This report shows monthly numbers of NHS Hospital and Community Health Services (HCHS) staff working in NHS Trusts and other core organisations in England (excluding primary care staff). Data are available as headcount and full-time equivalents and for all months from 30 September 2009 onwards. These data are a summary of the validated data extracted from the NHS HR and Payroll system. Additional statistics on staff in NHS Trusts and other core organisations and information for NHS Support Organisations and Central Bodies are published each: September (showing June statistics) December/January (showing September statistics) March (showing December statistics) June (showing March statistics) Quarterly NHS Staff Earnings, monthly NHS Staff Sickness Absence reports, and data relating to the General Practice workforce and the Independent Healthcare Provider workforce are also available via the Related Links below. We welcome feedback on the methodology and tables within this publication. Please email us with your comments and suggestions, clearly stating Monthly HCHS Workforce as the subject heading, via enquiries@nhsdigital.nhs.uk or 0300 303 5678.

Physicians (per 1,000 people) in United Kingdom was reported at 3.174 in 2022, according to the World Bank collection of development indicators, compiled from officially recognized sources. United Kingdom - Physicians - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

A List of UK Health Workers Who Have Died from COVID-19

Made machine-readable by hand from data from the UK newspaper "The Guardian", in this article: "Doctors, nurses, porters, volunteers: the UK health workers who have died from Covid-19" https://www.theguardian.com/world/2020/apr/16/doctors-nurses-porters-volunteers-the-uk-health-workers-who-have-died-from-covid-19

The Guardian is continuing to update the list day-by-day, as the COVID-19 pandemic continues. I do not plan to update this dataset, assuming, since the data collection biases are unknown, that nobody else will find it very interesting. I am not a copyright lawyer and do not know if this data is protected copyright, and if so, in which parts of the world.

Caveat: Creating this dataset from a newspaper article required a lot of hand work. I've done my best, but there may be mistakes.

Columns: Name age institution city: I have filled this in myself; I am ignorant of UK geography and there may well be mistakes date_of_death possible_ppe_issue: mostly blank, but I have filled in "yes" where the article mentions a person who had doubts about the adequacy of PPE (personal protective equipment) MED_SPEC: I have attempted to fill in a medical specialty from the values used on the Eurostat web site for Physicians by Medical Specialty" and "Nursing and caring professionals" tables. The idea is to be able to calculate a fraction of affected individuals by specialty.

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A detailed view of the NHS Medical and dental workforce including consultants, registrars and other doctors in training. Data for medical and dental staff are an annual snapshot of the number of doctors within hospital and community health services (HCHS) of the NHS. It excludes General Practitioners, GP practice staff and high street dentists. The detailed results contain further data tables for September 2012 for England by age, gender, specialty and grade and selected data by Strategic Health Authority area and individual organisation. This report is one of three that make up the NHS Staff 2002 - 2012 publication, along with: non-medical staff 2002 - 2012 general practice staff 2002 - 2012 For a general overview, see NHS Staff 2002 - 2012 overview

These statistics relate to the contracted positions within English NHS organisations and may include those where the person assigned to the position is temporarily absent, for example on maternity leave. Full Time Equivalent (FTE) refers to the proportion of each role’s full time contracted hours that the post holder is contracted to work. 1 would indicate they work a full set of hours, 0.5 that they work half time.

Note that the methodology for these statistics changed from December 2015. More information is available from the additional links below.

The average for 2020 based on 18 countries was 3.93 doctors per 1,000 people. The highest value was in Austria: 5.35 doctors per 1,000 people and the lowest value was in the United Kingdom: 3.03 doctors per 1,000 people. The indicator is available from 1960 to 2021. Below is a chart for all countries where data are available.

