Medical Doctors in the United States increased to 2.77 per 1000 people in 2019 from 2.74 per 1000 people in 2018. This dataset includes a chart with historical data for the United States Medical Doctors.

As of 2022, the number of licensed physicians in the United States and the District of Columbia amounted to 1,062,460 physicians. At the time, the national population was roughly 333 million, which yielded a physician-to-population ratio of 313 licensed physicians per 100,000 population. The density of licensed U.S. physicians has steadily increased since 2010.

Actual value and historical data chart for United States Physicians Per 1 000 People

The number of physicians across the United States reveals significant variations, with California leading the pack at nearly ******* active doctors as of April 2025. This concentration of medical professionals in populous states highlights the ongoing challenge of ensuring adequate healthcare access nationwide. The stark contrast between California's physician count and Wyoming's mere ***** doctors underscores the need for targeted efforts to address healthcare workforce shortages in less populated areas. Primary care and specialist distribution California leads also in both primary care physicians and specialists, accounting for over ** percent of each category nationally. This concentration of medical expertise in California reflects broader trends, with New York and Texas following as the states with the highest numbers of active primary care physicians. The distribution of specialists also mirrors national patterns, with psychiatry, surgery, and anaesthesiology among the most common specialties. Physician burnout While the number of physicians continues to grow, physician burnout remains a significant issue. There are large variations in rates of burnout depending on a physician's gender and specialty. For example, burnout is disproportionally high among women, affecting ** percent of female physicians and ** percent of male physicians. Meanwhile, emergency medicine physicians reported the highest levels of burnout among specialists, highlighting the need for targeted interventions to support the individual needs of doctors depending on their different circumstances.

Data on visits to physician offices, hospital outpatient departments and hospital emergency departments by selected population characteristics. Please refer to the PDF or Excel version of this table in the HUS 2019 Data Finder (https://www.cdc.gov/nchs/hus/contents2019.htm) for critical information about measures, definitions, and changes over time. Note that the data file available here has more recent years of data than what is shown in the PDF or Excel version. Data for 2017 physician office visits are not available. SOURCE: NCHS, National Ambulatory Medical Care Survey and National Hospital Ambulatory Medical Care Survey. For more information on the National Ambulatory Medical Care Survey and the National Hospital Ambulatory Medical Care Survey, see the corresponding Appendix entries at https://www.cdc.gov/nchs/data/hus/hus17_appendix.pdf.

According to a survey carried out in the United States in 2023, more than ** percent of respondents said that one of the reasons why they were interested in sharing data from their personal device with healthcare providers was to take a more active role in their health. Another motivation for sharing data from a wearable device for over ************** of respondents was to improve their health outcomes.

Health professionals, especially primary care physicians, are in high demand in many parts of the U.S. Some areas are experiencing health professional shortages. This map shows the ratio of population to primary care physicians in the U.S. Areas in dark red show where there are less primary care physicians per person.The data comes from County Health Rankings, a collaboration between the Robert Wood Johnson Foundation and the University of Wisconsin Population Health Institute, measure the health of nearly all counties in the nation and rank them within states. The layer used in the map comes from ArcGIS Living Atlas of the World, and the full documentation for the layer can be found here.County data are suppressed if, for both years of available data, the population reported by agencies is less than 50% of the population reported in Census or less than 80% of agencies measuring crimes reported data.

The National Ambulatory Medical Care Survey (NAMCS), conducted by the National Center for Health Statistics (NCHS), collects data on visits to physician offices to describe patterns of ambulatory care delivery in the United States. As part of NAMCS, the Physician Induction Interview collects information about practice characteristics at physician offices. Partway through the 2020 NAMCS, NCHS added questions to the Physician Induction Interview to assess physician experiences related to COVID-19 in office-based settings. The data include nationally representative estimates of experiences related to COVID-19 among office-based physicians in the United States, including: shortages of personal protective equipment (PPE) in the past 3 months; the ability to test for COVID-19 in the past 3 months; providers testing positive for COVID-19 in the past 3 months; turning away COVID-19 patients in the past 3 months; and telemedicine or telehealth technology use before and after March 2020. Estimates were derived from interviews with physicians in periods 3 and 4 of 2020 NAMCS and periods 1 through 4 of 2021 NAMCS, which occurred between December 15, 2020 and May 6, 2022. The data are considered preliminary, and the results may change with the final data release.

Comprehensive dataset containing 24,077 verified Doctor businesses in Ohio, United States with complete contact information, ratings, reviews, and location data.

