Comprehensive dataset of 268,061 Nurse practitioners in United States as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

United States - Employed full time: Wage and salary workers: Nurse practitioners occupations: 16 years and over: Women was 193.00000 Thous. of Persons in January of 2024, according to the United States Federal Reserve. Historically, United States - Employed full time: Wage and salary workers: Nurse practitioners occupations: 16 years and over: Women reached a record high of 194.00000 in January of 2022 and a record low of 65.00000 in January of 2012. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Employed full time: Wage and salary workers: Nurse practitioners occupations: 16 years and over: Women - last updated from the United States Federal Reserve on June of 2025.

ONC uses the SK&A Office-based Provider Database to calculate the counts of medical doctors, doctors of osteopathy, nurse practitioners, and physician assistants at the state and count level from 2011 through 2013. These counts are grouped as a total, as well as segmented by each provider type and separately as counts of primary care providers.

Graph and download economic data for Employed full time: Wage and salary workers: Nurse practitioners occupations: 16 years and over (LEU0257870000A) from 2011 to 2024 about nursing, occupation, full-time, salaries, workers, 16 years +, wages, employment, and USA.

Graph and download economic data for Employed full time: Median usual weekly nominal earnings (second quartile): Wage and salary workers: Nurse practitioners occupations: 16 years and over: Women (LEU0257870500A) from 2011 to 2024 about nursing, second quartile, occupation, females, full-time, salaries, workers, earnings, 16 years +, wages, median, employment, and USA.

Graph and download economic data for Employed full time: Median usual weekly nominal earnings (second quartile): Wage and salary workers: Nurse practitioners occupations: 16 years and over: Men (LEU0257870400A) from 2011 to 2011 about nursing, second quartile, occupation, full-time, males, salaries, workers, earnings, 16 years +, wages, median, employment, and USA.

United States - Employed full time: Median usual weekly nominal earnings (second quartile): Wage and salary workers: Nurse practitioners occupations: 16 years and over: Women was 2117.00000 $ in January of 2024, according to the United States Federal Reserve. Historically, United States - Employed full time: Median usual weekly nominal earnings (second quartile): Wage and salary workers: Nurse practitioners occupations: 16 years and over: Women reached a record high of 2117.00000 in January of 2024 and a record low of 1432.00000 in January of 2011. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Employed full time: Median usual weekly nominal earnings (second quartile): Wage and salary workers: Nurse practitioners occupations: 16 years and over: Women - last updated from the United States Federal Reserve on July of 2025.

Hospitals in New Mexico The term "hospital" ... means an institution which- (1) is primarily engaged in providing, by or under the supervision of physicians, to inpatients > (A) diagnostic services and therapeutic services for medical diagnosis, treatment, and care of injured, disabled, or sick persons, or > (B) rehabilitation services for the rehabilitation of injured, disabled, or sick persons; (...) (5) provides 24-hour nursing service rendered or supervised by a registered professional nurse, and has a licensed practical nurse or registered professional nurse on duty at all times; ... (...) (7) in the case of an institution in any State in which State or applicable local law provides for the licensing of hospitals, > (A) is licensed pursuant to such law or > (B) is approved, by the agency of such State or locality responsible for licensing hospitals, as meeting the standards established for such licensing; (Excerpt from Title XVIII of the Social Security Act [42 U.S.C. § 1395x(e)], http://www4.law.cornell.edu/uscode/html/uscode42/usc_sec_42_00001395---x000-.html) Included in this dataset are General Medical and Surgical Hospitals, Psychiatric and Substance Abuse Hospitals, and Specialty Hospitals (e.g., Children's Hospitals, Cancer Hospitals, Maternity Hospitals, Rehabilitation Hospitals, etc.). TGS has made a concerted effort to include all general medical/surgical hospitals in New Mexico. Other types of hospitals are included if they were represented in datasets sent by the state. Therefore, not all of the specialty hospitals in New Mexico are represented in this dataset. Hospitals operated by the Veterans Administration (VA) are included, even if the state they are located in does not license VA Hospitals. Nursing homes and Urgent Care facilities are excluded because they are included in a separate dataset. Locations that are administrative offices only are excluded from the dataset. Records with "-DOD" appended to the end of the [NAME] value are located on a military base, as defined by the Defense Installation Spatial Data Infrastructure (DISDI) military installations and military range boundaries. Text fields in this dataset have been set to all upper case to facilitate consistent database engine search results. All diacritics (e.g., the German umlaut or the Spanish tilde) have been replaced with their closest equivalent English character to facilitate use with database systems that may not support diacritics. The currentness of this dataset is indicated by the [CONTDATE] field. Based upon this field, the oldest record dates from 06/16/2008 and the newest record dates from 06/27/2008

