- Open Access
The UK medical education database (UKMED) what is it? Why and how might you use it?
BMC Medical Education volume 18, Article number: 6 (2018)
Educating doctors is expensive and poor performance by future graduates can literally cost lives. Whilst the practice of medicine is highly evidence based, medical education is much less so. Research on medical school selection, undergraduate progression, Fitness to Practise (FtP) and postgraduate careers has been hampered across the globe by the challenges of uniting the data required. This paper describes the creation, structure and access arrangements for the first UK-wide attempt to do so.
A collaborative approach has created a research database commencing with all entrants to UK medical schools in 2007 and 2008 (UKMED Phase 1). Here the content is outlined, governance arrangements considered, system access explained, and the potential implications of this new resource discussed. The data currently include achievements prior to medical school entry, admissions tests, graduation point information and also all subsequent data collected by the General Medical Council, including FtP, career progression, annual National Training Survey (NTS) responses, career choice and postgraduate exam performance data. UKMED has grown since the pilot phase with additional datasets; all subsequent years of students/trainees and stronger governance processes. The inclusion of future cohorts and additional information such as admissions scores or bespoke surveys or assessments is now being piloted. Thus, for instance, new scrutiny can be applied to selection techniques and the effectiveness of educational interventions. Data are available free of charge for approved studies from suitable research groups worldwide.
It is anticipated that UKMED will continue on a rolling basis. This has the potential to radically change the volume and types of research that can be envisaged and, therefore, to improve standards, facilitate workforce planning and support the regulation of medical education and training. This paper aspires to encourage proposals to utilise this exciting resource.
Medicine is a cornerstone of higher education globally, with high financial cost and academic resource requirements. Whilst there is no shortage of applicants, there is debate over equity of access, diversity and workforce requirements as well as other issues. In response, the UK Government announced in 2017 an additional 1500 (over 20%) medical school places  with the objective of producing graduates interested in less popular specialities and prepared to work with remote or deprived communities.
In the UK, it is estimated the current 7800 medical school entrants cost the state around £180,000 each (plus personal living expenses) to complete their primary medical qualification. However, this investment of approximately £1.5bn per annum has no organised research and development arm . Furthermore, the data on which to base selection decisions have never been systematically gathered, which explains why there has been a consistent paucity of robust longitudinal studies within medical education [3,4,5,6,7]. Lack of such UK wide studies was identified as a weakness in the field’s scholarly output by the 2014 UK Research Excellence Framework [8,9,10]. Rare events such as leaving medicine or Fitness to Practise (FtP) concerns, require collaboration to collate sufficient numbers for evaluation, and are particularly challenging to perform. The evident success of the multiple UK Birth Cohort Studies is encouraging but also testifies to the challenges of maintaining such as resource [11,12,13].
This paper outlines the concept as well as current and planned content of a novel UK national medical education research database (UKMED), and invites researchers and educationists internationally to consider how they might use it. The potential value of a mechanism for tracking the progress of students through medical school and into postgraduate practice, enabling a wide range of original studies to be conducted, has been recognised and is not without precedent. Collating and integrating such a large-scale database could enable high quality longitudinal studies to address significant research questions ranging from selection, through under- and postgraduate training, and eventually into clinical practice and patient outcomes.
Within the UK, the General Medical Council (GMC) has a statutory function under the Medical Act 1983 to co-ordinate the stages and promote high standards of medical education . A database linking educational outcomes gives the potential to explore the effect of policy changes at each stage of training (medical school, foundation school and postgraduate training programmes) independently. This is key as the GMC’s statutory function justifies using personal data about students and doctors in compliance with the Data Protection Act.
We are not the first to work towards these goals and internationally there have been four related initiatives that we are aware of:
The UK Medical Careers Research Group (MCRG) undertook sequential studies of graduate cohorts from 1973 leading to over 100 publications 
Jefferson Medical School has tracked its own graduates since 1964, leading to a highly effective research programme .
There are also multiple pre-admissions testing organisations that have an interest in evaluating the validity and utility of their assessments and have collaborated with academics in a range of studies. They have provided important insights, especially in USA and Canada, though typically, these organisations have been restricted to a limited range of relatively short-term outcomes (e.g. licensure exam data) or a small number of medical schools . However, even for non-profit making admissions test providers, funding research could be considered to potentially introduce bias.
