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.

Over 1.3 million people were employed by the NHS in June 2022 and 74.3% of them were white (out of people whose ethnicity was known).

In 2019, over **** thousand nurses in the United Kingdom held an Asian nationality, while **** thousand nurses had an EU nationality. Furthermore, there were approximately **** thousand Asian doctors in the UK, and **** thousand doctors with an EU nationality. The highest amount of NHS workers from the rest of the World were working as support to clinical staff, with *** thousand categorized in this staff group.

Make up of non-UK NHS workers

The highest share of healthcare employees who were from the EU occur in the younger age groups, with almost ** thousand employees in the period 2016 to 2018 aged under 34 years of age. While, ** thousand health care workers in the UK aged between 35 and 44 years are from outside of the EU. ** thousand NHS employees working in London were EU nationals, the highest amount of any region in the UK although London is one the most populated and most diverse region in the UK.

Impact of Brexit

In 2019, it was found that almost ** percent of healthcare professionals in the UK knew at least one colleague considering leaving their job due to Brexit. While ****** percent knew a co-worker, who had already left because of the Brexit situation. Due to the large number of workers from the EU in the NHS, the service could be very vulnerable to Brexit and the potential of many employees leaving due to Brexit.

In every year covered by the data, a lower percentage of white NHS staff experienced discrimination than staff from all other ethnic groups combined.

All NHS Staff refers to those directly employed by the NHS in Hospital and Community Health Services (HCHS) and by GP practices contracted to the NHS. It excludes high street dentists and ophthalmic practitioners. This publication is made up of three main staff group areas, which can be found by following the links: Non-Medical Staff 2000 - 2010 Medical and Dental Staff 2000 - 2010 General Practice Staff 2000 - 2010

The number of HCHS doctors leaving generally increased during the period reaching 21,000 leavers in the year 2022/23, the highest reported in the period analyzed, and over a seven percent increase from the figure reported a year earlier.

The General Practice Workforce series of Official Statistics presents a snapshot of the primary care general practice workforce. A snapshot statistic relates to the situation at a specific date, which for these workforce statistics is now the last calendar day each month. This monthly snapshot reflects the general practice workforce at 29 February 2024. These statistics present full-time equivalent (FTE) and headcount figures by four staff groups, (GPs, Nurses, Direct Patient Care (DPC) and administrative staff), with breakdowns of individual job roles within these high-level groups. For the purposes of NHS workforce statistics, we define full-time working to be 37.5 hours per week. Full-time equivalent is a standardised measure of the workload of an employed person. Using FTE, we can convert part-time and additional working hours into an equivalent number of full-time staff. For example, an individual working 37.5 hours would be classed as 1.0 FTE while a colleague working 30 hours would be 0.8 FTE. The term “headcount” relates to distinct individuals, and as the same person may hold more than one role, care should be taken when interpreting headcount figures. Please refer to the Using this Publication section for information and guidance about the contents of this publication and how it can and cannot be used. England-level time series figures for all job roles are available in the Excel bulletin tables back to September 2015 when this series of Official Statistics began. The Excel file also includes Sub-ICB Location-level FTE and headcount breakdowns for the current reporting period. CSVs containing practice-level summaries and Sub-ICB Location-level counts of individuals are also available. Please refer to the Publication content, analysis, and release schedule in the Using this publication section for more details of what’s available. We are continually working to improve our publications to ensure their contents are as useful and relevant as possible for our users. We welcome feedback from all users to PrimaryCareWorkforce@nhs.net.

The sickness absence rate of NHS staff in the North West of England stood at 6.23 percent in October 2024. Since April 2009, the sickness absence rate of the NHS workforce in this region ranged from four to almost eight percent. During the coronavirus pandemic, record highs of sickness absences among the NHS staff have been registered.

Hospital Episode Statistics (HES) is a curated data product containing details about admissions, outpatient appointments and historical accident and emergency attendances at NHS hospitals in England. Source: https://digital.nhs.uk/services/hospital-episode-statistics#analytical-technical-output-specification-and-data-dictionary

Contains public sector information licensed under the Open Government Licence v3.0. Licence: https://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/

Primary Dataset: Hospital Episode Statistics (HES) – (free from NHS Digital) Admitted Patient Care (APC) data Accident & Emergency (A&E) data Outpatients data

Supporting Data: NHS Trust performance ratings Staff numbers by trust Financial data from NHS foundation trusts

Description of hospital workers included in the study vs. overall population.