The average for 2020 based on 2 countries was 2.23 doctors per 1,000 people. The highest value was in Mexico: 2.41 doctors per 1,000 people and the lowest value was in Brazil: 2.05 doctors per 1,000 people. The indicator is available from 1960 to 2021. Below is a chart for all countries where data are available.

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.

Comprehensive dataset containing 626,830 verified Doctor businesses in United States with complete contact information, ratings, reviews, and location data.

About this Dataset

Limited dataset providing physician's name, company, city, state, zip, and website. Dataset is useful for someone looking to build a more complete lead generation list with web scrapping or data entry to supplement current information. List was developed from manual data entry and web search.

Category

Lead Generation

Keywords

doctors,medicine,physicians

Row Count

1775

Price

$29.99

This dataset comprises physician-level entries from the 1906 American Medical Directory, the first in a series of semi-annual directories of all practicing physicians published by the American Medical Association [1]. Physicians are consistently listed by city, county, and state. Most records also include details about the place and date of medical training. From 1906-1940, Directories also identified the race of black physicians [2].This dataset comprises physician entries for a subset of US states and the District of Columbia, including all of the South and several adjacent states (Alabama, Arkansas, Delaware, Florida, Georgia, Kansas, Kentucky, Louisiana, Maryland, Mississippi, Missouri, North Carolina, Oklahoma, South Carolina, Tennessee, Texas, Virginia, West Virginia). Records were extracted via manual double-entry by professional data management company [3], and place names were matched to latitude/longitude coordinates. The main source for geolocating physician entries was the US Census. Historical Census records were sourced from IPUMS National Historical Geographic Information System [4]. Additionally, a public database of historical US Post Office locations was used to match locations that could not be found using Census records [5]. Fuzzy matching algorithms were also used to match misspelled place or county names [6].The source of geocoding match is described in the “match.source” field (Type of spatial match (census_YEAR = match to NHGIS census place-county-state for given year; census_fuzzy_YEAR = matched to NHGIS place-county-state with fuzzy matching algorithm; dc = matched to centroid for Washington, DC; post_places = place-county-state matched to Blevins & Helbock's post office dataset; post_fuzzy = matched to post office dataset with fuzzy matching algorithm; post_simp = place/state matched to post office dataset; post_confimed_missing = post office dataset confirms place and county, but could not find coordinates; osm = matched using Open Street Map geocoder; hand-match = matched by research assistants reviewing web archival sources; unmatched/hand_match_missing = place coordinates could not be found). For records where place names could not be matched, but county names could, coordinates for county centroids were used. Overall, 40,964 records were matched to places (match.type=place_point) and 931 to county centroids ( match.type=county_centroid); 76 records could not be matched (match.type=NA).Most records include information about the physician’s medical training, including the year of graduation and a code linking to a school. A key to these codes is given on Directory pages 26-27, and at the beginning of each state’s section [1]. The OSM geocoder was used to assign coordinates to each school by its listed location. Straight-line distances between physicians’ place of training and practice were calculated using the sf package in R [7], and are given in the “school.dist.km” field. Additionally, the Directory identified a handful of schools that were “fraudulent” (school.fraudulent=1), and institutions set up to train black physicians (school.black=1).AMA identified black physicians in the directory with the signifier “(col.)” following the physician’s name (race.black=1). Additionally, a number of physicians attended schools identified by AMA as serving black students, but were not otherwise identified as black; thus an expanded racial identifier was generated to identify black physicians (race.black.prob=1), including physicians who attended these schools and those directly identified (race.black=1).Approximately 10% of dataset entries were audited by trained research assistants, in addition to 100% of black physician entries. These audits demonstrated a high degree of accuracy between the original Directory and extracted records. Still, given the complexity of matching across multiple archival sources, it is possible that some errors remain; any identified errors will be periodically rectified in the dataset, with a log kept of these updates.For further information about this dataset, or to report errors, please contact Dr Ben Chrisinger (Benjamin.Chrisinger@tufts.edu). Future updates to this dataset, including additional states and Directory years, will be posted here: https://dataverse.harvard.edu/dataverse/amd.References:1. American Medical Association, 1906. American Medical Directory. American Medical Association, Chicago. Retrieved from: https://catalog.hathitrust.org/Record/000543547.2. Baker, Robert B., Harriet A. Washington, Ololade Olakanmi, Todd L. Savitt, Elizabeth A. Jacobs, Eddie Hoover, and Matthew K. Wynia. "African American physicians and organized medicine, 1846-1968: origins of a racial divide." JAMA 300, no. 3 (2008): 306-313. doi:10.1001/jama.300.3.306.3. GABS Research Consult Limited Company, https://www.gabsrcl.com.4. Steven Manson, Jonathan Schroeder, David Van Riper, Tracy Kugler, and Steven Ruggles. IPUMS National Historical Geographic Information System: Version 17.0 [GNIS, TIGER/Line & Census Maps for US Places and Counties: 1900, 1910, 1920, 1930, 1940, 1950; 1910_cPHA: ds37]. Minneapolis, MN: IPUMS. 2022. http://doi.org/10.18128/D050.V17.05. Blevins, Cameron; Helbock, Richard W., 2021, "US Post Offices", https://doi.org/10.7910/DVN/NUKCNA, Harvard Dataverse, V1, UNF:6:8ROmiI5/4qA8jHrt62PpyA== [fileUNF]6. fedmatch: Fast, Flexible, and User-Friendly Record Linkage Methods. https://cran.r-project.org/web/packages/fedmatch/index.html7. sf: Simple Features for R. https://cran.r-project.org/web/packages/sf/index.html