Key examples of work to evaluate the validity and utility of pre-admissions testing are available from:
The UK Clinical Aptitude Test (UKCAT) 
Graduate Medical School Admissions Test (GAMSAT) 
The Health Professions Admission Test (HPAT) 
Undergraduate Medicine and Health Sciences Admission Test (UMAT) 
The Medical College Admission Test® (MCAT®) 
The UKCAT consortium commenced in 2005, and since 2006 around 20,000 applicants have sat this aptitude test each year. From its inception, the research potential of data on this scale was apparent but proved hard to realise, primarily because of data protection concerns. It took until 2012 for the first UK wide analyses to emerge  since when over 19 UKCAT related studies have been published . The consortium has enabled innovative approaches such as the UKCAT-12 study [29, 30], and a relevant proof of concept UG-PG matching study .
Hence, in 2011, the Medical Schools Council (MSC) and the GMC were asked to consider extending this database into a comprehensive and ongoing tracking system and research resource, which would link pre-admission metrics (e.g. performance in school level qualifications such as A-levels or Scottish Highers and performance on admissions tests like UKCAT), through graduation and into postgraduate careers. This wider database now also enables new and more detailed areas of research such as:
Recruitment and selection in terms of equity of access and impact on graduate qualities and workforce issues
Assessing the impact of variation or changes in undergraduate education such as comparing graduate and direct entry systems or traditional/integrated/problem-based curricula
Assessing variation in qualities of graduates using a range of outcome measures such as specialist postgraduate exam performance, career choice or FtP events
Equality and diversity in terms of access to and performance within medical careers
Workforce planning and career progression
Patient safety and FtP events
Improving all studies by enabling multivariate analysis to adjust for confounding variables, in particular prior academic attainment.
While no single approach can address all the issues, in this commentary we introduce UKMED as an innovation that offers the opportunity to better understand many of these complex dilemmas. Because patterns of background, performance and capability as a medical student, trainee or doctor often have international relevance and present issues common to all countries, researchers anywhere are invited to utilise UKMED’s unique potential.
Schema and implementation
How was UKMED piloted?
The UK Medical Education Database Phase 1 (2015–2016) was a collaboration that achieved the acquisition, linkage, governance and access to a broad range of routine data on all entrants to every UK medical school (N = 15,627) in 2007 and 2008. Extensive data were gathered from the point of application onwards, including graduation, the GMC’s National Training Survey (NTS) and career progression (see Fig. 1). Working across agencies enabled three ‘proof of concept’ longitudinal studies to be conducted. The intention was to establish a resource that could be expanded with the addition of successive cohorts and further datasets, ultimately including the planned UK national Medical Licensing Assessment which should provide a common academic outcome measure .
UKMED Phase 1 established complete coverage of UK medical school entrants and started to support multiple studies. See the UKMED website  for details of all approved studies, data dictionary including online coverage tool, and application forms.
The process has required extensive consultation and legal guidance to address data protection, management and academic governance issues. It was supported by joint leadership from the GMC and MSC, which, due to their roles with all UK medical schools, created a willingness to review and address issues such as data sharing agreements; privacy notices and establishing the GMC as the ‘Data Controller’. (As the Data Controller, the GMC has responsibility for ensuring compliance with the Data Protection Act.) This process took two years but overcame many hurdles, including concerns regarding Freedom of Information (exempt as a research database) and universal coverage as, by using HESA data, UKMED is able to include all UK students in established and emerging medical schools and including UKCAT and GAMSAT selection tests where relevant. Important limitations are acknowledged and discussed below.
Initial database content
In terms of structure based on Phase 1, HESA entry data defined cases for inclusion in UKMED (as not all those who start medical degrees progress to registration) and were linked to test provider data from UKCAT, and GAMSAT using the UCAS person ID [34, 35]. Graduates were matched to the GMC register using the medical school code and medical schools’ internal identifying number for each of their students , which the GMC receives as part of the provisional registration process, providing an efficient and reliable approach. There were no selection biases – all cases were included. The GMC number provides links to postgraduate data, including Annual Review of Competence Progression (ARCP) outcomes and royal college exam results; a full list is in the UKMED data dictionary .
