial Ischaemia National Audit Project\n- NICOR \u2013 NACRM: National Audit of Cardiac Rhythm Management\n- NICOR \u2013 NACSA: National Adult Cardiac Surgery Audit\n- NICOR \u2013 NHFA: National Heart Failure Audit\n- NICOR \u2013 PCI: Percutaneous Coronary Interventions\n- Secondary Uses Services Payment By Results\n- Sentinel Stroke National Audit Programme Clinical Dataset\n- SCOTLAND: \n- COVID Tests\n- Diabetes covariates\n- General Acute Inpatient and Day Case - Scottish Morbidity Record (SMR01)\n- National Records of Scotland (NRS) - Deaths Data\n- NHS Scotland General Practice (GP) Contact Data - Subset\n- Outpatient Appointments and Attendances - Scottish Morbidity Record (SMR00)\n- Prescribing Information System (PIS)\n- Scotland Accident and Emergency\n- Scottish Stroke Care Audit\n- WALES:\n- Annual District Death Daily (ADDD)\n- Annual District Death Extract (ADDE)\n- Covid Vaccination Dataset (CVVD)\n- COVID-19 Consolidated Deaths (CDDS)\n- COVID-19 Test Results (PATD)\n- Critical Care Dataset (CCDS)\n- Emergency Department Dataset (EDDS)\n- Emergency Department Dataset Daily (EDDD)\n- Intensive Care National Audit and Research Centre (ICCD) - Legacy - COVID only\n- Intensive Care National Audit and Research Centre (ICNC)\n- Outpatient Database for Wales (OPDW)\n- Outpatient Referral (OPRD)\n- Patient Episode Dataset for Wales (PEDW)\n- Welsh Demographic Service Dataset (WDSD)\n- Welsh Dispensing Dataset (WDDS) - Legacy\n- Welsh Longitudinal General Practice Dataset (WLGP) - Welsh Primary Care\n- Welsh Longitudinal GP Dataset - Welsh Primary Care (Daily COVID codes only) (GPCD)","latest_approval_date":"2021-03-31 12:00:00","manual_upload":true,"rejection_reason":null,"sublicence_arrangements":"No","public_benefit_statement":"For this project, we therefore propose to examine patterns in the prescription of medicines for these conditions. This will enable us to understand how the COVID-19 pandemic has had an impact on the control of CVD and its related conditions in the UK population. We will use this information to understand how many people are likely to be affected by cardiovascular disease in the future. It is hoped that this will enable more accurate planning for better patient care.  The scope of this project has been extended to understand the impact of COVID-19 on a number of clinical pathways using medicines as an approach.  This additional work is funded through a funding call by Health Data Research UK and the Alan Turing Institute as part of the wider Data and Connectivity National Core Study.\nVisit the BHF Data Science Centre website for more detailed information about project outputs. https:\/\/bhfdatasciencecentre.org\/projects\/ccu014\/","data_sensitivity_level":"De-Personalised","project_start_date":null,"project_end_date":null,"access_date":null,"accredited_researcher_status":null,"confidential_data_description":null,"dataset_linkage_description":null,"duty_of_confidentiality":null,"legal_basis_for_data_article6":null,"legal_basis_for_data_article9":null,"national_data_optout":null,"organisation_id":null,"privacy_enhancements":null,"request_category_type":null,"request_frequency":null,"access_type":"TRE","mongo_object_dar_id":null,"enabled":true,"last_activity":"2024-08-17 20:27:26","counter":50,"mongo_object_id":"61e6b93cb81d5c1cadd12240","mongo_id":"8847792740918621","user_id":null,"team_id":34,"application_id":null,"applicant_id":null,"sector_id":3,"status":"ACTIVE","datasets":[{"id":1378,"mongo_object_id":"66d02b586069f46cfb475efe","mongo_id":"18962119282350744","mongo_pid":"7e5f0247-f033-4f98-aed3-3d7422b9dc6d","datasetid":"66c108bb-031d-4c96-bea4-4d96e4e70da0","pid":"afa791e0-2ad7-45a3-aa26-6e466775381f","source":null,"discourse_topic_id":0,"is_cohort_discovery":false,"partner_context":"HDRUK","commercial_use":0,"state_id":0,"uploader_id":0,"metadataquality_id":0,"user_id":4777,"team_id":34,"views_count":0,"views_prev_count":0,"has_technical_details":1,"created":"2025-07-07T13:24:08.000000Z","updated":"2026-06-16T07:52:58.000000Z","submitted":"2025-07-07 13:24:08","published":null,"created_at":"2025-07-07T13:24:08.000000Z","updated_at":"2026-06-16T07:52:58.000000Z","deleted_at":null,"active_date":"2025-07-09 14:20:19","create_origin":"MANUAL","status":"ACTIVE","name":"Secure Data Environment for CVD-COVID-UK \/ COVID-IMPACT (England)","dataset_version_ids":[1657]},{"id":1379,"mongo_object_id":"66d02b586069f46cfb475efe","mongo_id":"18962119282350744","mongo_pid":"7e5f0247-f033-4f98-aed3-3d7422b9dc6d","datasetid":"66c108bb-031d-4c96-bea4-4d96e4e70da0","pid":"afa791e0-2ad7-45a3-aa26-6e466775381f","source":null,"discourse_topic_id":0,"is_cohort_discovery":false,"partner_context":"HDRUK","commercial_use":0,"state_id":0,"uploader_id":0,"metadataquality_id":0,"user_id":4777,"team_id":34,"views_count":0,"views_prev_count":0,"has_technical_details":1,"created":"2025-07-07T13:24:57.000000Z","updated":"2026-04-08T08:29:01.000000Z","submitted":"2025-07-07 13:24:57","published":null,"created_at":"2025-07-07T13:24:57.000000Z","updated_at":"2026-04-08T08:29:01.000000Z","deleted_at":null,"active_date":"2025-07-09 14:14:26","create_origin":"MANUAL","status":"ACTIVE","name":"Trusted Research Environment for CVD-COVID-UK (Wales)","dataset_version_ids":[1658]},{"id":1380,"mongo_object_id":"66d02b586069f46cfb475efe","mongo_id":"18962119282350744","mongo_pid":"7e5f0247-f033-4f98-aed3-3d7422b9dc6d","datasetid":"66c108bb-031d-4c96-bea4-4d96e4e70da0","pid":"afa791e0-2ad7-45a3-aa26-6e466775381f","source":null,"discourse_topic_id":0,"is_cohort_discovery":false,"partner_context":"HDRUK","commercial_use":0,"state_id":0,"uploader_id":0,"metadataquality_id":0,"user_id":4777,"team_id":34,"views_count":0,"views_prev_count":0,"has_technical_details":1,"created":"2025-07-07T13:25:28.000000Z","updated":"2026-04-08T08:19:15.000000Z","submitted":"2025-07-07 13:25:28","published":null,"created_at":"2025-07-07T13:25:28.000000Z","updated_at":"2026-04-08T08:19:15.000000Z","deleted_at":null,"active_date":"2025-07-09 14:35:00","create_origin":"MANUAL","status":"ACTIVE","name":"Trusted Research Environment for CVD-COVID-UK (Scotland)","dataset_version_ids":[1659]}],"publications":[{"id":3452,"created_at":"2026-07-28T14:56:21.000000Z","updated_at":"2026-07-28T14:56:21.000000Z","deleted_at":null,"active_date":"2026-07-28 14:56:21","first_publication_date":null,"paper_title":"Patterns of medication use across society from national primary care dispensing data","authors":"Caroline E. Dale, Rohan Takhar, Andrew Lambarth, Andrew Mason, Nathalie Conrad, Christopher Tomlinson, Spiros Denaxas, Mamas A. Mamas, Andrew D. Morris, Munir Pirmohamed, Kamlesh Khunti, Cathie Sudlow, Angela M. Wood, Naveed Sattar, Reecha Sofat","year_of_publication":"2026","paper_doi":"https:\/\/doi.org\/10.1038\/s44360-026-00134-w","publication_type":"Research articles","journal_name":"Nature Health","abstract":"This study analysed primary care medication dispensing in England from 1 November 2019 to 31 December 2024, covering 52.6 million individuals and 5.8 billion dispensed medications. We examined the impact of the COVID-19 pandemic on dispensing trends and the distribution of medications across society. Here, we show that the initiation of some medications\u2014notably those for gastrointestinal and mental health conditions\u2014declined and remained below pre-pandemic levels. In contrast, medications for cardiovascular disease (CVD) and diabetes showed sharp initial declines followed by recovery to or above pre-pandemic levels. Except for CVD medications, women had higher dispensing rates than men until late adulthood, including twice the volume of some antidepressants among younger adults. By age 50 years, around 15% of the population had been dispensed five or more concurrent medications, rising to 42% by age 70 years. The Bangladeshi and Pakistani groups had higher dispensing rates, with rate ratios of up to 2.5 by age 60 years, and individuals in the most deprived quintile had rates up to twice those of the least deprived by age 40 years. Our findings highlight both the pandemic\u2019s disruption of clinical pathways and potential inequities in medication use, demonstrating the value of medicines intelligence for monitoring and addressing health disparities.","url":null,"mongo_id":null,"publication_type_mk1":"","owner_id":4902,"status":"ACTIVE","team_id":34,"pivot":{"dur_id":67,"publication_id":3452,"user_id":4902,"application_id":null,"reason":null,"created_at":null,"updated_at":null}}],"tools":[],"keywords":[],"user":null,"team":{"id":34,"pid":"eff5a892-0a13-44e7-a353-73f7f6db954e","created_at":"2024-10-08T11:18:21.000000Z","updated_at":"2026-06-19T10:00:43.000000Z","deleted_at":null,"name":"BHF Data Science Centre","enabled":true,"allows_messaging":false,"workflow_enabled":false,"access_requests_management":false,"uses_5_safes":false,"is_admin":false,"team_logo":"\/teams\/bhf-dsc.png","member_of":"ALLIANCE","contact_point":null,"application_form_updated_by":"Qresearch webapp","application_form_updated_on":"0001-01-01 00:00:00","mongo_object_id":"607db9c2e1f9d3704d570ce2","notification_status":false,"is_question_bank":false,"is_provider":false,"url":null,"introduction":null,"dar_modal_header":"Important information about applying","dar_modal_content":"{\"type\":\"doc\",\"content\":[{\"type\":\"paragraph\",\"content\":[{\"type\":\"text\",\"text\":\"To apply for access to the BHF Data Science Centre please use the following link\"}]},{\"type\":\"paragraph\",\"content\":[{\"type\":\"text\",\"marks\":[{\"type\":\"link\",\"attrs\":{\"href\":\"https:\/\/bhfdatasciencecentre.org\/areas\/cvd-covid-uk-covid-impact\/\",\"target\":\"_blank\",\"rel\":\"noopener noreferrer nofollow\",\"class\":null}}],\"text\":\"https:\/\/bhfdatasciencecentre.org\/areas\/cvd-covid-uk-covid-impact\/\"}]}]}","dar_modal_footer":null,"is_dar":false,"service":null},"application":null,"users":[{"id":3040,"name":"System Generated","firstname":"System","lastname":"Generated","email":"email@hdruk.ac.uk","secondary_email":null,"preferred_email":"primary","email_verified_at":null,"secondary_email_verified_at":null,"provider":null,"created_at":"2024-10-08T11:15:21.000000Z","updated_at":"2025-02-13T16:10:02.000000Z","deleted_at":null,"sector_id":6,"organisation":null,"bio":null,"domain":null,"link":null,"orcid":null,"contact_feedback":0,"contact_news":0,"mongo_id":0,"mongo_object_id":"60659ec6031a06defa2922ef","is_admin":0,"terms":false,"hubspot_id":99200235059,"is_nhse_sde_approval":false,"laravel_through_key":67,"rquestroles":[],"cohort_discovery_roles":[],"cohort_discovery_nhs_sde":false},{"id":4902,"name":"Melissa Webb","firstname":"Melissa","lastname":"Webb","email":"Melissa.Webb@hdruk.ac.uk","secondary_email":null,"preferred_email":"primary","email_verified_at":null,"secondary_email_verified_at":null,"provider":"azure","created_at":"2025-07-04T15:50:22.000000Z","updated_at":"2026-07-28T14:36:37.000000Z","deleted_at":null,"sector_id":5,"organisation":null,"bio":null,"domain":null,"link":null,"orcid":null,"contact_feedback":0,"contact_news":0,"mongo_id":0,"mongo_object_id":null,"is_admin":0,"terms":true,"hubspot_id":3009101,"is_nhse_sde_approval":false,"rquestroles":[],"cohort_discovery_roles":[],"cohort_discovery_nhs_sde":false,"pivot":{"dur_id":67,"user_id":4902}}],"applications":[]},{"id":1594,"created_at":"2025-02-06T10:24:10.000000Z","updated_at":"2026-07-28T14:44:09.000000Z","deleted_at":null,"active_date":"2026-03-31 10:13:00","non_gateway_datasets":[],"non_gateway_applicants":["Robert Fletcher","Sadaf Farooqi","Reecha Sofat","Nathalie Conrad","Angela Wood"],"funders_and_sponsors":[""],"other_approval_committees":[""],"gateway_outputs_tools":null,"gateway_outputs_papers":null,"non_gateway_outputs":["https:\/\/doi.org\/10.1016\/S2213-8587(26)00120-8"],"project_title":"CCU096: Impact of COVID-19 on obesity and risks of cardio-renal-metabolic outcomes","project_id_text":"CCU096","organisation_name":"University of Cambridge, University of Liverpool, KU Leuven","organisation_sector":"3","lay_summary":"Obesity has been declared a worldwide epidemic by the World Health Organisation (WHO) and the number of people living with this condition continues to rise worldwide. It is a complex, chronic, and progressive condition, which substantially affects health, quality of life, and life expectancy. No study has fully examined the overall burden of obesity and how this has changed over the COVID-19 pandemic and in its aftermath, its associations with heart, kidney, and metabolic (i.e. diabetes) health, or how new treatments for obesity are being used in the population of England. This project will address these evidence gaps.\n\nThe aims of this study are to provide a comprehensive understanding of the patterns and impact of obesity in the population of England before, during and in the aftermath of the COVID-19 pandemic. The specific objectives are: \n\ni. To determine the annual incidence and overall prevalence of obesity (identified by both medical diagnoses and records of body-mass index [30 kg\/m\u00b2 or higher for individuals of white ethnicity or 27.5 kg\/m2 or higher for individuals of Asian, Chinese, Middle Eastern, Black African or African-Caribbean ethnicity, according to current NHS guidelines] calculated from patients\u2019 height and weight measurements during general practitioner appointments, within the entire population of England before, during, and in the aftermath of the COVID-19 pandemic; \nii. To evaluate the impact of obesity on hospitalisation rates, and key cardiovascular, kidney, and metabolic outcomes, in particular heart failure, chronic kidney disease, and diabetes (including in people with and without a history of COVID-19 diagnosis);\niii. To analyse how widely new therapies for the treatment of obesity (i.e. glucagon-like peptide-1 receptor agonists like Ozempic, Wegovy, and Mounjaro) have been used alongside existing interventions like bariatric surgery (i.e. weight loss surgery to alter the stomach or intestines of obese individuals so that they each less and absorb fewer calories) across the whole population of England before, during, and after the COVID-19 pandemic.\n\nThis research will address the challenge of understanding the scope and consequences of obesity, providing critical insights to inform healthcare strategies and to make best use of the available treatment interventions.","technical_summary":"This project accessed the following datasets within the Trusted Research Environment(s) for CVD-COVID-UK \/ COVID-IMPACT: \n- ENGLAND: \n- Civil Registration - Deaths\n- COVID-19 SARI-Watch (formerly CHESS)\n- Covid-19 Second Generation Surveillance System\n- Covid-19 UK Non-hospital Antibody Testing Results\n- Covid-19 UK Non-hospital Antigen Testing Results\n- COVID-19 Vaccination Adverse Reaction\n- COVID-19 Vaccination Status\n- Emergency Care Data Set (ECDS)\n- GPES Data for Pandemic Planning and Research (COVID-19)\n- Hospital Episode Statistics Accident and Emergency\n- Hospital Episode Statistics Admitted Patient Care\n- Hospital Episode Statistics Critical Care\n- Hospital Episode Statistics Outpatients\n- Medicines dispensed in Primary Care (NHSBSA data)\n- Secondary Uses Services Payment By Results\n- Uncurated Low Latency Hospital Data (Admitted Patient Care, Outpatients, Critical Care)","latest_approval_date":"2024-12-21 12:00:00","manual_upload":true,"rejection_reason":null,"sublicence_arrangements":null,"public_benefit_statement":"The potential impact of this work includes a deeper understanding of the scale of obesity in England, its occurrence in different sociodemographic groups - particularly by socioeconomic status, ethnicity, and geographic region - and its interaction with COVID-19. This research which will help to tailor interventions for obesity more effectively. By assessing how individuals with obesity interact with the healthcare system and identifying their risk for heart and kidney outcomes, the findings can support the development of targeted management strategies. This, in turn, will enable more efficient health resource planning and improve prevention efforts. Furthermore, by examining how novel medications for obesity are being implemented, this research will offer insights on whether these medications are being equitably distributed across various patient populations. These findings will inform public health policies to address obesity more effectively.\n\nVisit the BHF Data Science Centre website for more detailed information about project outputs. https:\/\/bhfdatasciencecentre.org\/projects\/ccu096\/","data_sensitivity_level":"De-Personalised","project_start_date":"2024-02-03 12:00:00","project_end_date":"2025-09-02 12:00:00","access_date":"2024-12-21 00:00:00","accredited_researcher_status":null,"confidential_data_description":null,"dataset_linkage_description":null,"duty_of_confidentiality":null,"legal_basis_for_data_article6":null,"legal_basis_for_data_article9":null,"national_data_optout":null,"organisation_id":null,"privacy_enhancements":null,"request_category_type":null,"request_frequency":null,"access_type":"TRE","mongo_object_dar_id":null,"enabled":true,"last_activity":null,"counter":0,"mongo_object_id":null,"mongo_id":null,"user_id":4068,"team_id":34,"application_id":null,"applicant_id":null,"sector_id":null,"status":"ACTIVE","datasets":[{"id":1378,"mongo_object_id":"66d02b586069f46cfb475efe","mongo_id":"18962119282350744","mongo_pid":"7e5f0247-f033-4f98-aed3-3d7422b9dc6d","datasetid":"66c108bb-031d-4c96-bea4-4d96e4e70da0","pid":"afa791e0-2ad7-45a3-aa26-6e466775381f","source":null,"discourse_topic_id":0,"is_cohort_discovery":false,"partner_context":"HDRUK","commercial_use":0,"state_id":0,"uploader_id":0,"metadataquality_id":0,"user_id":4777,"team_id":34,"views_count":0,"views_prev_count":0,"has_technical_details":1,"created":"2025-07-07T13:24:08.000000Z","updated":"2026-06-16T07:52:58.000000Z","submitted":"2025-07-07 13:24:08","published":null,"created_at":"2025-07-07T13:24:08.000000Z","updated_at":"2026-06-16T07:52:58.000000Z","deleted_at":null,"active_date":"2025-07-09 14:20:19","create_origin":"MANUAL","status":"ACTIVE","name":"Secure Data Environment for CVD-COVID-UK \/ COVID-IMPACT (England)","dataset_version_ids":[1657]}],"publications":[{"id":3451,"created_at":"2026-07-28T14:40:23.000000Z","updated_at":"2026-07-28T14:40:23.000000Z","deleted_at":null,"active_date":"2026-07-28 14:40:23","first_publication_date":null,"paper_title":"Whole-population trends in obesity across dimensions of inequality in England, 2019-25: a retrospective, longitudinal cohort study of 54 million adults.","authors":"Fletcher RA, Conrad N, Rockenschaub P, Logothetis SB, Neuen BL, Chalmers F, Dalakoti M, Raffetti E, Denaxas S, Khunti K, Farooqi IS, Arnott C, Di Angelantonio E, Danesh J, Sofat R, Sattar N, Wood AM, CVD-COVID-UK\/COVID-IMPACT Consortium.","year_of_publication":"2026","paper_doi":"https:\/\/doi.org\/10.1016\/S2213-8587(26)00120-8","publication_type":"Research articles","journal_name":"The Lancet Diabetes & Endocrinology","abstract":"Obesity is one of the 21st century's greatest public health challenges. Evidence on how inequalities intersect to shape the obesity burden is scarce, particularly since the COVID-19 pandemic. We aimed to investigate trends in the incidence and prevalence of obesity among adults in England, and to examine variation by age, sex, socioeconomic status, ethnicity, and geographical region. In this retrospective, longitudinal cohort study, we analysed whole-population, individual-level, anonymised, electronic health records with life-course data on the entire population of adults aged 18-99 years in England, accessed via the National Health Service England Secure Data Environment. We estimated age- and sex-standardised incidence and prevalence rates of obesity (BMI \u226530\u00b70 kg\/m<sup>2<\/sup> or clinician-assigned diagnosis) from 2019 to 2025 and used negative binomial regression to examine trends by age, sex, socioeconomic status, ethnicity, and geographical region (defined using middle layer super output areas: neighbourhood-level units comprised of 5000 to 15\u2008000 individuals).Between Nov 1, 2019, and April 30, 2025, we identified 54\u2008892\u2008390 adults with records in the National Health Service England Secure Data Environment. 