Received 17 February 2025: ‘may I have details of your independent doctor so I can check them out.’ Received 25 February 2025: ‘Please could you arrange for me to receive the Freedom of Information Act so that I can check the qualifications of your independent doctors.’ Our response I can confirm that the NHS Business Services Authority (NHSBSA) holds some of the information you have requested. Question 1 I can confirm that we do hold information on the names and General Medical Council numbers for independent medical assessors. Please note that this response does not relate to a specific claim or claimant. The request is being answered more generally given requests under FOIA are requester-blind, that is to say the identity of the requester is not taken into account when considering a request for information under FOIA. We consider the name and GMC number to be personal data under the Data Protection Act 2018. Disclosure of medical assessors’ names or GMC numbers would result in the identification of the medical assessors when entered into the GMC public register. Please be aware that I have decided not to release the names and GMC numbers of the medical assessors as this information falls under the exemption in section 40 subsections 2 and 3(A)(a) of the FOIA. As the requested information would allow a medical assessor to be identified, I consider this information is exempt. This is because it would breach the first data protection principle as: A. it is not fair to disclose medical assessors’ personal details to the world and is likely to cause damage or distress. B. these details are not of sufficient interest to the public to warrant an intrusion into the privacy of the medical assessor. The requested information is exempt if disclosure would contravene any of the data protection principles. For disclosure to comply with the lawfulness, fairness, and transparency principle, we either need the consent of the data subject(s) or there must be a legitimate interest in disclosure. In addition, the disclosure must be necessary to meet the legitimate interest and finally, the disclosure must not cause unwarranted harm. This means that the NHSBSA is therefore required to conduct a balancing exercise between the legitimate interest of the applicant in disclosure against the rights and freedoms of the medical assessor. While I acknowledge that you have a legitimate interest in disclosure of the information, the disclosure of the requested information would cause unwarranted harm. Disclosure under FOIA is to the world and therefore the NHSBSA has to consider the overall impact of the disclosure and its duty of care. The expectation of the medical assessors is that they will remain anonymous and will therefore not be subject to contact or pressure from claimants or campaigning groups. Given the certainty that the name and/or GMC number will identify the medical assessor there is a reasonable expectation that this information will not be disclosed under the FOIA. Disclosing this information would be unfair and as such this would breach the UK General Data Protection Regulation first data protection principle. Please see the following link to view the section 40 exemption in full: https://www.legislation.gov.uk/ukpga/2000/36/section/40 Question 2 I have established that the NHSBSA does not hold this information. This is because the medical qualifications and experience of the medical assessors are the responsibility of the third-party medical assessment supplier. I hope, however, that the following information provides reassurance on this point. All claims are assessed by the independent medical assessment supplier with a consistent approach. Each case is considered on its own merits, by an experienced independent medical assessor. The contract with our supplier does not require them to tell us details of the qualifications of the medical assessors or their experience. The contract requires that all assessments carried out are undertaken by suitably qualified and experienced registered medical practitioners. This includes being registered on the UK General Medical Council register, with a licence to practise and meet or exceed the following requirements: • they are a registered medical practitioner with at least five years’ post graduate experience; and • they have experience of the performance of medical and/ or disability assessment, addressing questions of causation and impact in the context of legislative or policy requirements to assist the decision maker