Developments since the phase 1 pilot
UKMED is a live project and the UKMED website outlines the current data available, approved research projects and their status . Key developments since the successful completion of Phase 1 are outlined in Fig. 2 and described below.
The UKMED population is now defined in two ways:
All those who started at a UK medical school since 2002 as defined by the HESA data (N = 110,78). The GMC has obtained historical data and now receives updates annually.
Those who have taken part in postgraduate training in the UK since 2012 as captured by the GMC annual census for the National Training Survey . This includes trainee doctors who obtained their primary medical qualification outside of the UK (N = 42,490). Inclusion of all doctors in postgraduate training allows UKMED to be used for studies looking at the predictive validity of selection methods used for postgraduate training programmes. It may also increase the opportunity for international comparisons.
Since piloting, UKMED has moved to an annual cycle collating a broader range of undergraduate and test-provider data as well as postgraduate performance and exam data. UKMED now includes:
Data from all medical royal and faculty exams sat from 1 August 2013 by any GMC registered doctor, updated annually
Data since 2014 from the UK Prescribing Safety Assessment (PSA) 
BMAT scores from 2003 
Data from the Multi-Specialty Recruitment Assessments used for postgraduate training programme selection
Data on practice history. GMC data collected for revalidation purposes originally provided from payroll systems by the four departments of Health: ESR – Electronic Staffing Records. PCIS – Primary Care Information System and SWISS – Scottish Workforce Information Standard System. This allows cases to be tracked through to post-training employment.
Enhancements to the governance processes
It is now possible for researchers to include data generated themselves in a UKMED research extract. This is subject to an information governance review to confirm legality and the presence of suitable identifier for linking purposes. Researchers doing this must make the data available to others via UKMED following completion of their study.
Utility and discussion
UKMED provides access to matched data via a safe haven for studies approved on the basis of their academic rigour and value . This approach helps address a number of privacy concerns that have hampered research using linked data in other contexts . It can only be accessed by application to ensure due diligence. Applications are reviewed by an expert panel against the publicly available criteria, including confirmation that only appropriate data are requested. On the basis of this review, a recommendation on each application is made by the UKMED Advisory Board to the GMC as data controller. There are two meetings a year at which applications are reviewed.
The GMC ensures compliance with the Data Protection Act by de-identifying the data: cases are assigned their own unique Study–Id and quasi-identifiers are recoded so unique cases cannot be identified in the extract . The safe haven further minimises the risk of re-identification; allowing the researchers to run analyses on the extract using the statistical packages of their choice, whilst preventing the export/import of data and re-identification through linkage. Researchers are under contract to use the data only for the purposes of the approved proposal. Analytic outputs are reviewed to ensure compliance with HESA statistical disclosure controls  prior to release to researchers, and all reports are screened prior to publication.
“Research limited to secondary use of information previously collected in the course of normal care (without an intention to use it for research at the time of collection) is generally excluded from REC review, provided that the patients or service users are not identifiable to the research team in carrying out the research.”
“Research involving staff: REC review is not normally required for research involving NHS or social care staff recruited as research participants by virtue of their professional role.”
This exemption only applies to data held exclusively in UKMED, so studies that introduce external data may need separate ethical approval and researchers may be required to obtain this from their local committee.
The GMC’s Information Governance Team reviews the privacy statements shown to data subjects; if data collection for a study has not yet commenced, UKMED recommends privacy notices make it clear that:
Identifiable data may be used for future research
Identifiable data may be shared with third parties to undertake the research.
Researchers access data by logging onto the safe haven portal provided by the University of Dundee Health Informatics Centre (HIC) . Once logged in remotely to the safe haven they are able to work using a Windows desktop and a range of statistical packages. Results are saved onto an output directory which is reviewed before sending to the researcher. Full details are described in the Process for completing UKMED research document .
It is anticipated that access will continue to be provided free of charge. When researchers wish to link additional data to UKMED, they may be asked to cover associated costs, with requests reviewed on a case-by-case basis.