2011 National Household Survey information on workers who work in New Westminster (regardless of municipality of residence). Information includes occupation, industry, employment income (before-tax), work activity, age and sex and education. Also contains descriptive information about the data source files and notes about the use of the data.

US Healthcare Readmissions and Mortality

Evaluating Hospital Performance

By Health [source]

About this dataset

This dataset contains detailed information about 30-day readmission and mortality rates of U.S. hospitals. It is an essential tool for stakeholders aiming to identify opportunities for improving healthcare quality and performance across the country. Providers benefit by having access to comprehensive data regarding readmission, mortality rate, score, measure start/end dates, compared average to national as well as other pertinent metrics like zip codes, phone numbers and county names. Use this data set to conduct evaluations of how hospitals are meeting industry standards from a quality and outcomes perspective in order to make more informed decisions when designing patient care strategies and policies

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This dataset provides data on 30-day readmission and mortality rates of U.S. hospitals, useful in understanding the quality of healthcare being provided. This data can provide insight into the effectiveness of treatments, patient care, and staff performance at different healthcare facilities throughout the country.

In order to use this dataset effectively, it is important to understand each column and how best to interpret them. The ‘Hospital Name’ column displays the name of the facility; ‘Address’ lists a street address for the hospital; ‘City’ indicates its geographic location; ‘State’ specifies a two-letter abbreviation for that state; ‘ZIP Code’ provides each facility's 5 digit zip code address; 'County Name' specifies what county that particular hospital resides in; 'Phone number' lists a phone contact for any given facility ;'Measure Name' identifies which measure is being recorded (for instance: Elective Delivery Before 39 Weeks); 'Score' value reflects an average score based on patient feedback surveys taken over time frame listed under ' Measure Start Date.' Then there are also columns tracking both lower estimates ('Lower Estimate') as well as higher estimates ('Higher Estimate'); these create variability that can be tracked by researchers seeking further answers or formulating future studies on this topic or field.; Lastly there is one more measure oissociated with this set: ' Footnote,' which may highlight any addional important details pertinent to analysis such as numbers outlying National averages etc..

This data set can be used by hospitals, research facilities and other interested parties in providing inciteful information when making decisions about patient care standards throughout America . It can help find patterns about readmitis/mortality along county lines or answer questions about preformance fluctuations between different hospital locations over an extended amount of time. So if you are ever curious about 30 days readmitted within US Hospitals don't hesitate to dive into this insightful dataset!

Research Ideas

• Comparing hospitals on a regional or national basis to measure the quality of care provided for readmission and mortality rates.
• Analyzing the effects of technological advancements such as telemedicine, virtual visits, and AI on readmission and mortality rates at different hospitals.
• Using measures such as Lower Estimate Higher Estimate scores to identify systematic problems in readmissions or mortality rate management at hospitals and informing public health care policy

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: Readmissions_and_Deaths_-_Hospital.csv | Column name | Description | |:-------------------------|:---------------------------------------------------------------------------------------------------| | Hospital Name ...

This file contains information on the commuting patterns of workers who live in New Westminster (regardless of which municipality their place of work is located in). The information is from the 2011 National Household Survey and contains mode of transportation, time leaving for work, commute duration and commuting destinations. Also contains descriptive information about the data source files and notes about the use of the data.

Non-expenditure healthcare data provide information on institutions providing healthcare in countries, on resources used and on output produced in the framework of healthcare provision.

Data on healthcare form a major element of public health information as they describe the capacities available for different types of healthcare provision as well as potential 'bottlenecks' observed. The quantity and quality of healthcare services provided and the work sharing established between the different institutions are a subject of ongoing debate in all countries. Sustainability - continuously providing the necessary monetary and personal resources needed - and meeting the challenges of ageing societies are the primary perspectives used when analysing and using the data.

The resource-related data refer to both human and technical resources, i.e. they relate to:

• Health care staff: 'manpower' active in the health care sector (doctors, dentists, nurses, etc.);
• Heath workforce migration: migration movements of doctors and nurses;
• Healthcare facilities: technical capacity dimensions (hospital beds, beds in nursing and residential care facilities, etc.).

The output-related data ('activities') refer to contacts between patients and the healthcare system, and to the treatment thereby received. Data are available for hospital discharges of in-patients and day cases, average length of stay of in-patients, consultations with medical professionals, and medical procedures performed in hospitals.

Annual national and regional data are provided in absolute numbers, percentages, and in population-standardised rates (per 100 000 inhabitants).

