******CONTEXT******: The data is about hospital patient data, a collection of data from the patient entering the hospital until his exit.

******CONTENT******: Date : The day patient visited Medication Revenue : the revenue of the medication Lab Cost : Lab cost paid by the patient Consultation Revenue : Revenue of the consultation Doctor Type : The type of doctor who treats the patient Financial Class : Patient financial Class Patient Type : (OUTPATIENT) Entry Time : Entered the (OUTPATIENT) & Hospital Post-Consultation Time : when the doctor tells the patients to enter the clinic room Completion Time : when the patients exit the clinic room or the building Patient ID : The unique Identity document

******Requirements******: Dose the patient type affect the waiting time? Is there a specific type of patient waiting a long time? Are we too busy? Do we have staffing issues? How much patients wait before the doctor can see them? What type of staff do we need or where do we need them? What days of the week are affected? How can we fix it?

Please up-vote if you find this dataset helpful!🖤!

The Open Database of Healthcare Facilities (ODHF) is a listing of health facilities across Canada. Facilities are classified into one of three types: ambulatory health care services, hospitals, and nursing and residential care facilities. The listing contains the names, addresses, and geo coordinates of facilities, as well as the facility type as assigned in the data source. The ODHF is based on data from authoritative sources that include among them all levels of government and public health and professional healthcare bodies. The ODHF is released as open data under the Open Government License - Canada and provided as a zipped comma-separated values (.csv) file.

The dataset contains information from a cohort of 799 patients admitted in the hospital for COVID-19, characterized with sociodemographic and clinical data. Retrospectively, from November 2020 to January 2021, data was collected from the medical records of all hospital admissions that occurred from March 1st, 2020, to December 31st, 2020. The analysis of these data can contribute to the definition of the clinical and sociodemographic profile of patients with COVID-19. Understanding these data can contribute to elucidating the sociodemographic profile, clinical variables and health conditions of patients hospitalized by COVID-19. To this end, this database contains a wide range of variables, such as: Month of hospitalization Sex Age group Ethnicity Marital status Paid work Admission to clinical ward Hospitalization in the Intensive Care Unit (ICU) COVID-19 diagnosis Number of times hospitalized by COVID-19 Hospitalization time in days Risk Classification Protocol Data is presented as a single Excel XLSX file: dataset.xlsx of clinical and sociodemographic characteristics of hospital admissions by COVID-19: retrospective cohort of patients in two hospitals in the Southern of Brazil. Researchers interested in studying the data related to patients affected by COVID-19 can extensively explore the variables described here. Approved by the Research Ethics Committee (No. 4.323.917/2020) of the Federal University of Santa Catarina.

The Healthcare Cost and Utilization Project (HCUP) Kids' Inpatient Database (KID) is the largest publicly available all-payer pediatric inpatient care database in the United States, containing data from two to three million hospital stays each year. Its large sample size is ideal for developing national and regional estimates and enables analyses of rare conditions, such as congenital anomalies, as well as uncommon treatments, such as organ transplantation. Developed through a Federal-State-Industry partnership sponsored by the Agency for Healthcare Research and Quality, HCUP data inform decision making at the national, State, and community levels. The KID is a sample of pediatric discharges from 4,000 U.S. hospitals in the HCUP State Inpatient Databases yielding approximately two to three million unweighted hospital discharges for newborns, children, and adolescents per year. About 10 percent of normal newborns and 80 percent of other neonatal and pediatric stays are selected from each hospital that is sampled for patients younger than 21 years of age. The KID contains clinical and resource use information included in a typical discharge abstract, with safeguards to protect the privacy of individual patients, physicians, and hospitals (as required by data sources). It includes discharge status, diagnoses, procedures, patient demographics (e.g., sex, age), expected source of primary payment (e.g., Medicare, Medicaid, private insurance, self-pay, and other insurance types), and hospital charges and cost. Restricted access data files are available with a data use agreement and brief online security training.

MIMIC-III is a large, freely-available database comprising deidentified health-related data associated with over forty thousand patients who stayed in critical care units of the Beth Israel Deaconess Medical Center between 2001 and 2012. The database includes information such as demographics, vital sign measurements made at the bedside (~1 data point per hour), laboratory test results, procedures, medications, caregiver notes, imaging reports, and mortality (including post-hospital discharge).MIMIC supports a diverse range of analytic studies spanning epidemiology, clinical decision-rule improvement, and electronic tool development. It is notable for three factors: it is freely available to researchers worldwide; it encompasses a diverse and very large population of ICU patients; and it contains highly granular data, including vital signs, laboratory results, and medications.