This dataset comprises physician-level entries from the 1906 American Medical Directory, the first in a series of semi-annual directories of all practicing physicians published by the American Medical Association [1]. Physicians are consistently listed by city, county, and state. Most records also include details about the place and date of medical training. From 1906-1940, Directories also identified the race of black physicians [2].This dataset comprises physician entries for a subset of US states and the District of Columbia, including all of the South and several adjacent states (Alabama, Arkansas, Delaware, Florida, Georgia, Kansas, Kentucky, Louisiana, Maryland, Mississippi, Missouri, North Carolina, Oklahoma, South Carolina, Tennessee, Texas, Virginia, West Virginia). Records were extracted via manual double-entry by professional data management company [3], and place names were matched to latitude/longitude coordinates. The main source for geolocating physician entries was the US Census. Historical Census records were sourced from IPUMS National Historical Geographic Information System [4]. Additionally, a public database of historical US Post Office locations was used to match locations that could not be found using Census records [5]. Fuzzy matching algorithms were also used to match misspelled place or county names [6].The source of geocoding match is described in the “match.source” field (Type of spatial match (census_YEAR = match to NHGIS census place-county-state for given year; census_fuzzy_YEAR = matched to NHGIS place-county-state with fuzzy matching algorithm; dc = matched to centroid for Washington, DC; post_places = place-county-state matched to Blevins & Helbock's post office dataset; post_fuzzy = matched to post office dataset with fuzzy matching algorithm; post_simp = place/state matched to post office dataset; post_confimed_missing = post office dataset confirms place and county, but could not find coordinates; osm = matched using Open Street Map geocoder; hand-match = matched by research assistants reviewing web archival sources; unmatched/hand_match_missing = place coordinates could not be found). For records where place names could not be matched, but county names could, coordinates for county centroids were used. Overall, 40,964 records were matched to places (match.type=place_point) and 931 to county centroids ( match.type=county_centroid); 76 records could not be matched (match.type=NA).Most records include information about the physician’s medical training, including the year of graduation and a code linking to a school. A key to these codes is given on Directory pages 26-27, and at the beginning of each state’s section [1]. The OSM geocoder was used to assign coordinates to each school by its listed location. Straight-line distances between physicians’ place of training and practice were calculated using the sf package in R [7], and are given in the “school.dist.km” field. Additionally, the Directory identified a handful of schools that were “fraudulent” (school.fraudulent=1), and institutions set up to train black physicians (school.black=1).AMA identified black physicians in the directory with the signifier “(col.)” following the physician’s name (race.black=1). Additionally, a number of physicians attended schools identified by AMA as serving black students, but were not otherwise identified as black; thus an expanded racial identifier was generated to identify black physicians (race.black.prob=1), including physicians who attended these schools and those directly identified (race.black=1).Approximately 10% of dataset entries were audited by trained research assistants, in addition to 100% of black physician entries. These audits demonstrated a high degree of accuracy between the original Directory and extracted records. Still, given the complexity of matching across multiple archival sources, it is possible that some errors remain; any identified errors will be periodically rectified in the dataset, with a log kept of these updates.For further information about this dataset, or to report errors, please contact Dr Ben Chrisinger (Benjamin.Chrisinger@tufts.edu). Future updates to this dataset, including additional states and Directory years, will be posted here: https://dataverse.harvard.edu/dataverse/amd.References:1. American Medical Association, 1906. American Medical Directory. American Medical Association, Chicago. Retrieved from: https://catalog.hathitrust.org/Record/000543547.2. Baker, Robert B., Harriet A. Washington, Ololade Olakanmi, Todd L. Savitt, Elizabeth A. Jacobs, Eddie Hoover, and Matthew K. Wynia. "African American physicians and organized medicine, 1846-1968: origins of a racial divide." JAMA 300, no. 3 (2008): 306-313. doi:10.1001/jama.300.3.306.3. GABS Research Consult Limited Company, https://www.gabsrcl.com.4. Steven Manson, Jonathan Schroeder, David Van Riper, Tracy Kugler, and Steven Ruggles. IPUMS National Historical Geographic Information System: Version 17.0 [GNIS, TIGER/Line & Census Maps for US Places and Counties: 1900, 1910, 1920, 1930, 1940, 1950; 1910_cPHA: ds37]. Minneapolis, MN: IPUMS. 2022. http://doi.org/10.18128/D050.V17.05. Blevins, Cameron; Helbock, Richard W., 2021, "US Post Offices", https://doi.org/10.7910/DVN/NUKCNA, Harvard Dataverse, V1, UNF:6:8ROmiI5/4qA8jHrt62PpyA== [fileUNF]6. fedmatch: Fast, Flexible, and User-Friendly Record Linkage Methods. https://cran.r-project.org/web/packages/fedmatch/index.html7. sf: Simple Features for R. https://cran.r-project.org/web/packages/sf/index.html

Data in .csv format relating to the paper Chiedozie et al. 2020 "How many medications do doctors in primary care use? An observational study of the DU90% indicator in primary care in England." Also contains eTables 5-8 in Excel format, and Stata do file for deriving the DU90% indicator.

