Nurses in the United States increased to 12.19 per 1000 people in 2023 from 12.05 per 1000 people in 2022. This dataset includes a chart with historical data for the United States Nurses.

This table provides an overview of the key figures on health and care available on StatLine. All figures are taken from other tables on StatLine, either directly or through a simple conversion. In the original tables, breakdowns by characteristics of individuals or other variables are possible. The period after the year of review before data become available differs between the data series. The number of exam passes/graduates in year t is the number of persons who obtained a diploma in school/study year starting in t-1 and ending in t.

Data available from: 2001

Status of the figures: 2024: The available figures are definite. 2023: Most available figures are definite Figures are provisional for: - perinatal mortality at pregnancy duration at least 24 weeks; - diagnoses known to the general practitioner; - supplied drugs; - AWBZ/Wlz-funded long term care; - persons employed in health and welfare; - persons employed in healthcare; - Mbo health care graduates; - Hbo nursing graduates / medicine graduates (university); - expenditures on health and welfare; - average distance to facilities. 2022: Most available figures are definite, figures are provisional for: - hospital admissions by some diagnoses; - physicians and nurses employed in care; - persons employed in health and welfare; - persons employed in healthcare; - expenditures on health and welfare; - profitability and operating results at institutions. 2021: Most available figures are definite, figures are provisional for: - expenditures on health and welfare. 2020 and earlier: All available figures are definite.

Changes as of 18 december 2024: - Distance to facilities: the figures withdrawn on 5 June have been replaced (unchanged). - Youth care: the previously published final results for 2021 and 2022 have been adjusted due to improvements in the processing. - Due to a revision of the statistics Expenditure on health and welfare 2021, figures for expenditure on health and welfare care have been replaced from 2021 onwards. - Due to the revision of the National Accounts, the figures on persons employed in health and welfare have been replaced for all years. - AWBZ/Wlz-funded long term care: from 2015, the series Wlz residential care including total package at home has been replaced by total Wlz care. This series fits better with the chosen demarcation of indications for Wlz care.

More recent figures have been added for: - crude birth rate; - live births to teenage mothers; - causes of death; - perinatal mortality at pregnancy duration at least 24 weeks; - life expectancy in perceived good health; - diagnoses known to the general practitioner; - supplied drugs; - AWBZ/Wlz-funded long term care; - youth care; - persons employed in health and welfare; - persons employed in healthcare; - expenditures on health and welfare; - average distance to facilities.

When will new figures be published? New figures will be published in July 2025.

This dataset, released in February 2021, contains the statistics for General Medical Practitioners, 2018; Specialist Practitioners (as reported, excluding GPs), 2018; Unknown/Not applicable Medical Practitioners, 2018; Total Medical Practitioners, 2018; Registered Nurses only, 2018; Registered Nurses who are also Midwives, 2018; Total Registered Nurses, 2018; Enrolled Nurses, 2018; Midwives (may also be a Registered Nurse or Enrolled Nurse), 2018; Total Nurses (Registered Nurses, Enrolled Nurses or Midwives, each person only counted once), 2018; Dentists, 2018; Total Dental Practitioners (includes Dentists, Oral health therapists, Dental hygienists, Dental therapists and Dental prosthetists), 2018.The data is by Local Government Area (LGA) 2016 geographic boundaries. For more information please see the data source notes on the data. Source: Compiled by PHIDU based on data from the National Health Workforce Dataset (NHWDS), 2018; and ABS Estimated Resident Population, 30 June 2018. AURIN has spatially enabled the original data. Data that was not shown/not applicable/not published/not available for the specific area ('#', '..', '^', 'np, 'n.a.', 'n.y.a.' in original PHIDU data) was removed.It has been replaced by by Blank cells. For other keys and abbreviations refer to PHIDU Keys.

Nurses in Denmark increased to 11.81 per 1000 people in 2021 from 11.65 per 1000 people in 2020. This dataset includes a chart with historical data for Denmark Nurses.

Abstract copyright UK Data Service and data collection copyright owner.

