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.

USA doctor profiles dataset. Name, medical specialty and phone numbers are the essentials fields. Dowload complete dataset from crawl feeds. Last extraction on 2 Oct 2021

This dataset provides values for MEDICAL DOCTORS reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

Comprehensive dataset containing 4,984 verified Occupational medical physician 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

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.

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Hyper target within and outside your desired markets with GDPR and CAN-SPAM compliant healthcare email lists that get integrated into your CRM and ESPs. Balance out the sales and marketing efforts by aligning goals using email lists from the healthcare industry. Build strong business relationships with potential clients through personalized campaigns. Call Infotanks Media for a free consultation. Explore new geographies and target markets with a focused approach using healthcare email lists. Align your sales teams and marketing teams through personalized email marketing campaigns to ensure they accomplish business goals together. Add value and grow revenue to take your business to the next level of success. Double up your business and revenue growth with email lists of healthcare professionals. Send segmented campaigns to monitor behaviors and understand the purchasing habits of your potential clients. Send follow up nurturing email marketing campaigns to attract your potential clients to become converted customers. Close deals sooner with detailed information of your prospects using the healthcare email list from Infotanks Media. Reach healthcare professionals on their preferred platform of communication with the email list of healthcare professionals. Identify, capture, explore, and grow in your target markets anywhere in the world with a fully verified, validated, and compliant email database of healthcare professionals. Move beyond the traditional approach and automate sales cycles with buying triggers sent through email marketing campaigns. Use the healthcare email list from Infotanks Media to engage with your targeted potential clients and get them to respond. Increase email marketing campaign response rate to convert better! Reach out to Infotanks Media to customize your healthcare email lists. Call today!"

Since 2008, the Centers for Disease Control and Prevention's National Center for Health Statistics has fielded a mail survey of office-based physicians, the National Electronic Health Records Survey (NEHRS). ONC helps fund this supplement to track office-based physician adoption and the use of EHRs for health information exchange and patient engagement. Starting in 2010, the NEHRS's sample size was increased to allow for state-level estimates. The data set estimates each measure nationally and individually for each state and the District of Columbia beginning in 2010, unless otherwise noted.

About this Dataset

Carefully curated list of “Surgeon / Doctor / Dentist” in USA. We strive to keep our customers satisfied, so they no longer have to worry about finding quality leads. Which is why we offer a 100% data guarantee. If there is any information missing or incorrect we will replace it for you. Contact us immediately. What you will find below: - Contact Name - Company - Email - Contact no. & more

Category

Health & Medicine

Keywords

USA Surgeon,USA Doctors,US Surgeons,US Doctors,US Dentist

Row Count

8768

Price

$4999.00

DR-DRR/Medical_Customer_care dataset hosted on Hugging Face and contributed by the HF Datasets community

Dataset

This dataset was created by huzefakhan

Contents

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.

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.

The Study of Women's Health Across the Nation (SWAN), is a multi-site longitudinal, epidemiologic study designed to examine the health of women during their middle years. The study examines the physical, biological, psychological, and social changes during this transitional period. The goal of SWAN's research is to help scientists, health care providers, and women learn how mid-life experiences affect health and quality of life during aging. The data include questions about doctor visits, medical conditions, medications, treatments, medical procedures, relationships, smoking, and menopause related information such as age at pre-, peri- and post-menopause, self-attitudes, feelings, and common physical problems associated with menopause.The study is co-sponsored by the National Institute on Aging (NIA), the National Institute of Nursing Research (NINR), the National Institutes of Health (NIH), and the NIH Office of Research on Women's Health. The study began in 1994. Between 1996 and 1997, 3,302 participants joined SWAN through 7 designated research centers. The research centers are located in the following communities: Detroit, MI; Boston, MA; Chicago, IL; Oakland and Los Angeles, CA; Newark, NJ; and Pittsburgh, PA. SWAN participants represent five racial/ethnic groups and a variety of backgrounds and cultures. This is the next phase of data collection after the original collection of the screening data (ICPSR 4368).

Dataset Card for Prescription-based prediction

  Dataset Summary

This is the dataset used in the Roam blog post Prescription-based prediction. It is derived from a variety of US open health datasets, but the bulk of the data points come from the Medicare Part D dataset and the National Provider Identifier dataset. The prescription vector for each doctor tells a rich story about that doctor's attributes, including specialty, gender, age, and region. There are 239,930 doctors… See the full description on the dataset page: https://huggingface.co/datasets/nateraw/prescriptionbasedprediction.

The MedDialog dataset (English) contains conversations (in English) between doctors and patients.It has 0.26 million dialogues. The data is continuously growing and more dialogues will be added. The raw dialogues are from healthcaremagic.com and icliniq.com. All copyrights of the data belong to healthcaremagic.com and icliniq.com.

This data package shows the Physician and Other Healthcare Information like Business Wire Healthcare Press Release Distribution List, Health Professional Shortage Area Mental and Dental Health, Physician Evaluation and Management Medicare Service Events and Physicians Malpractice Payments.

The Medicare Physician & Other Practitioners by Provider and Service dataset provides information on use, payments, and submitted charges organized by National Provider Identifier (NPI), Healthcare Common Procedure Coding System (HCPCS) code, and place of service.

Note: This full dataset contains more records than most spreadsheet programs can handle, which will result in an incomplete load of data. Use of a database or statistical software is required.