Medical affairs pharmaceutical physicians (MAPPs) are at risk for low work-related quality of life (WRQoL). The aim of this study was to develop, validate and implement the first WRQoL instrument for this population. A prospective observational cohort clinical study, the Medical Affairs Pharmaceutical Physician Work-related Quality of Life (MAPPWrQoL) Instrument Development and Patient Registry (MAPPWrQoLReg), was registered in November 2021 (NCT05123846). Thirteen MAPPs and 12 non-MAPPs participated in development and validation between December 2021 and January 2022. Development used the Jandhyala method for observing proportional group awareness and consensus. Discriminant validity analysis used the WRQoL Scale as a reference standard and assessed whether the instrument could differentiate between the groups. Twelve MAPPs and 12 non-MAPPs self-reported their WRQoL in the registry each month from February 2022. Recruitment and data collection are ongoing; 6-month data between February 2022 and August 2022 are reported here. Two participants were excluded from the registry. Chi-squared analysis showed a significant difference between the MAPPWRQoL instrument and WRQoL Scale (p = 1.029e-08) with acceptable sensitivity (89.19%) and specificity (75.00%). There were significant between-group differences for total scores at each follow-up (p = .003; n = 6 questions). Chi-squared analysis showed a significant difference between MAPPs’ and non-MAPPs’ ability to answer MAPPWRQoL instrument items (p = .002629), with acceptable sensitivity (91.9%) and near-acceptable specificity (66.7%). MAPPs’ WRQoL did not change significantly over 6 months. Discriminant validity of the 39-item MAPPWRQoL instrument was confirmed. The Jandhyala method successfully developed and validated a specific WRQoL instrument and may be applied to similar populations, such as junior doctors and UK general practitioners.

Could I please request the prescribing data for unlicensed cannabis based medicinal products [CBMP] prescribed privately, from Nov 2018 until the most recent available. I'm particularly looking for the item count, broken down on a monthly basis. Response A copy of the information is attached. Private prescribing of unlicensed cannabis-based medicines November 2018 to March 2023 NHS Prescription Services, within the NHS Business Services Authority (NHSBSA) process prescriptions for Pharmacy Contractors, Appliance Contractors, Dispensing Doctors and Personal Administration. This information is then used to make payments to pharmacists and appliance contractors in England for prescriptions dispensed in primary care settings. There are other arrangements in place for making payments to Dispensing Doctors and Personal Administration. This involves processing over 1 billion prescription items and payments totalling over £9 billion each year. The information gathered from this process is then used to provide information on costs and trends in prescribing in England and Wales to over 25,000 registered NHS and Department of Health and Social Care users. Data Source When prescriptions are processed by the NHSBSA data capture, prescriptions sometimes contain prescribing of medicines that were not populated on the NHSBSA drug database at the time. This type of order will be captured as an ‘unspecified drug.’ Data for prescribing of unlicensed cannabis-based medicines has been taken from data captured as unspecified prescribing. Unlicensed cannabis-based medicines are identified by an additional review process which occurs after the prescriptions have been processed. The items identified by this review are reported against the date that the prescription was written and not necessarily when they were submitted. Therefore, these figures may be subject to change if the prescription is submitted to the NHSBSA in a later month. This dataset This dataset shows total items per month for private prescriptions for unlicensed cannabis-based products. Time Period November 2018 to March 2023 (the latest available month currently). The Data is presented monthly. Organisation Data Data for private unlicensed prescriptions is limited to prescriptions dispensed in England. Items Shows the number of times a product appears on a prescription form not the quantity prescribed.