4\u2008131\u2008555 people had a first presentation of obesity during the study period, of whom 2\u2008278\u2008485 (55\u00b71%) were women and 1\u2008853\u2008070 (44\u00b79%) were men. 3\u2008106\u2008740 (75\u00b72%) of individuals were White, 482\u2008690 (11\u00b77%) were Asian or Asian British, and 291\u2008920 (7\u00b71%) were Black, Black British, Caribbean, or African. The median age at first presentation of obesity was 43 years (IQR 31-58), and mean BMI was 33\u00b74 kg\/m<sup>2<\/sup> (SD 10\u00b77). The overall age- and sex-standardised incidence of first-recorded obesity was 22 per 1000 person-years (95% CI 22-22) and increased by 4% over the study period (incidence rate ratio [IRR] 2024-2025 vs 2019-2020 1\u00b704, 95% CI 1\u00b701-1\u00b707), with marked variation across subgroups. The steepest increases over time occurred in those aged 20-29 years (IRR 1\u00b716, 95% CI 1\u00b708-1\u00b725) and in those aged 30-39 years (1\u00b719, 1\u00b713-1\u00b725). Obesity incidence was 35% greater in the most socioeconomically deprived quintile compared with the least deprived quintile (IRR 1\u00b735, 95% CI 1\u00b728-1\u00b742), with greater disparities in women (1\u00b754, 1\u00b745-1\u00b764), particularly Asian women (1\u00b794, 1\u00b786-2\u00b702). By 2025, overall obesity prevalence reached 30\u00b73%, rising from 26\u00b73% at the start of the study. There was considerable variation in obesity prevalence by age, sex, socioeconomic status, and ethnicity, ranging from 4\u00b73% in the least socioeconomically deprived White men aged 18-19 years to 66\u00b71% in the most deprived Black women aged 60-69 years, which was nearly double that in the least-deprived White women in the same age group (60-69 years; 34\u00b75%). Geographical disparities were striking, with prevalence varying between 8\u00b75% and 48\u00b71% (a nearly 6-fold difference), with the largest increases over time observed in most deprived regions.Obesity affects nearly one in three adults in England, with disproportionate burden among disadvantaged groups and widening disparities since the COVID-19 pandemic. Increasing rates among individuals of childbearing age risk perpetuating intergenerational cycles of health inequality. The intersection of sociodemographic determinants underscores the preventable nature of obesity and the need to address dimensions of inequality in prevention efforts.","url":null,"mongo_id":null,"publication_type_mk1":"","owner_id":4902,"status":"ACTIVE","team_id":34,"pivot":{"dur_id":1594,"publication_id":3451,"user_id":4902,"application_id":null,"reason":null,"created_at":null,"updated_at":null}}],"tools":[],"keywords":[],"user":{"id":4068,"name":"BHF Data Science Centre","firstname":"BHF","lastname":"Data Science Centre","email":"bhfdatasciencecentre@gmail.com","secondary_email":null,"preferred_email":"primary","email_verified_at":null,"secondary_email_verified_at":null,"provider":"google","created_at":"2024-10-14T14:16:07.000000Z","updated_at":"2025-12-18T18:28:50.000000Z","deleted_at":null,"sector_id":null,"organisation":"","bio":null,"domain":"","link":"","orcid":null,"contact_feedback":0,"contact_news":0,"mongo_id":0,"mongo_object_id":null,"is_admin":0,"terms":false,"hubspot_id":99185783604,"is_nhse_sde_approval":false,"rquestroles":[],"cohort_discovery_roles":[],"cohort_discovery_nhs_sde":false},"team":{"id":34,"pid":"eff5a892-0a13-44e7-a353-73f7f6db954e","created_at":"2024-10-08T11:18:21.000000Z","updated_at":"2026-06-19T10:00:43.000000Z","deleted_at":null,"name":"BHF Data Science Centre","enabled":true,"allows_messaging":false,"workflow_enabled":false,"access_requests_management":false,"uses_5_safes":false,"is_admin":false,"team_logo":"\/teams\/bhf-dsc.png","member_of":"ALLIANCE","contact_point":null,"application_form_updated_by":"Qresearch webapp","application_form_updated_on":"0001-01-01 00:00:00","mongo_object_id":"607db9c2e1f9d3704d570ce2","notification_status":false,"is_question_bank":false,"is_provider":false,"url":null,"introduction":null,"dar_modal_header":"Important information about applying","dar_modal_content":"{\"type\":\"doc\",\"content\":[{\"type\":\"paragraph\",\"content\":[{\"type\":\"text\",\"text\":\"To apply for access to the BHF Data Science Centre please use the following link\"}]},{\"type\":\"paragraph\",\"content\":[{\"type\":\"text\",\"marks\":[{\"type\":\"link\",\"attrs\":{\"href\":\"https:\/\/bhfdatasciencecentre.org\/areas\/cvd-covid-uk-covid-impact\/\",\"target\":\"_blank\",\"rel\":\"noopener noreferrer nofollow\",\"class\":null}}],\"text\":\"https:\/\/bhfdatasciencecentre.org\/areas\/cvd-covid-uk-covid-impact\/\"}]}]}","dar_modal_footer":null,"is_dar":false,"service":null},"application":null,"users":[{"id":3040,"name":"System Generated","firstname":"System","lastname":"Generated","email":"email@hdruk.ac.uk","secondary_email":null,"preferred_email":"primary","email_verified_at":null,"secondary_email_verified_at":null,"provider":null,"created_at":"2024-10-08T11:15:21.000000Z","updated_at":"2025-02-13T16:10:02.000000Z","deleted_at":null,"sector_id":6,"organisation":null,"bio":null,"domain":null,"link":null,"orcid":null,"contact_feedback":0,"contact_news":0,"mongo_id":0,"mongo_object_id":"60659ec6031a06defa2922ef","is_admin":0,"terms":false,"hubspot_id":99200235059,"is_nhse_sde_approval":false,"laravel_through_key":1594,"rquestroles":[],"cohort_discovery_roles":[],"cohort_discovery_nhs_sde":false},{"id":4902,"name":"Melissa Webb","firstname":"Melissa","lastname":"Webb","email":"Melissa.Webb@hdruk.ac.uk","secondary_email":null,"preferred_email":"primary","email_verified_at":null,"secondary_email_verified_at":null,"provider":"azure","created_at":"2025-07-04T15:50:22.000000Z","updated_at":"2026-07-28T14:36:37.000000Z","deleted_at":null,"sector_id":5,"organisation":null,"bio":null,"domain":null,"link":null,"orcid":null,"contact_feedback":0,"contact_news":0,"mongo_id":0,"mongo_object_id":null,"is_admin":0,"terms":true,"hubspot_id":3009101,"is_nhse_sde_approval":false,"rquestroles":[],"cohort_discovery_roles":[],"cohort_discovery_nhs_sde":false,"pivot":{"dur_id":1594,"user_id":4902}}],"applications":[]},{"id":3962,"created_at":"2026-07-22T16:42:19.000000Z","updated_at":"2026-07-22T16:47:24.000000Z","deleted_at":null,"active_date":"2026-07-22 16:47:25","non_gateway_datasets":[],"non_gateway_applicants":["Jessica Tyrrell"],"funders_and_sponsors":[""],"other_approval_committees":[""],"gateway_outputs_tools":null,"gateway_outputs_papers":null,"non_gateway_outputs":[""],"project_title":"Using genetics to understand lifelong health and disease","project_id_text":"OFHS260023","organisation_name":"University of Exeter","organisation_sector":"Academic Institute","lay_summary":"This study examines how our early life and biology shape health, wellbeing, and disease risk across life. By combining genetic information with questionnaires and health records, we aim to understand why some people develop serious physical or mental illnesses and why risks differ between individuals. First, we will look at early life and sex related factors such as birthweight, pregnancy outcomes, hormone levels, reproductive ageing, and major life changes, to see how they influence health. Second, we will test how these early risks contribute to major health problems, including heart and metabolic diseases, cancer, and premature death. Third, we will examine mental health conditions including schizophrenia, bipolar disorder, and major depression, and explore why people who have both mental and physical illnesses often face worse health outcomes. Finally, we will integrate findings to develop tools that can help identify who is at higher risk of disease and when prevention or treatment might work best. Our key research questions aim to address: how early biological and sex-specific factors influence lifelong health; why mental and physical illnesses often occur together; and whether genetic differences shape disease risk and progression. This work will support better prediction, prevention, and care across the lifespan. Common health problems such as diabetes, heart disease, and mental illness, arise from a complex interplay of genetics, biology, behavioural, and early-life factors across a person\u2019s lifetime. These include our genes, how our bodies work, and our behaviours throughout our lives. Traditional research can show links between these factors and disease, but it often cannot tell us what causes disease. Genetic research provides a powerful way to tackle this problem. By studying our genes and how small genetic differences affect health, we can learn more about the biological processes that lead to disease. This knowledge can create new opportunities to prevent illness and improve treatments. Because genetic variants are fixed at conception, they can be used as natural experiments to identify causal pathways between risk factors and health outcomes. Researchers can use them to understand which risk factors truly cause health problems and which ones are simply associated with them. This approach, known as Mendelian randomization, helps reveal which factors directly influence disease, and which may offer targets for prevention or treatment.","technical_summary":null,"latest_approval_date":"2026-03-26 12:00:00","manual_upload":true,"rejection_reason":null,"sublicence_arrangements":null,"public_benefit_statement":"This research will benefit the public by improving understanding of why common health problems including diabetes, heart disease, cancer, and mental illness develop, and why risk varies between people. Using genetic methods that help us get closer to identifying true cause and effect relationships, we aim to pinpoint the biological and life course factors that genuinely contribute to poor health, rather than those that are simply associated with it. Our work on sex differences and early-life development will examine how childhood health, hormones, and life transitions such as menopause influence later disease risk, helping address gaps in women\u2019s and midlife health. Work on metabolic health will clarify processes that lead to diabetes and related conditions, potentially supporting earlier identification of those at higher risk. Our mental health research will shed light on why some individuals experience worse physical health alongside depression and related disorders, while others remain resilient. Finally, our work on risk prediction will explore whether combining genetic information with routine health data can better identify people who are most likely to develop serious complications. Our research with Our Future Health will support progress toward more personalised prevention, earlier treatment, and better use of healthcare resources across the NHS.","data_sensitivity_level":null,"project_start_date":"2026-04-24 12:00:00","project_end_date":null,"access_date":null,"accredited_researcher_status":null,"confidential_data_description":null,"dataset_linkage_description":null,"duty_of_confidentiality":null,"legal_basis_for_data_article6":null,"legal_basis_for_data_article9":null,"national_data_optout":null,"organisation_id":null,"privacy_enhancements":null,"request_category_type":"Public Health 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Peters","firstname":"Amy","lastname":"Peters","email":"amy.peters@ourfuturehealth.org.uk","secondary_email":null,"preferred_email":"primary","email_verified_at":null,"secondary_email_verified_at":null,"provider":"azure","created_at":"2024-10-08T11:14:15.000000Z","updated_at":"2026-08-07T14:09:11.000000Z","deleted_at":null,"sector_id":5,"organisation":"Our Future Health","bio":null,"domain":null,"link":null,"orcid":null,"contact_feedback":0,"contact_news":0,"mongo_id":8640248443551848,"mongo_object_id":"6672f2b9713ab403a5cd5bf3","is_admin":0,"terms":true,"hubspot_id":31477292673,"is_nhse_sde_approval":false,"rquestroles":[],"cohort_discovery_roles":[],"cohort_discovery_nhs_sde":false},"team":{"id":86,"pid":"7edbcc67-610e-4234-a7ea-47e0e7f0b64c","created_at":"2024-10-08T11:18:56.000000Z","updated_at":"2026-06-19T09:14:29.000000Z","deleted_at":null,"name":"Our Future 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It is designed to enable the discovery and testing of more effective approaches to prevention, earlier detection and treatment of diseases.\"}]},{\"type\":\"paragraph\",\"content\":[{\"type\":\"text\",\"text\":\"The programme is collecting and linking multiple sources of health and health-relevant information, including genetic data, across a cohort of up to 5 million people. It is recruiting participants from all ages, backgrounds and communities to create a cohort that truly reflects the UK population.\"}]},{\"type\":\"paragraph\",\"content\":[{\"type\":\"text\",\"text\":\"Our Future Health will create a world-leading resource for researchers from universities, charities, the NHS and companies involved in health research to undertake discovery research on early indicators of disease. Researchers will also have the opportunity to re-contact participants on a risk-stratified basis for secondary studies.\"}]},{\"type\":\"paragraph\",\"content\":[{\"type\":\"text\",\"text\":\"Find out more on the Our Future Health website: \"},{\"type\":\"text\",\"marks\":[{\"type\":\"link\",\"attrs\":{\"href\":\"https:\/\/ourfuturehealth.org.uk\/\",\"target\":\"_blank\",\"rel\":\"noopener noreferrer nofollow\",\"class\":null}}],\"text\":\"https:\/\/ourfuturehealth.org.uk\/\"}]}]}","dar_modal_header":"Important information about applying","dar_modal_content":"{\"type\":\"doc\",\"content\":[{\"type\":\"paragraph\",\"content\":[{\"type\":\"text\",\"text\":\"To apply for access to the Our Future Health dataset, please use the following link.\"}]},{\"type\":\"paragraph\",\"content\":[{\"type\":\"text\",\"marks\":[{\"type\":\"link\",\"attrs\":{\"href\":\"https:\/\/research.ourfuturehealth.org.uk\/apply-to-access-the-data\/\",\"target\":\"_blank\",\"rel\":\"noopener noreferrer nofollow\",\"class\":null}}],\"text\":\"https:\/\/research.ourfuturehealth.org.uk\/apply-to-access-the-data\/\"}]}]}","dar_modal_footer":null,"is_dar":false,"service":null},"application":null,"users":[{"id":281,"name":"Amy Peters","firstname":"Amy","lastname":"Peters","email":"amy.peters@ourfuturehealth.org.uk","secondary_email":null,"preferred_email":"primary","email_verified_at":null,"secondary_email_verified_at":null,"provider":"azure","created_at":"2024-10-08T11:14:15.000000Z","updated_at":"2026-08-07T14:09:11.000000Z","deleted_at":null,"sector_id":5,"organisation":"Our Future Health","bio":null,"domain":null,"link":null,"orcid":null,"contact_feedback":0,"contact_news":0,"mongo_id":8640248443551848,"mongo_object_id":"6672f2b9713ab403a5cd5bf3","is_admin":0,"terms":true,"hubspot_id":31477292673,"is_nhse_sde_approval":false,"laravel_through_key":3962,"rquestroles":[],"cohort_discovery_roles":[],"cohort_discovery_nhs_sde":false}],"applications":[]},{"id":3961,"created_at":"2026-07-22T16:42:19.000000Z","updated_at":"2026-07-22T16:46:20.000000Z","deleted_at":null,"active_date":"2026-07-22 16:46:21","non_gateway_datasets":[],"non_gateway_applicants":["Yin Cao"],"funders_and_sponsors":[""],"other_approval_committees":[""],"gateway_outputs_tools":null,"gateway_outputs_papers":null,"non_gateway_outputs":[""],"project_title":"Risk factors across generations and their role in increasing burden of early-onset cancers","project_id_text":"OFHS240156","organisation_name":"Washington University in St Louis","organisation_sector":"Academic Institute","lay_summary":"The aim of this study is first to characterize the generational differences in cancer risk factors with the ultimate goal to elucidate risk factors that contribute to the rising burden and birth cohort effects of major early-onset cancers.\nAim 1: To characterize cancer risk factors across different generations, with a specific focus on assessing the impact of birth cohorts, defined as the time during which individuals are born. This aim seeks to explore how environmental, lifestyle, and genetic factors contributing to cancer risk vary between generations.\nAim 2: To elucidate the progression of signs and symptoms leading up to the diagnosis of early-onset cancers. This aim seeks to explore the clinical trajectory of patients by identifying patterns in symptom onset, duration, and severity prior to diagnosis.\nAim 3: To investigate life course risk factorsfor early-onset cancers by pooling data from over 15 cohorts worldwide. This aim will involve a comprehensive analysis of various exposures across different stages of life, to identify key contributors to early-onset cancers risk.\nWhile we will focus on early-onset bowel cancer in Aims 2 and 3, the analyses will serve as an exemplar for other major early-onset cancers, many of which are increasing in younger generations.Cancers are among the top causes of chronic diseases with noticeable burden on patients and health systems. In recent decades, there has been a noticeable increase in several types of cancer cases among younger people, particularly in developed countries like the UK; a trend that is not yet well understood. Furthermore, the role that various risk factors play in the development of cancer across different age groups and populations remains underexplored. The increasing burden of early-onset cancers suggests that younger generations may be exposed to unique or evolving risk factors, potentially linked to shifts generational changes.\nTo better understand the causes of these trends, it is important to study people from different generations and examine their exposure to potential risk factors over time. Also, investigating the signs and symptoms of cancers in younger generations will help in timely diagnosis of this disease. Additionally, identifying both established and emerging life course risk factors would majorly help to know the drivers of cancer in younger populations. The Our Future Health cohort provides an ideal opportunity to conduct this type of research, as it includes a large sample of individuals from multiple generations and offers follow-up data aimed for long-term tracking.","technical_summary":null,"latest_approval_date":"2025-06-04 12:00:00","manual_upload":true,"rejection_reason":null,"sublicence_arrangements":null,"public_benefit_statement":"Our study will benefit the public by addressing the critical need to understand why cancer rates are rising among younger generations. This research aims to uncover the lifestyle, environmental, and genetic factors that increase the risk of several cancers, offering valuable insights that can lead to targeted prevention strategies. For example, if certain dietary patterns or environmental exposures are found to significantly contribute to cancer development, we can implement appropriate earlier cancer screening approaches, and design public health campaigns to educate people on how to modify these risk factors. These efforts could empower individuals to make healthier choices, reducing their risk of developing cancer.\nIn addition to prevention, our research can improve early detection programs. Identifying high-risk groups, will enable healthcare providers to screen these populations more effectively, leading to earlier diagnosis and better treatment outcomes. Our findings will help lower the overall incidence of cancers, lighten the load on healthcare systems by reducing treatment costs, and, most importantly, save lives by preventing cancer from developing or ensuring that it is detected at an earlier, more treatable stage. 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It is designed to enable the discovery and testing of more effective approaches to prevention, earlier detection and treatment of diseases.\"}]},{\"type\":\"paragraph\",\"content\":[{\"type\":\"text\",\"text\":\"The programme is collecting and linking multiple sources of health and health-relevant information, including genetic data, across a cohort of up to 5 million people. It is recruiting participants from all ages, backgrounds and communities to create a cohort that truly reflects the UK population.\"}]},{\"type\":\"paragraph\",\"content\":[{\"type\":\"text\",\"text\":\"Our Future Health will create a world-leading resource for researchers from universities, charities, the NHS and companies involved in health research to undertake discovery research on early indicators of disease. Researchers will also have the opportunity to re-contact participants on a risk-stratified basis for secondary studies.\"}]},{\"type\":\"paragraph\",\"content\":[{\"type\":\"text\",\"text\":\"Find out more on the Our Future Health website: \"},{\"type\":\"text\",\"marks\":[{\"type\":\"link\",\"attrs\":{\"href\":\"https:\/\/ourfuturehealth.org.uk\/\",\"target\":\"_blank\",\"rel\":\"noopener noreferrer nofollow\",\"class\":null}}],\"text\":\"https:\/\/ourfuturehealth.org.uk\/\"}]}]}","dar_modal_header":"Important information about applying","dar_modal_content":"{\"type\":\"doc\",\"content\":[{\"type\":\"paragraph\",\"content\":[{\"type\":\"text\",\"text\":\"To apply for access to the Our Future Health dataset, please use the following link.\"}]},{\"type\":\"paragraph\",\"content\":[{\"type\":\"text\",\"marks\":[{\"type\":\"link\",\"attrs\":{\"href\":\"https:\/\/research.ourfuturehealth.org.uk\/apply-to-access-the-data\/\",\"target\":\"_blank\",\"rel\":\"noopener noreferrer nofollow\",\"class\":null}}],\"text\":\"https:\/\/research.ourfuturehealth.org.uk\/apply-to-access-the-data\/\"}]}]}","dar_modal_footer":null,"is_dar":false,"service":null},"application":null,"users":[{"id":281,"name":"Amy Peters","firstname":"Amy","lastname":"Peters","email":"amy.peters@ourfuturehealth.org.uk","secondary_email":null,"preferred_email":"primary","email_verified_at":null,"secondary_email_verified_at":null,"provider":"azure","created_at":"2024-10-08T11:14:15.000000Z","updated_at":"2026-08-07T14:09:11.000000Z","deleted_at":null,"sector_id":5,"organisation":"Our Future Health","bio":null,"domain":null,"link":null,"orcid":null,"contact_feedback":0,"contact_news":0,"mongo_id":8640248443551848,"mongo_object_id":"6672f2b9713ab403a5cd5bf3","is_admin":0,"terms":true,"hubspot_id":31477292673,"is_nhse_sde_approval":false,"laravel_through_key":3961,"rquestroles":[],"cohort_discovery_roles":[],"cohort_discovery_nhs_sde":false}],"applications":[]},{"id":3963,"created_at":"2026-07-22T16:42:19.000000Z","updated_at":"2026-07-22T16:42:47.000000Z","deleted_at":null,"active_date":"2026-07-22 16:42:48","non_gateway_datasets":[],"non_gateway_applicants":["Dipender Gill"],"funders_and_sponsors":[""],"other_approval_committees":[""],"gateway_outputs_tools":null,"gateway_outputs_papers":null,"non_gateway_outputs":[""],"project_title":"Leveraging human genetic data to inform drug discovery and development.","project_id_text":"OFHS260020","organisation_name":"Sequoia Genetics Ltd","organisation_sector":"Commercial","lay_summary":"This study will use large-scale genetic data from the Our Future Health programme to help identify and evaluate potential new drug targets for diseases where better treatments are needed. Genetic differences between people can act as natural experiments that help researchers understand how specific genes influence health and disease. By analysing these genetic differences across many participants, we can investigate whether modifying the activity of proteins that are coded for by those genes may have beneficial or harmful effects. First, we will identify DNA changes that can act as reliable indicators of how specific genes influence disease risk. This can help highlight genes whose proteins may serve as promising targets for new medicines or help validate targets already being explored by researchers. Second, we will examine the likely health effects of variation in the genes coding for these targets, including both potential benefits and possible side effects. Understanding these effects early can help guide the development of safer and more effective treatments. Finally, we will explore whether computers that learn from data (machine learning) can help us identify promising drug targets and predict their likely therapeutic outcomes. Together, this work aims to support the discovery and prioritisation of new medicines.Knowledge Gaps: Most drugs fail in clinical trials because their targets aren't validated in humans. Current drug development relies heavily on animal models, which don't always predict how drugs will work in people. Human genetic data has the potential to overcome these limitations.