[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

The complete data set of annual utilization data reported by hospitals contains basic licensing information including bed classifications; patient demographics including occupancy rates, the number of discharges and patient days by bed classification, and the number of live births; as well as information on the type of services provided including the number of surgical operating rooms, number of surgeries performed (both inpatient and outpatient), the number of cardiovascular procedures performed, and licensed emergency medical services provided.

The Healthcare Cost and Utilization Project (HCUP) Nationwide Readmissions Database (NRD) is a unique and powerful database designed to support various types of analyses of national readmission rates for all payers and the uninsured. The NRD includes discharges for patients with and without repeat hospital visits in a year and those who have died in the hospital. Repeat stays may or may not be related. The criteria to determine the relationship between hospital admissions is left to the analyst using the NRD. This database addresses a large gap in health care data - the lack of nationally representative information on hospital readmissions for all ages. Outcomes of interest include national readmission rates, reasons for returning to the hospital for care, and the hospital costs for discharges with and without readmissions. Unweighted, the NRD contains data from approximately 18 million discharges each year. Weighted, it estimates roughly 35 million discharges. Developed through a Federal-State-Industry partnership sponsored by the Agency for Healthcare Research and Quality, HCUP data inform decision making at the national, State, and community levels. The NRD is drawn from HCUP State Inpatient Databases (SID) containing verified patient linkage numbers that can be used to track a person across hospitals within a State, while adhering to strict privacy guidelines. The NRD is not designed to support regional, State-, or hospital-specific readmission analyses. The NRD contains more than 100 clinical and non-clinical data elements provided in a hospital discharge abstract. Data elements include but are not limited to: diagnoses, procedures, patient demographics (e.g., sex, age), expected source of payer, regardless of expected payer, including but not limited to Medicare, Medicaid, private insurance, self-pay, or those billed as ‘no charge, discharge month, quarter, and year, total charges, length of stay, and data elements essential to readmission analyses. The NIS excludes data elements that could directly or indirectly identify individuals. Restricted access data files are available with a data use agreement and brief online security training.

Comprehensive dataset containing 4 verified Heart hospital businesses in West Virginia, United States with complete contact information, ratings, reviews, and location data.

This dataset contains Hospital Chargemasters with prices in effect as of June 1 of their reporting year. Chargemasters consists of a list of average charges for 25 common outpatient procedures, and the estimated percentage change in gross revenue due to price changes each July 1.

For more on HCAI Chargemaster Data.