The National Ambulatory Medical Care Survey (NAMCS) is designed to meet the need for objective, reliable information about the provision and use of ambulatory medical care services in the United States. NAMCS began in 1973 as a national probability sample survey of visits to nonfederally employed office-based physicians. NCHS conducted the survey annually through 1981, again in 1985, and annually through 2021 (collection of visit data from physicians was stopped during 2020–2021 due to the burden placed on respondents by the COVID-19 pandemic). In 2006, a separate sample of Community Health Centers (CHCs) was added to the survey; the CHC component samples visits to both physicians and advanced practice providers (nurse practitioners, PAs [physician assistants and physician associates], and certified nurse midwives). Starting in 2012, in addition to the traditional NAMCS file, a separate data file for CHCs including physicians and advanced practice providers has been released.

In 2021, the former CHC sample of NAMCS was redesigned and launched as the NAMCS Health Center (HC) Component, collecting visit data from HCs using electronic health records, or EHR, systems of the participating health centers. The NAMCS Health Center Component contains critical data about health centers and the care they provide.

BackgroundGrowing physician maldistribution and population demographic shifts have contributed to large geographic variation in healthcare access and the emergence of advanced practice providers as contributors to the healthcare workforce. Current estimates of geographic accessibility of physicians and advanced practice providers rely on outdated “provider per capita” estimates that have shortcomings.PurposeTo apply state of the art methods to estimate spatial accessibility of physician and non-physician clinician groups and to examine factors associated with higher accessibility.MethodsWe used a combination of provider location, medical claims, and U.S. Census data to perform a national study of health provider accessibility. The National Plan and Provider Enumeration System was used along with Medicare claims to identify providers actively caring for patients in 2014 including: primary care physicians (i.e., internal medicine and family medicine), specialists, nurse practitioners, and chiropractors. For each U.S. ZIP code tabulation area, we estimated provider accessibility using the Variable-distance Enhanced 2 step Floating Catchment Area method and performed a Getis-Ord Gi* analysis for each provider group. Generalized linear models were used to examine associations between population characteristics and provider accessibility.ResultsNational spatial patterns of the provider groups differed considerably. Accessibility of internal medicine most resembled specialists with high accessibility in urban locales, whereas relative higher accessibility of family medicine physicians was concentrated in the upper Midwest. In our adjusted analyses independent factors associated with higher accessibility were very similar between internal medicine physicians and specialists–presence of a medical school in the county was associated with approximately 70% higher accessibility and higher accessibility was associated with urban locales. Nurse practitioners were similar to family medicine physicians with both having higher accessibility in rural locales.ConclusionsThe Variable-distance Enhanced 2 step Floating Catchment Area method is a viable approach to measure spatial accessibility at the national scale.

Success.ai’s Healthcare Industry Leads Data and B2B Contact Data for US Healthcare Professionals offers an extensive and verified database tailored to connect businesses with key executives and administrators in the healthcare industry across the United States. With over 170M verified profiles, including work emails and direct phone numbers, this dataset enables precise targeting of decision-makers in hospitals, clinics, and healthcare organizations.

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ABSTRACT Objectives: To identify the presence of occupational stress in nursing professionals of a university hospital in the inlands of the state of Minas Gerais and examine influence of sociodemographic and occupational characteristics in this disease. Methods: Cross-sectional, exploratory and quantitative study with 124 professional nurses from a university hospital in the inlands of the state of Minas Gerais. The adapted and validated Portuguese version of the Job Stress Scale (JSS) was used for the performance of the study. Results: Most professionals were women (87.9%) with a mean age of 40.2 years, 80.6% were nursing technicians and 71.8% of the sample had some degree of exposure to occupational stress. Conclusions: The occupational stress index was higher than that observed in previous studies. Data obtained in the study point to the need to implement institutional measures for the prevention of occupational stress, especially by strengthening social support at work.

