In 2024, according to the Bureau of Labor Statistics, there were *** million registered nurses in the United States. With over *** thousand registered nurses, California had the highest number of registered nurses in the U.S., followed by Texas and Florida. On the other hand, Wyoming was the state with the least registered nurses in 2024.

The National Sample Survey of Registered Nurses (NSSRN) Download makes data from the survey readily available to users in a one-stop download. The Survey has been conducted approximately every four years since 1977. For each survey year, HRSA has prepared two Public Use File databases in flat ASCII file format without delimiters. The 2008 data are also offerred in SAS and SPSS formats. Information likely to point to an individual in a sparsely-populated county has been withheld. General Public Use Files are State-based and provide information on nurses without identifying the County and Metropolitan Area in which they live or work. County Public Use Files provide most, but not all, the same information on the nurse from the General Public Use File, and also identifies the County and Metropolitan Areas in which the nurses live or work. NSSRN data are to be used for research purposes only and may not be used in any manner to identify individual respondents.

Graph and download economic data for Employed full time: Wage and salary workers: Registered nurses occupations: 16 years and over (LEU0254487900A) from 2000 to 2024 about registered nurses, nursing, occupation, full-time, salaries, workers, 16 years +, wages, employment, and USA.

In 2024, according to the Bureau of Labor Statistics, the annual mean wage of nurse practitioners in the United States stood at ******* U.S. dollars. With an annual mean wage of ******* U.S. dollars, registered nurses in California had the highest wages, followed by New York and Oregon. On the other hand, Tennessee had the lowest annual mean wages for nurse practitioners in 2024.

This dataset contains the custom geographies used to study the registered nurse workforce and identifies which areas are designated as Registered Nurse Shortage Areas (RNSA) as of the year labeled in the title.

Introduction

According to Nursing Workforce Statistics, The nursing workforce comprises various roles, including Registered Nurses (RNs), Nurse Practitioners (NPs), Licensed Practical Nurses (LPNs), and Nursing Assistants (CNAs), each with distinct responsibilities and educational requirements.

R.N.s, the largest group, typically hold an Associate or Bachelor's degree and provide comprehensive patient care. N.P.s with advanced degrees offer specialized hospital services.

LPNs and CNAs support patient care under supervision, with LPNs requiring a diploma or certificate and CNAs needing state certification.

The nursing workforce, which exceeds 3 million in the U.S., faces challenges such as shortages and burnout but also benefits from growth in advanced practice roles and diverse career opportunities.

In 2022, Switzerland had the highest number of practicing nurses per capita, that is, for every 1,000 population there were 18 practicing nurses in Switzerland. This is followed by Norway and Iceland. This statistic portrays the number of practicing nurses in selected countries as of 2021, per 1,000 population.

Nurses in the United States increased to 12.71 per 1000 people in 2024 from 12.36 per 1000 people in 2023. This dataset includes a chart with historical data for the United States Nurses.

Nurses in Sweden increased to 11.64 per 1000 people in 2022 from 11.54 per 1000 people in 2021. This dataset includes a chart with historical data for Sweden Nurses.

Cameroon CM: Nurses and Midwives: per 1000 People data was reported at 0.650 Ratio in 2022. This records an increase from the previous number of 0.193 Ratio for 2021. Cameroon CM: Nurses and Midwives: per 1000 People data is updated yearly, averaging 0.466 Ratio from Dec 2004 (Median) to 2022, with 10 observations. The data reached an all-time high of 1.549 Ratio in 2004 and a record low of 0.193 Ratio in 2021. Cameroon CM: Nurses and Midwives: per 1000 People data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Cameroon – Table CM.World Bank.WDI: Social: Health Statistics. Nurses and midwives include professional nurses, professional midwives, auxiliary nurses, auxiliary midwives, enrolled nurses, enrolled midwives and other associated personnel, such as dental nurses and primary care nurses.;World Health Organization's Global Health Workforce Statistics, OECD, supplemented by country data.;Weighted average;This is the Sustainable Development Goal indicator 3.c.1 [https://unstats.un.org/sdgs/metadata/].

This dataset contains the output of HCAI’s Supply and Demand Model for California’s Nursing Workforce. It includes the estimated supply and demand for providers in Full-Time Equivalent (FTE) for each role or role group included in the model for the years 2022-2023. These metrics can be compared by role, region, county, and year. For in-depth details on our modeling methodology, see our online comprehensive methodology documentation at https://hcai.ca.gov/wp-content/uploads/2025/05/Public-Modeling-Methodology-v.1.1_5-2025.pdf. Additional documentation is also provided on HCAI’s use of Lightcast data within the Supply & Demand model.