All credit for variables in AHRQ_included_variables.csv is attributed to

• AHRF: Area Health Resources Files (AHRF) 2009-2018. US Department of Health and
  Human Services, Health Resources and Services Administration, Bureau of Health Workforce,
  Rockville, MD.
• AHRQ SDOH Database (v1), from American Hospital Association Annual Survey Data,
  https://www.ahadata.com/,
• AHRQ SDOH Database (v1), from Health Resources and Services Administration. (2021, Nov 1).
  Area Health Resources Files [Database]. U.S. Department of Health and Human Services, Health
  Resources and Services Administration, Bureau of Health Workforce.
  https://data.hrsa.gov/data/download.
• AHRQ SDOH Database (v1), from the Foundation for AIDS Research. Opioid & Health Indicators
  Database. https://opioid.amfar.org/about/download
• AHRQ SDOH Database (v1), from United States Census Bureau. (n.d.). County Adjacency File
  [Data file]. https://www.census.gov/geographies/reference-files/2010/geo/countyadjacency.html.
• AHRQ SDOH Database (v1), from United States Census Bureau. County Business Patterns [Data
  series]. U.S. Department of Commerce, Bureau of the Census, Data User Services Division.
  https://www.census.gov/programs-surveys/cbp/data/tables.html.
• AHRQ SDOH Database (v1), from Local Education Agency (School District) Finance Survey (F-33)
  Data, v.1a—Provisional Common Core of Data (CCD) - CCD Data Files (ed.gov). Retrieved March
  2021 from https://nces.ed.gov/ccd/files.asp#Fiscal:1,LevelId:5,Page:1.
• AHRQ SDOH Database (v1), from Centers for Disease Control and Prevention, Division for Heart
  Disease and Stroke Prevention, Interactive Atlas of Heart Disease and Stroke,
  http://nccd.cdc.gov/DHDSPAtlas.
• AHRQ SDOH Database (v1), from Centers for Disease Control and Prevention. NCHHSTP
  AtlasPlus. Updated 2017. https://www.cdc.gov/nchhstp/atlas/index.htm.
• AHRQ SDOH Database (v1), from PLACES: Local Data for Better Health. Center for Disease
  Control and Prevention (2020 and 2021 Release). Retrieved January 2022 from:
  https://chronicdata.cdc.gov/browse?category=500+Cities+%26+Places&sortBy=newest&utf8.
• AHRQ SDOH Database (v1), from Centers for Disease Control and Prevention, National Center for
  Health Statistics. Multiple Cause of Death, 1999–2019, in CDC WONDER Online Database. (2021).
  Data are from the Multiple Cause of Death Files, 1999–2019, as compiled from data provided by
  the 57 vital statistics jurisdictions through the Vital Statistics Cooperative Program.
  http://wonder.cdc.gov/mcd-icd10.html.
• United States Census Bureau. Cartographic Boundary Files – Shapefile.
  https://www.census.gov/geographies/mapping-files/time-series/geo/carto-boundary-file.html.
• AHRQ SDOH Database (v1), from Robert Wood Johnson Foundation, & University of Wisconsin
  Population Health Institute. (2020). County Health Rankings & Roadmaps.
  https://www.countyhealthrankings.org.
• AHRQ SDOH Database (v1), from U.S. Department of Education. (n.d.). Civil Rights Data
  Collection. Retrieved September 2021, from https://ocrdata.ed.gov.
• AHRQ SDOH Database (v1), from Community Resilience Estimates Social, Economic, and
  Housing Statistics Division U.S. Census Bureau. Retrieved September 2021 from
  https://www.census.gov/programs-surveys/community-resilience-estimates.html.
• AHRQ SDOH Database (v1), from United States Environmental Protection Agency. Outdoor Air
  Quality Data. Air Quality Statistics Report. Retrieved March 2022 from:
  https://www.epa.gov/outdoor-air-quality-data/air-quality-statistics-report
• AHRQ SDOH Database (v1), from United States Environmental Protection Agency (EPA) National
  Walkability Index (2021). Retrieved June 2021 from: https://catalog.data.gov/dataset/walkabilityindex.
• AHRQ SDOH Database (v1), from U.S. Department of Agriculture, Economic Research Service.
  2010 Rural-Urban Commuting Area codes, from https://www.ers.usda.gov/data-products/ruralurban-commuting-area-codes.
• AHRQ SDOH Database (v1), from Economic Research Service (ERS), U.S. Department of
  Agriculture (USDA). Food Access Research Atlas, Retrieved November 2021 from
  https://www.ers.usda.gov/data-products/food-access-research-atlas/
• AHRQ SDOH Database (v1), from U.S. Department of Agriculture (USDA), Economic Research
  Service (ERS) 2020. Food Environment Atlas. Retrieved September 2021 from:
  https://www.ers.usda.gov/data-products/food-environment-atlas/data-access-anddocumentation-downloads/.
• AHRQ SDOH Database (v1), from LTCFocus Public Use Data sponsored by the National Institute
  on Aging (P01 AG027296) through a cooperative agreement with the Brown University School of
  Public Health. Available at www.ltcfocus.org. https://doi.org/10.26300/h9a2-2c26.