The creation, use and interpretation of prospective databases is complex. As Pearson  eloquently describes, it is impossible to predict the creative unexpected uses that tend to emerge over time, nor fully address the challenges presented. In particular, the difficulties managing missing data, defining socioeconomic class and equating prior academic attainment of students are challenges within UKMED. A project is underway amongst those conducting the early studies to create some UKMED standard approaches that can be used to simplify these issues. For instance, a syntax for calculating select derived variables using consistent methods is now available and a common approach for multiple imputation of missing values is being considered.
Details of accepted research proposals and their status are available on the UKMED website . In future, UKMED could enable:
The impact of selection tools to be evaluated in far greater detail and against a wide range of important outcome markers. The comprehensive scale and coverage allows for complex subgroup analysis exploring the impact of background and prior attainment as well as comparing selection tests. Access to retention data, common assessments such as the Situational Judgment Test for selection to the UK Foundation Programme and the PSA introduces new opportunities. Career choices, progression and postgraduate exam performance can be assessed and evaluated in the light of prior attainment and background
Validation studies in the event of the introduction of a UK Medical Licensing Assessment 
Studies into FtP information at the point of graduation, ARCP data and speciality selection information. Thus, an entirely new set of information has been made available for vital key (and relatively rare) performance outcome markers
Studies employing new fields in existing datasets, for instance the GMC’s annual NTS can be amended to include additional questions.
Studies linking external data, for example some centres already have data that merit inclusion (Multiple Mini Interview scores or Conscientiousness Index) and UKMED can be used to both improve and expedite the assessment of these emerging tools. Indeed, entirely new tools can now be designed, and data banked in anticipation that UKMED will provide a follow up mechanism in due course. In particular, this might apply to novel non-academic selection or assessment measures.
Finally, it is possible to conceive of ways in which UKMED might inspire not only new comparisons but generate new interventions. Medical schools could collaborate on testing alternative approaches to complex issues such as improving graduates’ resilience. Even cluster randomised trials might be considered feasible now an efficient follow up system is in place.
The limitations of UKMED.
The data in UKMED are administrative, collected by routine systems. Such data describe what happened but not why or how. There are situations within medical education where qualitative data are very informative. Questions such as unequal access to medical school, the reasons why graduates are reluctant to enter some specialities or work with remote or deprived communities, and doubts about the wider impact of different approaches to selection or education would benefit from qualitative or mixed methods approaches. At the medical school level, there is little data on student aspirations, motivations, interests, personalities and a host of other individual differences which probably underpin much variation. However, UKMED may in future collect some qualitative data in terms of ‘white space boxes’, for instance via the NTS or other independent surveys.
A further limitation concerns examination results. UKMED is collecting data on overall performance in selection tests, medical school examinations (in the form of selection scores for the Foundation Programme) and postgraduate examinations. However, those are total scores of individual applicants. Answers to individual items might be of interest but would produce an extremely complex dataset. Limited data on performance during the undergraduate course are available from some medical schools.
A different sort of limitation is that UKMED only collates data on individuals admitted to medical schools. At present, it does not collect data on individuals who applied but did not enter any UK school, limiting potential for research into selection processes and introducing range restriction issues. For example, if investigating the relationship between UKCAT scores and demographic variables only the better performing cases would be available for analysis within UKMED (such studies would be better conducted using the UKCAT database). Furthermore, even those who apply for medical school are a limited subset of the population as a whole. Recent work suggests that about 10% of 12-year olds put medicine as their first choice for a future career, a figure far removed from the 1% or so of individuals who eventually become doctors . Finding out about early self-selection is not easy, but might be possible if UKMED can be linked in future to large cohorts such as the Millennium Cohort Study .
The UKMED educational research database presents unique opportunities for multicentre longitudinal studies on ‘big numbers’ covering complex questions. Several studies have been completed and submitted for publication. Although based on UK students, the results have direct relevance for many countries. Research applications for access to datasets are not limited to those in the UK. The challenge is now to ensure that the medical education community takes full advantage of this outstanding new resource.
