BackgroundRelatively little is known about American medical student’s attitudes toward caring for the uninsured, limiting physician reimbursement and the role of cost-effectiveness data in medical decision-making. We assessed American medical student’s attitudes regarding these topics as well as demographic predictors of those attitudes, and compared them to practicing physicians.Methods and FindingsA survey instrument was explicitly designed to compare medical student attitudes with those previously reported by physicians. Between December 1st 2010 and March 27th 2011 survey responses were collected from more than 2% of the total estimated 2010–2011 US medical student population enrolled at 111 of 159 accredited US medical schools within the 50 United States (n = 2414 of possible 98197). Medical students were more likely to object to reimbursement cuts, and more likely to object to the use of cost effectiveness data in medical decision making than current physicians according to the literature. Specialty preference, political persuasion, and medical student debt were significant predictors of health policy attitudes. Medical students with anticipated debt in excess of $200,000 were significantly less willing to favor limiting reimbursement to improve patient access (OR: 0.73 [95% confidence interval (CI): 0.59–0.89]), and significantly more likely to object to using cost effectiveness data to limit treatments (OR 1.30, 95% CI 1.05–1.60) when compared to respondents with anticipated debt less than $200,000.ConclusionsWhen compared to physicians in the literature, future physicians may be less willing to favor cuts to physician reimbursements and may be more likely to object to the use of cost effectiveness data. Political orientation, specialty preference and anticipated debt may be important predictors of health policy attitudes among medical students. Early career medical providers with primary care ambitions and those who anticipate less debt may be more likely to support healthcare cost containment.

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.

The objective of this study was to evaluate the feasibility of providing a voluntary assignment list to third-year medical students in their pediatric clerkship. This is a retrospective single-center cross-sectional analysis of voluntary assignment completion during the 2019–2020 academic year. In total, 132 subjects who were part of our school’s traditional curriculum and rotated at the pediatric clerkship’s primary site and at our off-campus affiliate sites were included in this study. Subjects who were part of our integrated longitudinal curriculum were excluded.

The Colleges and Universities feature class/shapefile is composed of all Post Secondary Education facilities as defined by the Integrated Post Secondary Education System (IPEDS, http://nces.ed.gov/ipeds/), National Center for Education Statistics (NCES, https://nces.ed.gov/), US Department of Education for the 2018-2019 school year. Included are Doctoral/Research Universities, Masters Colleges and Universities, Baccalaureate Colleges, Associates Colleges, Theological seminaries, Medical Schools and other health care professions, Schools of engineering and technology, business and management, art, music, design, Law schools, Teachers colleges, Tribal colleges, and other specialized institutions. Overall, this data layer covers all 50 states, as well as Puerto Rico and other assorted U.S. territories. This feature class contains all MEDS/MEDS+ as approved by the National Geospatial-Intelligence Agency (NGA) Homeland Security Infrastructure Program (HSIP) Team. Complete field and attribute information is available in the ”Entities and Attributes” metadata section. Geographical coverage is depicted in the thumbnail above and detailed in the "Place Keyword" section of the metadata. This feature class does not have a relationship class but is related to Supplemental Colleges. Colleges and Universities that are not included in the NCES IPEDS data are added to the Supplemental Colleges feature class when found. This release includes the addition of 175 new records, the removal of 468 no longer reported by NCES, and modifications to the spatial location and/or attribution of 6682 records.

In this study, we analyze recommendations gathered from student evaluation team (SET) focus group meetings and analyzed whether these items were captured in open-ended comments within the online evaluations. Notes from 9 SET meetings for second-year medical student courses (academic year 2015-2016) taken by 2 second-year medical students (S.V.R. and A.C.) were analyzed. Feedback that included potential solutions was identified in a grounded theory-based approach and coded into the following 7 categories: issues related to specific teaching modalities used in courses, the overall course content, specific lectures (content and organization), sequencing of course events, administrative course components, exams, and study materials. Open-ended comments from online questionnaires were analyzed for the same 9 preclerkship courses for second-year medical students. In these online questionnaires, a 20-item Likert-style survey was followed by a request for comments related to the course. The survey was administered after the end of each course and 714 deidentified responses from second-year medical students were collected. The overall response rate of the online questionnaires was 66%. A total of 293 comments from the online questionnaires of the 9 preclerkship courses were analyzed. Online comments corresponding to SET meeting comments were identified.