• AHRQ SDOH Database (v1), from Centers for Medicare & Medicaid Services. (n.d.). Home Health
  Compare. https://www.medicare.gov/nursinghomecompare/search.html.
• AHRQ SDOH Database (v1), from the Department of Homeland Security. Homeland Infrastructure
  Foundation-Level Data (HIFLD)—Urgent Care Facilities. 2018. Available: https://hifldgeoplatform.opendata.arcgis.com/datasets/urgent-care-facilities.
• AHRQ SDOH Database (v1), from the Indian Health Service. IHS facilities. 2021. Available:
  https://www.ihs.gov/locations/.
• AHRQ SDOH Database (v1), from the Centers for Medicare & Medicaid Services. (2020, June). MA
  State/County Penetration, from https://www.cms.gov/Research-Statistics-DataandSystems/Statistics-Trends-and-Reports/MCRAdvPartDEnrolData/MA-State-CountyPenetration.
• AHRQ SDOH Database (v1), from the Centers for Medicare & Medicaid Services. (2020, June).
  Medicare Geographic Variation Public Use File, from https://www.cms.gov/Research-StatisticsData-and-Systems/Statistics-Trends-and-Reports/Medicare-Geographic-Variation/GV_PUF.
• AHRQ SDOH Database (v1), from the Centers for Disease Control and Prevention; U.S.
  Department of Health and Human Services; Agency for Toxic Substances and Disease Registry.
  Minority Health Social Vulnerability Index 2018. Retrieved July 2021 from
  https://www.minorityhealth.hhs.gov/minority-health-svi/.
• AHRQ SDOH Database (v1), from the Centers for Medicare & Medicaid Services, Office of Minority
  Health. (2020). Medicare Mapping Disparities Tool, from https://data.cms.gov/tools/mappingmedicare-disparities-by-population.
• AHRQ SDOH Database (v1), from the Health Resources and Services Administration. (2021).
  Medically Underserved Areas, from https://data.hrsa.gov/data/download.
• AHRQ SDOH Database (v1), from Ingram, D. D., & Franco, S. J. (2014, April). 2013 NCHS urban–
  rural classification scheme for counties. Vital and Health Statistics. Series 2, Data Evaluation and
  Methods Research. Number 166, 1–73. https://pubmed.ncbi.nlm.nih.gov/24776070.
• AHRQ SDOH Database (v1), from the Centers for Disease Control and Prevention. (2020).
  National Environmental Public Health Tracking Network from https://www.cdc.gov/ephtracking.
• AHRQ SDOH Database (v1), from the Centers for Medicare & Medicaid Services. (n.d.). Nursing
  Home Compare. https://www.medicare.gov/nursinghomecompare/search.html.
• Vose, R. S., Applequist, S., Squires, M., Durre, I., Menne, M. J., Williams, C. N., Jr., Fenimore, C.,
  Gleason, K., & Arndt, D. (2014). Improved Historical Temperature and Precipitation Time Series for
  U.S. Climate Divisions, Journal of Applied Meteorology and Climatology, 53(5), 1232-1251.
  Retrieved Oct 29, 2021, from https://journals.ametsoc.org/view/journals/apme/53/5/jamc-d-13-
  0248.1.xml.
  AHRQ SDOH Database (v1), from Vose, Russell S.; Applequist, Scott; Squires, Mike; Durre, Imke;
  Menne, Matthew J.; Williams, Claude N., Jr.; Fenimore, Chris; Gleason, Karin; Arndt, Derek (2014):
  NOAA Monthly U.S. Climate Divisional Database (NClimDiv). NOAA National Climatic Data Center.
  doi:10.7289/V5M32STR. Retrieved from: https://www.ncei.noaa.gov/data/climdiv/access/.
• AHRQ SDOH Database (v1), from National Center for Environmental Information (NCEI), National
  Environmental Satellite, Data and Information Service (NESDIS) and National Oceanic and
  Atmospheric Administration (NOAA), Storm Events Database 2009-2020 Retrieved August 2021
  from https://www.ncei.noaa.gov/pub/data/swdi/stormevents/csvfiles/.
• AHRQ SDOH Database (v1), from U.S. Census Bureau, Opportunity Insights. Opportunity Atlas [All
  Outcomes by Census Tract Race, Gender, and Parental Income Percentile, All Outcomes by
  County Race, Gender, and Parental Income Percentile] Retrieved October 2021 from:
  https://www.c
• AHRQ SDOH Database (v1), from National Center for Education Statistics. U.S. Skills Map: State
  and County Indicators of Adult Literacy and Numeracy. Retrieved March 2021 from:
  https://nces.ed.gov/surveys/piaac/skillsmap/.
• AHRQ SDOH Database (v1), from Centers for Medicare & Medicaid Services. (n.d.). Physician
  Compare. https://data.cms.gov/provider-data/topics/doctors-clinicians.
• AHRQ SDOH Database (v1), from Centers for Medicare & Medicaid Services. (n.d.). Provider of
  Services File. https://www.cms.gov/Research-Statistics-Data-and-Systems/Downloadable-PublicUse-Files/Provider-of-Services.
• AHRQ SDOH Database (v1), from Small Area Health Insurance Estimates. (n.d.). U.S. Census
  Bureau. https://www.census.gov/data/datasets/time-series/demo/sahie/estimates-acs.html
• AHRQ SDOH Database (v1), from Reardon, S. F., Ho, A. D., Shear, B. R., Fahle, E. M., Kalogrides, D.,
  Jang, H., &

