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.

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

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.

[IMPORTANT NOTE: Sample file posted on Datarade is not the complete dataset, as Datarade permits only a single CSV file. Visit https://www.careprecise.com/healthcare-provider-data-sample.htm for more complete samples.] The APD is the only physician database with this deep pool of data, available for immediate download in Microsoft Access format and CSV files, for use with spreadsheet, database or CRM software on Windows PC, Mac or Linux.:

100% of HIPAA-covered U.S. MD and DO physicians Their practice groups, with group size Independent practice indicator Sole proprietor indicator Their hospital affiliations All reported specialties, including the primary Years in practice Medical school attended Phone and fax numbers Rural/Urban practice indicator Practice and Mailing addresses Gender Current LEIE sanctions License Medicare practice PAC ID C-suite and director-level contacts for physician groups and hospitals Exclusive CoLoCode™ linkage between physicians practicing together and their group NPI records

Comprehensive dataset containing 207,879 verified Family practice physician businesses in United States with complete contact information, ratings, reviews, and location data.

Forecast: Density of Professionally Active Physicians in the US 2024 - 2028 Discover more data with ReportLinker!

[IMPORTANT NOTE: Sample file posted on Datarade is not the complete dataset, as Datarade permits only a single CSV file. Visit https://www.careprecise.com/healthcare-provider-data-sample.htm for more complete samples.] Updated every month, CarePrecise developed the AHD to provide a comprehensive database of U.S. hospital information. Extracted from the CarePrecise master provider database with information all of the 6.3 million HIPAA-covered US healthcare providers and additional sources, the Authoritative Hospital Database (AHD) contains records for all HIPAA-covered hospitals. In this database of hospitals we include bed counts, patient satisfaction data, hospital system ownership, hospital charges and cases by Zip Code®, and more. Most records include a cabinet-level or director-level contact. A PlaceKey is provided where available.

The AHD includes bed counts for 95% of hospitals, full contact information on 85%, and fax numbers for 62%. We include detailed patient satisfaction data, employee counts, and medical procedure volumes.

The AHD integrates directly with our extended provider data product to bring you the physicians and practice groups affiliated with the hospitals. This combination of data is the only commercially available hospital dataset of this depth.

NEW: Hospital NPI to CCN Rollup A CarePrecise Exclusive. Using advanced record-linkage technology, the AHD now includes a new file that makes it possible to mine the vast hospital information available in the National Provider Identifier registry database. Hospitals may have dozens of NPI records, each with its own information about a unit, listing facility type and/or medical specialties practiced, as well as separate contact names. To wield the power of this new feature, you'll need the CarePrecise Master Bundle, which contains all of the publicly available NPI registry data. These data are available in other CarePrecise data products.

Counts are approximate due to ongoing updates. Please review the current AHD information here: https://www.careprecise.com/detail_authoritative_hospital_database.htm

The AHD is sold as-is and no warranty is offered regarding accuracy, timeliness, completeness, or fitness for any purpose.

United States US: Physicians: per 1000 People data was reported at 2.568 Ratio in 2014. This records an increase from the previous number of 2.554 Ratio for 2013. United States US: Physicians: per 1000 People data is updated yearly, averaging 1.900 Ratio from Dec 1960 (Median) to 2014, with 39 observations. The data reached an all-time high of 2.704 Ratio in 2004 and a record low of 1.100 Ratio in 1960. United States US: Physicians: per 1000 People data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s USA – Table US.World Bank: Health Statistics. Physicians include generalist and specialist medical practitioners.; ; World Health Organization's Global Health Workforce Statistics, OECD, supplemented by country data.; Weighted average;

This study was conducted in order to gather detailed specialty-specific data for most of the physician population of the United States. Each member of sample groups of physicians in each of 24 specialty areas completed numerically coded "log diaries" (self-enumerated questionnaires) over the course of one week during the survey data gathering period of 1976-1978. With the information obtained from the log diaries, three data files containing detailed information on the activities of the physicians surveyed and on the characteristics of their practices were prepared for each of the 24 specialty areas: allergy, cardiology, dermatology, emergency medicine, endocrinology, family practice, gastroenterology, general practice, general surgery, hematology, infectious diseases, internal medicine, nephrology, neurological surgery, neurology, obstetrics/gynecology, oncology, ophthalmology, orthopaedic surgery, otorhinolaryngology, pediatrics, psychiatry, pulmonary diseases, and rheumatology. As a result, there are 71 discrete datafiles in this dataset (emergency medicine has only two files). Parts 1-24 contain detailed information about each physician's medical or surgical practice, e.g., specialty, major professional activity, board certifications, type of practice, physician's opinion concerning distribution of specialties in the community, number of hours per week worked and in what capacity, and type of employees in physician's practice and number of hours worked. Parts 28-48 contain data on each patient the physician saw in person during the week in which he or she kept the log diary. Parts 49-71 hold the data derived from each encounter the physician had via telephone with a patient during the same period. The data in the latter two groups of files contain patient age, sex, problem focus, role, source, and diagnoses.