Academy of Medical Royal Colleges
Annual Review of Competence Progression
Biomedical Admissions Test
Foundation Programme Application System
Fitness to Practise
Graduate Medical School Admissions Test
General Medical Council
Health Education England
Higher Education Statistics Agency
Health Informatics Centre, University of Dundee
The Health Professions Admission Test
Medical College Admission Test
UK Medical Careers Research Group
Medical schools council
National Training Survey
UK wide portal for recruitment to postgraduate medical training programmes.
Prescribing Safety Assessment.
Research Ethics Committee.
Situational Judgement Test.
United Kingdom Foundation Programme Office.
UK Clinical Aptitude Test.
United Kingdom Medical Education Database.
Undergraduate Medicine and Health Sciences Admission Test.
McManus IC (11 October 2016). Hunt promises 25% more medical students in 2018. Br Med J 2016;355: i5480.
Archer J, McManus IC, Woolf K, Monrouxe L, Illing J, Bullock A, Robert T. Without proper research funding, how can medical education be evidence based? Br Med J. 2015;350:h3445.
McManus IC, Powis DA, Wakeford R, Ferguson E, James D, Richards P. Intellectual aptitude tests and a levels for selecting UK school leaver entrants for medical school. Br Med J. 2005;331:555–9.
Parry JM, Mathers JM, Stevens A, Parsons A, Lilford R, Spurgeon P, Thomas H. Admissions processes for five year medical courses at English schools: review. Br Med J. 2006;332:1005–9.
Cook DA, Andriole DA, Durning SJ, Roberts NK, Triola MM. Longitudinal research databases in medical education: facilitating the study of educational outcomes over time and across institutions. Acad Med. 2010;85(8):1340–6.
Patterson F, Knight A, Dowell J, Nicholson S, Cousans F, Cleland J. How effective are selection methods in medical education? A systematic review. Med Educ. 2016;50(1):36–60.
Rees EL, Hawarden AW, Dent G, Hays R, Bates J, Hassell AB. Evidence regarding the utility of multiple mini-interview (MMI) for selection to undergraduate health programs: a BEME systematic review: BEME guide no. 37. Med Teach. 2016;38(5):443–55.
Rees C, Francis B, Pollard A. The state of medical education research: what can we learn from the outcomes of the UK research excellence framework? Med Educ. 2015;49(5):446–8.
Butz WP, Torrey BB. Some Frontiers in social science. Science 2006;312(5782):1898-1900. Available at: https://doi.org https://doi.org/10.1126/science.1130121. Accessed 19 Feb 2017.
MRC Population Health Sciences Group. Maximising the value of UK population cohorts. MRC Strategic Review of the Largest UK Population Cohort Studies. Swindon: 2014 Available at: https://www.mrc.ac.uk/publications/browse/?keywords=maximising&searchSectionID=4BC7DBBA-1972-4E8B-898B31A1B8A6EEB0. Accessed 19 Feb 2017.
Pearson H. The Life Project: The Extraordinary Story of Our Ordinary Lives. London: Penguin Books; 2016.
Williams C, Birch EE, Emmett PM, Northstone K. Avon longitudinal study of pregnancy and childhood study team. Stereoacuity at age 3.5 y in children born full-term is associated with prenatal and postnatal dietary factors: a report from a population-based cohort study. Am J Clin Nutr. 2001;73(2):316–22.
Bath S, Steer C, Golding J, Emmett P, Rayman M. Effect of inadequate iodine status in UK pregnant women on cognitive outcomes in their children: results from the Avon longitudinal study of parents and children. Lancet. 2013;382(9889):331–7.
The Medical Act (1983). Available at: http://www.gmc-uk.org/about/legislation/medical_act.asp#5. Accessed 24 Jan 2017.
University of Oxford Unit of Health-Care Epidemiology. UK Medical Careers Research Group Available at: http://www.uhce.ox.ac.uk/ukmcrg/. Accessed 18 Nov 2017.
McManus C. University College London Research Department of Clinical, Educational and Health Psychology. Medical Education Studies. 1999. http://www.ucl.ac.uk/medical-education/medical-education-studies. Accessed 28 Dec 2016.
McManus IC, Keeling A, Paice E. Stress, burnout and doctors' attitudes to work are determined by personality and learning style: a twelve year longitudinal study of UK medical graduates. BMCMed. 2004;2:29.