Under the Freedom of Information Act 2000, I request the following information: The number of individuals of all ages who were prescribed contraceptives in the financial years 2019-2020, 2021-2020, 2020-2021, 2021-2022 and 2022-2023 in community settings (GP surgeries and pharmacies) broken down by contraceptive method. I would also like the proportion these represent of contraception users. For example, X proportion of those on contraception are using the Mirena coil. If possible, I would also appreciate if this were broken down by age of those prescriptions too. To clarify, I mean patients. I also mean both contraceptive drugs and appliances/devices Response A copy of the information is attached. Please read the following information to ensure correct understanding of the data. Fewer than five Please be aware that I have decided not to release the full details where the total number of individuals falls below five. This is because the individuals could be identified, when combined with other information that may be in the public domain or reasonably available. This information falls under the exemption in section 40 subsections 2 and 3 (a) of the Freedom of Information Act (FOIA). This is because it would breach the first data protection principle as: a - It is not fair to disclose individual’s personal details to the world and is likely to cause damage or distress. b - These details are not of sufficient interest to the public to warrant an intrusion into the privacy of the individual. Please click the weblink to see the exemption in full: www.legislation.gov.uk/ukpga/2000/36/section/40 NHS Business Services Authority (NHSBSA) - NHS Prescription Services process prescriptions for Pharmacy Contractors, Appliance Contractors, Dispensing Doctors, and Personal Administration with information then used to make payments to pharmacists and appliance contractors in England for prescriptions dispensed in primary care settings (other arrangements are in place for making payments to Dispensing Doctors and Personal Administration). This involves processing over one billion prescription items and payments totalling over £9 billion each year. The information gathered from this process is then used to provide information on costs and trends in prescribing in England and Wales to over 25,000 registered NHS and Department of Health and Social Care (DHSC) users. Data Source: ePACT2 - Data in ePACT2 is sourced from the NHSBSA Data Warehouse and is derived from products prescribed on prescriptions and dispensed in the Community. The data captured from prescription processing is used to calculate reimbursement and remuneration. It includes items prescribed in England, Wales, Scotland, Northern Ireland, Guernsey/Alderney, Jersey, and Isle of Man which have been dispensed in the community in England. English prescribing that has been dispensed in Wales, Scotland, Guernsey/Alderney, Jersey, and Isle of Man is also included. The data excludes: • Items not dispensed, disallowed and those returned to the contractor for further clarification. • Prescriptions prescribed and dispensed in prisons, hospitals, and private prescriptions. • Items prescribed but not presented for dispensing or not submitted to NHS Prescription Services by the dispenser. Dataset - The data is limited to presentations prescribed in BNF sections 0703 Contraceptives and BNF section 2104 Contraceptive Devices. Data is presented at BNF Sub Paragraph and BNF Presentation level. Time Period - Financial years 2019/20, 2020/21, 2021/22, 2022/23 and 2023/24 (April 2023 - January 2024). Data is currently available up to and including January 2024. Organisation Data - The data is for prescribing in England regardless of where dispensed in the community. British National Formulary (BNF) Sub Paragraph and Presentation Code – The BNF Code is a 15-digit code in which the first seven digits are allocated according to the categories in the BNF, and the last eight digits represent the medicinal product, form, strength and the link to the generic equivalent product. NHS Prescription Services has created pseudo BNF chapters, which are not published, for items not included in BNF chapters 1 to 15. Most of such items are dressings and appliances which NHS Prescription Services has classified into four pseudo BNF chapters (20 to 23). Patient Identification - Where patient identifiable figures have been reported they are based on the information captured during the prescription processing activities. Please note, patient details cannot be captured from every prescription form and based on the criteria used for this analysis, patient information (NHS number) was only available for 98.28% of prescription items. The unique patient count figures are based on a distinct count of NHS number as captured from the prescription image. Patient ages are based on the age as captured from the prescription image and relates to the patient's age at the time of prescribing/dispensing. Please note it is possible that a single patient may be included in the results for more than one age band where a patient has received prescribing at different ages during a financial year. The figures for the number of identifiable patients should not be combined and reported at any other level than provided as this may result in the double counting of patients. For example, a single patient could appear in the results for multiple presentations or both financial years. Patient Age - Shows the age of the patient, if recorded. Data Quality for patient age - NHSBSA stores information on the age of the recipient of each prescription as it was read by computer from images of paper prescriptions or as attached to messages sent through the electronic prescription system. The NHSBSA does not validate, verify or manually check the resulting information as part of the routine prescription processing. There are some data quality issues with the ages of patients prescribed the products. The NHSBSA holds prescription images for 18 months. A sample of the data was compared to the images of the paper prescription forms from which the data was generated where these images are still available. These checks revealed issues in the reliability of age data, in particular the quality of the stored age data was poor for patients recorded as aged two years and under. When considering the accuracy of age data, it is expected that a small number of prescriptions may be allocated against any given patient age incorrectly. Application of Disclosure Control to information services (prescriptions) products- ePACT 2 data is not published statistics - it is available to authorised NHS users who are subject to Caldicott Guardian approval. We have no plans to apply disclosure control to data released to ePACT 2 users. These users are under an obligation to protect the anonymity of any patients when reusing this data or releasing derived information publicly. All requests that fall under the FOI process are subject to the NHSBSA Anonymisation and Pseudonymisation Standard. The application of the techniques described in the standard is judged on a case-by-case basis (by NHSBSA Information Governance) in respect of what techniques should be applied. The ICO typically rules on a case-by-case basis too so each case or challenge or appeal is judged on its own merits. FOI rules apply to data that we hold as part of our normal course of business.