This study comprises the second round of the physician survey component of the Community Tracking Study (CTS) sponsored by the Robert Wood Johnson Foundation. The CTS is a national study designed to track changes in the American health care system and the effects of the changes on care delivery and on individuals. Central to the design of the CTS is its community focus. Sixty sites (51 metropolitan areas and 9 nonmetropolitan areas) were randomly selected to form the core of the CTS and to be representative of the nation as a whole. As in the first round of the physician survey (COMMUNITY TRACKING STUDY PHYSICIAN SURVEY, 1996-1997: UNITED STATES), the second round was administered to physicians in the 60 CTS sites and to a supplemental national sample of physicians. The survey instrument collected information on physician supply and specialty distribution, practice arrangements and physician ownership of practices, physician time allocation, sources of practice revenue, level and determinants of physician compensation, provision of charity care, career satisfaction, physicians' perceptions of their ability to deliver care, views on care management strategies, and various other aspects of physicians' practice of medicine. In addition, primary care physicians (PCPs) were asked to recommend courses of action in response to some vignettes of clinical presentations for which there was no prescribed method of treatment. Dataset 3, the Site and County Crosswalk Data File, identifies the counties that constitute each CTS site. Dataset 4, the Physician Survey Summary File, contains site-level estimates and standard errors of the estimates for selected physician characteristics, e.g., the percentage of physicians who were foreign medical school graduates, the mean age of physicians, and the mean percentage of patient care practice revenue from Medicaid.

Forecast: Density of Physicians Licensed to Practice in the US 2024 - 2028 Discover more data with ReportLinker!

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.

Objective: To assess US physicians’ attitudes towards using shared decision-making (SDM) to achieve cost containment. Design: Cross-sectional mailed survey. Setting: US medical practice. Participants: 3897 physicians were randomly selected from the AMA Physician Masterfile. Of these, 2556 completed the survey. Main outcome measures: Level of enthusiasm for “Promoting better conversations with patients as a means of lowering healthcare costs†; degree of agreement with “Decision support tools that show costs would be helpful in my practice†and agreement with “should promoting SDM be legislated to control overall healthcare costs†. Results: Of 2556 respondents (response rate (RR) 65%), two-thirds (67%) were ‘very enthusiastic’ about promoting SDM as a means of reducing healthcare costs. Most (70%) agreed decision support tools that show costs would be helpful in their practice, but only 24% agreed with legislating SDM to control costs. Compared with physicians with billing-only compensati...

Despite decades of low utilization, telemedicine adoption expanded at an unprecedented rate during the COVID-19 pandemic. This study examined quantitative and qualitative data provided by a national online sample of 228 practicing physicians (64% were women, and 75% were White) to identify facilitators and barriers to the adoption of telemedicine in the United States (U.S.) at the beginning of the COVID-19 pandemic. Logistic regressions were used to predict the most frequently endorsed (20% or more) barriers and facilitators based on participant demographics and practice characteristics. The top five reported barriers were: lack of patient access to technology (77.6%), insufficient insurance reimbursement (53.5%), diminished doctor-patient relationship (46.9%), inadequate video/audio technology (46.1%), and diminished quality of delivered care (42.1%). The top five reported facilitators were: better access to care (75.4%), increased safety (70.6%), efficient use of time (60.5%), lower cost for patients (43%), and effectiveness (28.9%). Physicians’ demographic and practice setting characteristics significantly predicted their endorsement of telemedicine barriers and facilitators. Older physicians were less likely to endorse inefficient use of time (p < 0.001) and potential for medical errors (p = 0.034) as barriers to telemedicine use compared to younger physicians. Physicians working in a medical center were more likely to endorse inadequate video/audio technology (p = 0.037) and lack of patient access to technology (p = 0.035) as a barrier and more likely to endorse lower cost for patients as a facilitator (p = 0.041) than providers working in other settings. Male physicians were more likely to endorse inefficient use of time as a barrier (p = 0.007) than female physicians, and White physicians were less likely to endorse lower costs for patients as a facilitator (p = 0.012) than physicians of color. These findings provide important context for future implementation strategies for healthcare systems attempting to increase telemedicine utilization.

A listing of all registered prescriptive delegation between physicians and Physician Assistants or Advanced Practice Registered Nurses. Additional information can be found at https://www.tmb.state.tx.us/page/look-up-a-license.

The National Electronic Health Records Survey (NEHRS) is an annual survey of non-federally employed, office-based physicians practicing in the United States (excluding those in the specialties of anesthesiology, radiology, and pathology). NEHRS began in 2008 and was originally designed as an annual mail supplement to the National Ambulatory Medical Care Survey (NAMCS). Since 2012, NEHRS has been administered as a survey independent of NAMCS. Data from NEHRS can be used to produce state and national estimates of EHR adoption and capabilities, burden associated with EHRs, and progress physicians have made towards meeting the policy goals of the HITECH Act. In more recent years, survey questions have also asked about Promoting Interoperability programs, sponsored by the Centers for Medicare and Medicaid Services (CMS).