The Primary Care Management Module (PCMM) was developed to assist VA facilities in implementing Primary Care. PCMM supports both Primary Care and non-Primary Care teams. The software allows the user to set up and define a team, assign positions to the team, assign staff to the positions, assign patients to the team, and assign patients to a Primary Care Provider (PCP) or Associate Provider (AP). In a Primary Care setting, patients are assigned a PCP, Associate Provider (AP) and/or a Transition Patient Advocate (TPA) who is responsible for delivering essential health care, coordinating all health care services, and serving as the point of access for specialty care. The PCP is supported by a team of professionals which may include nurses, pharmacists, social workers, etc. Associate Providers are non-physician clinicians (such as Physicians Assistants, Nurse Practitioners or Residents) who may provide care under the supervision of a presiding PCP. The PCMM software is considered to be an important component to measure patient demand and the PCPs capacity to meet that demand and to reduce wait times. PCMM was developed to assist facilities in implementing primary care for veterans. It uses the site's data to identify patients and to assign them to a PCP. PCMM provides tools to facilitate the startup process, automating such tasks as identifying patients to be assigned to primary care; assigning patients to teams, and assigning patients to practitioners via team positions.

The National Ambulatory Medical Care Survey (NAMCS) is designed to meet the need for objective, reliable information about the provision and use of ambulatory medical care services in the United States. NAMCS began in 1973 as a national probability sample survey of visits to nonfederally employed office-based physicians. NCHS conducted the survey annually through 1981, again in 1985, and annually through 2021 (collection of visit data from physicians was stopped during 2020–2021 due to the burden placed on respondents by the COVID-19 pandemic). In 2006, a separate sample of Community Health Centers (CHCs) was added to the survey; the CHC component samples visits to both physicians and advanced practice providers (nurse practitioners, PAs [physician assistants and physician associates], and certified nurse midwives). Starting in 2012, in addition to the traditional NAMCS file, a separate data file for CHCs including physicians and advanced practice providers has been released.

In 2021, the former CHC sample of NAMCS was redesigned and launched as the NAMCS Health Center (HC) Component, collecting visit data from HCs using electronic health records, or EHR, systems of the participating health centers. The NAMCS Health Center Component contains critical data about health centers and the care they provide.

These data are part of NACJD's Fast Track Release and are distributed as they were received from the data depositor. The files have been zipped by NACJD for release, but not checked or processed except for the removal of direct identifiers. Users should refer to the accompanying readme file for a brief description of the files available with this collection and consult the investigator(s) if further information is needed. To address the under-reporting and under-prosecution of adult sexual assaults, some communities in the United States implemented the Sexual Assault Nurse Examiner (SANE) Program whereby specially trained nurses provide comprehensive psychological, medical, and forensic services for sexual assault to improve post-assault care for victims and the criminal justice system response. The SANE Practitioner Toolkit was created to teach SANE program staff how to evaluate whether prosecution rates increased in their communities after the implantation of their SAME program. Six SANE programs were selected and provided with comprehensive technical assistance to help them work through the steps in the Toolkit in order to evaluate whether the program was having a beneficial impact on prosecution rates. This study evaluated the effectiveness of the SANE program to increase prosecution rates of sexual assaults through the SANE Practitioner Evaluation Toolkit, and the technical assistance process and resources provided to the sites improved their evaluative abilities.

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US Nursing Education Market Size 2025-2029

The US nursing education market size is forecast to increase by USD 161.9 billion at a CAGR of 30% between 2024 and 2029.

US Nursing Education Market is experiencing significant growth, driven by the increasing demand for competency-based learning and the integration of advanced technologies such as Augmented Reality (AR) and Virtual Reality (VR) in nursing education. The shift towards competency-based learning is a response to the evolving healthcare landscape and the need for nurses to possess a higher level of skills and knowledge to provide effective patient care. Furthermore, the use of AR and VR technologies in nursing education offers learning experiences, enabling students to practice complex procedures in a safe and controlled environment. However, the market is not without challenges.
One of the significant challenges is the lack of standardized assessment metrics to measure the effectiveness of nursing education programs. This challenge hampers the ability to evaluate the success of educational initiatives and the readiness of graduates to enter the workforce. To capitalize on the market opportunities and navigate these challenges effectively, companies must focus on developing innovative solutions that address the need for competency-based learning and provide reliable assessment metrics. Additionally, investing in the integration of AR and VR technologies can offer a competitive edge in the market.