In the United States, the large majority of registered nurses are *****. From 2014 to 2017, only *** out of ten registered nurses were men. Although nursing remains predominately a female profession, the share of male registered nurses has slightly increased over the years.

Dataset for Continuous Stress Monitoring of Hospital Nurses

The growing accessibility of wearable tech has opened doors to continuously monitor various physiological factors. Detecting stress early has become pivotal, aiding individuals in proactively managing their health against the detrimental effects of prolonged stress exposure. This paper presents an exclusive stress detection dataset cultivated within the natural environment of a hospital. Compiled during the COVID-19 outbreak, this dataset encompasses the biometric data of nurses. Analyzing stress in a workplace setting is intricate due to the multifaceted social, cultural, and psychological elements inherent in dealing with stressful circumstances. Hence, our dataset not only encompasses physiological data but also contextual information surrounding stress events. Key physiological metrics such as electrodermal activity, heart rate, and skin temperature of the nurse subjects were continuously monitored. Additionally, a periodic survey administered via smartphones captured contributing factors linked to detected stress events. The database housing these signals, stress occurrences, and survey responses is publicly accessible on Dryad.

Project Overview This project delves into leveraging wearable device-derived physiological signals to gauge stress levels among nurses operating within a hospital environment. The dataset comprises details acquired from nurses wearing watches that tracked their heart rate, skin temperature, and electrodermal activity (EDA) while simultaneously reporting their stress levels.

The primary goal revolves around evaluating various machine learning models to forecast stress levels based on recorded physiological signals. Additionally, the project investigates the most pertinent physiological indicators for stress detection and offers insights to enhance the accuracy and dependability of stress detection via wearable tech.

Dataset Description:

Data Collection Context: Period: Data gathered over one week from 15 female nurses aged 30 to 55 years, during regular shifts at a hospital. Collection Phases: Two phases - Phase-I (April 15, 2020, to August 6, 2020) and Phase-II (October 8, 2020, to December 11, 2020). Exclusion Criteria: Pregnancy, heavy smoking, mental disorders, chronic or cardiovascular diseases.

Data Captured: Physiological Variables Monitored: Electrodermal activity, Heart Rate, and skin temperature of the nurse subjects. Survey Responses: Periodic smartphone-administered surveys capturing contributing factors to detected stress events. Measurement Technologies: Utilized Empatica E4 for data collection, specifically focusing on Galvanic Skin Response and Blood Volume Pulse (BVP) readings.

Study Procedure: Approval: University's Institutional Review Board approved the study protocol (FA19–50 INFOR). Consent and Enrollment: Nurse subjects were enrolled after expressing interest and obtaining hospital compliance. Study Design: Conducted in three phases, each including 7 nurses. No incentives were provided, and anonymization of data was ensured.

Data Availability: Public Release: A database containing signals, stress events, and survey responses is publicly available on Dryad. Anonymization: Unique identifiers assigned to subjects to maintain anonymity.

Merge CSV File Information: This dataset comprises approximately 11.5 million entries across nine columns: X, Y, Z: Orientation data (256 unique entries each). EDA, HR, TEMP: Physiological measurements (EDA: 274,452 unique, HR: 6,268 unique, TEMP: 599 unique). id: 18 categorical identifiers. datetime: Extensive date and time entries (10.6 million unique). label: Categorical states or classes (three unique entries). The dataset offers a wide array of continuous physiological measurements alongside orientation data, facilitating stress detection, health monitoring, and related research endeavours.

Requirements Python 3.7 or higher and Jupyter Notebook are prerequisites. The necessary Python packages are enumerated in the requirements.txt file. To execute the code, installation of the following libraries is mandatory: pandas, numpy, sci-kit-learn, and matplotlib.

Graph and download economic data for Employed full time: Median usual weekly nominal earnings (second quartile): Wage and salary workers: Registered nurses occupations: 16 years and over (LEU0254541300A) from 2000 to 2024 about registered nurses, nursing, second quartile, occupation, full-time, salaries, workers, earnings, 16 years +, wages, median, employment, and USA.

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 stock of nurses according to country from where they obtained their first nursing qualification, as at end of the referenced year. .hidden { display: none }

Graph and download economic data for All Employees, Nursing and Residential Care Facilities (CEU6562300001) from Jan 1990 to Sep 2025 about nursing homes, nursing, health, establishment survey, education, residential, services, employment, and USA.

