With NHS contact database selections covering CEO's, Directors and Management decision makers, responsive marketing lists can be purchased by job role, seniority level, size and region.

With NHS management database selections covering CEO's, Directors and Management level decision makers, responsive contact data can be purchased by job role, seniority level, size and region.

Content

This dataset is the complete UK NHS (National Health Service) Hospital Database including geospatial data.

Acknowledgements

Data is available on the NHS website under the Open Government Licence.

Cover photo by Camilo Jimenez on Unsplash Unsplash Images are distributed under a unique Unsplash License.

• OrganisationID

• OrganisationCode

• OrganisationType

• SubType

• Sector

• OrganisationStatus

• IsPimsManaged

• OrganisationName

• Address1

• Address2

• Address3

• City

• County

• Postcode

• Latitude

• Longitude

• ParentODSCode

• ParentName

• Phone

• Email

Our NHS doctors database holds current medical and clinical specialists, and has valid doctors email addresses, for responsive clinical marketing and medical research.

BackgroundThere is limited evidence about how vocational rehabilitation (VR) for people with multiple sclerosis (MS) can be delivered through the United Kingdom’s (UK) National Health Service (NHS) and how it works.AimTo understand the mechanisms and context for implementing a VR intervention for people with MS in the NHS and develop an explanatory programme theory.MethodsA realist evaluation, including a review of evidence followed by semi-structured interviews. A realist review about VR for people with MS in the NHS was conducted on six electronic databases (PubMed, MEDLINE, PsychINFO, Web of Science, CINAHL, and EMBASE) with secondary purposive searches. Included studies were assessed for relevance and rigour. Semi-structured interviews with people with MS, employers, and healthcare professionals, were conducted remotely. Data were extracted, analysed, and synthesised to refine the programme theory and produce a logic model.ResultsData from 13 studies, and 19 interviews (10 people with MS, five employers, and four healthcare professionals) contributed to producing the programme theory. The resulting programme theory explains the implementation of VR in the NHS for MS populations, uncovering the complex interplay between the healthcare and employment sectors to influence health and employment outcomes. VR programmes that offer timely support, tailored to the needs of the person with MS, and that support and empower the employee beyond the healthcare context are most likely associated with improved employment outcomes, for example, job retention.ConclusionEmbedding VR support within the NHS requires substantial cultural and organisational change (e.g., increased staff numbers, training, and awareness about the benefits of work). This study emphasises the need to routinely identify people with MS at risk of job loss and follow a collaborative approach to address employment issues. This realist evaluation provides insight on how to improve the quality of care available to people with MS.

.NHS.UK Whois Database, discover comprehensive ownership details, registration dates, and more for .NHS.UK TLD with Whois Data Center.

Published: 28 November 2017 - This is a report on outpatient activity in English NHS hospitals and English NHS-commissioned activity in the independent sector. This annual publication covers the financial year ending March 2017. It contains final data and replaces the provisional data that are published each month. The data are taken from the Hospital Episodes Statistics (HES) data warehouse. HES contains records of all admissions, appointments and attendances for patients admitted to NHS hospitals in England. This publication includes analysis of more than 100 million outpatient appointments recorded in HES data during the 12 month period. A number of breakdowns are provided including by patient's age, gender, whether the appointment was attended or not and by provider. Note that this report counts the number of outpatient appointments rather than the number of patients. The purpose of this publication is to inform and support strategic and policy-led processes for the benefit of patient care. This document will also be of interest to researchers, journalists and members of the public interested in NHS hospital activity in England.

The National Highway System (NHS) dataset and its geometries was updated on August 08, 2025 from the Federal Highway Administration (FHWA) and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). The National Highway System consists of roadways important to the nation's economy, defense, and mobility. The National Highway System (NHS) includes the following subsystems of roadways: Interstate - The Eisenhower Interstate System of highways, Other Principal Arterials - highways in rural and urban areas which provide access between an arterial and a major port, airport, public transportation facility, or other intermodal transportation facility, Strategic Highway Network (STRAHNET) - a network of highways which are important to the United States' strategic defense policy and which provide defense access, continuity and emergency capabilities for defense purposes, Major Strategic Highway Network Connectors - highways which provide access between major military installations and highways which are part of the Strategic Highway Network, Intermodal Connectors - highways providing access between major intermodal facilities and the other four subsystems making up the National Highway System. A specific highway route may be on more than one subsystem. A data dictionary, or other source of attribute information, is accessible at https://doi.org/10.21949/1529838