What will be the size of the US Nursing Education Market during the forecast period?

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The nursing education market in the US is experiencing significant growth and innovation, driven by the demand for advanced nursing informatics solutions and continuing education units. This trend is reflected in the development of nurse recruitment strategies that leverage telehealth platforms and nursing curriculum tailored to healthcare technology. Nursing salary trends continue to influence the market, as nursing informatics specialists become increasingly essential for effective healthcare data management. Nursing simulation software and nursing career pathways are key components of nursing education trends, providing clinical experience and patient safety initiatives that align with patient-centered care and improved health outcomes.
Accreditation standards and nursing faculty recruitment are also critical areas of focus, as institutions seek to maintain high educational standards and remain competitive. Patient portals, mobile health apps, and nursing education consultants are essential tools for nursing workforce development, enabling professional growth and leadership training. Nursing ethics committees and clinical data analytics further enhance the quality of nursing education and research, ensuring that the nursing profession remains at the forefront of healthcare innovation.

How is this market segmented?

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Type

  Graduate courses
  Postgraduate courses


End-user

  Hospitals
  Home healthcare services


Program Type

  Associate Degree
  Bachelor's Degree
  Master's Degree
  Doctoral Programs


Delivery Mode

  On-Campus
  Online
  Hybrid


Institution Types

  Universities
  Community Colleges
  Vocational Schools


Geography

  US

By Type Insights

The graduate courses segment is estimated to witness significant growth during the forecast period.

The nursing education market in the US is experiencing significant growth due to the rising enrollment in undergraduate and graduate nursing programs. This trend is driven by the increasing demand for specialized nursing professionals in various fields, such as geriatric nursing, mental health nursing, and critical care nursing. The American Nurses Association and other nursing organizations advocate for continued nursing education as a means of addressing health disparities and improving patient care. E-learning platforms, nursing simulation labs, and clinical skills training are essential components of graduate nursing programs, providing students with the necessary theoretical and practical knowledge.

Nursing informatics, healthcare reform, and patient safety are key areas of focus, with data analytics and clinical decision support playing crucial roles. The nursing workforce is evolving, with an emphasis on nurse retention, nursing leadership, and nursing professional development. Online nursing programs, mobile health, and wearable technology are transforming nursing education, making it more accessible and flexible. Nursing evaluation, nursing diagnosis, and nursing standards are integral parts of nursing education, ensuring that students are prepared for the nursing l

The CMS EHR Incentive Programs provide incentives to eligible office-based providers and hospitals to adopt electronic health records. Both the Medicare and Medicaid programs have separate criteria and eligible participants. These measures track the percentage of physicians, nurse practitioners, physician assistants, short-term general, Critical Access, and Children's hospitals that have demonstrated meaningul use of certified electronic health record technology and/or adopted, implemented, or ugraded any electronic health record. These measures track the rate of adoption and use of EHR technology certified by HHS in addition to adoption of other non-certified EHR technology. These measures are cumulative, representing the most recent data.

AbstractBackground Moral injury is an emerging explanation of burnout and suicidality, but remains poorly quantified in at-risk practitioners. We hypothesized that COVID-19 pandemic-related moral injury differs between frontline clinicians, genders, age, and country of practice. Methods We conducted an online cross-sectional survey of international physicians, nurses, nurse practitioners, paramedics and respiratory therapists between April and June 2020. We included the adapted version of the Expressions of Moral Injury Scale (EMIS). The primary outcome was differences in moral injury scores between clinician roles. Results Three hundred and two clinicians participated, including physicians (61% [n=184]), nurses (28% [n=85]), and nurse practitioners (5% [n=14]). The median age was 39 (IQR 32-76), females comprised 54% of the respondents, and the majority resided in Canada (n =183 [61%]) or the United States (US; n = 106 [35%]). Emergency medicine (88% [n=265]), and intensive care (6% [n=17]) were the main specialties responding. Median moral injury scores across multiple domains were higher for nurses compared to physicians, as well as for younger, and female respondents. Moral injury scores were also significantly higher for respondents from the United States, the United Kingdom and Australia, compared to Canada. Conclusions Our research suggests that during COVID-19, measures of moral injury differ across roles, gender and place of work. Future research is warranted to better understand the impact of moral injury on clinicians’ psychological well-being during the COVID-19 pandemic. MethodsThis dataset was collected through the Qualtrics online survey application.