\nSignificance of Research: Human genetic information offers another way to study disease and potential treatments. Natural differences in genes between people can show how changes in certain biological processes affect health. In some cases, these natural differences can mimic the effects of medicines that increase or reduce the activity of a gene. Studying these patterns can help researchers understand which genes or biological pathways may be good targets for new treatments.\nAnticipated Research Significance: The Our Future Health programme provides a unique opportunity to do this research at a very large scale. It includes genetic information and health data from many volunteers across the UK. By analysing these data together, we can identify patterns that link genes with health outcomes and gain insights into how diseases develop. This information can help guide future research into new medicines and improve our understanding of human health.","technical_summary":null,"latest_approval_date":"2026-04-24 12:00:00","manual_upload":true,"rejection_reason":null,"sublicence_arrangements":null,"public_benefit_statement":"This research supports the discovery and development of safer and more effective medicines for common health conditions. These include illnesses that affect many people in the UK, such as heart disease, diabetes, respiratory conditions, immune-related diseases, brain disorders, and kidney disease. These conditions lead to poor health, disability, and early death, and many still lack effective treatments.\nBy studying genetic and health information from the Our Future Health programme, this research will explore how natural differences in genes influence health and disease. These genetic differences can provide clues about whether changing certain biological processes in the body might improve health or cause unwanted effects. Learning this early in the research process can help identify which treatments are most likely to work.\nThis approach may help reduce the number of medicines that fail during clinical trials and allow researchers to focus on the most promising treatments. Longer term, this could speed up the development of new medicines, ensure they are safer and more effective. It may also help identify groups of people who are more likely to benefit from certain treatments or experience side effects. Overall, this work aims to contribute to better treatments, and longer, healthier lives for the public.","data_sensitivity_level":null,"project_start_date":"2026-04-29 12:00:00","project_end_date":null,"access_date":null,"accredited_researcher_status":null,"confidential_data_description":null,"dataset_linkage_description":null,"duty_of_confidentiality":null,"legal_basis_for_data_article6":null,"legal_basis_for_data_article9":null,"national_data_optout":null,"organisation_id":null,"privacy_enhancements":null,"request_category_type":"Public Health 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It is designed to enable the discovery and testing of more effective approaches to prevention, earlier detection and treatment of diseases.\"}]},{\"type\":\"paragraph\",\"content\":[{\"type\":\"text\",\"text\":\"The programme is collecting and linking multiple sources of health and health-relevant information, including genetic data, across a cohort of up to 5 million people. It is recruiting participants from all ages, backgrounds and communities to create a cohort that truly reflects the UK population.\"}]},{\"type\":\"paragraph\",\"content\":[{\"type\":\"text\",\"text\":\"Our Future Health will create a world-leading resource for researchers from universities, charities, the NHS and companies involved in health research to undertake discovery research on early indicators of disease. Researchers will also have the opportunity to re-contact participants on a risk-stratified basis for secondary studies.\"}]},{\"type\":\"paragraph\",\"content\":[{\"type\":\"text\",\"text\":\"Find out more on the Our Future Health website: \"},{\"type\":\"text\",\"marks\":[{\"type\":\"link\",\"attrs\":{\"href\":\"https:\/\/ourfuturehealth.org.uk\/\",\"target\":\"_blank\",\"rel\":\"noopener noreferrer nofollow\",\"class\":null}}],\"text\":\"https:\/\/ourfuturehealth.org.uk\/\"}]}]}","dar_modal_header":"Important information about applying","dar_modal_content":"{\"type\":\"doc\",\"content\":[{\"type\":\"paragraph\",\"content\":[{\"type\":\"text\",\"text\":\"To apply for access to the Our Future Health dataset, please use the following link.\"}]},{\"type\":\"paragraph\",\"content\":[{\"type\":\"text\",\"marks\":[{\"type\":\"link\",\"attrs\":{\"href\":\"https:\/\/research.ourfuturehealth.org.uk\/apply-to-access-the-data\/\",\"target\":\"_blank\",\"rel\":\"noopener noreferrer nofollow\",\"class\":null}}],\"text\":\"https:\/\/research.ourfuturehealth.org.uk\/apply-to-access-the-data\/\"}]}]}","dar_modal_footer":null,"is_dar":false,"service":null},"application":null,"users":[],"applications":[]},{"id":1271,"created_at":"2024-10-25T18:17:23.000000Z","updated_at":"2026-07-10T16:39:03.000000Z","deleted_at":null,"active_date":"2026-03-31 10:13:00","non_gateway_datasets":[],"non_gateway_applicants":["Daniel Smith"],"funders_and_sponsors":[""],"other_approval_committees":[""],"gateway_outputs_tools":null,"gateway_outputs_papers":null,"non_gateway_outputs":["10.1136\/bmjment-2025-301706","https:\/\/doi.org\/10.1101\/2025.11.23.25340839"],"project_title":"Mood disorders within Our Future Health","project_id_text":"OFHS240114","organisation_name":"University of Edinburgh","organisation_sector":"Academic Institute","lay_summary":"This study aims to find out how common mood disorders are within the Our Future Health (OFH) Cohort. We will also assess whether people with a history of mood disorders are more likely to have physical health problems. Finally, we will investigate how lifestyle factors and genetics contribute to both mood disorders and physical health problems. The over-arching goal of this study is to generate new hypotheses on the mechanisms that drive associations between mood disorders and physical health problems. We will: a) identify the prevalence of mood disorders in the OFH cohort; b) describe patterns of comorbidity between mood disorders and physical health conditions (such as obesity, diabetes and cardiovascular disease); and c) conduct an assessment of how lifestyle factors (such as activity levels, sleep, smoking and alcohol use) and genetic factors (defined by family history and polygenic risk scores) influence the risk of different patterns of mental\/physical health comorbidity. These analyses will then be used to generate new hypotheses on the mechanisms that may drive comorbidity between mood disorders and physical ill-health.\nThere are several areas where this research will address knowledge gaps. The large Our Future Health cohort will allow us to obtain a clear picture of how common different types of mood disorder are within the UK general population. We will also be able to assess how certain aspects of lifestyle (eg, smoking, alcohol use, exercise and sleep) are associated with different types of mood disorder (such as depression, anxiety disorders and bipolar disorder). Similarly, we will identify patterns of physical health problems in people with mood disorders. By doing this, we will generate theories about how mental and physical health problems might be related to each other.","technical_summary":null,"latest_approval_date":"2024-08-22 12:00:00","manual_upload":true,"rejection_reason":null,"sublicence_arrangements":null,"public_benefit_statement":"Mood disorders are common and for many individuals they are associated poor quality of life and reduced life span. Increasingly, mood disorders are considered 'whole body' phenomena (affecting both physical and mental wellbeing). As such, the data within Our Future Health has the potential to improve our understanding of which factors (both generic and non-genetic) contribute most to different patterns of adverse physical health outcomes in people with depression, anxiety disorders or bipolar disorder.\nThe added value and benefit for the public will be a better understanding of how lifestyle, environmental and genetic factors influence patterns of comorbidity between mood disorders and physical health problems. In the future, this may inform the design of new treatment approaches, for example, to prevent obesity or diabetes in people with a personal or family history of depression, anxiety disorder or bipolar disorder. 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Researchers will also have the opportunity to re-contact participants on a risk-stratified basis for secondary studies.\"}]},{\"type\":\"paragraph\",\"content\":[{\"type\":\"text\",\"text\":\"Find out more on the Our Future Health website: \"},{\"type\":\"text\",\"marks\":[{\"type\":\"link\",\"attrs\":{\"href\":\"https:\/\/ourfuturehealth.org.uk\/\",\"target\":\"_blank\",\"rel\":\"noopener noreferrer nofollow\",\"class\":null}}],\"text\":\"https:\/\/ourfuturehealth.org.uk\/\"}]}]}","dar_modal_header":"Important information about applying","dar_modal_content":"{\"type\":\"doc\",\"content\":[{\"type\":\"paragraph\",\"content\":[{\"type\":\"text\",\"text\":\"To apply for access to the Our Future Health dataset, please use the following link.\"}]},{\"type\":\"paragraph\",\"content\":[{\"type\":\"text\",\"marks\":[{\"type\":\"link\",\"attrs\":{\"href\":\"https:\/\/research.ourfuturehealth.org.uk\/apply-to-access-the-data\/\",\"target\":\"_blank\",\"rel\":\"noopener noreferrer nofollow\",\"class\":null}}],\"text\":\"https:\/\/research.ourfuturehealth.org.uk\/apply-to-access-the-data\/\"}]}]}","dar_modal_footer":null,"is_dar":false,"service":null},"application":null,"users":[{"id":281,"name":"Amy Peters","firstname":"Amy","lastname":"Peters","email":"amy.peters@ourfuturehealth.org.uk","secondary_email":null,"preferred_email":"primary","email_verified_at":null,"secondary_email_verified_at":null,"provider":"azure","created_at":"2024-10-08T11:14:15.000000Z","updated_at":"2026-08-07T14:09:11.000000Z","deleted_at":null,"sector_id":5,"organisation":"Our Future Health","bio":null,"domain":null,"link":null,"orcid":null,"contact_feedback":0,"contact_news":0,"mongo_id":8640248443551848,"mongo_object_id":"6672f2b9713ab403a5cd5bf3","is_admin":0,"terms":true,"hubspot_id":31477292673,"is_nhse_sde_approval":false,"laravel_through_key":1271,"rquestroles":[],"cohort_discovery_roles":[],"cohort_discovery_nhs_sde":false}],"applications":[]},{"id":3380,"created_at":"2026-02-26T16:03:49.000000Z","updated_at":"2026-06-24T19:05:16.000000Z","deleted_at":null,"active_date":"2026-06-24 19:05:18","non_gateway_datasets":["ICHT Fetal Link Dataset"],"non_gateway_applicants":["T.G. 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The ultimate aim of the study is to create an algorithm, through machine learning\/ artificial intelligence that will improve recognition and management of abnormal foetal heart traces in future (Artificial Intelligence is a set of instructions which are written in a computer program. The instructions run a computer programme which performs mathematical tests on data. The instructions that allow the AI to work are called an \u2018algorithm\u2019).\nThis study will principally consist of the collection of foetal heart rate tracings, which are stored in digital form, from women in labour. Foetal heart rate monitoring is used to monitor foetal well being in labour. 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It can then be translated into decision support for clinicians undertaking care of women in labour, to identify abnormalities more quickly in labour.\nAn automated and reliable Artificial Intelligence based tool will reduce human error leading to improved health benefits and reduction of adverse outcomes for babies and mothers in labour. \nIssues related to use of patient information is mitigated through the use of de-identified information at the point of extraction as well as analysis and the use of secure servers of Imperial College Healthcare NHS Trust and the Big Data Analytic Unit (at Imperial College London) to link and store, as well as analyse this data, respectively.\nThe initial Pilot data has allowed the team to develop a process of data management and also has shown potential for development of a novel machine learning process (algorithm). This however was Pilot data on 100 patients only and to show transferable results to a wider population, much more data is required to ensure development of a safe and robust machine learning process to improve earlier identification of abnormalities in foetal heart rate tracings.","technical_summary":null,"latest_approval_date":"2021-09-24 12:00:00","manual_upload":true,"rejection_reason":null,"sublicence_arrangements":"No","public_benefit_statement":"It is known that approximately half of all stillbirths and a quarter of neonatal deaths result from complications during labour and childbirth, which remains a major concern for women, their families and the healthcare professionals who are tasked with providing gold-standard care for them. A significant contributory factor to this is misinterpretation of foetal heart monitoring. \nIn addition, misinterpretation of foetal heart monitoring results in inappropriate intervention by caesarean section and instrumental deliveries, as well as being associated with maternal and foetal morbidity and mortality. An artificial intelligence system that can more specifically interpret and predict the foetal tracings will address the concerns above and therefore will hold the potential to improve outcomes for women and children. 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The aim of this project is to equip and empower patients known to be at high risk of acute coronary syndromes to seek urgent medical help without going to the hospital, if they experience symptoms, and to make a decision to present to the emergency services whenever necessary. This serves two aims:\n1) Ensuring that patients present appropriately to the emergency services if needed, and\n2) To prevent unnecessary presentations, as assessed by well-validated technologies coupled with an urgent remote consultation with a specialist.","technical_summary":null,"latest_approval_date":"2021-11-10 12:00:00","manual_upload":true,"rejection_reason":null,"sublicence_arrangements":"No","public_benefit_statement":"The telemonitoring system would reduce hospital readmission for patients with high cardiovascular risk post-acute coronary syndrome by using well-validated technologies coupled with an urgent remote consultation with a cardiologist.","data_sensitivity_level":"De-Personalised","project_start_date":"2021-11-10 12:00:00","project_end_date":null,"access_date":null,"accredited_researcher_status":"Unknown","confidential_data_description":null,"dataset_linkage_description":null,"duty_of_confidentiality":"Not applicable","legal_basis_for_data_article6":null,"legal_basis_for_data_article9":null,"national_data_optout":"Not applicable","organisation_id":null,"privacy_enhancements":null,"request_category_type":"Public Health Research","request_frequency":"One-off","access_type":"TRE","mongo_object_dar_id":null,"enabled":true,"last_activity":null,"counter":0,"mongo_object_id":null,"mongo_id":null,"user_id":4777,"team_id":93,"application_id":null,"applicant_id":"NIBDAPC_2021_NA_0004","sector_id":4,"status":"ACTIVE","datasets":[],"publications":[],"tools":[],"keywords":[],"user":{"id":4777,"name":"Giselle Kerry","firstname":"Giselle","lastname":"Kerry","email":"Giselle.Kerry@hdruk.ac.uk","secondary_email":"giselle.kerry@hotmail.co.uk","preferred_email":"primary","email_verified_at":null,"secondary_email_verified_at":"2025-09-24 09:18:19","provider":"azure","created_at":"2025-06-05T13:51:42.000000Z","updated_at":"2026-08-07T09:22:46.000000Z","deleted_at":null,"sector_id":6,"organisation":null,"bio":null,"domain":null,"link":null,"orcid":null,"contact_feedback":0,"contact_news":0,"mongo_id":0,"mongo_object_id":null,"is_admin":1,"terms":true,"hubspot_id":128489718789,"is_nhse_sde_approval":false,"rquestroles":["GENERAL_ACCESS"],"cohort_discovery_roles":["GENERAL_ACCESS"],"cohort_discovery_nhs_sde":false},"team":{"id":93,"pid":"d9d1f252-1f5d-45cf-b8b3-078dfaf739fb","created_at":"2024-10-08T11:19:00.000000Z","updated_at":"2026-06-22T14:33:54.000000Z","deleted_at":null,"name":"Imperial College Healthcare NHS Trust","enabled":true,"allows_messaging":false,"workflow_enabled":false,"access_requests_management":false,"uses_5_safes":false,"is_admin":false,"team_logo":null,"member_of":"ALLIANCE","contact_point":null,"application_form_updated_by":"Qresearch webapp","application_form_updated_on":"0001-01-01 00:00:00","mongo_object_id":"66d1e270b5df0006bb95e5e3","notification_status":false,"is_question_bank":false,"is_provider":false,"url":null,"introduction":null,"dar_modal_header":"Important information about applying","dar_modal_content":"{\"type\":\"doc\",\"content\":[{\"type\":\"paragraph\",\"content\":[{\"type\":\"text\",\"text\":\"To apply for access to the Imperial College Healthcare NHS Trust dataset please use the following link:\"}]},{\"type\":\"paragraph\",\"content\":[{\"type\":\"text\",\"marks\":[{\"type\":\"link\",\"attrs\":{\"href\":\"https:\/\/www.imperial.ac.uk\/medicine\/research-and-impact\/groups\/icare\/icare-facility\/information-for-researchers\/\",\"target\":\"_blank\",\"rel\":\"noopener noreferrer nofollow\",\"class\":null}}],\"text\":\"https:\/\/www.imperial.ac.uk\/medicine\/research-and-impact\/groups\/icare\/icare-facility\/information-for-researchers\/\"}]}]}","dar_modal_footer":null,"is_dar":false,"service":null},"application":null,"users":[],"applications":[]},{"id":3378,"created_at":"2026-02-26T16:03:49.000000Z","updated_at":"2026-06-24T19:04:57.000000Z","deleted_at":null,"active_date":"2026-06-24 19:04:59","non_gateway_datasets":["NIHR HIC Colorectal Cancer Dataset"],"non_gateway_applicants":["Ben Glampson"],"funders_and_sponsors":["Harpreet Wasan"],"other_approval_committees":[""],"gateway_outputs_tools":null,"gateway_outputs_papers":null,"non_gateway_outputs":["https:\/\/informatics.bmj.com\/content\/29\/1\/e100535."],"project_title":"NIHR HIC \u2013 Colorectal Cancer Theme","project_id_text":"NIBDAPC_2021_0003","organisation_name":"Imperial College Healthcare NHS Trust","organisation_sector":"Government Agency (Health and Adult Social Care)","lay_summary":"Imperial College Healthcare NHS Trust collects data on its patients who have colorectal cancer. This includes data on diagnosis, surgery and treatment, all of which is recorded on electronic patient record systems as part of the routine care process. The Trust is in the process of extracting the data from these systems, and structuring it into one database with all patient identifiable information (such as patient names and NHS numbers) de-identified . Other cancer centres around the country would follow a similar process, and these structured databases would be sent to a research team in Oxford University Hospitals. From there, these can be combined to form one larger research database. Approved researchers can then use this research database to answer important research questions relating to the care and outcomes of colorectal cancer patients. This work aims to identify best practices relating to care of cancer patients and ultimately improve outcomes for these patients.","technical_summary":null,"latest_approval_date":"2021-07-30 12:00:00","manual_upload":true,"rejection_reason":null,"sublicence_arrangements":"No","public_benefit_statement":"Researchers wishing to access the Colorectal Cancer Theme research database (which contains data from all participating sites) must submit a project proposal application to the Theme\u2019s Scientific Steering Committee. This Committee is made up of the Theme Clinical Leads from all participating sites. As part of the application process, researchers must outline how the research question has been identified as a priority for the benefit of patients. Applications that do not adequately answer this will not be approved by the Scientific Steering Committee, and access to the data will therefore not be granted.","data_sensitivity_level":"De-Personalised","project_start_date":"2021-07-30 12:00:00","project_end_date":null,"access_date":"2021-08-01 00:00:00","accredited_researcher_status":"Unknown","confidential_data_description":null,"dataset_linkage_description":null,"duty_of_confidentiality":"Not applicable","legal_basis_for_data_article6":null,"legal_basis_for_data_article9":null,"national_data_optout":"Not