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.

This dataset contains detailed demographic and health-related information for individuals alongside their corresponding medical insurance charges. It includes features such as age, sex, BMI, number of children, smoking status, region, and total insurance cost. This dataset is covered from the USA.

The dataset is ideal for building and evaluating machine learning models that predict healthcare costs based on personal and lifestyle factors.

Dataset Features

1. age: Age of the individual in years.

2. sex: Biological sex of the individual (male or female).

3. BMI: Body Mass Index — the numeric measure of body fat based on height and weight.

4. children: Number of dependent children covered by the insurance plan.

5. smoker: Smoking status of the individual (yes or no).

6. region: Geographic region of the individual within the United States (northeast, northwest, southeast, or southwest).

7. charges: Individual medical insurance cost billed by the insurer.

Distribution

• Format: CSV (Comma-Separated Values)

• Data Volume: Rows: 1,338 records

• 7 Columns: age, sex, BMI, children, smoker, region, charges

• File Size: Approximately 56 KB

Usage

This dataset is ideal for a variety of applications:

Medical Cost Prediction: Train regression models to estimate insurance charges based on demographic and lifestyle factors

Health Economics Research: Analyze how factors like smoking, BMI, and age impact healthcare costs.

Geographic Coverage:

• United States: the dataset includes individuals from four regions: northeast, northwest, southeast, and southwest.

• Time Range: The exact dates of data collection are not specified, but the data reflects typical insurance and demographic patterns observed in recent years.

• Demographics: Includes a diverse range of individuals: Age Range: From 18 to 64 years old Gender: Male and female Lifestyle Factors: Smoking status and BMI Dependents: Number of children covered by the insurance

License

CC0

Who Can Use It

• Data Scientists: For training machine learning models.
• Researchers: For academic or scientific studies.
• Businesses: For analysis, insights, or AI development.

Note: After May 3, 2024, this dataset will no longer be updated because hospitals are no longer required to report data on COVID-19 hospital admissions, hospital capacity, or occupancy data to HHS through CDC’s National Healthcare Safety Network (NHSN). The related CDC COVID Data Tracker site was revised or retired on May 10, 2023.

This dataset represents weekly COVID-19 hospitalization data and metrics aggregated to national, state/territory, and regional levels. COVID-19 hospitalization data are reported to CDC’s National Healthcare Safety Network, which monitors national and local trends in healthcare system stress, capacity, and community disease levels for approximately 6,000 hospitals in the United States. Data reported by hospitals to NHSN and included in this dataset represent aggregated counts and include metrics capturing information specific to COVID-19 hospital admissions, and inpatient and ICU bed capacity occupancy.

Reporting information:

• As of December 15, 2022, COVID-19 hospital data are required to be reported to NHSN, which monitors national and local trends in healthcare system stress, capacity, and community disease levels for approximately 6,000 hospitals in the United States. Data reported by hospitals to NHSN represent aggregated counts and include metrics capturing information specific to hospital capacity, occupancy, hospitalizations, and admissions. Prior to December 15, 2022, hospitals reported data directly to the U.S. Department of Health and Human Services (HHS) or via a state submission for collection in the HHS Unified Hospital Data Surveillance System (UHDSS).
• While CDC reviews these data for errors and corrects those found, some reporting errors might still exist within the data. To minimize errors and inconsistencies in data reported, CDC removes outliers before calculating the metrics. CDC and partners work with reporters to correct these errors and update the data in subsequent weeks.
• Many hospital subtypes, including acute care and critical access hospitals, as well as Veterans Administration, Defense Health Agency, and Indian Health Service hospitals, are included in the metric calculations provided in this report. Psychiatric, rehabilitation, and religious non-medical hospital types are excluded from calculations.
• Data are aggregated and displayed for hospitals with the same Centers for Medicare and Medicaid Services (CMS) Certification Number (CCN), which are assigned by CMS to counties based on the CMS Provider of Services files.
• Full details on COVID-19 hospital data reporting guidance can be found here: https://www.hhs.gov/sites/default/files/covid-19-faqs-hospitals-hospital-laboratory-acute-care-facility-data-reporting.pdf

Metric details:

• Time Period: timeseries data will update weekly on Mondays as soon as they are reviewed and verified, usually before 8 pm ET. Updates will occur the following day when reporting coincides with a federal holiday. Note: Weekly updates might be delayed due to delays in reporting. All data are provisional. Because these provisional counts are subject to change, including updates to data reported previously, adjustments can occur. Data may be updated since original publication due to delays in reporting (to account for data received after a given Thursday publication) or data quality corrections.
• New COVID-19 Hospital Admissions (count): Number of new admissions of patients with laboratory-confirmed COVID-19 in the previous week (including both adult and pediatric admissions) in the entire jurisdiction.
• New COVID-19 Hospital Admissions (7-Day Average): 7-day average of new admissions of patients with laboratory-confirmed COVID-19 in the previous week (including both adult and pediatric admissions) in the entire jurisdiction.
• Cumulative COVID-19 Hospital Admissions: Cumulative total number of admissions of patients with labo

Once-eliminated vaccine-preventable childhood diseases, such as measles, are resurging across the United States. Understanding the spatio-temporal trends in vaccine exemptions is crucial to targeting public health intervention to increase vaccine uptake and anticipating vulnerable populations as cases surge. However, prior available data on childhood disease vaccination is either on too rough a spatial scale for this spatially-heterogeneous issue, or is only available for small geographic regions, making general conclusions infeasible. Here, we have collated school vaccine exemption data across the United States and provide it at the county-level for all years available. We demonstrate the fine-scale spatial heterogeneity in vaccine exemption levels, and show that many counties may fall below the herd immunity threshold. We also show that vaccine exemptions increase over time in most states, and non-medical exemptions are highly prevalent where allowed. Our dataset also highlights the need for greater data sharing and standardized reporting across the United States.

Methods We collected data from all US states where school vaccine exemption information was freely available from the Department of Health website in any format. We were able to locate that data in 24 states. Within these states, the number of years available varied relatively widely, between 19 years in California and a single year in 6 states. The most represented year in our dataset was 2017 (corresponding to school year 2017-2018). Because the dataset was compiled in June-July 2019, we note that it is possible that additional data for recent years may not be available, or that data may have become available in additional states not included in our dataset.

The data format varied widely between states, and exemptions were reported either as a number of exemptions or as a percentage of the enrolled students. We have elected to use number of students rather than percentages, and have transformed data as needed. For most states included in our dataset, the data are provided at the county level. In several states (Arizona, Colorado, Illinois, Maine, Michigan, South Dakota, Tennessee, Vermont, Oregon, and Washington), the data was provided at the school level, which we aggregated to the county. Additional data processing was necessary in some cases. In Virginia, data was provided by school name, but county or city information was not included. We used a list of public and private schools to match school names with their respective county using fuzzy matching (with the fuzzywuzzy Python package) with an 80\% matching requirement. Our algorithm was unable to find a suitable match for between 3.8\% and 6.8\% of schools (depending on year), and these schools were not included in the final counts at the county level. Similarly, in Idaho, data at the school level included city information but county was not provided. We first matched city and county names, before aggregating the exemption data at the county level. Finally in New York state, exemptions were provided as percentages at the school level but enrollment information was not included. We obtained enrollment for public and private schools separately from the New York State Education Department, and used the school unique code to calculate exemption number from enrollment and exemption percentages. We then aggregated these numbers at the county level.

States reported data for exemptions based on varying definitions, so we selected data records based on data availability to make the data comparable cross states. We aimed to achieve parsimonious definitions of total medical exemptions, total non-medical exemptions, and total exemptions, which includes both types of exemptions. We define medical exemptions as reported total medical exemptions. In Florida, permanent medical exemptions were reported separately from temporary medical exemptions, so permanent medical exemptions was chosen to represent total medical exemptions. To define total non-medical exemptions, we considered the state law regarding non-medical exemptions and the data availability. If the state reported total aggregated non-medical exemptions, that was selected as total non-medical exemptions. If the state reported only religious exemptions and only allows religious exemptions, that was selected as total non-medical exemptions. If the state reported only religious exemptions, but also allows philosophical exemptions, that was considered missing data. If the state allows philosophical exemptions and only reports philosophical exemptions, that was selected as total non-medical exemptions, as the state may not differentiate religious from philosophical. If the state allows philosophical exemptions and reports both religious and philosophical exemptions separately, these values were summed for total non-medical exemptions. To define total exemptions, if the state reported a total exemptions value, this value was used. If the state did not report a total exemptions value, but reported values for total medical exemptions and total non-medical exemptions, as defined above, these were summed for total exemptions. If the state was missing either medical or non-medical exemptions, but reported the total number of students with completed vaccinations, the total exemptions was the difference between the number of students enrolled and the number of students completed.