Comprehensive dataset containing 4,984 verified Occupational medical physician businesses in United States with complete contact information, ratings, reviews, and location data.

In 2022, over three in ten licensed physicians in the United States were 60 years of age or older. In comparison, just one quarter were over the age of 60 years in 2010. This trend towards older physicians can be seen more clearly by comparing the average age of licensed physicians in 2022, which was 51.9 years, to 2010, in which the mean age was 50.7.

Graph and download economic data for Employment for Health Care and Social Assistance: Offices of Physicians (NAICS 62111) in the United States (IPURN62111W200000000) from 1987 to 2024 about offices, physicians, healthcare, social assistance, health, NAICS, IP, employment, and USA.

The U.S. Census Bureau regularly collects information for many metropolitan areas in the United States, including data on number of physicians and number (and size) of hospitals. This dataset has such information for 83 different metropolitan areas.

Context Financial relationships between manufacturers of drugs, devices, biologicals, and medical supplies and healthcare providers (physicians, non-physician practitioners, and teaching hospitals) are common and often serve important functions. However, these ties can also create potential conflicts of interest.

The CMS (Centers for Medicare & Medicaid Services) Open Payments program is a U.S. federal initiative designed to increase transparency around these financial relationships. By publicly reporting data on payments and other transfers of value, the program helps patients and researchers better understand the nature and extent of these collaborations.

While the raw data is incredibly rich, its comprehensive and detailed structure can be challenging for quick analysis or machine learning applications. This dataset is a cleaned, processed, and user-friendly version of the original Open Payments data, specifically prepared to facilitate straightforward data exploration, visualization, and predictive modeling.

Content This dataset contains records of payments made by manufacturers to healthcare providers in the United States. The original, multi-part fields for products, specialties, and licenses have been simplified to focus on the primary entry for each record, and columns with low variance or sparse data have been removed.

The dataset includes the following columns: | Column Name | Description | | ------------------------- | -------------------------------------------------------------------------------------------------------- | | payment_id | System-assigned unique identifier for the payment transaction. | | payment_amount | The total value of the payment in U.S. Dollars. | | payment_number | The number of individual payments included in the total amount. | | address_full | The full primary business street address of the payment recipient. | | address_country | The primary business country of the recipient. | | address_state | The primary business state of the recipient (2-letter abbreviation). | | address_city | The primary business city of the recipient. | | zip_code | The 5 or 9-digit zip code for the recipient's primary business location. | | payment_day | The day of the month the payment was made. | | payment_month | The month the payment was made. | | payment_year | The year the payment was made. | | publication_day | The day of the month the payment record was published. | | publication_month | The month the payment record was published. | | publication_year | The year the payment record was published. | | change_type | An indicator showing if the record is new or added (NEW, ADD). | | indicator_third_party | Indicates if payment was made to a third party (ENTITY, INDIVIDUAL, NO THIRD PARTY PAYMENT). | | indicator_related_product | Indicates if the payment was related to a specific product (YES, NO). | | indicator_covered | Indicates if the related product is "covered" under Open Payments rules (UNKNOWN, NON-COVERED, COVERED). | | identity_type | The professional designation of the payment recipient (NON-PHYSICIAN PRACTITIONER, PHYSICIAN). | | first_name | The first name of the covered recipient. | | last_name | The last name of the covered recipient. | | manufacturer_name | The name of the company that made the payment. | | manufacturer_state | The state where the paying company is located. | | manufacturer_country | The country where the paying compan...

Comprehensive dataset containing 48,907 verified Emergency care physician businesses in United States with complete contact information, ratings, reviews, and location data.