applicable","organisation_id":null,"privacy_enhancements":null,"request_category_type":"Public Health Research","request_frequency":"One-off","access_type":"TRE","mongo_object_dar_id":null,"enabled":true,"last_activity":null,"counter":0,"mongo_object_id":null,"mongo_id":null,"user_id":4777,"team_id":93,"application_id":null,"applicant_id":"NIBDAPC_2021_BG_0003","sector_id":4,"status":"ACTIVE","datasets":[],"publications":[],"tools":[],"keywords":[],"user":{"id":4777,"name":"Giselle Kerry","firstname":"Giselle","lastname":"Kerry","email":"Giselle.Kerry@hdruk.ac.uk","secondary_email":"giselle.kerry@hotmail.co.uk","preferred_email":"primary","email_verified_at":null,"secondary_email_verified_at":"2025-09-24 09:18:19","provider":"azure","created_at":"2025-06-05T13:51:42.000000Z","updated_at":"2026-08-07T09:22:46.000000Z","deleted_at":null,"sector_id":6,"organisation":null,"bio":null,"domain":null,"link":null,"orcid":null,"contact_feedback":0,"contact_news":0,"mongo_id":0,"mongo_object_id":null,"is_admin":1,"terms":true,"hubspot_id":128489718789,"is_nhse_sde_approval":false,"rquestroles":["GENERAL_ACCESS"],"cohort_discovery_roles":["GENERAL_ACCESS"],"cohort_discovery_nhs_sde":false},"team":{"id":93,"pid":"d9d1f252-1f5d-45cf-b8b3-078dfaf739fb","created_at":"2024-10-08T11:19:00.000000Z","updated_at":"2026-06-22T14:33:54.000000Z","deleted_at":null,"name":"Imperial College Healthcare NHS Trust","enabled":true,"allows_messaging":false,"workflow_enabled":false,"access_requests_management":false,"uses_5_safes":false,"is_admin":false,"team_logo":null,"member_of":"ALLIANCE","contact_point":null,"application_form_updated_by":"Qresearch webapp","application_form_updated_on":"0001-01-01 00:00:00","mongo_object_id":"66d1e270b5df0006bb95e5e3","notification_status":false,"is_question_bank":false,"is_provider":false,"url":null,"introduction":null,"dar_modal_header":"Important information about applying","dar_modal_content":"{\"type\":\"doc\",\"content\":[{\"type\":\"paragraph\",\"content\":[{\"type\":\"text\",\"text\":\"To apply for access to the Imperial College Healthcare NHS Trust dataset please use the following link:\"}]},{\"type\":\"paragraph\",\"content\":[{\"type\":\"text\",\"marks\":[{\"type\":\"link\",\"attrs\":{\"href\":\"https:\/\/www.imperial.ac.uk\/medicine\/research-and-impact\/groups\/icare\/icare-facility\/information-for-researchers\/\",\"target\":\"_blank\",\"rel\":\"noopener noreferrer nofollow\",\"class\":null}}],\"text\":\"https:\/\/www.imperial.ac.uk\/medicine\/research-and-impact\/groups\/icare\/icare-facility\/information-for-researchers\/\"}]}]}","dar_modal_footer":null,"is_dar":false,"service":null},"application":null,"users":[],"applications":[]},{"id":3377,"created_at":"2026-02-26T16:03:49.000000Z","updated_at":"2026-06-24T19:04:45.000000Z","deleted_at":null,"active_date":"2026-06-24 19:04:47","non_gateway_datasets":["Lung Nodules Dataset"],"non_gateway_applicants":["Prashanthi Ratnakumar"],"funders_and_sponsors":["Susannah Bloch"],"other_approval_committees":[""],"gateway_outputs_tools":null,"gateway_outputs_papers":null,"non_gateway_outputs":[""],"project_title":"Investigating Lung Nodule Management","project_id_text":"NIBDAPC_2021_0002","organisation_name":"Imperial College Healthcare NHS Trust","organisation_sector":"Government Agency (Health and Adult Social Care)","lay_summary":"Lung cancer is one of the most common cancers within the UK, and continues to be diagnosed at late stages, where curative treatment cannot be offered. The symptom burden and mortality in advanced lung cancer is significant. Early diagnosis can be increased by efficient pick-up and surveillance of lung nodules, small spots on the lung which are common incidental findings when CT scans are done for other reasons in healthcare. The majority of lung nodules are not concerning, but up to 10% of lung nodules can become cancerous. Careful follow-up scans under specialists (Respiratory services) detect nodule growth, enabling us to identify and curatively treat lung cancers as early as possible. This is vital to improve lung cancer survival. Although national guidelines guide surveillance, variation still exists in practice, and follow-up relies on individual clinicians reading lengthy reports. This poses a significant safety risk to patients, of loss to follow-up or delay in referral. This project utilises computer coding to develop a search strategy which acts as a safety-net to identify scans reporting a lung nodule needing specialist input. Automating this process reduces risk of losing patients, and crucially of missing any opportunities to diagnose lung cancer at an early stage. The first stage will refine coding developed collaboratively with the Royal Marsden Informatics team, to accurately identify scans reporting lung nodules. The second stage will retrospectively test the code and cross-link findings with electronic patient records to evaluate if referral occurred, and how referral time correlates with stage and treatment if cancer was diagnosed. From this, we will analyse which patient groups are particularly at risk of delay. Finally, this project will directly improve clinical care for patients as it can be implemented into hospital systems to reduce variation in follow up, supporting efficient early cancer diagnosis pathways.","technical_summary":null,"latest_approval_date":"2021-07-30 12:00:00","manual_upload":true,"rejection_reason":null,"sublicence_arrangements":"No","public_benefit_statement":"As part of the pilot work towards this study, which is supported by the West London Cancer Alliance (Royal Marsden Partners), a feedback exercise was undertaken with patients under lung nodule surveillance, or who had recently completed their surveillance. The local care team spoke to patients to invite them to a short telephone interview with one of the study team (PR), in conjunction with their local care team, to seek feedback on the nodule surveillance service. One of the common themes identified through feedback at multiple Trusts was anxiety around the long waiting time of surveillance for a potentially pre-malignant lesion, and the concern about \u201cbeing forgotten\u201d, a sentiment which was amplified by the cessation of routine care during the first wave of the Covid-19 pandemic. This directly highlights the value of our research question around how an iterative machine learning approach can offer a safety net to patients.","data_sensitivity_level":"De-Personalised","project_start_date":"2021-08-02 12:00:00","project_end_date":null,"access_date":"2021-08-02 00:00:00","accredited_researcher_status":"Unknown","confidential_data_description":null,"dataset_linkage_description":null,"duty_of_confidentiality":"Not applicable","legal_basis_for_data_article6":null,"legal_basis_for_data_article9":null,"national_data_optout":"Not applicable","organisation_id":null,"privacy_enhancements":null,"request_category_type":"Public Health Research","request_frequency":"One-off","access_type":"TRE","mongo_object_dar_id":null,"enabled":true,"last_activity":null,"counter":0,"mongo_object_id":null,"mongo_id":null,"user_id":4777,"team_id":93,"application_id":null,"applicant_id":"NIBDAPC_2021_PR_0002","sector_id":4,"status":"ACTIVE","datasets":[],"publications":[],"tools":[],"keywords":[],"user":{"id":4777,"name":"Giselle Kerry","firstname":"Giselle","lastname":"Kerry","email":"Giselle.Kerry@hdruk.ac.uk","secondary_email":"giselle.kerry@hotmail.co.uk","preferred_email":"primary","email_verified_at":null,"secondary_email_verified_at":"2025-09-24 09:18:19","provider":"azure","created_at":"2025-06-05T13:51:42.000000Z","updated_at":"2026-08-07T09:22:46.000000Z","deleted_at":null,"sector_id":6,"organisation":null,"bio":null,"domain":null,"link":null,"orcid":null,"contact_feedback":0,"contact_news":0,"mongo_id":0,"mongo_object_id":null,"is_admin":1,"terms":true,"hubspot_id":128489718789,"is_nhse_sde_approval":false,"rquestroles":["GENERAL_ACCESS"],"cohort_discovery_roles":["GENERAL_ACCESS"],"cohort_discovery_nhs_sde":false},"team":{"id":93,"pid":"d9d1f252-1f5d-45cf-b8b3-078dfaf739fb","created_at":"2024-10-08T11:19:00.000000Z","updated_at":"2026-06-22T14:33:54.000000Z","deleted_at":null,"name":"Imperial College Healthcare NHS Trust","enabled":true,"allows_messaging":false,"workflow_enabled":false,"access_requests_management":false,"uses_5_safes":false,"is_admin":false,"team_logo":null,"member_of":"ALLIANCE","contact_point":null,"application_form_updated_by":"Qresearch webapp","application_form_updated_on":"0001-01-01 00:00:00","mongo_object_id":"66d1e270b5df0006bb95e5e3","notification_status":false,"is_question_bank":false,"is_provider":false,"url":null,"introduction":null,"dar_modal_header":"Important information about applying","dar_modal_content":"{\"type\":\"doc\",\"content\":[{\"type\":\"paragraph\",\"content\":[{\"type\":\"text\",\"text\":\"To apply for access to the Imperial College Healthcare NHS Trust dataset please use the following link:\"}]},{\"type\":\"paragraph\",\"content\":[{\"type\":\"text\",\"marks\":[{\"type\":\"link\",\"attrs\":{\"href\":\"https:\/\/www.imperial.ac.uk\/medicine\/research-and-impact\/groups\/icare\/icare-facility\/information-for-researchers\/\",\"target\":\"_blank\",\"rel\":\"noopener noreferrer nofollow\",\"class\":null}}],\"text\":\"https:\/\/www.imperial.ac.uk\/medicine\/research-and-impact\/groups\/icare\/icare-facility\/information-for-researchers\/\"}]}]}","dar_modal_footer":null,"is_dar":false,"service":null},"application":null,"users":[],"applications":[]},{"id":3376,"created_at":"2026-02-26T16:03:49.000000Z","updated_at":"2026-06-24T19:04:36.000000Z","deleted_at":null,"active_date":"2026-06-24 19:04:38","non_gateway_datasets":["NIHR HIC Diabetes Dataset"],"non_gateway_applicants":["Rustam Rae"],"funders_and_sponsors":["Neil Hill"],"other_approval_committees":[""],"gateway_outputs_tools":null,"gateway_outputs_papers":null,"non_gateway_outputs":["https:\/\/doi.org\/10.1016\/j.jdiacomp.2023.108474"],"project_title":"Diabetes In-Patient Hypoglycaemia Prediction Model","project_id_text":"NIBDAPC_2021_0001","organisation_name":"Oxford University Hospitals NHS Foundation Trust","organisation_sector":"Government Agency (Health and Adult Social Care)","lay_summary":"We wish to use anonymised patient data to confirm the efficacy of a model that can predict people at risk of hypoglycaemia in during their hospital admission. If this works it may be possible to use this model in real-time to identify individuals at risk and take pre-emptive steps to prevent or mitigate the risk of hypoglycaemia.","technical_summary":null,"latest_approval_date":"2021-06-25 12:00:00","manual_upload":true,"rejection_reason":null,"sublicence_arrangements":"No","public_benefit_statement":"Between May and October 2018, 525 hypoglycaemic harms were recorded amongst participating NHS Trusts in the The National Diabetes Audit. As well as these harms being linked to poorer outcomes, hypoglycaemia requires significant medical and nursing resources to manage and the cost of an inpatient hospital stay is increased by 40% for those exposed. For example, if the number of hypoglycaemic episodes in people with diabetes and acute stroke were halved, this could lead to savings of more than \u00a36,000,000. \nCurrent means of detecting blood glucose levels in hospitalised patients are inadequate. Hypoglycaemia is recognised only if patients are symptomatic and are able to communicate this to healthcare professionals. This is clearly not feasible for large numbers of patients (including those with reduced level of consciousness, difficulty communicating, or cognitive impairment). In addition, around 30% people with diabetes have impaired awareness of hypoglycaemia. Healthcare professionals establish blood glucose levels by checking capillary blood glucose (CBG) levels. This method is sensitive but its efficacy in preventing harm from hypoglycaemia is a function of how often it is performed. Approximately 20% of hospital beds in the UK are occupied by people with diabetes, and such a large amount of bedside CBG testing is unfeasible for most hospitals. Changes in nursing staffing levels on general wards may also impact on capacity to undertake regular CBG monitoring. This means the risk of hypoglycaemia is set to rise. Mitigating this increased risk is essential to prevent harm.","data_sensitivity_level":"De-Personalised","project_start_date":"2021-06-25 12:00:00","project_end_date":null,"access_date":"2021-08-13 00:00:00","accredited_researcher_status":"Unknown","confidential_data_description":null,"dataset_linkage_description":null,"duty_of_confidentiality":"Not applicable","legal_basis_for_data_article6":null,"legal_basis_for_data_article9":null,"national_data_optout":"Not applicable","organisation_id":null,"privacy_enhancements":null,"request_category_type":"Public Health Research","request_frequency":"One-off","access_type":"TRE","mongo_object_dar_id":null,"enabled":true,"last_activity":null,"counter":0,"mongo_object_id":null,"mongo_id":null,"user_id":4777,"team_id":93,"application_id":null,"applicant_id":"NIBDAPC_2021_RR_0001","sector_id":4,"status":"ACTIVE","datasets":[],"publications":[],"tools":[],"keywords":[],"user":{"id":4777,"name":"Giselle Kerry","firstname":"Giselle","lastname":"Kerry","email":"Giselle.Kerry@hdruk.ac.uk","secondary_email":"giselle.kerry@hotmail.co.uk","preferred_email":"primary","email_verified_at":null,"secondary_email_verified_at":"2025-09-24 09:18:19","provider":"azure","created_at":"2025-06-05T13:51:42.000000Z","updated_at":"2026-08-07T09:22:46.000000Z","deleted_at":null,"sector_id":6,"organisation":null,"bio":null,"domain":null,"link":null,"orcid":null,"contact_feedback":0,"contact_news":0,"mongo_id":0,"mongo_object_id":null,"is_admin":1,"terms":true,"hubspot_id":128489718789,"is_nhse_sde_approval":false,"rquestroles":["GENERAL_ACCESS"],"cohort_discovery_roles":["GENERAL_ACCESS"],"cohort_discovery_nhs_sde":false},"team":{"id":93,"pid":"d9d1f252-1f5d-45cf-b8b3-078dfaf739fb","created_at":"2024-10-08T11:19:00.000000Z","updated_at":"2026-06-22T14:33:54.000000Z","deleted_at":null,"name":"Imperial College Healthcare NHS Trust","enabled":true,"allows_messaging":false,"workflow_enabled":false,"access_requests_management":false,"uses_5_safes":false,"is_admin":false,"team_logo":null,"member_of":"ALLIANCE","contact_point":null,"application_form_updated_by":"Qresearch webapp","application_form_updated_on":"0001-01-01 00:00:00","mongo_object_id":"66d1e270b5df0006bb95e5e3","notification_status":false,"is_question_bank":false,"is_provider":false,"url":null,"introduction":null,"dar_modal_header":"Important information about applying","dar_modal_content":"{\"type\":\"doc\",\"content\":[{\"type\":\"paragraph\",\"content\":[{\"type\":\"text\",\"text\":\"To apply for access to the Imperial College Healthcare NHS Trust dataset please use the following link:\"}]},{\"type\":\"paragraph\",\"content\":[{\"type\":\"text\",\"marks\":[{\"type\":\"link\",\"attrs\":{\"href\":\"https:\/\/www.imperial.ac.uk\/medicine\/research-and-impact\/groups\/icare\/icare-facility\/information-for-researchers\/\",\"target\":\"_blank\",\"rel\":\"noopener noreferrer nofollow\",\"class\":null}}],\"text\":\"https:\/\/www.imperial.ac.uk\/medicine\/research-and-impact\/groups\/icare\/icare-facility\/information-for-researchers\/\"}]}]}","dar_modal_footer":null,"is_dar":false,"service":null},"application":null,"users":[],"applications":[]},{"id":3382,"created_at":"2026-02-26T16:03:50.000000Z","updated_at":"2026-06-24T19:04:25.000000Z","deleted_at":null,"active_date":"2026-06-24 19:04:27","non_gateway_datasets":["NIHR HIC Ovarian Cancer Dataset"],"non_gateway_applicants":["Laura Tookman"],"funders_and_sponsors":["Deidre Lyons"],"other_approval_committees":[""],"gateway_outputs_tools":null,"gateway_outputs_papers":null,"non_gateway_outputs":[""],"project_title":"NLP and Pathway Modelling in Ovarian Cancer to Understand Inequalities","project_id_text":"NIBDAPC_2021_0007","organisation_name":"Imperial College Healthcare NHS Trust","organisation_sector":"Government Agency (Health and Adult Social Care)","lay_summary":"Ovarian cancer is the most lethal gynaecological malignancy, diagnosed in over 7000 patients each year in the UK and prognosis remains poor. The recent national ovarian cancer pilot audit has clearly revealed significant inequalities in the management of patients on a national level. We do not yet understand the reasons underlying these differences and the full impact of these inequalities on outcome. There is therefore a significant unmet need to understand treatment pathways for all women with ovarian cancer level and correlate these data with outcome. \n\nIt is only by ensuring accurate, correct records, fully analysing and reviewing our data that we can really understand the challenges that are faced when treating patients with ovarian cancer. We propose to develop methods to utilise the wealth of routine data held in NHS records.  We will develop the processes that allow robust, relevant and comprehensive data collection and analysis to be performed automatically to assess the care given to all patients.\n\nThis data will be used to identify any inequalities in care of patients with ovarian cancer (e.g. variations with age, ethnicity or region) and develop methods to feedback this information to the clinical teams. Once this is understood we can begin to effect change and improve care for patients.","technical_summary":null,"latest_approval_date":"2022-01-14 12:00:00","manual_upload":true,"rejection_reason":null,"sublicence_arrangements":"No","public_benefit_statement":"The results from the ovarian cancer feasibility pilot audit1 have clearly revealed inequalities in the management of patients with ovarian cancer on a national level. We are yet to appreciate fully the reasons underlying these differences and the full impact of these inequalities on outcome. There is therefore a significant unmet need to be able to understand treatment pathways for all women with ovarian cancer on a local and national level and correlate these data with outcome. \n\nThere is a wealth of data held across NHS systems that could help understand the management of patients with ovarian cancer. Curation of these data on the scale needed to effect change is near impossible using the current methods of manual data collection. This project proposes to address this problem by creating a collaboration with clinicians, informatics teams and data analysts. This project aims to design innovative programmatic linkage, curation and analysis pipelines for data using text analysis. This will enable us to:\n\n1. Answer important clinical relevant questions regarding the management of patients with ovarian cancer\n2. Identify areas of inequality of care (e.g. inequalities with age, ethnicity, region). To use this knowledge to develop ways to improve this and improve direct care (for example, engagement with local GPs, cultural groups, patients\u2019 groups, addressing the needs of the older women).  \n3. Identify ways to improve efficacy of the service to benefit both patients and healthcare professionals\n4. Provide evidence of accuracy of the text analysis and explore how it can be used with clinician validation to improve the quality of the clinical record \n\nOnce developed, we will share our learning and techniques with other tumour sites and also other Trusts across the UK so that collection and analysis of relevant data is possible on a national level and healthcare professionals can have access to the data to drive ongoing improvement.","data_sensitivity_level":"De-Personalised","project_start_date":"2022-01-14 12:00:00","project_end_date":null,"access_date":"2022-01-14 00:00:00","accredited_researcher_status":"Unknown","confidential_data_description":null,"dataset_linkage_description":null,"duty_of_confidentiality":"Not applicable","legal_basis_for_data_article6":null,"legal_basis_for_data_article9":null,"national_data_optout":"Not applicable","organisation_id":null,"privacy_enhancements":null,"request_category_type":"Public Health Research","request_frequency":"One-off","access_type":"TRE","mongo_object_dar_id":null,"enabled":true,"last_activity":null,"counter":0,"mongo_object_id":null,"mongo_id":null,"user_id":4777,"team_id":93,"application_id":null,"applicant_id":"NIBDAPC_2021_LT_0007","sector_id":4,"status":"ACTIVE","datasets":[],"publications":[],"tools":[],"keywords":[],"user":{"id":4777,"name":"Giselle Kerry","firstname":"Giselle","lastname":"Kerry","email":"Giselle.Kerry@hdruk.ac.uk","secondary_email":"giselle.kerry@hotmail.co.uk","preferred_email":"primary","email_verified_at":null,"secondary_email_verified_at":"2025-09-24 09:18:19","provider":"azure","created_at":"2025-06-05T13:51:42.000000Z","updated_at":"2026-08-07T09:22:46.000000Z","deleted_at":null,"sector_id":6,"organisation":null,"bio":null,"domain":null,"link":null,"orcid":null,"contact_feedback":0,"contact_news":0,"mongo_id":0,"mongo_object_id":null,"is_admin":1,"terms":true,"hubspot_id":128489718789,"is_nhse_sde_approval":false,"rquestroles":["GENERAL_ACCESS"],"cohort_discovery_roles":["GENERAL_ACCESS"],"cohort_discovery_nhs_sde":false},"team":{"id":93,"pid":"d9d1f252-1f5d-45cf-b8b3-078dfaf739fb","created_at":"2024-10-08T11:19:00.000000Z","updated_at":"2026-06-22T14:33:54.000000Z","deleted_at":null,"name":"Imperial College Healthcare NHS Trust","enabled":true,"allows_messaging":false,"workflow_enabled":false,"access_requests_management":false,"uses_5_safes":false,"is_admin":false,"team_logo":null,"member_of":"ALLIANCE","contact_point":null,"application_form_updated_by":"Qresearch webapp","application_form_updated_on":"0001-01-01 00:00:00","mongo_object_id":"66d1e270b5df0006bb95e5e3","notification_status":false,"is_question_bank":false,"is_provider":false,"url":null,"introduction":null,"dar_modal_header":"Important information about applying","dar_modal_content":"{\"type\":\"doc\",\"content\":[{\"type\":\"paragraph\",\"content\":[{\"type\":\"text\",\"text\":\"To apply for access to the Imperial College Healthcare NHS Trust dataset please use the following link:\"}]},{\"type\":\"paragraph\",\"content\":[{\"type\":\"text\",\"marks\":[{\"type\":\"link\",\"attrs\":{\"href\":\"https:\/\/www.imperial.ac.uk\/medicine\/research-and-impact\/groups\/icare\/icare-facility\/information-for-researchers\/\",\"target\":\"_blank\",\"rel\":\"noopener noreferrer nofollow\",\"class\":null}}],\"text\":\"https:\/\/www.imperial.ac.uk\/medicine\/research-and-impact\/groups\/icare\/icare-facility\/information-for-researchers\/\"}]}]}","dar_modal_footer":null,"is_dar":false,"service":null},"application":null,"users":[],"applications":[]},{"id":3381,"created_at":"2026-02-26T16:03:50.000000Z","updated_at":"2026-06-24T19:04:14.000000Z","deleted_at":null,"active_date":"2026-06-24 19:04:16","non_gateway_datasets":["NIHR DiAlS Dataset"],"non_gateway_applicants":["Ceire Costelloe"],"funders_and_sponsors":["Graham Cooke"],"other_approval_committees":[""],"gateway_outputs_tools":null,"gateway_outputs_papers":null,"non_gateway_outputs":[""],"project_title":"Digital Alerting to Improve Sepsis Detection and Patient Outcomes in NHS Trusts (DiAlS)","project_id_text":"NIBDAPC_2021_0006","organisation_name":"Institute of Cancer Research","organisation_sector":"Academic Institute","lay_summary":"Sepsis is a serious disease, most commonly caused by a bacterial infection and can be the cause of death.  