Data for this publication are extracted each month as a snapshot in time from the Primary Care Registration database within the NHAIS (National Health Application and Infrastructure Services) system. This release is an accurate snapshot as at 1 March 2024. GP Practice; Primary Care Network (PCN); Sub Integrated Care Board Locations (SICBL); Integrated Care Board (ICB) and NHS England Commissioning Region level data are released in single year of age (SYOA) and 5-year age bands, both of which finish at 95+, split by gender. In addition, organisational mapping data is available to derive PCN; SICBL; ICB and Commissioning Region associated with a GP practice and is updated each month to give relevant organisational mapping. Quarterly publications in January, April, July and October will include Lower Layer Super Output Area (LSOA) populations.

The Leeds Teaching Hospitals NHS Trust (LTHT) OMOP database is a robust, longitudinal dataset constructed using data from the electronic health records (EHR) of patients treated and diagnosed at Leeds Teaching Hospitals NHS Trust since 2003. This comprehensive resource is mapped to the OMOP CDM, ensuring interoperability with other OMOP databases, and enabling privacy-preserving, large-scale, multi-centre studies.

Encompassing a wide array of clinical data, the database includes information on demographics, diagnoses, procedures, medications and laboratory results. A particular strength lies in its detailed cancer-specific data, which supports in-depth analyses of treatment outcomes, survival rates, and disease progression. This makes it an invaluable resource for researchers focusing on oncology, as well as those interested in broader secondary care settings.

Researchers can draw insights from the LTHT OMOP database through federated analytics approaches as well as through the use of standardised OHDSI tools, which enable secure, privacy-preserving analyses across multiple institutions, eliminating the need to access individual-level patient data.

Notably, the LTHT OMOP database has been instrumental in several high-profile studies:

• HERON Network: LTHT is a member of the HERON network, funded by HDR UK, which focuses on enhancing the quality and impact of cancer research through federated analytics. LTHT participated in a study examining the use of antibiotics which are in the WHO watchlist for high risk of antimicrobial resistance. • DigiONE Pilot Studies: These studies analyse harmonised routine care data from OMOP databases in 6 digitally mature European hospitals. Three studies have been conducted to date, focusing on the impact of the COVID-19 pandemic on cancer care, on metastatic non-small cell lung cancer, and on HER2-/HR+ metastatic breast cancer. • FALCON-Lung Study: This study focused on the uptake of immune checkpoint inhibitors for metastatic non-small cell lung cancer across the world, and implemented a clinically validated line of therapy algorithm using systemic anti-cancer therapy data in the OMOP databases of 17 international institutions.

In summary, the LTHT OMOP database stands as a robust resource for secondary care research, particularly in oncology. Its comprehensive, high-quality data, combined with a commitment to national and international collaboration, positions it as a cornerstone for advancing healthcare research and improving patient outcomes.

The LTHT OMOP database consists of the following tables and data:

• Visit occurrence: includes inpatient and outpatient admissions for all patients that are or have been part of the cancer pathway, as well as all in-patient admissions for all other patients. The visit_detail table has not been populated. • Condition occurrence: populated with all diagnoses in the Trust since 2003. • Drug exposure: populated. Includes all anti-cancer drugs (chemotherapy and immunotherapy), and selected antibiotics medication (all antibiotics that are in the WHO watchlist for antimicrobial resistance, as well as access antibiotics). Plans to extend this to all medication prescribed. • Procedure occurrence: populated. Includes surgical and radiotherapy procedures delivered to patients with cancer, as well as all surgical procedures delivered to all other patients. • Measurement: populated with weight, height, TNM staging, performance status, and metastasis location data. • Observation: populated with ethnicity, IMD quintile, clinical trial participation (cancer only) and cancer histology data. • Device exposure: not populated. • Death: populated from ONS.

Attendance information for all hospital outpatient appointments. The data are collected and coded at each hospital. Administrative information is collected from the central PAS (Patient Administrative System), such as specialty of care, appointment date and attendance status.

This dataset contains all scheduled outpatient appointments, including those where the patient failed to attend.