Wherever applicable, the definitions and classifications of the System of Health Accounts (SHA) are followed, e.g. International Classification for Health Accounts - Providers of health care (ICHA-HP). For hospital discharges, the International Shortlist for Hospital Morbidity Tabulation (ISHMT) is used. Surgical procedures are classified according to a shortlist mapped to ICD-9-CM.

These healthcare data are largely based on administrative data sources in the countries. Therefore, they reflect the country-specific way of organising healthcare and may not always be completely comparable.

The NIHR Bioresource consists of several groups of participants: ~70k from the general population and blood donors (COMPARE, INTERVAL and STRIDES studies); ~19k with one of ~50 rare diseases (RD) including a ~5k pilot for GEL; ~30k with Inflammatory Bowel Disease (IBD) which include the members of Gut Reaction, the Health Data Research Hub for IBD; and ~20k with Anxiety or depression (GLAD study). It intends to extend recruitment in all areas, and to other rare and common disease groups, with a target of ~300k by 2022. The NIHR BioResource acquires demographic details – e.g. age, sex, ethnicity - from participants at recruitment. This is used to pre-screen or match participants when inviting them to take part in experimental medicine studies. De-identified versions of this data are available to researchers investigating the feasibility of future studies.

A set of interviews with NHS COVID-19 frontline staff to investigate the influence of COVID deployment on non-technical factors for healthcare delivery (leadership, social support & cohesion, communication, shared mental models, co-ordination) and expected moderating factors (occupational background, preparedness, work-life balance and home situation, proximity, workforce allocation models) and the impact on perceived teamwork, performance, individual team member well-being, resilience and team member employment retention intentions for NHS COVID-team members. The interviews with medical staff consisted of demographic questions collecting some special category data (e.g., ethnicity, job title, living arrangements during COVID), a 12-item standardised measure of wellbeing (administered using the GHQ-12, a short form General Health Questionnaire) and an 8 item Work Life Balance Scale (Schwartz et al., 2019; Sexton et al., 2017). These are not included in the interview transcripts. The interview schedule then followed a topic based semi-structured component (informed by themes identified in our previous work (Reid et al., 2018; 2016; Schilling, 2019), the wider literature, and our preliminary conceptual framework across these four main areas: 1) the creation of teams and the experience of teamwork, social support, shared communication patterns, co- ordination and mental models; 2) the role of leaders in establishing teamwork, social support, shared communication patterns, co-ordination and mental models; 3) perceived individual and team performance, well-being, resilience and retention intentions; 4) moderating factors including occupational background, preparedness, home life, work-life balance and any other issues arising during COVID-team membership.

A key component of the NHS (and global) response to the COVID-19 pandemic has been to reinforce acute and critical care capacity, through an unprecedented re-deployment of personnel from different care pathways into fluid teams consisting of volunteers, student doctors and nurses, and in some cases military personnel [1-4]. These COVID-teams provide a unique opportunity to examine the interaction of many of the established factors for successful delivery of medical teamwork and care. Current evidence suggests that without common teamwork, shared communication patterns and clear leadership structures, the ad-hoc and fluid nature of these COVID-teams increases risk to patient outcomes, delivery of care [5-9] and team member resilience, mental-health and retention [10,11].

This project will examine how non-technical factors for healthcare delivery (leadership, social support; cohesion, communication, shared mental models, co-ordination) and expected moderating factors (occupational background, preparedness, work-life balance, home situation, proximity, workforce allocation models) impact on perceived COVID-teamworking and performance, individual team member well-being and team member employment retention intentions. It will be a mixed methods cross-sectional exploratory study of COVID-team members, clinical directors and senior hospital managers across a wide range of partnered NHS Trusts. Qualitative interviews will identify key themes and will be followed up by a more widely recruited confirmatory survey examining longer term individual well-being and retention intentions.

Frequency and percentage of demographic characteristics (N = 340).

This report contains information on 112,200 jobs for people employed (directly and indirectly) by local authority adult social services departments in England as at September 2018. The report will be of interest to central government (for policy development, monitoring and workforce planning), local government (for benchmarking), charities, academics and the general public. The report does not include information on staff employed in the independent sector (private and voluntary) or children's social services departments (published separately by the Department for Education). This report has used data collected by the National Minimum Data Set for Social Care (NMDS-SC) for the past seven years (from 2011). The NMDS-SC is managed by Skills for Care (SfC) on behalf of the Department of Health and Social Care (DHSC) and has been collecting information about social care providers and their staff since early 2006. Please note: On 6th March 2019, the CSV file was updated to correct some discrepancies which included missing ‘Jobs by sickness days’ data. None of the other remaining publication outputs were affected.