Identifying patients with sepsis as early as possible means treatment with antibiotics is started quickly and increases the chance of survival. There are lots of ways of identifying patients who may have sepsis based on their clinical condition. For example, high or low temperature and fast breathing rate. Most of these measurements can be combined to create a score, if the score is high sepsis should be considered. The introduction of electronic health records in hospitals in the UK has meant that these scores can be included in the system and nurses and doctors can be \u2018alerted\u2019 that the patient may have sepsis. \nOur earlier research at ICHT demonstrated that the introduction of a digital sepsis alert was associated with more patients receiving antibiotics in the target of one hour after identification and fewer patients dying. We want to expand this work to include sites from other areas of the UK. Different hospitals have used different methods of creating a score and introduced the digital alerting systems in different ways. We currently don\u2019t know which method works best, and how. This research will assess whether different digital alerts, and the way in which they were introduced results in better outcomes for patients.\nWe will use statistical methods to analyse patient digital health records tol allow us to find out if patients are doing better in hospitals when a digital alert is present and whether different alerting systems perform better than others. We will focus on whether or not patients have received the recommended care and whether they have better health outcomes.","technical_summary":null,"latest_approval_date":"2021-11-10 12:00:00","manual_upload":true,"rejection_reason":null,"sublicence_arrangements":"No","public_benefit_statement":"Sepsis is a current national and international priority, which is readily treatable with antibiotics, a valuable resource which needs careful stewardship.\nThe proposed research area of improved rapid diagnosis and treatment for sepsis has been highlighted by the James Lind Alliance (JLA) Priority Setting Partnership as a key priority area in Emergency medicine and by a UK academic and patient group team with an interest in healthcare associated infection. \nIn order to secure funding from the NIHR we carried out an extensive literature review and held discussions with sepsis survivors, the UK Sepsis Trust, clinicians and researchers from a range of NHS Trusts and Universities. We presented our initial work on digital sepsis alerts at a range of international conferences, generating further collaborations. This process enabled us to refine the research question. In addition to the quantitative analysis of data from digital health records the DiAlS project includes a qualitative work package to develop our understanding of clinicians and patients\u2019 views on digital alerts. \n\nThis project is vitally important, we are now in a digital age and all hospitals are striving to be paperless. As companies and researchers have increased access to clinical data more and more algorithms are being\ndeveloped to identify patients with sepsis. There is little evidence available to support Trusts in selecting the most effective algorithms, thresholds and patient cohorts to apply the algorithm. In addition, there is limited evidence that the introduction of screening tools of any kind is associated with improved patient outcomes when it comes to patients with sepsis, and there is limited evidence on the most appropriate implementation methodology for optimisation of digital alerts.","data_sensitivity_level":"De-Personalised","project_start_date":"2021-11-10 12:00:00","project_end_date":null,"access_date":"2022-12-15 00:00:00","accredited_researcher_status":"Unknown","confidential_data_description":null,"dataset_linkage_description":null,"duty_of_confidentiality":"Not applicable","legal_basis_for_data_article6":null,"legal_basis_for_data_article9":null,"national_data_optout":"Not applicable","organisation_id":null,"privacy_enhancements":null,"request_category_type":"Public Health Research","request_frequency":"One-off","access_type":"TRE","mongo_object_dar_id":null,"enabled":true,"last_activity":null,"counter":0,"mongo_object_id":null,"mongo_id":null,"user_id":4777,"team_id":93,"application_id":null,"applicant_id":"NIBDAPC_2021_CC_0006","sector_id":3,"status":"ACTIVE","datasets":[],"publications":[],"tools":[],"keywords":[],"user":{"id":4777,"name":"Giselle Kerry","firstname":"Giselle","lastname":"Kerry","email":"Giselle.Kerry@hdruk.ac.uk","secondary_email":"giselle.kerry@hotmail.co.uk","preferred_email":"primary","email_verified_at":null,"secondary_email_verified_at":"2025-09-24 09:18:19","provider":"azure","created_at":"2025-06-05T13:51:42.000000Z","updated_at":"2026-08-07T09:22:46.000000Z","deleted_at":null,"sector_id":6,"organisation":null,"bio":null,"domain":null,"link":null,"orcid":null,"contact_feedback":0,"contact_news":0,"mongo_id":0,"mongo_object_id":null,"is_admin":1,"terms":true,"hubspot_id":128489718789,"is_nhse_sde_approval":false,"rquestroles":["GENERAL_ACCESS"],"cohort_discovery_roles":["GENERAL_ACCESS"],"cohort_discovery_nhs_sde":false},"team":{"id":93,"pid":"d9d1f252-1f5d-45cf-b8b3-078dfaf739fb","created_at":"2024-10-08T11:19:00.000000Z","updated_at":"2026-06-22T14:33:54.000000Z","deleted_at":null,"name":"Imperial College Healthcare NHS Trust","enabled":true,"allows_messaging":false,"workflow_enabled":false,"access_requests_management":false,"uses_5_safes":false,"is_admin":false,"team_logo":null,"member_of":"ALLIANCE","contact_point":null,"application_form_updated_by":"Qresearch webapp","application_form_updated_on":"0001-01-01 00:00:00","mongo_object_id":"66d1e270b5df0006bb95e5e3","notification_status":false,"is_question_bank":false,"is_provider":false,"url":null,"introduction":null,"dar_modal_header":"Important information about applying","dar_modal_content":"{\"type\":\"doc\",\"content\":[{\"type\":\"paragraph\",\"content\":[{\"type\":\"text\",\"text\":\"To apply for access to the Imperial College Healthcare NHS Trust dataset please use the following link:\"}]},{\"type\":\"paragraph\",\"content\":[{\"type\":\"text\",\"marks\":[{\"type\":\"link\",\"attrs\":{\"href\":\"https:\/\/www.imperial.ac.uk\/medicine\/research-and-impact\/groups\/icare\/icare-facility\/information-for-researchers\/\",\"target\":\"_blank\",\"rel\":\"noopener noreferrer nofollow\",\"class\":null}}],\"text\":\"https:\/\/www.imperial.ac.uk\/medicine\/research-and-impact\/groups\/icare\/icare-facility\/information-for-researchers\/\"}]}]}","dar_modal_footer":null,"is_dar":false,"service":null},"application":null,"users":[],"applications":[]},{"id":3393,"created_at":"2026-02-26T16:03:51.000000Z","updated_at":"2026-06-24T19:04:02.000000Z","deleted_at":null,"active_date":"2026-06-24 19:04:05","non_gateway_datasets":["NIHR HIC Viral Hepatitis Dataset"],"non_gateway_applicants":["Ben Glampson"],"funders_and_sponsors":["Graham Cooke"],"other_approval_committees":[""],"gateway_outputs_tools":null,"gateway_outputs_papers":null,"non_gateway_outputs":["http:\/\/dx.doi.org\/10.1136\/bmjhci-2020-100145"," https:\/\/doi.org\/10.12688\/wellcomeopenres.17522.1","https:\/\/doi.org\/10.1093\/ije\/dyac127"],"project_title":"NIHR HIC \u2013 Viral Hepatitis Theme","project_id_text":"NIBDAPC_2022_0018","organisation_name":"Imperial College Healthcare NHS Trust","organisation_sector":"Government Agency (Health and Adult Social Care)","lay_summary":"Imperial College Healthcare NHS Trust collects data on its hepatitis patients as part of routnine care. This includes data patient demographics, treatment, lab tests, imaging reports and liver disease progression; all of which is recorded on electronic patient record systems as part of the routine care process. The Trust is in the process of extracting the data from these systems, and structuring it into one database with all patient identifiable information (such as patient names and NHS numbers) de-identified . Other infectious disease centres around the country would follow a similar process, and these structured databases would be sent to a research team in Oxford University Hospitals.\nFrom there, these can be combined to form one larger research database. Approved researchers can then use this research database to answer important research questions relating to the care and outcomes of hepatitis patients. This work aims to identify best practices relating to care of hepatitis patients and ultimately improve outcomes for these patients.","technical_summary":null,"latest_approval_date":"2022-10-07 12:00:00","manual_upload":true,"rejection_reason":null,"sublicence_arrangements":"No","public_benefit_statement":"Researchers wishing to access the Viral Hepatitis Theme research database (which contains data from all participating sites) must submit a project proposal application to the Theme\u2019s Scientific Steering Committee. This Committee is made up of the Theme Clinical Leads from all participating sites. As part of the application process, researchers must outline how the research question has been identified as a priority for the benefit of patients.\nApplications that do not adequately answer this will not be approved by the Scientific Steering Committee, and access to the data will therefore not be granted.","data_sensitivity_level":"De-Personalised","project_start_date":"2022-10-07 12:00:00","project_end_date":null,"access_date":"2022-10-07 00:00:00","accredited_researcher_status":"Unknown","confidential_data_description":null,"dataset_linkage_description":null,"duty_of_confidentiality":"Not applicable","legal_basis_for_data_article6":null,"legal_basis_for_data_article9":null,"national_data_optout":"Not applicable","organisation_id":null,"privacy_enhancements":null,"request_category_type":"Public Health Research","request_frequency":"One-off","access_type":"TRE","mongo_object_dar_id":null,"enabled":true,"last_activity":null,"counter":0,"mongo_object_id":null,"mongo_id":null,"user_id":4777,"team_id":93,"application_id":null,"applicant_id":"NIBDAPC_2022_BG_0018","sector_id":4,"status":"ACTIVE","datasets":[],"publications":[],"tools":[],"keywords":[],"user":{"id":4777,"name":"Giselle Kerry","firstname":"Giselle","lastname":"Kerry","email":"Giselle.Kerry@hdruk.ac.uk","secondary_email":"giselle.kerry@hotmail.co.uk","preferred_email":"primary","email_verified_at":null,"secondary_email_verified_at":"2025-09-24 09:18:19","provider":"azure","created_at":"2025-06-05T13:51:42.000000Z","updated_at":"2026-08-07T09:22:46.000000Z","deleted_at":null,"sector_id":6,"organisation":null,"bio":null,"domain":null,"link":null,"orcid":null,"contact_feedback":0,"contact_news":0,"mongo_id":0,"mongo_object_id":null,"is_admin":1,"terms":true,"hubspot_id":128489718789,"is_nhse_sde_approval":false,"rquestroles":["GENERAL_ACCESS"],"cohort_discovery_roles":["GENERAL_ACCESS"],"cohort_discovery_nhs_sde":false},"team":{"id":93,"pid":"d9d1f252-1f5d-45cf-b8b3-078dfaf739fb","created_at":"2024-10-08T11:19:00.000000Z","updated_at":"2026-06-22T14:33:54.000000Z","deleted_at":null,"name":"Imperial College Healthcare NHS Trust","enabled":true,"allows_messaging":false,"workflow_enabled":false,"access_requests_management":false,"uses_5_safes":false,"is_admin":false,"team_logo":null,"member_of":"ALLIANCE","contact_point":null,"application_form_updated_by":"Qresearch webapp","application_form_updated_on":"0001-01-01 00:00:00","mongo_object_id":"66d1e270b5df0006bb95e5e3","notification_status":false,"is_question_bank":false,"is_provider":false,"url":null,"introduction":null,"dar_modal_header":"Important information about applying","dar_modal_content":"{\"type\":\"doc\",\"content\":[{\"type\":\"paragraph\",\"content\":[{\"type\":\"text\",\"text\":\"To apply for access to the Imperial College Healthcare NHS Trust dataset please use the following link:\"}]},{\"type\":\"paragraph\",\"content\":[{\"type\":\"text\",\"marks\":[{\"type\":\"link\",\"attrs\":{\"href\":\"https:\/\/www.imperial.ac.uk\/medicine\/research-and-impact\/groups\/icare\/icare-facility\/information-for-researchers\/\",\"target\":\"_blank\",\"rel\":\"noopener noreferrer nofollow\",\"class\":null}}],\"text\":\"https:\/\/www.imperial.ac.uk\/medicine\/research-and-impact\/groups\/icare\/icare-facility\/information-for-researchers\/\"}]}]}","dar_modal_footer":null,"is_dar":false,"service":null},"application":null,"users":[],"applications":[]},{"id":3392,"created_at":"2026-02-26T16:03:51.000000Z","updated_at":"2026-06-24T19:03:53.000000Z","deleted_at":null,"active_date":"2026-06-24 19:03:55","non_gateway_datasets":["ICHT COVID-19 Research Dataset"],"non_gateway_applicants":["Paul Aylin "],"funders_and_sponsors":["Alison Holmes"],"other_approval_committees":[""],"gateway_outputs_tools":null,"gateway_outputs_papers":null,"non_gateway_outputs":["https:\/\/pubmed.ncbi.nlm.nih.gov\/34596212"],"project_title":"Impact of COVID-19 on Antibiotic Prescribing in North West London","project_id_text":"NIBDAPC_2022_0017","organisation_name":"Imperial College London","organisation_sector":"Academic Institute","lay_summary":"Antibiotics are medications used to treat bacterial infections, which are very common in England. If antibiotics were not provided when needed, infection might get worse and sometimes be vital. However, if using antibiotics when unnecessary, the pathogens will become resistant to the medication and make treating future infections impossible. Therefore we must carefully monitoring how antibiotics are used. The COVID-19 pandemic has affected how infections were managed and treated with antibiotics, for example, some hospitalised COVID-19 patients were treated with antibiotics despite such medication cannot cure COVID-19 which is a viral infection. On the other hand, increased work pressure on hospital laboratories might have delayed the confirmation of bacterial infections that required antibiotic treatment. In this project, we have been supported by the individual level de-identifided patient data collected from three hospitals from ICHT, to continue monitoring whether antibiotic prescribing was appropriate. We also aimed to assess the impact of multiple complex factors, such as different COVID-19 variant, hospital admission patient mix, changes in guidelines, might have influenced other infectious diseases other than COVID-19. ","technical_summary":null,"latest_approval_date":"2022-09-21 12:00:00","manual_upload":true,"rejection_reason":null,"sublicence_arrangements":"No","public_benefit_statement":"Tackling AMRis one of the nation\u2019s highest public health priorities. Our work is in line with UK\u2019s 20 year vision of AMR, and England\u2019s national strategy for infectious diseases. Benefited from the unique setting of our research group (NIHR HPRU in HCAI and AMR), which is a close collaboration between Imperial College, UKHSA, and local NHS trusts, research outputs can be disseminated and translated to policy and patient management guidelines rapidly to enhance the quality of care. In addition, the highly granular patient level data in iCARE environment provides knowledge complementary to the data produced by UKHSA at national level (covered larger population but less detailed). This work will help identify any sub-optimal prescribing practices in secondary care, any newly emerged drug resistant infections, and any unintended conseuqnces associated with national prescribing guidelines and prescribing interventions.","data_sensitivity_level":"De-Personalised","project_start_date":"2022-12-01 12:00:00","project_end_date":null,"access_date":"2022-12-01 00:00:00","accredited_researcher_status":"Unknown","confidential_data_description":null,"dataset_linkage_description":null,"duty_of_confidentiality":"Not applicable","legal_basis_for_data_article6":null,"legal_basis_for_data_article9":null,"national_data_optout":"Not applicable","organisation_id":null,"privacy_enhancements":null,"request_category_type":"Public Health Research","request_frequency":"One-off","access_type":"TRE","mongo_object_dar_id":null,"enabled":true,"last_activity":null,"counter":0,"mongo_object_id":null,"mongo_id":null,"user_id":4777,"team_id":93,"application_id":null,"applicant_id":"NIBDAPC_2022_PA_0017","sector_id":3,"status":"ACTIVE","datasets":[],"publications":[],"tools":[],"keywords":[],"user":{"id":4777,"name":"Giselle Kerry","firstname":"Giselle","lastname":"Kerry","email":"Giselle.Kerry@hdruk.ac.uk","secondary_email":"giselle.kerry@hotmail.co.uk","preferred_email":"primary","email_verified_at":null,"secondary_email_verified_at":"2025-09-24 09:18:19","provider":"azure","created_at":"2025-06-05T13:51:42.000000Z","updated_at":"2026-08-07T09:22:46.000000Z","deleted_at":null,"sector_id":6,"organisation":null,"bio":null,"domain":null,"link":null,"orcid":null,"contact_feedback":0,"contact_news":0,"mongo_id":0,"mongo_object_id":null,"is_admin":1,"terms":true,"hubspot_id":128489718789,"is_nhse_sde_approval":false,"rquestroles":["GENERAL_ACCESS"],"cohort_discovery_roles":["GENERAL_ACCESS"],"cohort_discovery_nhs_sde":false},"team":{"id":93,"pid":"d9d1f252-1f5d-45cf-b8b3-078dfaf739fb","created_at":"2024-10-08T11:19:00.000000Z","updated_at":"2026-06-22T14:33:54.000000Z","deleted_at":null,"name":"Imperial College Healthcare NHS Trust","enabled":true,"allows_messaging":false,"workflow_enabled":false,"access_requests_management":false,"uses_5_safes":false,"is_admin":false,"team_logo":null,"member_of":"ALLIANCE","contact_point":null,"application_form_updated_by":"Qresearch webapp","application_form_updated_on":"0001-01-01 00:00:00","mongo_object_id":"66d1e270b5df0006bb95e5e3","notification_status":false,"is_question_bank":false,"is_provider":false,"url":null,"introduction":null,"dar_modal_header":"Important information about applying","dar_modal_content":"{\"type\":\"doc\",\"content\":[{\"type\":\"paragraph\",\"content\":[{\"type\":\"text\",\"text\":\"To apply for access to the Imperial College Healthcare NHS Trust dataset please use the following link:\"}]},{\"type\":\"paragraph\",\"content\":[{\"type\":\"text\",\"marks\":[{\"type\":\"link\",\"attrs\":{\"href\":\"https:\/\/www.imperial.ac.uk\/medicine\/research-and-impact\/groups\/icare\/icare-facility\/information-for-researchers\/\",\"target\":\"_blank\",\"rel\":\"noopener noreferrer nofollow\",\"class\":null}}],\"text\":\"https:\/\/www.imperial.ac.uk\/medicine\/research-and-impact\/groups\/icare\/icare-facility\/information-for-researchers\/\"}]}]}","dar_modal_footer":null,"is_dar":false,"service":null},"application":null,"users":[],"applications":[]},{"id":3391,"created_at":"2026-02-26T16:03:51.000000Z","updated_at":"2026-06-24T19:03:44.000000Z","deleted_at":null,"active_date":"2026-06-24 19:03:46","non_gateway_datasets":["ICHT COVID-19 Research Dataset"],"non_gateway_applicants":["William Bolton"],"funders_and_sponsors":["Alison Holmes"],"other_approval_committees":[""],"gateway_outputs_tools":null,"gateway_outputs_papers":null,"non_gateway_outputs":[""],"project_title":"Optimizing antimicrobial use in multi-morbid patients through intelligent clinical decision support","project_id_text":"NIBDAPC_2022_0016","organisation_name":"Imperial College London","organisation_sector":"Academic Institute","lay_summary":"Antibiotics are drugs that treat bacterial infections; however, the overuse of antibiotics is driving antimicrobial resistance (AMR) (which is when a bacterial infection is difficult to treat with an antibiotic). AMR is a global challenge that promises to have significant negative effects on health and society. One way to address AMR is to only use antibiotics to treat bacterial infections instead of viral infections, as infections caused by virus do not improve with antibiotics. This can be done through artificial intelligence (AI) where software used by computers mimic aspects of human intelligence. This is a powerful technology that enables us to understand data and make predictions using computers. AI is increasingly being used within medicine and has great potential to provide meaningful benefit with regards to infections and antibiotics. Despite a strong association being shown between other medical conditions and different infection-related risks and outcomes, to date limited AI research has focused on antibiotic use in patients with more than one long-term health condition. This project will use health data to understand the use of antibiotics in patients with more than one long-term health condition and predict patient outcomes and the most appropriate antibiotic treatment through using AI. Ultimately such technology will be incorporated into clinical decision support systems (CDSSs) to provide information to healthcare professionals so they can make good clinical decisions on antibiotic use.