To estimate county of residence of Filipinx healthcare workers who died of COVID-19, we retrieved data from the Kanlungan website during the month of December 2020.22 In deciding who to include on the website, the AF3IRM team that established the Kanlungan website set two standards in data collection. First, the team found at least one source explicitly stating that the fallen healthcare worker was of Philippine ancestry; this was mostly media articles or obituaries sharing the life stories of the deceased. In a few cases, the confirmation came directly from the deceased healthcare worker's family member who submitted a tribute. Second, the team required a minimum of two sources to identify and announce fallen healthcare workers. We retrieved 86 US tributes from Kanlungan, but only 81 of them had information on county of residence. In total, 45 US counties with at least one reported tribute to a Filipinx healthcare worker who died of COVID-19 were identified for analysis and will hereafter be referred to as “Kanlungan counties.” Mortality data by county, race, and ethnicity came from the National Center for Health Statistics (NCHS).24 Updated weekly, this dataset is based on vital statistics data for use in conducting public health surveillance in near real time to provide provisional mortality estimates based on data received and processed by a specified cutoff date, before data are finalized and publicly released.25 We used the data released on December 30, 2020, which included provisional COVID-19 death counts from February 1, 2020 to December 26, 2020—during the height of the pandemic and prior to COVID-19 vaccines being available—for counties with at least 100 total COVID-19 deaths. During this time period, 501 counties (15.9% of the total 3,142 counties in all 50 states and Washington DC)26 met this criterion. Data on COVID-19 deaths were available for six major racial/ethnic groups: Non-Hispanic White, Non-Hispanic Black, Non-Hispanic Native Hawaiian or Other Pacific Islander, Non-Hispanic American Indian or Alaska Native, Non-Hispanic Asian (hereafter referred to as Asian American), and Hispanic. People with more than one race, and those with unknown race were included in the “Other” category. NCHS suppressed county-level data by race and ethnicity if death counts are less than 10. In total, 133 US counties reported COVID-19 mortality data for Asian Americans. These data were used to calculate the percentage of all COVID-19 decedents in the county who were Asian American. We used data from the 2018 American Community Survey (ACS) five-year estimates, downloaded from the Integrated Public Use Microdata Series (IPUMS) to create county-level population demographic variables.27 IPUMS is publicly available, and the database integrates samples using ACS data from 2000 to the present using a high degree of precision.27 We applied survey weights to calculate the following variables at the county-level: median age among Asian Americans, average income to poverty ratio among Asian Americans, the percentage of the county population that is Filipinx, and the percentage of healthcare workers in the county who are Filipinx. Healthcare workers encompassed all healthcare practitioners, technical occupations, and healthcare service occupations, including nurse practitioners, physicians, surgeons, dentists, physical therapists, home health aides, personal care aides, and other medical technicians and healthcare support workers. County-level data were available for 107 out of the 133 counties (80.5%) that had NCHS data on the distribution of COVID-19 deaths among Asian Americans, and 96 counties (72.2%) with Asian American healthcare workforce data. The ACS 2018 five-year estimates were also the source of county-level percentage of the Asian American population (alone or in combination) who are Filipinx.8 In addition, the ACS provided county-level population counts26 to calculate population density (people per 1,000 people per square mile), estimated by dividing the total population by the county area, then dividing by 1,000 people. The county area was calculated in ArcGIS 10.7.1 using the county boundary shapefile and projected to Albers equal area conic (for counties in the US contiguous states), Hawai’i Albers Equal Area Conic (for Hawai’i counties), and Alaska Albers Equal Area Conic (for Alaska counties).20