The Organisation for Economic Co-operation and Development (OECD) Health Statistics offers the most comprehensive source of comparable statistics on health and health systems across OECD countries. It is an essential tool for health researchers and policy advisors in governments, the private sector and the academic community, to carry out comparative analyses and draw lessons from international comparisons of diverse health care systems. Within UKDS.Stat the data are presented in the following databases:

Health status

This datasets presents internationally comparable statistics on morbidity and mortality with variables such as life expectancy, causes of mortality, maternal and infant mortality, potential years of life lost, perceived health status, infant health, dental health, communicable diseases, cancer, injuries, absence from work due to illness. The annual data begins in 2000.

Non-medical determinants of health

This dataset examines the non-medical determinants of health by comparing food, alcohol, tobacco consumption and body weight amongst countries. The data are expressed in different measures such as calories, grammes, kilo, gender, population. The data begins in 1960.

Healthcare resources

This dataset includes comparative tables analyzing various health care resources such as total health and social employment, physicians by age, gender, categories, midwives, nurses, caring personnel, personal care workers, dentists, pharmacists, physiotherapists, hospital employment, graduates, remuneration of health professionals, hospitals, hospital beds, medical technology with their respective subsets. The statistics are expressed in different units of measure such as number of persons, salaried, self-employed, per population. The annual data begins in 1960.

Healthcare utilisation

This dataset includes statistics comparing different countries’ level of health care utilisation in terms of prevention, immunisation, screening, diagnostics exams, consultations, in-patient utilisation, average length of stay, diagnostic categories, acute care, in-patient care, discharge rates, transplants, dialyses, ICD-9-CM. The data is comparable with respect to units of measures such as days, percentages, population, number per capita, procedures, and available beds.

Health Care Quality Indicators

This dataset includes comparative tables analyzing various health care quality indicators such as cancer care, care for acute exacerbation of chronic conditions, care for chronic conditions and care for mental disorders. The annual data begins in 1995.

Pharmaceutical market

This dataset focuses on the pharmaceutical market comparing countries in terms of pharmaceutical consumption, drugs, pharmaceutical sales, pharmaceutical market, revenues, statistics. The annual data begins in 1960.

Long-term care resources and utilisation

This dataset provides statistics comparing long-term care resources and utilisation by country in terms of workers, beds in nursing and residential care facilities and care recipients. In this table data is expressed in different measures such as gender, age and population. The annual data begins in 1960.

Health expenditure and financing

This dataset compares countries in terms of their current and total expenditures on health by comparing how they allocate their budget with respect to different health care functions while looking at different financing agents and providers. The data covers the years starting from 1960 extending until 2010. The countries covered are Australia, Austria, Belgium, Canada, Chile, Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Israel, Italy, Japan, Korea, Luxembourg, Mexico, Netherlands, New Zealand, Norway, Poland, Portugal, Slovak Republic, Slovenia, Spain, Sweden, Switzerland, Turkey, United Kingdom, and United States.

Social protection

This dataset introduces the different health care coverage systems such as the government/social health insurance and private health insurance. The statistics are expressed in percentage of the population covered or number of persons. The annual data begins in 1960.

Demographic references

This dataset provides statistics regarding general demographic references in terms of population, age structure, gender, but also in term of labour force. The annual data begins in 1960.

Economic references

This dataset presents main economic indicators such as GDP and Purchasing power parities (PPP) and compares countries in terms of those macroeconomic references as well as currency rates, average annual wages. The annual data begins in 1960.

These data were first provided by the UK Data Service in November...

Forecast: Number of Nurses Graduates in the US 2024 - 2028 Discover more data with ReportLinker!

Number of Nurses: Registered: Karnataka: General Nursing and Midwives data was reported at 231,643.000 Person in 2022. This stayed constant from the previous number of 231,643.000 Person for 2021. Number of Nurses: Registered: Karnataka: General Nursing and Midwives data is updated yearly, averaging 231,643.000 Person from Dec 2005 (Median) to 2022, with 15 observations. The data reached an all-time high of 231,643.000 Person in 2022 and a record low of 64,308.000 Person in 2007. Number of Nurses: Registered: Karnataka: General Nursing and Midwives data remains active status in CEIC and is reported by Central Bureau of Health Intelligence. The data is categorized under India Premium Database’s Health Sector – Table IN.HLB005: Health Human Resources: Number of Nurses: Registered.

Nurses in France increased to 9.66 per 1000 people in 2021 from 9.44 per 1000 people in 2020. This dataset includes a chart with historical data for France Nurses.