We searched the following electronic databases from January 1946 to 2016: English language (Medline, Embase), Chinese (CNKI, Wanfang, VIP), Japanese (Ichushi), Russian (Yandex, Rambler), and Spanish and Portuguese (SciELO). The search strategy was based on the Medline search described by Wilson et al (excluding steps 24–34). Sample search strategy can be found in the full paper Searches were supplemented by a survey of national members from the World Organization of National Colleges, Academies and Academic Associations of General Practitioners/Family Physicians (WONCA), who were asked to search the grey literature in their respective country for evidence relating to consultation length in either English or their native language. The grey literature search also included the WHO/INRUD database (2000–2016) and Robert Graham data repository (2009–2016). One author (GI) screened all references and excluded duplicate records and those that were not eligible based on our selection criteria for considering studies. Two authors (GI and ALN) then applied the criteria to the short-listed references for full-text screening. We included observational studies including cross-sectional studies, surveys and cohorts of consultation length with primary care physicians. Primary care physicians were defined broadly as any medically qualified physician who provides primary care. Terms for primary care physicians differ according to different settings and include general practitioners (GPs), family doctors, family practitioners and other physicians working in primary healthcare settings and who perform primary healthcare task. Studies set in secondary care and randomised controlled trials were excluded.

This dataset supports the University of Southampton Doctoral thesis "How should wellbeing be measured in UK doctors? A salutogenic, consensus approach, towards a Core Outcome set for doctor wellbeing measurement" by Simons G 2022. This dataset contains: Systematic review (chapter 2).csv Expert survey (chapter 4).csv Regional doctor survey (chapter 5).csv Regional doctor survey readme.txt National survey (chapter 6).csv National survey readme.txt Delphi survey (chapter 8).csv Licence:CC BY NC

This report is one of four publications that make up the NHS Staff 2004 - 2014 publication, along with: Non Medical staff 2004 - 2014 General Practice Staff 2004 - 2014 NHS Staff 2004 - 2014 Overview report This report provides a detailed view of the NHS Medical and dental workforce including consultants, registrars and other doctors in training as at 30 September 2004 to 2014. This is an annual snapshot of the number of doctors within hospital and community health services (HCHS) of the NHS. It excludes General Practitioners, GP practice staff and high street dentists. The detailed results section contain further data tables for September 2014 for England by age, gender, specialty and grade and selected data by Health Education England area and individual organisation. NHS staff data (excluding GPs and their staff) is also published each month, the December 2014 data is published on the same day as these reports.

The Dispensing Practice Name and Address dataset is produced monthly and provides the name and address details of dispensing practices for each primary care organisation at Sub Integrated Care Board Level (SICBL). It also includes: • how many GPs are in each practice and how many of them are dispensing GPs • practice name • practice address A dispensing practice is defined as a practice with at least one active dispensing GP. The information NHS Prescription Services hold on practices is supplied to NHS Prescription Services by primary care organisations (PCOs). You can view all definitions for the fields included in the dataset in the Dispensing Practice Name and Address data dictionary (XLSX: 12KB) This data was previously published on the NHSBSA Information Services Portal. Changes have been made to the data since migration to the Open Data Portal and you can read about these changes in the Dispensing Practice Name and Address migration changes documentation (ODT: 217KB).