The dataset contains information of 213 cancer patients undergoing clinical or surgical treatment characterized on sociodemographic and clinical data as well as data from the Care Transition Measure (CTM 15-Brazil). Data collection was carried out 7 to 30 days after their discharge from hospital from June to August 2019. Understanding these data can contribute to improving quality of care transitions and avoiding hospital readmissions. To this end, this dataset contains a broad array of variables:

*gender

*age group

*place of residence

*race

*marital status

*schooling

*paid work activity

*type of treatment

*cancer staging

*metastasis

*comorbidities

*main complaint

*continue use medication

*diagnosis

*cancer type

*diagnostic year

*oncology treatment

*first hospitalization

*readmission in the last 30 days

*number of hospitalizations in the last 30 days

*readmission in the last 6 months

*number of hospitalizations in the last 6 months

*readmission in the last year

*number of hospitalizations in the last year

*questions 1-15 from CTM 15-Brazil

The data are presented as a single Excel XLSX file: cancer patient´s care transitions dataset.xlsx.

The analyses of the present dataset have the potential to generate hospital readmission prevention strategies to be implemented by the hospital team. Researchers who are interested in CTs of cancer patients can extensively explore the variables described here.

The project from which these data were extracted was approved by the institution’s research ethics committee (approval n. 3.266.259/2019) at Associação Hospital de Caridade Ijuí, Rio Grande do Sul, Brazil.

WA-APCD - Washington All-Payer Claims Database The WA-APCD is the state’s most complete source of health care eligibility, medical claims, pharmacy claims, and dental claims insurance data. It contains claims from more than 50 data suppliers, spanning commercial, Medicaid, and Medicare managed care. The WA-APCD has historical claims data for five years (2013-2017), with ongoing refreshes scheduled quarterly. Workers' compensation data from the Washington Department of Labor & Industries will be added in fall 2018. Download the attachment for the data dictionary and more information about WA-APCD and the data.

Comprehensive dataset containing 447 verified Hospital businesses in Saitama, Japan with complete contact information, ratings, reviews, and location data.

The construction of diabetes dataset was explained. The data were collected from the Iraqi society, as they data were acquired from the laboratory of Medical City Hospital and (the Specializes Center for Endocrinology and Diabetes-Al-Kindy Teaching Hospital). Patients' files were taken and data extracted from them and entered in to the database to construct the diabetes dataset. The data consist of medical information, laboratory analysis. The data attribute are: The data consist of medical information, laboratory analysis… etc. The data that have been entered initially into the system are: No. of Patient, Sugar Level Blood, Age, Gender, Creatinine ratio(Cr), Body Mass Index (BMI), Urea, Cholesterol (Chol), Fasting lipid profile, including total, LDL, VLDL, Triglycerides(TG) and HDL Cholesterol , HBA1C, Class (the patient's diabetes disease class may be Diabetic, Non-Diabetic, or Predict-Diabetic).

Comprehensive dataset containing 10 verified Maternity hospital businesses in Michigan, United States with complete contact information, ratings, reviews, and location data.

This database offers addresses, phone numbers, administrator names and state registration or licensure status for Minnesota health care providers. Federal certification classifications are also included. Provider types in the directory are boarding care homes, home health agencies, home care providers, hospices, hospitals, housing with services, nursing homes and supervised living facilities and other non-long term care providers. Providers can be identified by type, county, city or name. This page provides a link to download current data from the MDH database. The link works best in Internet Explorer and Firefox. This data is provided in tabular format. There is no assoicated geographic dataset; results require geocoding to be mapped. A link to the file with the field names and definitions is also provided below.

The notes field contains the full MEDLINE (Ovid) search strategy for studies on psychiatric day treatment and mental disorders. The search file in this dataset contains the full MEDLINE (Ovid), EMBASE (Ovid), PsycInfo (Ovid) and CINAHL (EBSCO) strategies from February 14, 2025. The remaining files are the original database downloads for each search as well as the database results once records from before 2005 removed; access to these files are restricted due to licensing agreements.

Hospitals: A full list of health services, including name, address, contact details and Local Health District is available for download in CSV format.

Comprehensive dataset containing 1 verified Maternity hospital businesses in Wisconsin, United States with complete contact information, ratings, reviews, and location data.