This project investigated various routes of entry to the UK of labour migrants coming from a single source country. Additionally, face-to-face interviews were conducted with recruiters, experts and healthcare professionals involved in training and administration in the Philippines. A total of 73 transcripts were compiled, 19 from care home assistants/nurses, 19 from domestic workers, 18 from hospital nurses, 13 from Philippine fieldwork (including student nurses), 2 from UK based recruitment agencies, 1 from a migrant organisation and 1 from a UK care home. Data and literature on health worker emigration patterns were gathered from local research bodies. The mission of the Centre is to provide a strategic, integrated approach to understanding contemporary and future migration dynamics across sending areas and receiving contexts in the UK and EU. In 2003, Filipinos made up the largest and most visible group of internationally recruited nurses in the UK. Of roughly 13,000 overseas nationals registered with the Nursing and Midwifery Council (NMC) that year, around 5,600, or almost half, came from the Philippines. They also figured prominently in private care homes and in the provision of care in private households. While there are various nationalities contributing to the care workforce, this project narrowed its focus on care workers from the Philippines due to it being a sector that is heavily segmented by ‘race,’ nationality, as well as immigration status. Focusing on one nationality also allowed us to investigate various routes of entry in the UK of labour migrants coming from a single source country. Additionally, fieldwork was carried out in the Philippines between November and December 2004 in order to asses the effect of nursing and care work recruitment from the sending country perspective. A series of interviews were conducted with recruiters, academics, experts and healthcare professionals involved in training and administration. Data and literature on health worker emigration patterns were gathered from local research bodies. The following findings were observed: (1) Many care workers arrived in the UK via other countries, highlighting the wide scope of multinational recruitment agencies. (2) Filipino care workers arriving via Singapore and the Middle East tended to enter via student visas, but employers assigned them more work than their immigration status allowed (they worked 35-40 hours compared to the regulated 20 hours) (3) Nurses working in care homes experienced more difficulty applying for registration, and were in some cases discouraged by employers. (4) Regulatory conditions differ significantly between public and private care providers. Recruitment to private nursing homes is particularly unregulated. 73 face-to-face interviews were conducted and transcribed from 19 care home assistants/nurses, 19 domestic workers, 18 hospital nurses, 13 Philippine fieldwork (including student nurses), 2 UK based recruitment agencies, a migrant organisation and a UK care home. No sampling method was used, it was totally universe. Data and literature on health worker emigration patterns were gather from local research bodies.

Nurses in Japan increased to 13.02 per 1000 people in 2022 from 12.85 per 1000 people in 2020. This dataset includes a chart with historical data for Japan Nurses.

BackgroundNurses are the largest occupational group in the health field, with inestimable value in realizing universal health coverage, and nurses’ physical and mental health has become an ordinary global reality. Compared with explicit absence, nurses’ presenteeism has a more lasting impact and significant harm and loss. It has become an essential factor affecting nurses’ physical and mental health, declining quality of healthcare services, and elevated healthcare-related risks. There is a lack of research exploring whether occupational coping self-efficacy influences nurses’ presenteeism behavior, especially in less-developed regions of China.ObjectiveThis study aimed to investigate the current status of ICU nurses’ occupational coping self-efficacy and presenteeism in public hospitals in western China and to explore the impact of ICU nurses’ occupational coping self-efficacy on presenteeism.MethodsA cross-sectional research design selected 722 ICU nurses in western China from January to February 2023 as survey respondents. A general information questionnaire, Occupational Coping Self-Efficacy Scale (OCSE-N), and Stanford Presenteeism Scale (SPS-6) were used. SPSS 21.0 software was used for statistical analysis. Pearson correlation analysis and multivariate hierarchical regression were used to explore the influence of ICU nurses’ occupational coping self-efficacy on presenteeism.ResultsA total of 722 ICU nurses completed the questionnaire. The OCSE-N score of ICU nurses was (22.24 ± 6.15), and the SPS-6 score was (16.83 ± 4.24). The high presenteeism was 67.23%. Correlation analysis showed that in ICU nurses, OCSE-N total score was negatively correlated with SPS-6 total score (r = −0.421, p 

Number of Nurses: Registered: Kerala: General Nursing and Midwives data was reported at 329,492.000 Person in 2022. This records an increase from the previous number of 315,620.000 Person for 2021. Number of Nurses: Registered: Kerala: General Nursing and Midwives data is updated yearly, averaging 215,708.000 Person from Dec 2005 (Median) to 2022, with 15 observations. The data reached an all-time high of 329,492.000 Person in 2022 and a record low of 77,596.000 Person in 2005. Number of Nurses: Registered: Kerala: General Nursing and Midwives data remains active status in CEIC and is reported by Central Bureau of Health Intelligence. The data is categorized under India Premium Database’s Health Sector – Table IN.HLB005: Health Human Resources: Number of Nurses: Registered.