We also considered disease-specific exemptions reports. If a state reported the number of exemptions for a vaccine specific to a given infection, that value was used. If the state did not report exemptions, but did provide the total number complete for that disease, the difference between the enrolled students and the completed students was used. For pertussis-specific vaccination, we used DTaP exemptions where available, and TDaP exemptions where DTaP was not available. For measles-specific vaccination, if separate reports were available for measles, mumps, and rubella, the value for measles was used. If measles was not available, then the mumps or rubella exemptions were used, if available.

The data in the figures is only data reported for kindergartens in states where kindergarten-specific data was available, or K-12 data in states where kindergarten-specific data was not reported. States reported age groups heterogeneously, and data by other age groups is available in the data file.

United States Health Insurance: Total Hospital & Medical Expenses: Medicare data was reported at 342.426 USD bn in 2023. This records an increase from the previous number of 294.298 USD bn for 2022. United States Health Insurance: Total Hospital & Medical Expenses: Medicare data is updated yearly, averaging 127.385 USD bn from Dec 2007 (Median) to 2023, with 17 observations. The data reached an all-time high of 342.426 USD bn in 2023 and a record low of 47.083 USD bn in 2007. United States Health Insurance: Total Hospital & Medical Expenses: Medicare data remains active status in CEIC and is reported by National Association of Insurance Commissioners. The data is categorized under Global Database’s United States – Table US.RG022: Health Insurance: Operations by Lines of Business.

United States Health Insurance: Total Hospital & Medical Expenses: Vision data was reported at 3.595 USD bn in 2023. This records an increase from the previous number of 3.274 USD bn for 2022. United States Health Insurance: Total Hospital & Medical Expenses: Vision data is updated yearly, averaging 1.832 USD bn from Dec 2007 (Median) to 2023, with 17 observations. The data reached an all-time high of 3.595 USD bn in 2023 and a record low of 997.000 USD mn in 2007. United States Health Insurance: Total Hospital & Medical Expenses: Vision data remains active status in CEIC and is reported by National Association of Insurance Commissioners. The data is categorized under Global Database’s United States – Table US.RG022: Health Insurance: Operations by Lines of Business.

The Textbooks Corpus in MedRAG

This HF dataset contains the chunked snippets from the Textbooks corpus used in MedRAG. It can be used for medical Retrieval-Augmented Generation (RAG).

  Dataset Details





  Dataset Descriptions

Textbooks is a collection of 18 widely used medical textbooks, which are important references for students taking the United States Medical Licensing Examination (USLME). In MedRAG, the textbooks are processed as chunks with no more than 1000… See the full description on the dataset page: https://huggingface.co/datasets/MedRAG/textbooks.

United States Health Insurance: Total Underwriting Deductions: Comprehensive Hospital & Medical data was reported at 267.145 USD bn in 2023. This records an increase from the previous number of 249.045 USD bn for 2022. United States Health Insurance: Total Underwriting Deductions: Comprehensive Hospital & Medical data is updated yearly, averaging 219.054 USD bn from Dec 2007 (Median) to 2023, with 17 observations. The data reached an all-time high of 267.145 USD bn in 2023 and a record low of 178.187 USD bn in 2007. United States Health Insurance: Total Underwriting Deductions: Comprehensive Hospital & Medical data remains active status in CEIC and is reported by National Association of Insurance Commissioners. The data is categorized under Global Database’s United States – Table US.RG022: Health Insurance: Operations by Lines of Business.

After May 3, 2024, this dataset and webpage will no longer be updated because hospitals are no longer required to report data on COVID-19 hospital admissions, and hospital capacity and occupancy data, to HHS through CDC’s National Healthcare Safety Network. Data voluntarily reported to NHSN after May 1, 2024, will be available starting May 10, 2024, at COVID Data Tracker Hospitalizations.

The following dataset provides facility-level data for hospital utilization aggregated on a weekly basis (Sunday to Saturday). These are derived from reports with facility-level granularity across two main sources: (1) HHS TeleTracking, and (2) reporting provided directly to HHS Protect by state/territorial health departments on behalf of their healthcare facilities.