McManus IC, Smithers E, Partridge P, Keeling A, Fleming PR. A levels and intelligence as predictors of medical careers in UK doctors: 20 year prospective study. Br Med J. 2003;327(7407):139–42.
Kaur B, Carberry A, Hogan N, Roberton D, Beilby J. The medical schools outcomes database project: Australian medical student characteristics. BMC Med Educ. 2014;14:180.
Puddey IB, Mercer AA, Playford DE, Pougnault SM, Riley GJ. Medical student selection criteria as predictors of intended rural practice following graduation. BMC Med Educ. 2014;14:218–9.
Gonnella JS, Hojat M, Veloski J. The Jefferson longitudinal study of medical education. Acad Med. 2011;86(3):404. https://doi.org/10.1097/ACM.0b013e31820bb3e7.
Norcini JJ, Boulet JR, Opalek A & Dauphinee WD. The Relationship Between Licensing Examination Performance and the Outcomes of Care by International Medical School Graduates. Acad Med 2014;89(8):1157-62.
UK Clinical Aptitude Test. Published Research Available at: http://www.ukcat.ac.uk/our-research/published-research/. Accessed 28 Dec 2016.
Graduate Medical School Admissions Test (GAMSAT) Research Available at: https://gamsat.acer.edu.au/research1. Accessed 28 Dec 2016.
Health Professions Admission Test–Ireland Research Available at: https://hpat-ireland.acer.edu.au/research. Accessed 28 Dec 2016.
Undergraduate Medicine and Health Sciences Admission Test. Research Available at: https://umat.acer.edu.au/umat-research. Accessed 28 Dec 2016.
Association of American Medical Colleges. 2015. Annotated Bibliography of MCAT ® Research Available at: https://students-residents.aamc.org/content/article/annotated-bibliography-mcat-research/. Accessed 28 Dec 2016.
Tiffin PA, Dowell JS, McLachlan JC. Widening access to UK medical education for underrepresented socioeconomic groups: modelling the impact of the UKCAT in the 2009 cohort. Br Med J. 2012;344:e1805.
McManus IC, Dewberry C, Nicholson S, Dowell J. The UKCAT-12 study: educational attainment, aptitude test performance, demographic and socio-economic contextual factors as predictors of first year outcome in a collaborative study of twelve UK medical schools. BMC Med. 2013;11:244.
Tiffin P, Mwandigha L, Paton L, Hesselgreaves H, McLachlan J, Finn G, Kasim A. Predictive validity of the UKCAT for medical school undergraduate performance: a national prospective cohort study. BMC Med. 2016;14:140.
MacKenzie R, Dowell J, Ayasina D, Cleland J. Do personality attributes assessed on medical school admission predict exit performance? A UK-wide longitudinal cohort study. Adv in Health Sci Educ. 2016;22(2)365–385.
General Medical Council (GMC) Press Release 4 June 2015. GMC Council approves development of UK medical licensing assessment Available at: http://www.gmc-uk.org/news/26549.asp. Accessed 28 Dec 2016.
UK Medical Education Database (UKMED) http://www.ukmed.ac.uk. Accessed 5 Nov 2017.
Graduate Medical School Admissions Test (GAMSAT). 2017. Available at: https://gamsat.acer.edu.au/. Accessed 28 Dec 2016.
Higher Education Statistics Agency (HESA). HESA UCAS Personal Identifier Student Record 2016/17 Available at: https://www.hesa.ac.uk/collection/c16051/c/ucasperid/.html/.html/. Accessed 28 Dec 2016.
Higher Education Statistics Agency (HESA). HESA Unique Student Identifier (HUSID) Student Record 2015/16 Available at: https://www.hesa.ac.uk/collection/c15051/a/husid/. Accessed 28 Dec 2016.
UK Medical Education Database (UKMED). 2016. UKMED – 2017 v1 Data dictionary Available at: http://www.ukmed.ac.uk/documents/UKMED_data_dictionary.pdf. Accessed 28 Dec 2016.
GMC National Trainee Survey. 2017. Available at: https://www.gmc-uk.org/education/surveys.asp. Accessed 18 Nov 2017.