IntroductionHealth education is an important part of nursing care. Verbal health education is a common practice in surgical wards, which is time-consuming and laborious. Thus, this study aims to evaluate whether multimedia health education reduces nurses’ workload without compromising patient and family satisfaction in a surgical context.MethodsWe conducted a parallel-group, prospective randomized controlled trial at the Hepatobiliary Surgery Institute of Lanzhou University’s First Hospital between July 2019 and May 2022. Eligible patients (≥18 years) with general surgical conditions and acceptable for surgery were randomly assigned (1:1) to receive a multi-media health education group or a standard health education group. Randomization was performed by an independent statistician using a computer-generated randomization list. The nurses’ workload and satisfaction were the main outcomes; the anxiety level of patients and the variables affecting nurse workload were the secondary outcomes.ResultsA total of 184 eligible participants were randomly assigned to receive multimedia health education and standard health education. The results showed that multimedia health education can shorten the time [15.21 (0.63)vs.16.94 (3.96)] nurses spend on health education during patient admissions, the difference being statistically significant (p 

This dataset includes information from surveys about quality improvement administered to newly licensed registered nurses who participate in the RN Work Project. The purpose of this study was to describe what newly licensed registered nurses working in hospitals learned about quality improvement in their education programs and workplaces.

Quality improvement topics covered by the survey include patient-centered care; evidence-based practice; standardized practices for restraint and seclusion, infection control and pain management; use of information technology or strategies to reduce reliance on memory; participation in analyzing errors and designing system improvements; use of national patient safety resources, initiatives or regulations; and use of specific quality improvement models, specifically:

• FADE (Focus, Analyze, Develop, Execute, and Evaluate)
• PDSA (Plan, Do Study, Act)
• Six-Sigma-DMAIC/DMADV (Define, Measure, Analyze, Improve, Control) or (Define, Measure, Analyze, Design, Verify)
• CQI (Continuous Quality Improvement)
• TQM (Total Quality Management)

Clinical Nurse Scheduling Software Market Outlook

The global market size for Clinical Nurse Scheduling Software in 2023 is estimated to be approximately $800 million and is projected to reach around $1.5 billion by 2032, growing at a Compound Annual Growth Rate (CAGR) of 7.2%. This significant growth can be attributed to the increasing demand for efficient healthcare operations, the rise in the number of healthcare facilities, and the growing adoption of advanced technology for workforce management.

One of the primary growth factors driving the Clinical Nurse Scheduling Software market is the pressing need for optimized staffing in healthcare facilities. As the healthcare industry continues to expand with an aging population and the rise in chronic diseases, the demand for skilled nursing staff has surged. Efficient scheduling software helps in addressing staffing shortages, reducing burnout, and ensuring that patient care is not compromised. Such software solutions also enable better resource allocation, thereby enhancing the overall efficiency of healthcare operations.

Another significant growth factor is the technological advancements in software development. The introduction of Artificial Intelligence (AI) and Machine Learning (ML) in clinical nurse scheduling software has revolutionized workforce management. These advanced technologies can predict staffing needs based on historical data, patient influx, and other variables, offering a more dynamic and responsive scheduling system. This results in reduced operational costs and improved patient care, making it an attractive investment for healthcare providers.

The increasing adoption of cloud-based solutions also plays a crucial role in the market's growth. Cloud-based scheduling software offers the flexibility and scalability needed to meet the varying demands of healthcare institutions. It eliminates the need for heavy infrastructure investments and allows for real-time updates and mobile accessibility. This shift towards cloud-based solutions is particularly beneficial for small to medium-sized healthcare facilities that may not have the resources for extensive IT infrastructure.