BackgroundHealth literacy experts and the American Medical Association have developed recommended communication techniques for healthcare providers given that effective communication has been shown to greatly improve health outcomes. The purpose of this study was to determine the number and types of communication techniques routinely used by Maryland physicians.MethodsIn 2010, a 30-item survey was mailed to a random sample of 1,472 Maryland family physicians and pediatricians, with 294 surveys being returned and usable. The survey contained questions about provider and practice characteristics, and 17 items related to communication techniques, including seven basic communication techniques. Physicians’ use of recommended communication techniques was analyzed using descriptive statistics, analysis of variance, and ordinary least squares regression.ResultsFamily physicians routinely used an average of 6.6 of the 17 total techniques and 3.3 of the seven basic techniques, whereas pediatricians routinely used 6.4 and 3.2 techniques, respectively. The use of simple language was the only technique that nearly all physicians routinely utilized (Family physicians, 91%; Pediatricians, 93%). Physicians who had taken a communications course used significantly more techniques than those who had not. Physicians with a low percentage of patients on Medicaid were significantly less likely to use the recommended communication techniques compared to those providers who had high proportion of their patient population on Medicaid.ConclusionsOverall, the use of recommended communication techniques was low. Additionally, many physicians were unsure of the effectiveness of several of the recommended techniques, which could suggest that physicians are unaware of valuable skills that could enhance their communication. The findings of this study suggest that communications training should be given a higher priority in the medical training process in the United States.

To explore the professional knowledge, experience, goals, and self-assessed competencies of 5th-year medical students, AMD alumni, and other practicing physicians in and around Liberia, U.S. academic faculty developed a survey instrument, which was reviewed and revised by AMD faculty and administrators. Survey responses were offered on a five-point Likert scale. A separate section of the survey presented the current AMD curriculum, asking AMD alumni to assess its relevance to training future physicians for the practice of medicine and to determine whether each course had been delivered in a manner that AMD alumni felt was either adequate or inadequate. This survey was developed to capture a wide range of data related to physician training and pipeline development in Liberia. Results relevant to undergraduate medical education and curriculum revision at AMD are presented here. A research team comprised of Liberian and U.S. faculty and staff administered the survey on paper to 124 re...

Return-On-Total-Capital Time Series for Cross Country Healthcare Inc. Cross Country Healthcare, Inc. provides talent management services for healthcare clients in the United States. The company operates in two segments, Nurse and Allied Staffing and Physician Staffing. The Nurse and Allied Staffing segment provides traditional staffing, recruiting, and value-added total talent solutions, including temporary and permanent placement of travel and local nurse, and allied professionals, as well as healthcare leaders within nursing, allied, human resources, and finance; vendor neutral and managed services programs; and education healthcare, in-home care, and outsourcing services. This segment offers staffing solutions for registered nurses, licensed practical nurses, certified nurse assistants, practitioners, pharmacists, and other allied professionals on per diem and short-term assignments; and clinical and non-clinical professionals on long-term assignments, as well as workforce solutions, including MSP, VMS, RPO, project management, and other outsourcing and consultative services. It also provides retained search services for healthcare professionals, as well as contingent search and recruitment process outsourcing services. The company serves public and private acute care and non-acute care hospitals, government facilities, local and national healthcare plans, managed care providers, public and charter schools, outpatient clinics, ambulatory care facilities, correctional facilities, PACE programs, physician practice groups, and other healthcare providers. The Physician Staffing segment provides physicians in various specialties, certified registered nurse anesthetists, nurse practitioners, and physician assistants as independent contractors on temporary assignments. This segment serves various healthcare facilities, such as acute and non-acute care facilities, medical group practices, government facilities, and managed care organizations. The company was founded in 1986 and is headquartered in Boca Raton, Florida.

Operating-Income Time Series for Cross Country Healthcare Inc. Cross Country Healthcare, Inc. provides talent management services for healthcare clients in the United States. The company operates in two segments, Nurse and Allied Staffing and Physician Staffing. The Nurse and Allied Staffing segment provides traditional staffing, recruiting, and value-added total talent solutions, including temporary and permanent placement of travel and local nurse, and allied professionals, as well as healthcare leaders within nursing, allied, human resources, and finance; vendor neutral and managed services programs; and education healthcare, in-home care, and outsourcing services. This segment offers staffing solutions for registered nurses, licensed practical nurses, certified nurse assistants, practitioners, pharmacists, and other allied professionals on per diem and short-term assignments; and clinical and non-clinical professionals on long-term assignments, as well as workforce solutions, including MSP, VMS, RPO, project management, and other outsourcing and consultative services. It also provides retained search services for healthcare professionals, as well as contingent search and recruitment process outsourcing services. The company serves public and private acute care and non-acute care hospitals, government facilities, local and national healthcare plans, managed care providers, public and charter schools, outpatient clinics, ambulatory care facilities, correctional facilities, PACE programs, physician practice groups, and other healthcare providers. The Physician Staffing segment provides physicians in various specialties, certified registered nurse anesthetists, nurse practitioners, and physician assistants as independent contractors on temporary assignments. This segment serves various healthcare facilities, such as acute and non-acute care facilities, medical group practices, government facilities, and managed care organizations. The company was founded in 1986 and is headquartered in Boca Raton, Florida.