\n\nNovember 2023 Update\nArtificial intelligence (AI) technology to understand patients\u2019 historical medical conditions has been developed. It has been shown to be good at predicting patient death and is able to find historical patient cases that are similar to any patient of interest. Healthcare professionals can use this to learn about previous clinical scenarios and make appropriate clinical decisions. Work on using this technology to learn how to improve antibiotic use, prevent resistance and improve patient outcomes is ongoing. ","technical_summary":null,"latest_approval_date":"2022-10-03 12:00:00","manual_upload":true,"rejection_reason":null,"sublicence_arrangements":"No","public_benefit_statement":"Multi-morbidity and antimicrobial resistance (AMR) are significant challenges to healthcare. Co-morbid conditions put individuals at a high risk of developing an infection with a greater chance of poor outcomes, including increased length of hospital stay, chance of readmission and risk of death. These patients also often fail to be treated appropriately due to polypharmacy and a lack of evidence. Our work will focus on researching intelligent clinical decision support systems (CDSS) to provide clinicians with the information they need to help support antimicrobial decision making in this complex patient population.","data_sensitivity_level":"De-Personalised","project_start_date":"2022-11-17 12:00:00","project_end_date":null,"access_date":"2022-11-17 00:00:00","accredited_researcher_status":"Unknown","confidential_data_description":null,"dataset_linkage_description":null,"duty_of_confidentiality":"Not applicable","legal_basis_for_data_article6":null,"legal_basis_for_data_article9":null,"national_data_optout":"Not applicable","organisation_id":null,"privacy_enhancements":null,"request_category_type":"Public Health Research","request_frequency":"One-off","access_type":"TRE","mongo_object_dar_id":null,"enabled":true,"last_activity":null,"counter":0,"mongo_object_id":null,"mongo_id":null,"user_id":4777,"team_id":93,"application_id":null,"applicant_id":"NIBDAPC_2022_WB_0016","sector_id":3,"status":"ACTIVE","datasets":[],"publications":[],"tools":[],"keywords":[],"user":{"id":4777,"name":"Giselle Kerry","firstname":"Giselle","lastname":"Kerry","email":"Giselle.Kerry@hdruk.ac.uk","secondary_email":"giselle.kerry@hotmail.co.uk","preferred_email":"primary","email_verified_at":null,"secondary_email_verified_at":"2025-09-24 09:18:19","provider":"azure","created_at":"2025-06-05T13:51:42.000000Z","updated_at":"2026-08-07T09:22:46.000000Z","deleted_at":null,"sector_id":6,"organisation":null,"bio":null,"domain":null,"link":null,"orcid":null,"contact_feedback":0,"contact_news":0,"mongo_id":0,"mongo_object_id":null,"is_admin":1,"terms":true,"hubspot_id":128489718789,"is_nhse_sde_approval":false,"rquestroles":["GENERAL_ACCESS"],"cohort_discovery_roles":["GENERAL_ACCESS"],"cohort_discovery_nhs_sde":false},"team":{"id":93,"pid":"d9d1f252-1f5d-45cf-b8b3-078dfaf739fb","created_at":"2024-10-08T11:19:00.000000Z","updated_at":"2026-06-22T14:33:54.000000Z","deleted_at":null,"name":"Imperial College Healthcare NHS Trust","enabled":true,"allows_messaging":false,"workflow_enabled":false,"access_requests_management":false,"uses_5_safes":false,"is_admin":false,"team_logo":null,"member_of":"ALLIANCE","contact_point":null,"application_form_updated_by":"Qresearch webapp","application_form_updated_on":"0001-01-01 00:00:00","mongo_object_id":"66d1e270b5df0006bb95e5e3","notification_status":false,"is_question_bank":false,"is_provider":false,"url":null,"introduction":null,"dar_modal_header":"Important information about applying","dar_modal_content":"{\"type\":\"doc\",\"content\":[{\"type\":\"paragraph\",\"content\":[{\"type\":\"text\",\"text\":\"To apply for access to the Imperial College Healthcare NHS Trust dataset please use the following link:\"}]},{\"type\":\"paragraph\",\"content\":[{\"type\":\"text\",\"marks\":[{\"type\":\"link\",\"attrs\":{\"href\":\"https:\/\/www.imperial.ac.uk\/medicine\/research-and-impact\/groups\/icare\/icare-facility\/information-for-researchers\/\",\"target\":\"_blank\",\"rel\":\"noopener noreferrer nofollow\",\"class\":null}}],\"text\":\"https:\/\/www.imperial.ac.uk\/medicine\/research-and-impact\/groups\/icare\/icare-facility\/information-for-researchers\/\"}]}]}","dar_modal_footer":null,"is_dar":false,"service":null},"application":null,"users":[],"applications":[]},{"id":3390,"created_at":"2026-02-26T16:03:51.000000Z","updated_at":"2026-06-24T19:03:35.000000Z","deleted_at":null,"active_date":"2026-06-24 19:03:37","non_gateway_datasets":["ICHT MATIS Dataset"],"non_gateway_applicants":["Nichola Cooper"],"funders_and_sponsors":["Erik Mayer"],"other_approval_committees":[""],"gateway_outputs_tools":null,"gateway_outputs_papers":null,"non_gateway_outputs":[""],"project_title":"Multi-Arm Trial of Inflammatory Signal Inhibitors for COVID-19 (MATIS)","project_id_text":"NIBDAPC_2022_0015","organisation_name":"Imperial College London","organisation_sector":"Academic Institute","lay_summary":"This research is being undertaken on a lung disease called COVID-19. This condition is caused by a type of virus called SARS-CoV-2. In people who have been admitted to hospital with COVID-19 pneumonia (lung infection), many will develop severe disease, which can result in needing ventilation and some people may not survive. There is currently no cure or effective treatment for COVID-19, although steroids, including dexamethasone has shown some improvement, we still need to find new treatments to stop people getting more sick.\nThere is a lot of evidence now that some of what makes people sick is the body\u2019s response to the virus. Steroids work a little bit on this, but not enough. This study aims to find out whether some other treatments, which have been used for other diseases could stop the development of severe disease in patients who have been hospitalised with COVID-19. These treatments are anti-inflammatory treatments and they show promise, however, nobody knows if any of them will turn out to be more effective in helping patients recover than the usual standard of care.\nThis data collected will be used as part of a long covid substudy to assess the longer term clinical outcomes from patients who were enrolled onto the trial. We will assess long term outcomes including death, patients being admitted to hospital again and blood clots in order to determine whether the study drugs have any impact on these outcomes and\/or long covid.","technical_summary":null,"latest_approval_date":"2022-07-29 12:00:00","manual_upload":true,"rejection_reason":null,"sublicence_arrangements":"No","public_benefit_statement":"COVID-19 is a signficiant public health problem, and has resulted in significant morbidity and mortality worldwide. The MATIS trial aimed to trial new medications in the treatment of COVID-19. This follow-up substudy aims to assess the impact of these medications (Ruxolitinib and fostamatinib) on the longer term patient outcomes, as well as the development of long covid. This may change current COVID guidelines and inform future prioritised research and therefore will be potentially of significant benefit for patients. Many MATIS trial participants on follow up phone calls and visits have been concerned by ongoing COVID symptoms and Long covid has been highly publicised in the media, and therefore we believe this to be an important concern and patient-focused.","data_sensitivity_level":"De-Personalised","project_start_date":null,"project_end_date":null,"access_date":null,"accredited_researcher_status":"Unknown","confidential_data_description":null,"dataset_linkage_description":null,"duty_of_confidentiality":"Not applicable","legal_basis_for_data_article6":null,"legal_basis_for_data_article9":null,"national_data_optout":"Not applicable","organisation_id":null,"privacy_enhancements":null,"request_category_type":"Public Health Research","request_frequency":"One-off","access_type":"TRE","mongo_object_dar_id":null,"enabled":true,"last_activity":null,"counter":0,"mongo_object_id":null,"mongo_id":null,"user_id":4777,"team_id":93,"application_id":null,"applicant_id":"NIBDAPC_2022_NC_0015","sector_id":3,"status":"ACTIVE","datasets":[],"publications":[],"tools":[],"keywords":[],"user":{"id":4777,"name":"Giselle Kerry","firstname":"Giselle","lastname":"Kerry","email":"Giselle.Kerry@hdruk.ac.uk","secondary_email":"giselle.kerry@hotmail.co.uk","preferred_email":"primary","email_verified_at":null,"secondary_email_verified_at":"2025-09-24 09:18:19","provider":"azure","created_at":"2025-06-05T13:51:42.000000Z","updated_at":"2026-08-07T09:22:46.000000Z","deleted_at":null,"sector_id":6,"organisation":null,"bio":null,"domain":null,"link":null,"orcid":null,"contact_feedback":0,"contact_news":0,"mongo_id":0,"mongo_object_id":null,"is_admin":1,"terms":true,"hubspot_id":128489718789,"is_nhse_sde_approval":false,"rquestroles":["GENERAL_ACCESS"],"cohort_discovery_roles":["GENERAL_ACCESS"],"cohort_discovery_nhs_sde":false},"team":{"id":93,"pid":"d9d1f252-1f5d-45cf-b8b3-078dfaf739fb","created_at":"2024-10-08T11:19:00.000000Z","updated_at":"2026-06-22T14:33:54.000000Z","deleted_at":null,"name":"Imperial College Healthcare NHS Trust","enabled":true,"allows_messaging":false,"workflow_enabled":false,"access_requests_management":false,"uses_5_safes":false,"is_admin":false,"team_logo":null,"member_of":"ALLIANCE","contact_point":null,"application_form_updated_by":"Qresearch webapp","application_form_updated_on":"0001-01-01 00:00:00","mongo_object_id":"66d1e270b5df0006bb95e5e3","notification_status":false,"is_question_bank":false,"is_provider":false,"url":null,"introduction":null,"dar_modal_header":"Important information about applying","dar_modal_content":"{\"type\":\"doc\",\"content\":[{\"type\":\"paragraph\",\"content\":[{\"type\":\"text\",\"text\":\"To apply for access to the Imperial College Healthcare NHS Trust dataset please use the following link:\"}]},{\"type\":\"paragraph\",\"content\":[{\"type\":\"text\",\"marks\":[{\"type\":\"link\",\"attrs\":{\"href\":\"https:\/\/www.imperial.ac.uk\/medicine\/research-and-impact\/groups\/icare\/icare-facility\/information-for-researchers\/\",\"target\":\"_blank\",\"rel\":\"noopener noreferrer nofollow\",\"class\":null}}],\"text\":\"https:\/\/www.imperial.ac.uk\/medicine\/research-and-impact\/groups\/icare\/icare-facility\/information-for-researchers\/\"}]}]}","dar_modal_footer":null,"is_dar":false,"service":null},"application":null,"users":[],"applications":[]},{"id":3389,"created_at":"2026-02-26T16:03:51.000000Z","updated_at":"2026-06-24T19:03:23.000000Z","deleted_at":null,"active_date":"2026-06-24 19:03:26","non_gateway_datasets":["ICHT VTE EHR Dataset"],"non_gateway_applicants":["Sneha Jha"],"funders_and_sponsors":["Erik Mayer"],"other_approval_committees":[""],"gateway_outputs_tools":null,"gateway_outputs_papers":null,"non_gateway_outputs":[""],"project_title":"Identifying the risk and true-incidence of in-hospital VTE using electronic healthcare records","project_id_text":"NIBDAPC_2022_0014","organisation_name":"Imperial College London","organisation_sector":"Academic Institute","lay_summary":"Blood clots, also called venous thromboembolisms (VTE) occur as either a clot in a deep vein, usually an arm or leg (Deep vein thrombosis (DVT)) or a clot that has broken off and travelled to the lungs (pulmonary embolism (PE)). They can happen to anybody and can cause serious illness, disability, and in some cases, death. Even though it causes a significant number of deaths and disability in the UK and worldwide, VTE is preventable and treatable if discovered in time. Identifying who developed a serious blood clot during their stay in the hospital is an important step in managing and preventing the illness, financial costs, and deaths associated with it.\nThe current methods of detecting VTE depend largely on administrative data available after the patient is discharged. This method is known to have a number of drawbacks. The medical billing codes appear much later after a patient is discharged and are often not dated precisely. It makes it challenging to differentiate between events that occurred before the hospitalization and those that were acquired during the hospital stay. This makes the timely surveillance of the blood clots both inefficient and inaccurate. \nThe detection and estimate of the actual number of patients who developed VTE during their hospital stay can be significantly improved by using the clinical narrative text available as part of the electronic health records. The results of imaging, such as ultrasounds, chest CT scans etc, that identify VTE are summarized in free-form text reports. While it is easy for human experts to identify an event by reading these manually, it is time-consuming and costly. This project proposes to apply advanced analysis techniques to detect instances of VTE from this free text data available digitally. Automating parts of this process could help reduce the time and cost significantly and help clinicians to manage the risk and treatment of VTE more efficiently in acute care hospital settings.","technical_summary":null,"latest_approval_date":"2022-06-24 12:00:00","manual_upload":true,"rejection_reason":null,"sublicence_arrangements":"No","public_benefit_statement":"Hospital-acquired venous thromboembolism (VTE) covers VTE that occurs in hospital and within 90 days after a hospital admission. It is a common and potentially preventable problem but accounts for thousands of deaths annually in the NHS, and fatal pulmonary embolism remains a common cause of in-hospital death. Treatment of non-fatal symptomatic VTE and related long-term conditions is associated with a considerable cost to the health service. Without improvements and use of better data-driven processes to identify the incidence of VTE, the number of people affected by VTE can be expected to increase. People admitted to hospital or mental health units have varied risk factors for VTE. Although anyone can develop a blood clot, over half of blood clots are related to a recent hospitalization or surgery.\nA more accurate estimate of hospital-acquired VTE will allow us to improve and support monitoring and prevention of its occurrence. It will also lead to a better assessment of the public health burden of VTE by providing more accurate picture of the health and economic impact of VTE, including identifying high-risk groups and settings. It can also inform the development of improved monitoring tools to measure the success of prevention activities by tracking and monitoring trends in HA-VTE occurrence over time.\nThe current methods used to measure safety events around VTE rely primarily on incident reports, which detect only a small proportion of events. Data-driven methods can be designed to automatically detect errors of omission, such as patients who are overdue for medication, monitoring, patients who lack appropriate surveillance after treatment, and patients who are not provided with follow-up care after receiving abnormal laboratory or radiological tests results. Combining different sources of data could potentially help detect some of the incidents of in-hospital VTE that are often overlooked and under-reported. Automating the process of detecting events through statistical processing of narrative text data, already present in the electronic health records could greatly reduce the time and cost required for monitoring VTE rates.","data_sensitivity_level":"De-Personalised","project_start_date":"2022-10-17 12:00:00","project_end_date":null,"access_date":"2022-10-17 00:00:00","accredited_researcher_status":"Unknown","confidential_data_description":null,"dataset_linkage_description":null,"duty_of_confidentiality":"Not applicable","legal_basis_for_data_article6":null,"legal_basis_for_data_article9":null,"national_data_optout":"Not applicable","organisation_id":null,"privacy_enhancements":null,"request_category_type":"Public Health Research","request_frequency":"One-off","access_type":"TRE","mongo_object_dar_id":null,"enabled":true,"last_activity":null,"counter":0,"mongo_object_id":null,"mongo_id":null,"user_id":4777,"team_id":93,"application_id":null,"applicant_id":"NIBDAPC_2022_SJ_0014","sector_id":3,"status":"ACTIVE","datasets":[],"publications":[],"tools":[],"keywords":[],"user":{"id":4777,"name":"Giselle Kerry","firstname":"Giselle","lastname":"Kerry","email":"Giselle.Kerry@hdruk.ac.uk","secondary_email":"giselle.kerry@hotmail.co.uk","preferred_email":"primary","email_verified_at":null,"secondary_email_verified_at":"2025-09-24 09:18:19","provider":"azure","created_at":"2025-06-05T13:51:42.000000Z","updated_at":"2026-08-07T09:22:46.000000Z","deleted_at":null,"sector_id":6,"organisation":null,"bio":null,"domain":null,"link":null,"orcid":null,"contact_feedback":0,"contact_news":0,"mongo_id":0,"mongo_object_id":null,"is_admin":1,"terms":true,"hubspot_id":128489718789,"is_nhse_sde_approval":false,"rquestroles":["GENERAL_ACCESS"],"cohort_discovery_roles":["GENERAL_ACCESS"],"cohort_discovery_nhs_sde":false},"team":{"id":93,"pid":"d9d1f252-1f5d-45cf-b8b3-078dfaf739fb","created_at":"2024-10-08T11:19:00.000000Z","updated_at":"2026-06-22T14:33:54.000000Z","deleted_at":null,"name":"Imperial College Healthcare NHS Trust","enabled":true,"allows_messaging":false,"workflow_enabled":false,"access_requests_management":false,"uses_5_safes":false,"is_admin":false,"team_logo":null,"member_of":"ALLIANCE","contact_point":null,"application_form_updated_by":"Qresearch webapp","application_form_updated_on":"0001-01-01 00:00:00","mongo_object_id":"66d1e270b5df0006bb95e5e3","notification_status":false,"is_question_bank":false,"is_provider":false,"url":null,"introduction":null,"dar_modal_header":"Important information about applying","dar_modal_content":"{\"type\":\"doc\",\"content\":[{\"type\":\"paragraph\",\"content\":[{\"type\":\"text\",\"text\":\"To apply for access to the Imperial College Healthcare NHS Trust dataset please use the following link:\"}]},{\"type\":\"paragraph\",\"content\":[{\"type\":\"text\",\"marks\":[{\"type\":\"link\",\"attrs\":{\"href\":\"https:\/\/www.imperial.ac.uk\/medicine\/research-and-impact\/groups\/icare\/icare-facility\/information-for-researchers\/\",\"target\":\"_blank\",\"rel\":\"noopener noreferrer nofollow\",\"class\":null}}],\"text\":\"https:\/\/www.imperial.ac.uk\/medicine\/research-and-impact\/groups\/icare\/icare-facility\/information-for-researchers\/\"}]}]}","dar_modal_footer":null,"is_dar":false,"service":null},"application":null,"users":[],"applications":[]},{"id":3388,"created_at":"2026-02-26T16:03:51.000000Z","updated_at":"2026-06-24T19:03:13.000000Z","deleted_at":null,"active_date":"2026-06-24 19:03:15","non_gateway_datasets":["ICHT NHSX Research Dataset"],"non_gateway_applicants":["Kelsey Flott"],"funders_and_sponsors":["Erik Mayer"],"other_approval_committees":[""],"gateway_outputs_tools":null,"gateway_outputs_papers":null,"non_gateway_outputs":[""],"project_title":"Identifying the role of Digital and IT in the safety of Healthcare","project_id_text":"NIBDAPC_2022_0013","organisation_name":"NHSE\/I Transformation Directorate ","organisation_sector":"Government Agency (Health and Adult Social Care)","lay_summary":"Patient safety is a national priority and an important part of any quality health system. The National Patient Safety Strategy explains that improving safety will save lives and save costs. Improving safety across the whole NHS, however, is a challenging and long term task that requires collaboration between national organisations, local healthcare providers and patients. Improving safety also requires us to measure safety: we cannot improve what we cannot measure. This is why we need to use patient safety data like incident reporting, complaints, and other forms of patient and staff reported feedback to understand where the safety issues are and identify areas for improvement. \n\nSpecifically in this work we are concerned with the digital aspect of patient safety. Following the pandemic, the increase in the use of digital technologies across the health service has been extreme. Now it is much more common for any patient to use a digital technology to interact with the health service, whether it is in booking their appointment, having a virtual consultation or simply accessing their records. There is also a growing use of technologies for healthcare staff who use digital systems to care for patients, record data and manage things like medicines, imaging, care plans and more operational things like their own workflow. All of these technologies can help in building safer systems, but they also come with risks to safety. In addition to understanding what the most prevalent safety issues are, we need to know whether digital systems are contributing to safety risks and also where they could be used to support safety improvements.