In the context of healthcare staffing, Per Diem Nurse Staffing has emerged as a flexible and cost-effective solution for many healthcare facilities. This staffing model allows hospitals and clinics to meet fluctuating patient demands without the long-term commitment of full-time staff. By leveraging per diem staff, healthcare providers can maintain optimal nurse-to-patient ratios, ensuring high-quality care even during peak times. This approach not only helps in managing labor costs but also provides nurses with the flexibility to choose shifts that fit their schedules, thereby enhancing job satisfaction and reducing burnout. As the demand for adaptable staffing solutions grows, the integration of per diem staffing with advanced scheduling software becomes increasingly vital.

Regionally, North America holds the largest market share due to the well-established healthcare infrastructure and the rapid adoption of advanced technologies. However, Asia Pacific is expected to witness the highest growth rate, driven by the increasing number of healthcare facilities and government initiatives to improve healthcare services. Europe also shows promising growth potential with the ongoing digital transformation in the healthcare sector.

Component Analysis

The component segment of the Clinical Nurse Scheduling Software market is divided into Software and Services. The software segment holds a significant share due to the increasing need for advanced scheduling solutions. Modern software applications offer a range of features including shift swapping, automated scheduling, and predictive analytics, which are essential for optimizing nurse management. These functionalities not only improve operational efficiency but also enhance staff satisfaction by providing more flexibility and transparency in scheduling.

On the services front, there is a growing demand for implementation, training, and support services. As healthcare facilities adopt new scheduling software, the need for seamless integration with existing systems becomes critical. Service providers offer customized solutions that ensure the software is efficiently integrated and optimized according to the specific needs of the facility. Additionally, training services are crucial for staff to effectively use the new software, t

India Number of Nurses: Registered: General Nursing and Midwives data was reported at 1,980,536.000 Person in 2017. This records an increase from the previous number of 1,900,837.000 Person for 2016. India Number of Nurses: Registered: General Nursing and Midwives data is updated yearly, averaging 1,406,006.000 Person from Dec 2005 (Median) to 2017, with 11 observations. The data reached an all-time high of 1,980,536.000 Person in 2017 and a record low of 908,962.000 Person in 2005. India Number of Nurses: Registered: General Nursing and Midwives data remains active status in CEIC and is reported by Central Bureau of Health Intelligence. The data is categorized under India Premium Database’s Health Sector – Table IN.HLB005: Health Human Resources: Number of Nurses: Registered.

Disaster Healthcare Volunteers (DHV) is a program that registers and credentials health professionals who may wish to volunteer during disaster including doctors, nurses, paramedics, pharmacists, dentists, mental health practitioners, etc. DHV may be used by local officials to support a variety of local needs, including augmenting medical staff at healthcare facilities or supporting mass vaccination clinics. DHV is California’s Emergency System for the Advance Registration of Volunteer Health Professionals (ESAR-VHP). This dataset lists the number of volunteers by their organizations.

Totals and percentages of nursing and residential care facility employee annual hours worked, for nurses, physicians and therapists, direct care support workers, and indirect care employees, in 2019, by 2017 NAICS (North American Industry Classification System), for Canada, provinces and territories, annual.

Number of Nurses: Registered: Punjab: General Nursing and Midwives data was reported at 76,680.000 Person in 2022. This stayed constant from the previous number of 76,680.000 Person for 2021. Number of Nurses: Registered: Punjab: General Nursing and Midwives data is updated yearly, averaging 76,680.000 Person from Dec 2005 (Median) to 2022, with 15 observations. The data reached an all-time high of 76,680.000 Person in 2022 and a record low of 45,801.000 Person in 2010. Number of Nurses: Registered: Punjab: General Nursing and Midwives data remains active status in CEIC and is reported by Central Bureau of Health Intelligence. The data is categorized under India Premium Database’s Health Sector – Table IN.HLB005: Health Human Resources: Number of Nurses: Registered.

JQ05 - Nurses. Published by Department of Health. Available under the license Creative Commons Attribution 4.0 (CC-BY-4.0).Nurses’ data is compiled by the Department of Health as part of the Non-Monetary Health Care Statistics, administered jointly by Eurostat, OECD and WHO in fulfilment of the European regulation (EU) 2022/2294. These statistics are compiled and published on an annual basis and refer to the number of practicing nurses and nurses licensed to practice in the Republic of Ireland, as at end of the referenced ending calendar year....