These statistics are derived from the National Community Child Health Database (NCCHD). This data source is provided to the Welsh Government by Digital Health and Care Wales (DHCW). The NCCHD was established in 2004 and consists of anonymised records for all children born, resident or treated in Wales and born after 1987. The database brings together data from local Community Child Health System databases which are held by local health boards (LHBs), and its main function is to provide an online record of a child’s health and care from birth to leaving school age. The statistics used in this release are based on the data recorded at birth and shortly after birth. Full details of every data item available on both the Maternity Indicators dataset and National Community Child Health Database are available through the NHS Wales Data Dictionary: http://www.datadictionary.wales.nhs.uk/#!WordDocuments/datasetstructure20.htm. Gestational age is based on the best estimate available for when pregnancy started, based on either date of last menstrual period or from an ultrasound scan.

This data package contains the hospital bed availability and occupancy data by consultant main specialty and sector as well as data on inpatient and outpatient related hospital activity in England. It also contains information on Sub-Saharan public hospitals.

The iCARE SDE is a cloud-based, big data analytics platform sitting within Imperial College Healthcare NHS Trust (ICHT) NHS infrastructure. This, combined with the iCARE Team’s robust method of data de-identification, make the Environment an incredibly secure platform. The fact that it can be accessed remotely using the Trust’s Virtual Desktop Infrastructure means that researchers can perform their work remotely and are therefore not constrained by location. (imperial.dcs@nhs.net)

The iCARE SDE enables clinicians, researchers and data scientists to access large-scale, highly curated databases for the purposes of research, clinical audit and service evaluation. The iCARE SDE enables advanced data analytics through a scalable virtual infrastructure supporting Azure Machine Learning, Python, R and STATA and a large variety of snowflake SQL tooling.

The main iCARE data model is a HRA REC approved database covering all routinely captured information from Imperial College Healthcare Trust (ICHT) Electronic Health Record and 39 linked (at the patient-level) clinical and non-clinical systems. It contains data for all patients from 2015 onwards and is updated weekly as a minimum, and close to real-time when required. It includes inpatient, outpatient, A&E, pathology, cancer, imaging treatments, e-prescribing, procedures, clinical notes, Consent, clinical trials, tissue bank samples, Patient safety and incidents, Patient experience, Staffing and environment data.

Data can also be linked to primary care data for the 2.8million population in Northwest London, HRA REC approved, Whole Systems Integrated Care (WSIC) hosted database and other health and social care providers when approved.

On a project-by-project basis the model can be expanded to curate and include new data (including multi-modality data), that is either captured routinely or through approved research and clinical trials. There are streamlined processes to approve and curate new data (imperial.dataaccessrequest@nhs.net) and data will always remain hosted in the SDE.

The Child Health System in Wales; includes birth registration and monitoring of child health examinations and immunisations.

The Child Health System in Wales; includes birth registration and monitoring of child health examinations and immunisations.

The dataset brings together data from local Child Health System databases which are held by NHS Trusts and used by them to administer child immunisation and health surveillance programmes.

The dataset contains all children born, resident or treated in Wales and born after 1987.

This report contains generic information about pacing and ICD practice in the United Kingdom and Republic of Ireland up to and including 2000.

This zip file contains the Code History Database for the United Kingdom as at April 2018. To download the zip file click the Download button. The Code History Database (CHD) contains the GSS nine-character codes, where allocated, for current and new statistical geographies from 1 January 2009. The codes consist of a simple alphanumeric structure; the first three characters (ANN) represent the area entity (i.e. type; or category of geography) and the following six characters (NNNNNN) represent the specific area instance. The CHD provides multiple functionality including details of codes, relationships, hierarchies and archived data. The CHD can be used in conjunction with the Register of Geographic Codes (RGC) that summarises the range of area instances within each geographic entity. The GSS Coding and Naming policy for some statistical geographies was implemented on 1 January 2011. From this date, where new codes have been allocated they should be used in all exchanges of statistics and published outputs that normally include codes. For further information on this product, please read the user guide and version notes contained within the product zip file. Updated GeographiesUpdates to Parishes (E04) (name change), Wards (E05) (name change), NMD (E07) (name change), Clinical Commissioning Groups in England (E38), NHS (Region, Local Office) (E39) and NHS England Regions (E40)Updates to Council Areas (S13) and Wards (S13)Updates to the Change History, SI Details, Name Changes, Equivalents table and Information table.Database ChangesUpdates to form design to account for December 2017 version have been made.