\n\nIn order to address these issues, we plan to work between NHS England (NHSX), Imperial College Healthcare NHS Trust and NHS Resolution to analyse patient and staff reported data about safety. It is critical to ensure data comes from both staff and patient perspectives. We will also be working with patients to ensure we are using patient safety data appropriately and properly considering patient perspectives.","technical_summary":null,"latest_approval_date":"2022-06-13 12:00:00","manual_upload":true,"rejection_reason":null,"sublicence_arrangements":"No","public_benefit_statement":"The research question is a priority area, and could provide the following benefits to patients and the public:\n\nKnowledge creation and intelligence generation: In the first instance, this project is intended as a fact finding exercise, the results of which may help to guide future research, policy, and service evaluation. There are currently a range of data sets (including but not limited to incident reporting data, Friends & Family Test data, complaints data, and claims data), each of which are of great instructive value, but currently operate in silos with little coordination between them. Triangulating these myriad datasets and analysing them for patient safety and Health IT insights will improve the quality of care provided by preempting, preventing, and mitigating adverse events. A root cause analysis of key patient safety incidents may help to catalyse quality improvement projects (QIPs) in a manner that is systematic and needs-driven, rather than the piecemeal approach to QI we see today. \n\nBlueprinting and scaling learning: We recognise the importance of QI initiatives that are sustainable and scalable throughout the healthcare system, instead of being patchy and anecdotal. As such, we are keen to use Imperial College Healthcare NHS Trust as the pilot site, through which to refine methodologies and blueprint best practice to take elsewhere. We acknowledge that Imperial is uniquely positioned to participate in this project by virtue of its data science resource power and expertise, and may also experience patient safety and Health IT incidents that are unique to the local health economy. To that end, whilst the methodologies used here cannot simply be transplanted elsewhere, this project, and the blueprint\/approach used herein, could certainly be of interest to the wider Trust and ICS community. \n\nPatient outcomes: Using the insights generated and delivering targeted QI initiatives may have the potential to reduce the burden of unsafe care, from an individual patient, organisational, and health system wide perspective. By identifying and proactively implementing mitigations in the highest priority areas, adverse patient outcomes (such as deteriorations in care and avoidable escalation to more specialised units, prolonged lengths of stay, and failed discharges and frequent readmissions) can be reduced. \n\nFinancial benefits: In the longer term, improved clinical outcomes stand to also benefit the organisation and health system as a whole. Cost savings from frequent readmissions and delayed discharges, as well as reducing the number of claims made against the NHS (and, in turn, the amount paid out), can increase the availability of resources for trusts to reinvest in ongoing quality and safety initiatives. \n\nPatient empowerment and improving the patient experience: Patients and the public currently contribute vast amounts of data and information to the NHS, the aim of which is to improve the quality of care offered. Nevertheless, there currently does not exist a feedback mechanism for patients to be made aware of improvements in their local service, as a result of suggestions made (e.g. \u2018you said, we did\u2019). This project aims to improve demand signalling for the highest priority areas, in turn empowering patients (and healthcare professionals), and providing the confidence that resources are being directed where the system is most in need of them. Through publishing patient facing communications, for example, it is hoped this project will facilitate a sense of buy-in and trust in the NHS. Furthermore, it is hoped that this project could set the wheels in motion for an improved patient experience when navigating the care pathway, and increased confidence and satisfaction with the system.","data_sensitivity_level":"De-Personalised","project_start_date":null,"project_end_date":null,"access_date":null,"accredited_researcher_status":"Unknown","confidential_data_description":null,"dataset_linkage_description":null,"duty_of_confidentiality":"Not applicable","legal_basis_for_data_article6":null,"legal_basis_for_data_article9":null,"national_data_optout":"Not applicable","organisation_id":null,"privacy_enhancements":null,"request_category_type":"Public Health Research","request_frequency":"One-off","access_type":"TRE","mongo_object_dar_id":null,"enabled":true,"last_activity":null,"counter":0,"mongo_object_id":null,"mongo_id":null,"user_id":4777,"team_id":93,"application_id":null,"applicant_id":"NIBDAPC_2022_KF_0013","sector_id":4,"status":"ACTIVE","datasets":[],"publications":[],"tools":[],"keywords":[],"user":{"id":4777,"name":"Giselle Kerry","firstname":"Giselle","lastname":"Kerry","email":"Giselle.Kerry@hdruk.ac.uk","secondary_email":"giselle.kerry@hotmail.co.uk","preferred_email":"primary","email_verified_at":null,"secondary_email_verified_at":"2025-09-24 09:18:19","provider":"azure","created_at":"2025-06-05T13:51:42.000000Z","updated_at":"2026-08-07T09:22:46.000000Z","deleted_at":null,"sector_id":6,"organisation":null,"bio":null,"domain":null,"link":null,"orcid":null,"contact_feedback":0,"contact_news":0,"mongo_id":0,"mongo_object_id":null,"is_admin":1,"terms":true,"hubspot_id":128489718789,"is_nhse_sde_approval":false,"rquestroles":["GENERAL_ACCESS"],"cohort_discovery_roles":["GENERAL_ACCESS"],"cohort_discovery_nhs_sde":false},"team":{"id":93,"pid":"d9d1f252-1f5d-45cf-b8b3-078dfaf739fb","created_at":"2024-10-08T11:19:00.000000Z","updated_at":"2026-06-22T14:33:54.000000Z","deleted_at":null,"name":"Imperial College Healthcare NHS Trust","enabled":true,"allows_messaging":false,"workflow_enabled":false,"access_requests_management":false,"uses_5_safes":false,"is_admin":false,"team_logo":null,"member_of":"ALLIANCE","contact_point":null,"application_form_updated_by":"Qresearch webapp","application_form_updated_on":"0001-01-01 00:00:00","mongo_object_id":"66d1e270b5df0006bb95e5e3","notification_status":false,"is_question_bank":false,"is_provider":false,"url":null,"introduction":null,"dar_modal_header":"Important information about applying","dar_modal_content":"{\"type\":\"doc\",\"content\":[{\"type\":\"paragraph\",\"content\":[{\"type\":\"text\",\"text\":\"To apply for access to the Imperial College Healthcare NHS Trust dataset please use the following link:\"}]},{\"type\":\"paragraph\",\"content\":[{\"type\":\"text\",\"marks\":[{\"type\":\"link\",\"attrs\":{\"href\":\"https:\/\/www.imperial.ac.uk\/medicine\/research-and-impact\/groups\/icare\/icare-facility\/information-for-researchers\/\",\"target\":\"_blank\",\"rel\":\"noopener noreferrer nofollow\",\"class\":null}}],\"text\":\"https:\/\/www.imperial.ac.uk\/medicine\/research-and-impact\/groups\/icare\/icare-facility\/information-for-researchers\/\"}]}]}","dar_modal_footer":null,"is_dar":false,"service":null},"application":null,"users":[],"applications":[]},{"id":3387,"created_at":"2026-02-26T16:03:51.000000Z","updated_at":"2026-06-24T19:03:03.000000Z","deleted_at":null,"active_date":"2026-06-24 19:03:05","non_gateway_datasets":["ICHT COVID-19 Research Dataset"],"non_gateway_applicants":["Timothy Miles Rawson"],"funders_and_sponsors":["James Price"],"other_approval_committees":[""],"gateway_outputs_tools":null,"gateway_outputs_papers":null,"non_gateway_outputs":["https:\/\/doi.org\/10.1016\/j.landig.2025.01.010"],"project_title":"Optimising Prescribing for Drug Resistant Bacteraemia in the COVID-19 Context","project_id_text":"NIBDAPC_2022_0012","organisation_name":"Imperial College London","organisation_sector":"Academic Institute","lay_summary":"Antibiotics are medications used to treat bacterial infections. If antibiotics were not provided when needed, infection might get worse or even kill the patients. However, antibiotics can also cause the germs to become resistant to the medication and make treating future infections impossible. For drug resistant germs, the treatment options are even more limited. COVID-19 has made the problems more challenging because of the different burdens on health care system. Therefore, we must choose carefully how and when antibiotics are used for these germs. In this project, we have been supported by the individual level de-identified patient data collected from three hospitals from ICHT to explore the effects of different treatment options for these infections and try to find the best suitable options in the future.","technical_summary":null,"latest_approval_date":"2022-04-14 12:00:00","manual_upload":true,"rejection_reason":null,"sublicence_arrangements":"No","public_benefit_statement":"Tackling AMRis one of the nation\u2019s highest public health priorities. Our work is in line with UK\u2019s 20 year vision of AMR, and England\u2019s national strategy for infectious diseases. Benefited from the unique setting of our research group (NIHR HPRU in HCAI and AMR), which is a close collaboration between Imperial College, UKHSA, and local NHS trusts, research outputs can be disseminated and translated to policy and patient management guidelines rapidly to enhance the quality of care. \nThis work will help improve the precision in antibiotic prescribing for drug resistant pathogens to increase the chance of better clinical outcomes and reduce overall drug exposure during therapy.","data_sensitivity_level":"De-Personalised","project_start_date":"2022-05-03 12:00:00","project_end_date":null,"access_date":"2022-05-03 00:00:00","accredited_researcher_status":"Unknown","confidential_data_description":null,"dataset_linkage_description":null,"duty_of_confidentiality":"Not applicable","legal_basis_for_data_article6":null,"legal_basis_for_data_article9":null,"national_data_optout":"Not applicable","organisation_id":null,"privacy_enhancements":null,"request_category_type":"Public Health Research","request_frequency":"One-off","access_type":"TRE","mongo_object_dar_id":null,"enabled":true,"last_activity":null,"counter":0,"mongo_object_id":null,"mongo_id":null,"user_id":4777,"team_id":93,"application_id":null,"applicant_id":"NIBDAPC_2022_TMR_0012","sector_id":3,"status":"ACTIVE","datasets":[],"publications":[],"tools":[],"keywords":[],"user":{"id":4777,"name":"Giselle Kerry","firstname":"Giselle","lastname":"Kerry","email":"Giselle.Kerry@hdruk.ac.uk","secondary_email":"giselle.kerry@hotmail.co.uk","preferred_email":"primary","email_verified_at":null,"secondary_email_verified_at":"2025-09-24 09:18:19","provider":"azure","created_at":"2025-06-05T13:51:42.000000Z","updated_at":"2026-08-07T09:22:46.000000Z","deleted_at":null,"sector_id":6,"organisation":null,"bio":null,"domain":null,"link":null,"orcid":null,"contact_feedback":0,"contact_news":0,"mongo_id":0,"mongo_object_id":null,"is_admin":1,"terms":true,"hubspot_id":128489718789,"is_nhse_sde_approval":false,"rquestroles":["GENERAL_ACCESS"],"cohort_discovery_roles":["GENERAL_ACCESS"],"cohort_discovery_nhs_sde":false},"team":{"id":93,"pid":"d9d1f252-1f5d-45cf-b8b3-078dfaf739fb","created_at":"2024-10-08T11:19:00.000000Z","updated_at":"2026-06-22T14:33:54.000000Z","deleted_at":null,"name":"Imperial College Healthcare NHS Trust","enabled":true,"allows_messaging":false,"workflow_enabled":false,"access_requests_management":false,"uses_5_safes":false,"is_admin":false,"team_logo":null,"member_of":"ALLIANCE","contact_point":null,"application_form_updated_by":"Qresearch webapp","application_form_updated_on":"0001-01-01 00:00:00","mongo_object_id":"66d1e270b5df0006bb95e5e3","notification_status":false,"is_question_bank":false,"is_provider":false,"url":null,"introduction":null,"dar_modal_header":"Important information about applying","dar_modal_content":"{\"type\":\"doc\",\"content\":[{\"type\":\"paragraph\",\"content\":[{\"type\":\"text\",\"text\":\"To apply for access to the Imperial College Healthcare NHS Trust dataset please use the following link:\"}]},{\"type\":\"paragraph\",\"content\":[{\"type\":\"text\",\"marks\":[{\"type\":\"link\",\"attrs\":{\"href\":\"https:\/\/www.imperial.ac.uk\/medicine\/research-and-impact\/groups\/icare\/icare-facility\/information-for-researchers\/\",\"target\":\"_blank\",\"rel\":\"noopener noreferrer nofollow\",\"class\":null}}],\"text\":\"https:\/\/www.imperial.ac.uk\/medicine\/research-and-impact\/groups\/icare\/icare-facility\/information-for-researchers\/\"}]}]}","dar_modal_footer":null,"is_dar":false,"service":null},"application":null,"users":[],"applications":[]},{"id":3386,"created_at":"2026-02-26T16:03:51.000000Z","updated_at":"2026-06-24T19:02:53.000000Z","deleted_at":null,"active_date":"2026-06-24 19:02:55","non_gateway_datasets":["NIHR HIC Ovarian Cancer Dataset"],"non_gateway_applicants":["Sandrine Rendel"],"funders_and_sponsors":["Iain McNeish"],"other_approval_committees":[""],"gateway_outputs_tools":null,"gateway_outputs_papers":null,"non_gateway_outputs":[""],"project_title":"Imperial College Healthcare Tissue Bank","project_id_text":"NIBDAPC_2022_0011","organisation_name":"Imperial College London","organisation_sector":"Academic Institute","lay_summary":"Biological samples are only useful for research if they are annotated with information. The Tissue Bank already records a small amount of clinical data in its dedicated, secured database, however, researchers would greatly benefit from having access to further clinical information from patients who donated their samples for research. Examples of the data proposed to be included in this automatic transfer are: height, weight, BMI, smoking status. In addition, this will include information about treatments such as length and type of cancer therapy treatments and responses to these.\nResearchers in the future will use this information to compile more specific categories of samples during their analysis. Better grouping of samples with similar properties can highlight subtle differences that were not obvious previously without the access to this additional clinical information.\nThis study is a pilot and will look to curate de-identified data that can be used to support future research and cohort finding once Tissue samples are linked to the patient in the Electronic health record. ","technical_summary":null,"latest_approval_date":"2022-03-07 12:00:00","manual_upload":true,"rejection_reason":null,"sublicence_arrangements":"No","public_benefit_statement":"This project is beneficial to enhancing the value of research projects using the samples donated by patients of the Imperial College Healthcare NHS Trust. This will in turn allow researchers to better understand various disease mechanisms and their correlations with clinical information.","data_sensitivity_level":"De-Personalised","project_start_date":"2022-03-25 12:00:00","project_end_date":null,"access_date":"2022-03-25 00:00:00","accredited_researcher_status":"Unknown","confidential_data_description":null,"dataset_linkage_description":null,"duty_of_confidentiality":"Not applicable","legal_basis_for_data_article6":null,"legal_basis_for_data_article9":null,"national_data_optout":"Not applicable","organisation_id":null,"privacy_enhancements":null,"request_category_type":"Public Health Research","request_frequency":"One-off","access_type":"TRE","mongo_object_dar_id":null,"enabled":true,"last_activity":null,"counter":0,"mongo_object_id":null,"mongo_id":null,"user_id":4777,"team_id":93,"application_id":null,"applicant_id":"NIBDAPC_2022_SR_0011","sector_id":3,"status":"ACTIVE","datasets":[],"publications":[],"tools":[],"keywords":[],"user":{"id":4777,"name":"Giselle Kerry","firstname":"Giselle","lastname":"Kerry","email":"Giselle.Kerry@hdruk.ac.uk","secondary_email":"giselle.kerry@hotmail.co.uk","preferred_email":"primary","email_verified_at":null,"secondary_email_verified_at":"2025-09-24 09:18:19","provider":"azure","created_at":"2025-06-05T13:51:42.000000Z","updated_at":"2026-08-07T09:22:46.000000Z","deleted_at":null,"sector_id":6,"organisation":null,"bio":null,"domain":null,"link":null,"orcid":null,"contact_feedback":0,"contact_news":0,"mongo_id":0,"mongo_object_id":null,"is_admin":1,"terms":true,"hubspot_id":128489718789,"is_nhse_sde_approval":false,"rquestroles":["GENERAL_ACCESS"],"cohort_discovery_roles":["GENERAL_ACCESS"],"cohort_discovery_nhs_sde":false},"team":{"id":93,"pid":"d9d1f252-1f5d-45cf-b8b3-078dfaf739fb","created_at":"2024-10-08T11:19:00.000000Z","updated_at":"2026-06-22T14:33:54.000000Z","deleted_at":null,"name":"Imperial College Healthcare NHS Trust","enabled":true,"allows_messaging":false,"workflow_enabled":false,"access_requests_management":false,"uses_5_safes":false,"is_admin":false,"team_logo":null,"member_of":"ALLIANCE","contact_point":null,"application_form_updated_by":"Qresearch webapp","application_form_updated_on":"0001-01-01 00:00:00","mongo_object_id":"66d1e270b5df0006bb95e5e3","notification_status":false,"is_question_bank":false,"is_provider":false,"url":null,"introduction":null,"dar_modal_header":"Important information about applying","dar_modal_content":"{\"type\":\"doc\",\"content\":[{\"type\":\"paragraph\",\"content\":[{\"type\":\"text\",\"text\":\"To apply for access to the Imperial College Healthcare NHS Trust dataset please use the following link:\"}]},{\"type\":\"paragraph\",\"content\":[{\"type\":\"text\",\"marks\":[{\"type\":\"link\",\"attrs\":{\"href\":\"https:\/\/www.imperial.ac.uk\/medicine\/research-and-impact\/groups\/icare\/icare-facility\/information-for-researchers\/\",\"target\":\"_blank\",\"rel\":\"noopener noreferrer nofollow\",\"class\":null}}],\"text\":\"https:\/\/www.imperial.ac.uk\/medicine\/research-and-impact\/groups\/icare\/icare-facility\/information-for-researchers\/\"}]}]}","dar_modal_footer":null,"is_dar":false,"service":null},"application":null,"users":[],"applications":[]},{"id":3385,"created_at":"2026-02-26T16:03:51.000000Z","updated_at":"2026-06-24T19:02:42.000000Z","deleted_at":null,"active_date":"2026-06-24 19:02:44","non_gateway_datasets":["Severe Hyperglycaemia Diabetes Dataset"],"non_gateway_applicants":["Emily Chan"],"funders_and_sponsors":["Neil Hill"],"other_approval_committees":[""],"gateway_outputs_tools":null,"gateway_outputs_papers":null,"non_gateway_outputs":[""],"project_title":"Using Regression Analysis to Identify the Characteristics of Diabetic Inpatients at-risk of Persistent Severe Hyperglycaemia Towards Earlier Intervention","project_id_text":"NIBDAPC_2022_0010","organisation_name":"Imperial College Healthcare NHS Trust","organisation_sector":"Government Agency (Health and Adult Social Care)","lay_summary":"Diabetes is a common disease which affects up to 20% of patients in hospital. High blood glucose levels (hyperglycaemia) is common in patients with diabetes. Despite being preventable, patients still suffer from severe hyperglycaemia whilst in hospital. If left untreated, hyperglycaemia can cause an increased risk of other serious clinical complications such as infection, can lead to patients staying longer in hospital, and can also increase a patient\u2019s risk of death. Therefore, further research is needed to support clinicians in better managing hyperglycaemia in hospitalised patients with diabetes. \n\nThis research will look to make use of routinely collected data from Imperial College Healthcare NHS Trust\u2019s (ICHT) electronic health record EHR, Cerner, to predict characteristcs of patients who are at greater risk of severe hyperglycaemia. This model may then be used in practice to inform clinicians whether a patient is at greater risk of severe hyperglycaemia and support clinicians in implementing preventative clinical interventions to avoid patients developing severe hyperglycaemia in hospital.","technical_summary":null,"latest_approval_date":"2022-02-11 12:00:00","manual_upload":true,"rejection_reason":null,"sublicence_arrangements":"No","public_benefit_statement":"It is estimated that 4.8 million people in the UK have diabetes, and this figure is predicted to continue to increase in parallel with the UK\u2019s aging population (Diabetes UK, 2020). As such, diabetes is one of the most prevalent chronic diseases and, along with its related risk factors and comorbidities, places huge strains on the current healthcare landscape. The importance of improving outcomes for patients with diabetes in hospital settings is fundamental to relieving pressure on other areas of the healthcare system and enabling a more sustainable healthcare system. \n \nOne in ten acute hospital beds are occupied by patients with diabetes (NDA, 2019). High blood glucose levels (hyperglycaemia) commonly occurs in people with diabetes. Diabetic ketoacidosis (DKA) and hyperglycaemic hyperosmolar state (HHS) are complications of hyperglycaemia that a particular concern for inpatients with diabetes. Poorly managed hyperglycaemia in hospital settings is associated with increased risk of complications (both in hospital as well as post discharge ), longer length of stay, and mortality (Dhatariya et al., 2020, Pasquel et al., 2021, Pratiwi et al., 2021, Umpierrez et al., 2002). Whilst some areas of inpatient diabetes management have improved since the first National Diabetes Audit in 2010, the management of hyperglycaemia and its related complications remains unchanged. Although preventable, 1 in 25 inpatients with Type 1 diabetes developed DKA in 2019 (NDA, 2019). Therefore, inpatient management of hyperglycaemia continues to present a significant area of concern for patient outcomes and the wider healthcare landscape. \n\nFurthermore, the NHS Long Term Plan places digital development at the heart of measures to improve health and care, and to deliver services more sustainably (NHS, 2019). A key aspect of this requires the use of healthcare data to generate evidence that enables transformation and improvement of services. A \u2018learning health system\u2019 (LHS) continuously analyses data collected as