Lebanon LB: Nurses and Midwives: per 1000 People data was reported at 2.562 Ratio in 2014. This records an increase from the previous number of 2.523 Ratio for 2011. Lebanon LB: Nurses and Midwives: per 1000 People data is updated yearly, averaging 1.982 Ratio from Dec 2001 (Median) to 2014, with 7 observations. The data reached an all-time high of 2.562 Ratio in 2014 and a record low of 1.184 Ratio in 2005. Lebanon LB: Nurses and Midwives: per 1000 People data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Lebanon – Table LB.World Bank: Health Statistics. Nurses and midwives include professional nurses, professional midwives, auxiliary nurses, auxiliary midwives, enrolled nurses, enrolled midwives and other associated personnel, such as dental nurses and primary care nurses.; ; World Health Organization's Global Health Workforce Statistics, OECD, supplemented by country data.; Weighted average;

Comprehensive dataset containing 30,092 verified Registered general nurse businesses in United States with complete contact information, ratings, reviews, and location data.

This dataset provides quarterly data submitted by nursing homes, detailing their daily nurse and non-nurse staffing levels. The data includes the hours staff are paid to work each day, categorized by staff roles such as Registered Nurses (RN), Licensed Practical Nurses (LPN), Certified Nurse Aides (CNA), and other non-nurse roles like therapists and social workers. It also incorporates the facility's daily census, derived from Minimum Data Set (MDS) submissions. The data is collected through the Payroll Based Journal (PBJ) system, a CMS initiative to obtain verifiable staffing information from nursing facilities.

Data Source:

• Centers for Medicare & Medicaid Services (CMS)
• Payroll Based Journal (PBJ) system

Purpose & Usage:

• Quality Rating: Used to calculate the staffing component of the CMS Five-Star Quality Rating System for nursing homes.
• Staffing Evaluation: Helps in evaluating the adequacy of staffing levels in nursing facilities.
• Value-Based Purchasing: Will be used in Skilled Nursing Facility (SNF) Value-Based Purchasing programs.
• Enforcement of Standards: CMS uses PBJ data to enhance the enforcement of federal nursing home staffing standards and investigate noncompliance.
• Research & Analysis: Useful for researchers and analysts studying nursing home staffing, quality of care, and healthcare policy.
• Sales & Strategy: Potentially valuable for staffing agencies to understand staffing trends and optimize resource allocation.

Update Frequency:

• Typically updated quarterly (4 times a year).

Key Data Points:

• Daily staffing hours by various staff categories (RN Director of Nursing, Registered Nurses, Licensed Practical Nurses, Certified Nurse Aides, etc.)
• Employee and contract hours for each staff category
• Facility's daily census
• Facility information (Medicare Provider Number, Name, City, State, County)
• Calendar Quarter and Work Date

Data Dictionary:

• A data dictionary is available which provides definitions for each column in the dataset. You can find it here: Payroll Based Journal Daily Nurse Staffing Data Dictionary. This data dictionary is also listed as a featured resource on the main dataset page: Payroll Based Journal Daily Nurse Staffing - CMS Data.

The CMS Program Statistics - Medicare Skilled Nursing Facility tables provide use and payment data for skilled nursing facilities. For additional information on enrollment, providers, and Medicare use and payment, visit the CMS Program Statistics page. Below is the list of tables: MDCR SNF 1. Medicare Skilled Nursing Facilities: Utilization, Program Payments, and Cost Sharing for Original Medicare Beneficiaries, by Type of Entitlement, Yearly Trend MDCR SNF 2. Medicare Skilled Nursing Facilities: Utilization, Program Payments, and Cost Sharing for Original Medicare Beneficiaries, by Demographic Characteristics and Medicare-Medicaid Enrollment Status MDCR SNF 3. Medicare Skilled Nursing Facilities: Utilization, Program Payments, and Cost Sharing for Original Medicare Beneficiaries, by Area of Residence MDCR SNF 4. Medicare Skilled Nursing Facilities: Utilization, Program Payments, and Cost Sharing for Original Medicare Beneficiaries, by Type of Entitlement and Covered Days of Care MDCR SNF 5. Medicare Skilled Nursing Facilities: Utilization, Program Payments, and Cost Sharing for Original Medicare Beneficiaries, by Type of Facility and Bedsize MDCR SNF 6. Medicare Skilled Nursing Facilities: Distribution of Medicare Covered Skilled Nursing Facility Days, by State of Provider and Major Resource Utilization Groups (RUG)-III (versions 2013-2018 only)