The hospital population includes all hospitals registered with Centers for Medicare & Medicaid Services (CMS) as of June 1, 2020. It includes non-CMS hospitals that have reported since July 15, 2020. It does not include psychiatric, rehabilitation, Indian Health Service (IHS) facilities, U.S. Department of Veterans Affairs (VA) facilities, Defense Health Agency (DHA) facilities, and religious non-medical facilities.

For a given entry, the term “collection_week” signifies the start of the period that is aggregated. For example, a “collection_week” of 2020-11-15 means the average/sum/coverage of the elements captured from that given facility starting and including Sunday, November 15, 2020, and ending and including reports for Saturday, November 21, 2020.

Reported elements include an append of either “_coverage”, “_sum”, or “_avg”.

• A “_coverage” append denotes how many times the facility reported that element during that collection week.
• A “_sum” append denotes the sum of the reports provided for that facility for that element during that collection week.
• A “_avg” append is the average of the reports provided for that facility for that element during that collection week.

The file will be updated weekly. No statistical analysis is applied to impute non-response. For averages, calculations are based on the number of values collected for a given hospital in that collection week. Suppression is applied to the file for sums and averages less than four (4). In these cases, the field will be replaced with “-999,999”.

A story page was created to display both corrected and raw datasets and can be accessed at this link: https://healthdata.gov/stories/s/nhgk-5gpv

This data is preliminary and subject to change as more data become available. Data is available starting on July 31, 2020.

Sometimes, reports for a given facility will be provided to both HHS TeleTracking and HHS Protect. When this occurs, to ensure that there are not duplicate reports, deduplication is applied according to prioritization rules within HHS Protect.

For influenza fields listed in the file, the current HHS guidance marks these fields as optional. As a result, coverage of these elements are varied.

For recent updates to the dataset, scroll to the bottom of the dataset description.

On May 3, 2021, the following fields have been added to this data set.

• hhs_ids
• previous_day_admission_adult_covid_confirmed_7_day_coverage
• previous_day_admission_pediatric_covid_confirmed_7_day_coverage
• previous_day_admission_adult_covid_suspected_7_day_coverage
• previous_day_admission_pediatric_covid_suspected_7_day_coverage
• previous_week_personnel_covid_vaccinated_doses_administered_7_day_sum
• total_personnel_covid_vaccinated_doses_none_7_day_sum
• total_personnel_covid_vaccinated_doses_one_7_day_sum
• total_personnel_covid_vaccinated_doses_all_7_day_sum
• previous_week_patients_covid_vaccinated_doses_one_7_day_sum
• previous_week_patients_covid_vaccinated_doses_all_7_day_sum

On May 8, 2021, this data set has been converted to a corrected data set. The corrections applied to this data set are to smooth out data anomalies caused by keyed in data errors. To help determine which records have had corrections made to it. An additional Boolean field called is_corrected has been added.

On May 13, 2021 Changed vaccination fields from sum to max or min fields. This reflects the maximum or minimum number reported for that metric in a given week.

On June 7, 2021 Changed vaccination fields from max or min fields to Wednesday reported only. This reflects that the number reported for that metric is only reported on Wednesdays in a given week.

On September 20, 2021, the following has been updated: The use of analytic dataset as a source.

On January 19, 2022, the following fields have been added to this dataset:

• inpatient_beds_used_covid_7_day_avg
• inpatient_beds_used_covid_7_day_sum
• inpatient_beds_used_covid_7_day_coverage

On April 28, 2022, the following pediatric fields have been added to this dataset:

• all_pediatric_inpatient_bed_occupied_7_day_avg
• all_pediatric_inpatient_bed_occupied_7_day_coverage
• all_pediatric_inpatient_bed_occupied_7_day_sum
• all_pediatric_inpatient_beds_7_day_avg
• all_pediatric_inpatient_beds_7_day_coverage
• all_pediatric_inpatient_beds_7_day_sum
• previous_day_admission_pediatric_covid_confirmed_0_4_7_day_sum
• previous_day_admission_pediatric_covid_confirmed_12_17_7_day_sum
• previous_day_admission_pediatric_covid_confirmed_5_11_7_day_sum
• previous_day_admission_pediatric_covid_confirmed_unknown_7_day_sum
• staffed_icu_pediatric_patients_confirmed_covid_7_day_avg
• staffed_icu_pediatric_patients_confirmed_covid_7_day_coverage
• staffed_icu_pediatric_patients_confirmed_covid_7_day_sum
• staffed_pediatric_icu_bed_occupancy_7_day_avg
• staffed_pediatric_icu_bed_occupancy_7_day_coverage
• staffed_pediatric_icu_bed_occupancy_7_day_sum
• total_staffed_pediatric_icu_beds_7_day_avg
• total_staffed_pediatric_icu_beds_7_day_coverage
• total_staffed_pediatric_icu_beds_7_day_sum

On October 24, 2022, the data includes more analytical calculations in efforts to provide a cleaner dataset. For a raw version of this dataset, please follow this link: https://healthdata.gov/Hospital/COVID-19-Reported-Patient-Impact-and-Hospital-Capa/uqq2-txqb

Due to changes in reporting requirements, after June 19, 2023, a collection week is defined as starting on a Sunday and ending on the next Saturday.

The Health Services Training Report (HST) Database tracks the overall number of Personnel and Accounting Integrated Data Systems (PAID) and Without Compensation (WOC) Trainee positions by the cooperating academic institutions for all medical center approved health services programs. Information in the database comes from all Veterans Affairs Medical Centers (VAMCs) who have Office of Academic Affiliations (OAA) approved HST programs. Worksheets and memos are distributed to participating VAMCs by the OAA annually. VAMC personnel enter the information electronically into the database located at the OAA Support Center (OAASC) in St. Louis, Missouri. The main user of this database is the OAA.

About this dataset

Context

The dataset tabulates the Medical Lake population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Medical Lake across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of Medical Lake was 4,957, a 1.27% decrease year-by-year from 2022. Previously, in 2022, Medical Lake population was 5,021, an increase of 2.95% compared to a population of 4,877 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Medical Lake increased by 1,086. In this period, the peak population was 5,064 in the year 2010. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Medical Lake is shown in this column.
• Year on Year Change: This column displays the change in Medical Lake population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Medical Lake Population by Year. You can refer the same here

The dataset represents Emergency Medical Services (EMS) locations in the United States and its territories. EMS Stations are part of the Fire Stations / EMS Stations HSIP Freedom sub-layer, which in turn is part of the Emergency Services and Continuity of Government Sector, which is itself a part of the Critical Infrastructure Category. The EMS stations dataset consists of any location where emergency medical service (EMS) personnel are stationed or based out of, or where equipment that such personnel use in carrying out their jobs is stored for ready use. Ambulance services are included even if they only provide transportation services, but not if they are located at, and operated by, a hospital. If an independent ambulance service or EMS provider happens to be collocated with a hospital, it will be included in this dataset. The dataset includes both private and governmental entities. A concerted effort was made to include all emergency medical service locations in the United States and its territories. This dataset is comprised completely of license free data. 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. At the request of NGA, text fields in this dataset have been set to all upper case to facilitate consistent database engine search results. At the request of NGA, 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 12/29/2004 and the newest record dates from 01/11/2010.This dataset represents the EMS stations of any location where emergency medical service (EMS) personnel are stationed or based out of, or where equipment that such personnel use in carrying out their jobs is stored for ready use. Homeland Security Use Cases: Use cases describe how the data may be used and help to define and clarify requirements. 1. An assessment of whether or not the total emergency medical services capability in a given area is adequate. 2. A list of resources to draw upon by surrounding areas when local resources have temporarily been overwhelmed by a disaster - route analysis can determine those entities that are able to respond the quickest. 3. A resource for Emergency Management planning purposes. 4. A resource for catastrophe response to aid in the retrieval of equipment by outside responders in order to deal with the disaster. 5. A resource for situational awareness planning and response for Federal Government events.

Multiple choice question answering based on the United States Medical License Exams (USMLE). The dataset is collected from the professional medical board exams. It covers three languages: English, simplified Chinese, and traditional Chinese, and contains 12,723, 34,251, and 14,123 questions for the three languages, respectively.

Graph and download economic data for Expenditures: Medical Services by Education: Total, Less Than College Graduate (CXUMEDSERVSLB1302M) from 1996 to 2012 about no college, medical, expenditures, education, services, and USA.