UK Prescibing Safety Assessment. 2016. Available at: https://prescribingsafetyassessment.ac.uk/. Accessed 18 Nov 2017.
Cambridge Assessment The BioMedical Admissions Test. 2017. http://www.admissionstestingservice.org/for-test-takers/bmat/. Accessed 18 Nov 2017.
UK Medical Education Database (UKMED). UKMED Process for completing UKMED Research v3 December 2016 Available at: https://www.ukmed.ac.uk/documents/UKMED_research_application_form.pdf [Accessed 14 November 2017].
Hayden JL. The big peek. Nature. 2015;525:440–2.
Sweeney L. Achieving k-anonymity privacy protection using generalization and suppression. Int. J. Uncertainty Fuzziness Knowledge Based Syst. 2002;10(5):571–88.
Higher Education Statistics Agency (HESA). Statistical Confidentiality Policy. Available at: https://www.hesa.ac.uk/about/regulation/official-statistics/confidentiality [Accessed 28 December 2016].
NHS Research Authority. Is NHS REC Review required? 2017. Available at: http://www.hra.nhs.uk/resources/before-you-apply/is-nhs-rec-review-required/. Accessed 28 Dec 2016.
Queen Mary University of London Ethics of Research Committee. UKMED ethics exemption letter dated 10 December 2015. 2017. http://www.ukmed.ac.uk/documents/UKMED_research_projects_ethics_exemption.pdf. Accessed 19 Feb 2017.
Health Informatics Centre. Safe Haven User Guide October 2015. 2015. Available at: https://medicine.dundee.ac.uk/sites/medicine.dundee.ac.uk/files/Safe%20haven%20User%20Guide.pdf. Accessed 28 Dec 2016.
McManus IC, Ng-Knight T, Riglin L, Frederickson N, Shelton K, Rice F. Doctor, builder, soldier, lawyer, teacher, dancer, shopkeeper. Vet: Exploratory study of which eleven-year olds would like to become a doctor BMC Psychology. 2015;3:38.
Millennium Cohort Study. Centre for Longitudinal Studies Available at: http://www.cls.ioe.ac.uk/page.aspx?sitesectionid=851. Accessed 14 Nov 2017.
The development of UKMED has been led by the MSC and GMC but would not have been possible without the opportunity to build upon and learn from the work of the UKCAT consortium. In addition, we should like to acknowledge the key contribution of multiple agencies (and their staff) without which this collaboration would not be possible: AoMRC, GAMSAT, HEE, HESA, HIC, UKCAT and UKFPO.
Academy of Medical Royal Colleges (Jane Dacre)
General Medical Council (Peter McNair, Ganesan Gurusamy, Andrew Ledgard)
Graduate Medical School Admissions Test (Veronica Vele and
Marita MacMahon Ball)
Health Education England (Jonathan Howes)
Health Informatics Centre School of Medicine, Dundee University (Mike Ghattas)
Higher Education Statistics Agency (James McLaren, Denise Jones, Claire Morris)
UK Clinical Aptitude Test (Rachel Greatrix)
UK Foundation Programme Office (Charlotte Dainter, Kim Walker and Janet Brown)
HESA Student Record 2007/08 and 2008/09. Copyright. The Higher Education Statistics Agency Limited cannot accept responsibility for any inferences or conclusions derived by third parties from data or other information supplied by it.
The development of UKMED has not been directly funded by but has received support from the General Medical Council and Medical Schools Council as well as staff time from all participating organisations.
Availability of data and materials
Not applicable (see https://www.ukmed.ac.uk).
Ethics approval and consent to participate
Consent for publication
Katie Petty-Saphon, Sandra Nicholson and Jennifer Cleland all currently hold unremunerated roles on the UKCAT executive and have an interest in the success of this charity. Jon Dowell has previously done so. Steve Thornton provides consultancy advice for commercial organisations unrelated to UKMED and is a Trustee of a National charity (Wellbeing of Women).
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
About this article
Cite this article
Dowell, J., Cleland, J., Fitzpatrick, S. et al. The UK medical education database (UKMED) what is it? Why and how might you use it?. BMC Med Educ 18, 6 (2018). https://doi.org/10.1186/s12909-017-1115-9
- Medical schools
- Fitness to Practise