DECOVID, a multi-centre research consortium, was founded in March 2020 by two United Kingdom (UK) National Health Service (NHS) Foundation Trusts (comprising three acute care hospitals) and three research institutes/universities: University Hospitals Birmingham (UHB), University College London Hospitals (UCLH), University of Birmingham, University College London and The Alan Turing Institute. The original aim of DECOVID was to share harmonised electronic health record (EHR) data from UCLH and UHB to enable researchers affiliated with the DECOVID consortium to answer clinical questions to support the COVID-19 response.   ​​   ​​The DECOVID database has now been placed within the infrastructure of PIONEER, a Health Data Research (HDR) UK funded data hub that contains data from acute care providers, to make the DECOVID database accessible to external researchers not affiliated with the DECOVID consortium.  

This highly granular dataset contains 256,804 spells and 165,414 hospitalised patients. The data includes demographics, serial physiological measurements, laboratory test results, medications, procedures, drugs, mortality and readmission.

Geography: UHB is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & > 120 ITU bed capacity. UCLH provides first-class acute and specialist services in six hospitals in central London, seeing more than 1 million outpatient and 100,000 admissions per year. Both UHB and UCLH have fully electronic health records. Data has been harmonised using the OMOP data model. Data set availability: Data access is available via the PIONEER Hub for projects which will benefit the public or patients. This can be by developing a new understanding of disease, by providing insights into how to improve care, or by developing new models, tools, treatments, or care processes. Data access can be provided to NHS, academic, commercial, policy and third sector organisations. Applications from SMEs are welcome. There is a single data access process, with public oversight provided by our public review committee, the Data Trust Committee. Contact pioneer@uhb.nhs.uk or visit www.pioneerdatahub.co.uk for more details.

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

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

To reduce mortality For information on the definitions of what these indicators include, please see the relevant specification. From 2016 onwards, mortality counts within the Compendium Mortality Indicator set are based on a bespoke extract taken from the Primary Care Mortality Database (PCMD) maintained by NHS Digital. PCMD is updated monthly using a file of death records from ONS and is continually subject to amendment. It is already well established that late registrations have a small impact on counts. This bespoke extract may be taken at a different time to that of the mortality data published by ONS and as such this may cause some small differences between ONS and NHS Digital mortality figures for a given year.

A five-year framework for GP contract reform to implement The NHS Long Term Plan published in January 2019 describes significant investment in Primary Care Networks (PCNs) through the Network Contract Directed Enhanced Service (DES). As part of the Network Contract DES, investment is being made to support the expansion of the primary care workforce and the implementation of seven national Network Service Requirements between 2019-20 and 2023-24 through the Additional Roles Reimbursement Scheme (ARRS). The fourth year of the Network Contract DES began in April 2022. The data being collected expands on the indicators developed in 2019-20 and 2020-21 around the ARRS with thirteen service specifications, covering: Access Anticipatory care Cancer Cardiovascular disease prevention Early cancer diagnosis Enhanced health in care homes Environmental sustainability Personalised care Respiratory care Structured medication reviews and medicines optimisation Tackling health inequalities Vaccination and immunisation Workforce The data is collected via the General Practice Extraction Service (GPES) monthly and will be published on a monthly basis at GP Practice level. Initially, the collection for the 2022-23 service year only included data for payment indicators only; management information indicators were added to the collection in August. As of July 2023, the Investment and Impact Fund (IIF) annual publication is also published on this page. IIF is an incentive scheme focussed on supporting PCNs to deliver high quality care to their population, and the delivery of the priority objectives articulated in the NHS Long Term Plan and in Investment and Evolution; a five-year GP contract framework. The scheme contains indicators that focus on where PCNs can contribute significantly towards the ‘triple aim’: Improving health and saving lives (e.g. through improvements in medicines safety) Improving the quality of care for people with multiple morbidities (e.g. through increasing referrals to social prescribing services) Helping to make the NHS more sustainable. NOTE: .csv files may not open in applications such as Microsoft Excel due to the number of rows included in the dataset. Users may wish to import the file directly into a database, or alternatively use a text editor to split the file for import/use in applications such as Microsoft Excel. To view data quality notices for this publication please navigate to the supporting information which is linked below in the resource links section.