part of routine care, to monitor outcomes, identify improvements in care, and implement changes on the basis of evidence (Foley and Fairmichael, 2015, Foley et al., 2021). This research will use data to support clinical monitoring, and encourage earlier intervention of hospitalised patients with diabetes, and is therefore closely aligned with LHS principles. Furthermore, the output of this research will present an opportunity to implement a LHS into practice and, in doing so, directly addresses aims and objectives stipulated within the NHS Long Term Plan. ","data_sensitivity_level":"De-Personalised","project_start_date":"2022-04-06 12:00:00","project_end_date":null,"access_date":"2022-04-06 00:00:00","accredited_researcher_status":"Unknown","confidential_data_description":null,"dataset_linkage_description":null,"duty_of_confidentiality":"Not applicable","legal_basis_for_data_article6":null,"legal_basis_for_data_article9":null,"national_data_optout":"Not applicable","organisation_id":null,"privacy_enhancements":null,"request_category_type":"Public Health Research","request_frequency":"One-off","access_type":"TRE","mongo_object_dar_id":null,"enabled":true,"last_activity":null,"counter":0,"mongo_object_id":null,"mongo_id":null,"user_id":4777,"team_id":93,"application_id":null,"applicant_id":"NIBDAPC_2022_EC_0010","sector_id":4,"status":"ACTIVE","datasets":[],"publications":[],"tools":[],"keywords":[],"user":{"id":4777,"name":"Giselle Kerry","firstname":"Giselle","lastname":"Kerry","email":"Giselle.Kerry@hdruk.ac.uk","secondary_email":"giselle.kerry@hotmail.co.uk","preferred_email":"primary","email_verified_at":null,"secondary_email_verified_at":"2025-09-24 09:18:19","provider":"azure","created_at":"2025-06-05T13:51:42.000000Z","updated_at":"2026-08-07T09:22:46.000000Z","deleted_at":null,"sector_id":6,"organisation":null,"bio":null,"domain":null,"link":null,"orcid":null,"contact_feedback":0,"contact_news":0,"mongo_id":0,"mongo_object_id":null,"is_admin":1,"terms":true,"hubspot_id":128489718789,"is_nhse_sde_approval":false,"rquestroles":["GENERAL_ACCESS"],"cohort_discovery_roles":["GENERAL_ACCESS"],"cohort_discovery_nhs_sde":false},"team":{"id":93,"pid":"d9d1f252-1f5d-45cf-b8b3-078dfaf739fb","created_at":"2024-10-08T11:19:00.000000Z","updated_at":"2026-06-22T14:33:54.000000Z","deleted_at":null,"name":"Imperial College Healthcare NHS Trust","enabled":true,"allows_messaging":false,"workflow_enabled":false,"access_requests_management":false,"uses_5_safes":false,"is_admin":false,"team_logo":null,"member_of":"ALLIANCE","contact_point":null,"application_form_updated_by":"Qresearch webapp","application_form_updated_on":"0001-01-01 00:00:00","mongo_object_id":"66d1e270b5df0006bb95e5e3","notification_status":false,"is_question_bank":false,"is_provider":false,"url":null,"introduction":null,"dar_modal_header":"Important information about applying","dar_modal_content":"{\"type\":\"doc\",\"content\":[{\"type\":\"paragraph\",\"content\":[{\"type\":\"text\",\"text\":\"To apply for access to the Imperial College Healthcare NHS Trust dataset please use the following link:\"}]},{\"type\":\"paragraph\",\"content\":[{\"type\":\"text\",\"marks\":[{\"type\":\"link\",\"attrs\":{\"href\":\"https:\/\/www.imperial.ac.uk\/medicine\/research-and-impact\/groups\/icare\/icare-facility\/information-for-researchers\/\",\"target\":\"_blank\",\"rel\":\"noopener noreferrer nofollow\",\"class\":null}}],\"text\":\"https:\/\/www.imperial.ac.uk\/medicine\/research-and-impact\/groups\/icare\/icare-facility\/information-for-researchers\/\"}]}]}","dar_modal_footer":null,"is_dar":false,"service":null},"application":null,"users":[],"applications":[]},{"id":3384,"created_at":"2026-02-26T16:03:51.000000Z","updated_at":"2026-06-24T19:02:31.000000Z","deleted_at":null,"active_date":"2026-06-24 19:02:33","non_gateway_datasets":["ICHT Maternity Dataset"],"non_gateway_applicants":["Phillip Bennett"],"funders_and_sponsors":["Lynne Sykes"],"other_approval_committees":[""],"gateway_outputs_tools":null,"gateway_outputs_papers":null,"non_gateway_outputs":[""],"project_title":"ABO Blood Group Status and Pregnancy Outcomes","project_id_text":"NIBDAPC_2022_0009","organisation_name":"Imperial College Healthcare NHS Trust","organisation_sector":"Government Agency (Health and Adult Social Care)","lay_summary":"Preterm Birth affects 7-8% of pregnancies in the UK. Around 70% of preterm birth is spontaneous (with the remaining 30% accounted for by medical interventions for complications of pregnancy (indicated preterm birth)). It is one of the leading causes of neonatal morbidity and mortality world-wide. Despite much effort, the mechanisms of labour, and preterm birth are not fully understood. The composition of the maternal vaginal microbiome and the cervico vaginal maternal immune response have been shown to modulate risk of preterm birth. It is plausiable that the blood group antigens secreted into the cervico vaginal fluid alter the risk of preterm birth by influencing which bacteria colonise in the vaginal. . This study aims to establish if there is a link between maternal blood group (ABO status) and preterm birth. Routinely collected data about women, their health in pregnancy and pregnancy outcomes will be extracted from electronic patient records. All data extracted will be pseudo-anonymised at the point of extraction, so that no woman can be identified as a result of the work. The researchers will then review the data to see if f maternal ABO status is linked to pregnancy outcomes, such as preterm birth and prelabour preterm rupture of membranes.","technical_summary":null,"latest_approval_date":"2022-01-28 12:00:00","manual_upload":true,"rejection_reason":null,"sublicence_arrangements":"No","public_benefit_statement":"In 2017 as part of its Safer Maternity Care Strategy the Department of Health introduced a target to reduce the preterm birth rate in the UK to 6% by 2025. Currently, the preterm birth rate is 7-8%, and has risen consecutively across the last three decades. In order to address and achieve this target, it is essential that our understanding of the mechanisms that underpin preterm birth is improved, as without this, the development of new therapeutic strategies is limited. Not only is preterm birth a leading cause of neonatal morbidity and mortality, but it also can also have lifelong effects for the parents and families of those affected, including a significant psychological burden. Therefore, projects such as this one, which aim to better understand preterm birth are of huge potential benefit to patients and to wider society.","data_sensitivity_level":"De-Personalised","project_start_date":"2022-03-22 12:00:00","project_end_date":null,"access_date":"2022-03-22 00:00:00","accredited_researcher_status":"Unknown","confidential_data_description":null,"dataset_linkage_description":null,"duty_of_confidentiality":"Not applicable","legal_basis_for_data_article6":null,"legal_basis_for_data_article9":null,"national_data_optout":"Not applicable","organisation_id":null,"privacy_enhancements":null,"request_category_type":"Public Health Research","request_frequency":"One-off","access_type":"TRE","mongo_object_dar_id":null,"enabled":true,"last_activity":null,"counter":0,"mongo_object_id":null,"mongo_id":null,"user_id":4777,"team_id":93,"application_id":null,"applicant_id":"NIBDAPC_2022_PB_0009","sector_id":4,"status":"ACTIVE","datasets":[],"publications":[],"tools":[],"keywords":[],"user":{"id":4777,"name":"Giselle Kerry","firstname":"Giselle","lastname":"Kerry","email":"Giselle.Kerry@hdruk.ac.uk","secondary_email":"giselle.kerry@hotmail.co.uk","preferred_email":"primary","email_verified_at":null,"secondary_email_verified_at":"2025-09-24 09:18:19","provider":"azure","created_at":"2025-06-05T13:51:42.000000Z","updated_at":"2026-08-07T09:22:46.000000Z","deleted_at":null,"sector_id":6,"organisation":null,"bio":null,"domain":null,"link":null,"orcid":null,"contact_feedback":0,"contact_news":0,"mongo_id":0,"mongo_object_id":null,"is_admin":1,"terms":true,"hubspot_id":128489718789,"is_nhse_sde_approval":false,"rquestroles":["GENERAL_ACCESS"],"cohort_discovery_roles":["GENERAL_ACCESS"],"cohort_discovery_nhs_sde":false},"team":{"id":93,"pid":"d9d1f252-1f5d-45cf-b8b3-078dfaf739fb","created_at":"2024-10-08T11:19:00.000000Z","updated_at":"2026-06-22T14:33:54.000000Z","deleted_at":null,"name":"Imperial College Healthcare NHS Trust","enabled":true,"allows_messaging":false,"workflow_enabled":false,"access_requests_management":false,"uses_5_safes":false,"is_admin":false,"team_logo":null,"member_of":"ALLIANCE","contact_point":null,"application_form_updated_by":"Qresearch webapp","application_form_updated_on":"0001-01-01 00:00:00","mongo_object_id":"66d1e270b5df0006bb95e5e3","notification_status":false,"is_question_bank":false,"is_provider":false,"url":null,"introduction":null,"dar_modal_header":"Important information about applying","dar_modal_content":"{\"type\":\"doc\",\"content\":[{\"type\":\"paragraph\",\"content\":[{\"type\":\"text\",\"text\":\"To apply for access to the Imperial College Healthcare NHS Trust dataset please use the following link:\"}]},{\"type\":\"paragraph\",\"content\":[{\"type\":\"text\",\"marks\":[{\"type\":\"link\",\"attrs\":{\"href\":\"https:\/\/www.imperial.ac.uk\/medicine\/research-and-impact\/groups\/icare\/icare-facility\/information-for-researchers\/\",\"target\":\"_blank\",\"rel\":\"noopener noreferrer nofollow\",\"class\":null}}],\"text\":\"https:\/\/www.imperial.ac.uk\/medicine\/research-and-impact\/groups\/icare\/icare-facility\/information-for-researchers\/\"}]}]}","dar_modal_footer":null,"is_dar":false,"service":null},"application":null,"users":[],"applications":[]},{"id":3383,"created_at":"2026-02-26T16:03:51.000000Z","updated_at":"2026-06-24T19:02:19.000000Z","deleted_at":null,"active_date":"2026-06-24 19:02:21","non_gateway_datasets":["ICHT AI Clinician Dataset"],"non_gateway_applicants":["Matthieu Komorowski"],"funders_and_sponsors":["Anthony Gordon"],"other_approval_committees":[""],"gateway_outputs_tools":null,"gateway_outputs_papers":null,"non_gateway_outputs":[""],"project_title":"Retrospective Validation of the AI Clinician Algorithm for Optimal Sepsis Treatment","project_id_text":"NIBDAPC_2021_0008","organisation_name":"Imperial College London","organisation_sector":"Academic Institute","lay_summary":"Sepsis (severe infections with a high risk of death) represents a global healthcare challenge, a leading cause of mortality and the most expensive condition treated in hospitals. Additionally, sepsis is a central contributor to most deaths related to COVID-19 infections. It was recognized as a top priority by the James Lind Alliance, a consortium bringing together patients and clinicians to prioritise the most pressing unanswered questions and inform the NIHR.\n\nA cornerstone of the treatment of sepsis is the administration of intravenous fluids (sterile salty water given directly in the veins) and vasopressors (drugs that constrict the blood vessels to normalise the blood pressure). However, there is huge controversy around the individual dosing of these drugs in a given patient. A tool to personalise these medications could improve patient outcomes. \n\nOur contribution to the field was the development of a new method to suggest the correct dose of medications to doctors, which was created using artificial intelligence algorithms applied to large medical databases in the USA. This tool has the potential to drastically improve sepsis management, save lives and precious ICU resources.\n\nNow, we would like to test this AI system retrospectively using UK data from ICHT, without influencing patient care or actually using the AI in the NHS. One way to do this is to check whether patients who received (in the past) the dose recommended by the AI had better outcomes. We also intend to re-calibrate the model using UK data, which involves re-training the existing model with new UK data, and check whether this improves model performance in this patient population.\n\nTo conclude, accessing ICHT data to validate the model represents a crucial step towards clinical validation of our AI tool, which we are conducting in parallel via an NIHR\/NHS-X AI in Health and Care Award. We aim to publish the output of this work in the scientific and lay press, to maximise its impact.","technical_summary":null,"latest_approval_date":"2021-12-17 12:00:00","manual_upload":true,"rejection_reason":null,"sublicence_arrangements":"No","public_benefit_statement":"First and foremost, it is essential to improve the management of sepsis in order to decrease sepsis-related morbidity mortality and to reduce healthcare expenditures. Reducing sepsis mortality by even a few percent represents hundreds or thousands of lives saved annually in the UK alone, and several tens of millions of pounds in direct and indirect costs. The COVID-19 pandemic, with staff shortages and increased number of critically ill patients, has only rendered the need for such systems more acute. \n\nThe importance of the problem for the NHS and the NIHR was clearly highlighted in several reports and strategy documents. The James Lind Alliance (which informs the NIHR by bringing together patients, carers and clinicians to identify and prioritise top unanswered questions that they agree are the most important) lists our exact same question in the top priorities for Emergency Medicine: \u201cIn patients with sepsis does a liberal fluid resuscitation strategy versus early vasopressor use result in increased morbidity and mortality?\u201d. Sepsis is also listed in the James Lind Alliance top 10 priorities in intensive care.\n\nThe 2014 National Information Board strategy report \u201cPersonalised health and care 2020\u201d sets out proposals intending to \u201c\u2018bring forward life-saving treatments and support innovation and growth\u201d, and to support the development of new medicines and treatments \u201cparticularly in light of breakthroughs in (\u2026) tackling infectious diseases\u201d. Tailoring clinical management to an individual patient is the very principle of my approach. NHS Digital is now in charge of delivering this strategy in its mission to \u201ctransform health and care through technology\u201d.\n\nThe 2018 NHS-commissioned report \u201cThinking on its own: AI in the NHS\u201d stated that \u201cAI could support the delivery of the NHS\u2019s Five Year Forward View, which aims to narrow (\u2026) gaps in health provision\u201c (Harwich & Laycock, 2018). More specifically, it highlighted that AI could support \u201cthe reduction of the care and the quality gap (\u2026) as [AI] can give all health professionals (\u2026) access to cutting edge diagnostics and treatments tailored to individual need\u201d (Harwich & Laycock, 2018). The report also stressed that public trust was \u201cvital for the successful development of AI\u201d, and that solutions had to be found to \u201covercome concerns of both the public and healthcare professionals\u201d, which is why we put such a strong emphasis on involving these entities and other stakeholders such as HDR UK (Harwich & Laycock, 2018).","data_sensitivity_level":"De-Personalised","project_start_date":null,"project_end_date":null,"access_date":null,"accredited_researcher_status":"Unknown","confidential_data_description":null,"dataset_linkage_description":null,"duty_of_confidentiality":"Not applicable","legal_basis_for_data_article6":null,"legal_basis_for_data_article9":null,"national_data_optout":"Not applicable","organisation_id":null,"privacy_enhancements":null,"request_category_type":"Public Health Research","request_frequency":"One-off","access_type":"TRE","mongo_object_dar_id":null,"enabled":true,"last_activity":null,"counter":0,"mongo_object_id":null,"mongo_id":null,"user_id":4777,"team_id":93,"application_id":null,"applicant_id":"NIBDAPC_2021_MK_0008","sector_id":3,"status":"ACTIVE","datasets":[],"publications":[],"tools":[],"keywords":[],"user":{"id":4777,"name":"Giselle Kerry","firstname":"Giselle","lastname":"Kerry","email":"Giselle.Kerry@hdruk.ac.uk","secondary_email":"giselle.kerry@hotmail.co.uk","preferred_email":"primary","email_verified_at":null,"secondary_email_verified_at":"2025-09-24 09:18:19","provider":"azure","created_at":"2025-06-05T13:51:42.000000Z","updated_at":"2026-08-07T09:22:46.000000Z","deleted_at":null,"sector_id":6,"organisation":null,"bio":null,"domain":null,"link":null,"orcid":null,"contact_feedback":0,"contact_news":0,"mongo_id":0,"mongo_object_id":null,"is_admin":1,"terms":true,"hubspot_id":128489718789,"is_nhse_sde_approval":false,"rquestroles":["GENERAL_ACCESS"],"cohort_discovery_roles":["GENERAL_ACCESS"],"cohort_discovery_nhs_sde":false},"team":{"id":93,"pid":"d9d1f252-1f5d-45cf-b8b3-078dfaf739fb","created_at":"2024-10-08T11:19:00.000000Z","updated_at":"2026-06-22T14:33:54.000000Z","deleted_at":null,"name":"Imperial College Healthcare NHS Trust","enabled":true,"allows_messaging":false,"workflow_enabled":false,"access_requests_management":false,"uses_5_safes":false,"is_admin":false,"team_logo":null,"member_of":"ALLIANCE","contact_point":null,"application_form_updated_by":"Qresearch webapp","application_form_updated_on":"0001-01-01 00:00:00","mongo_object_id":"66d1e270b5df0006bb95e5e3","notification_status":false,"is_question_bank":false,"is_provider":false,"url":null,"introduction":null,"dar_modal_header":"Important information about applying","dar_modal_content":"{\"type\":\"doc\",\"content\":[{\"type\":\"paragraph\",\"content\":[{\"type\":\"text\",\"text\":\"To apply for access to the Imperial College Healthcare NHS Trust dataset please use the following link:\"}]},{\"type\":\"paragraph\",\"content\":[{\"type\":\"text\",\"marks\":[{\"type\":\"link\",\"attrs\":{\"href\":\"https:\/\/www.imperial.ac.uk\/medicine\/research-and-impact\/groups\/icare\/icare-facility\/information-for-researchers\/\",\"target\":\"_blank\",\"rel\":\"noopener noreferrer nofollow\",\"class\":null}}],\"text\":\"https:\/\/www.imperial.ac.uk\/medicine\/research-and-impact\/groups\/icare\/icare-facility\/information-for-researchers\/\"}]}]}","dar_modal_footer":null,"is_dar":false,"service":null},"application":null,"users":[],"applications":[]},{"id":3426,"created_at":"2026-02-26T16:03:52.000000Z","updated_at":"2026-06-24T19:02:06.000000Z","deleted_at":null,"active_date":"2026-06-24 19:02:08","non_gateway_datasets":["N\/A - this is a consent to contact IHKB study; no data is being made available"],"non_gateway_applicants":["Alastair Webb"],"funders_and_sponsors":["Clinical Sponsor is not required"],"other_approval_committees":[""],"gateway_outputs_tools":null,"gateway_outputs_papers":null,"non_gateway_outputs":[""],"project_title":"ACCESS@ICL: An All-inclusive Cohort for the Comprehensive Examination of Sporadic Small Vessel Disease @ Imperial College London - IHKB Study","project_id_text":"NIBDAPC_2025_0051","organisation_name":"Imperial College London","organisation_sector":"Academic Institute","lay_summary":"We can see the effects of chronic changes to the blood vessels deep inside the brain on a brain scan as people get older. This is called \u2018small vessel disease\u2019 but is present in the majority of older people and is often a feature just of ageing. However, when these changes become more severe they are one of the most frequent reasons people have strokes, bleeds in the brain or develop dementia. However, we currently can\u2019t treat these changes as we don\u2019t fully understand why this happens, or what medications to use. This is partly because these changes vary a lot from one person to another and we don\u2019t have good ways of measuring what is wrong with the blood vessels.\n\nThis research study aims to improve our understanding of cerebral small vessel disease to identify new ways to treat it. In particular, we aim to include a much broader range of people with small vessel disease than in previous studies, which have usually only included people who have already either had a strokes or already have difficulties with their thinking. We will then do more detailed measurements of how their blood vessels work, how the condition affects them, and then to continue to keep in touch with people to understand what medical difficulties they develop in the future.","technical_summary":null,"latest_approval_date":"2025-10-27 12:00:00","manual_upload":true,"rejection_reason":null,"sublicence_arrangements":"Yes","public_benefit_statement":"Cerebral small vessel disease affects more than half of everyone over 65 and is a leading cause of stroke, memory loss, mood disturbances and difficulty walking. Yet it is often only spotted by accident on routine scans, and there are no treatments to slow or prevent its harmful effects.\n\nWe know this research is an urgent priority for patients and the public for three main reasons. First, the condition touches a very large and growing number of people as our population ages, placing a heavy burden on individuals, families and health services. Second, we have met with focus groups of stroke survivors, people living with vascular dementia and their carers, who gave a clear opinion that the study would help to understand how people are affected by the disease, and help to develop new treatments. Third, doctors and charities working in this area have emphasised the need for research that brings together all forms of the disease\u2014whether or not someone has had a stroke or memory problems\u2014to gain a fuller understanding of what causes it and how it progresses.\n\nBy studying people with the first signs of this disease on their brain scans, whether they have symptoms or not, our project will reveal its hidden mechanisms, help doctors to identify those at greatest risk and lay the groundwork for new therapies. 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