Timely access to emergency care can significantly reduce mortality. International benchmarks for access to emergency hospital care have been established to guide ambitions for universal health care by 2030. However, there is no complete geo-coded inventory of hospital services in Africa in relation to how populations might access these services. We assembled a geocoded inventory of public hospitals across 48 countries and islands of sub-Saharan Africa from 100 different sources. A cost distance algorithm based on the location of 4908 public hospitals, population distributions and road networks were used to compute the proportion of populations living within a combined walking and motorised travel time of 2 hours to emergency hospital services. We estimate that 286 million (29%) people and 64 million (28%) women of child bearing age are located more than 2 hours from the nearest hospital. Marked differences were observed within and between countries. Only 17 countries reached the international benchmark of more than 80% of their populations living within a 2-hour travel time of the nearest hospital.

This map service includes the acute and non-acute care hospitals in Massachusetts.Acute care hospitals are those licensed under MGL Chapter 111, section 51 and which contain a majority of medical-surgical, pediatric, obstetric, and maternity beds, as defined by the Massachusetts Department of Public Health (DPH). The features in this layer are based on database information provided to MassGIS from the DPH, Office of Emergency Medical Services (OEMS) and the Center for Health Information and Analysis (CHIA).All hospitals in the state that have a 24-hour emergency department are included in this layer, but not all facilities in this layer have an emergency department (the ER_STATUS field stores this data). Other attributes include cohort, adult and pediatric trauma levels, and special public funding. See CHIA's Massachusetts Acute Hospital Profiles page for more information. CHIA reviewed the final revision in November 2018.Non-acute care hospitals in Massachusetts are typically identified as psychiatric, rehabilitation, and chronic care facilities, along with some non-acute specialty hospitals, using the Massachusetts Department of Public Health (DPH) and Department of Mental Health (DMH) license criteria as well as a listing on the state's Bureau of Hospitals website. The non-acute care hospitals are based on database information provided by the DPH and the Center for Health Information and Analysis (CHIA). CHIA reviewed this layer in November 2018.Non-acute care hospitals in this layer do not contain 24/7 emergency departments.See the full data layer descriptions:Acute care hospitalsNon-acute care hospitalsMap service also available

The Open Database of Healthcare Facilities (ODHF) is a collection of open data containing the names, types, and locations of health facilities across Canada. It is released under the Open Government License - Canada. The ODHF compiles open, publicly available, and directly-provided data on health facilities across Canada. Data sources include regional health authorities, provincial, territorial and municipal governments, and public health and professional healthcare bodies. This database aims to provide enhanced access to a harmonized listing of health facilities across Canada by making them available as open data. This database is a component of the Linkable Open Data Environment (LODE).

This feature layer contains locations of Hospitals for 50 US states, Washington D.C., US territories of Puerto Rico, Guam, American Samoa, Northern Mariana Islands, Palau, and Virgin Islands. The dataset only includes hospital facilities based on data acquired from various state departments or federal sources which has been referenced in the SOURCE field. Hospital facilities which do not occur in these sources will be not present in the database. The source data was available in a variety of formats (pdfs, tables, webpages, etc.) which was cleaned and geocoded and then converted into a spatial database. The database does not contain nursing homes or health centers. Hospitals have been categorized into children, chronic disease, critical access, general acute care, long term care, military, psychiatric, rehabilitation, special, and women based on the range of the available values from the various sources after removing similarities. In this update the TRAUMA field was populated for 172 additional hospitals and helipad presence were verified for all hospitals.

AbstractThis feature layer describes the location of Australia’s public hospitals, operational at some point during the 2022-23 Financial Year. A public hospital is defined as a hospital included in the Australian Institute of Health and Welfare’s (AIHW) National Public Hospital Establishment (NPHE) database, for the relevant financial year.The NPHE database holds data for each public hospital in Australia, including public acute hospitals, psychiatric hospitals, drug and alcohol hospitals and dental hospitals in all states and territories. Hence, public hospitals not administered by the state and territory health authorities (hospitals operated by correctional authorities for example, and hospitals in offshore territories) are not included.For this feature layer, an NPHE hospital is only included if it was also included in the AIHW’s count of public hospitals as reported in the online Hospital Resources data tables for the relevant financial year (2022-23: https://www.aihw.gov.au/getmedia/06f2c08c-f00f-439e-b57c-9a825ffc505c/Hospital-resources-tables-2022-23.xlsx).As noted in the AIHW’s technical appendix accompanying the NPHE data, the number of public hospitals reported can be affected by administrative and/or reporting arrangements and is not necessarily a measure of the number of physical hospital buildings or campuses. For more information on the 2022-23 NPHE see: https://www.aihw.gov.au/getmedia/747ae239-e38b-45a9-8d83-0ac6a3e0726f/hospital-resources-2022-23-appendix.pdfThe original data source for the public hospital layer is not the same as the Commonwealth’s list of declared hospitals (https://www.health.gov.au/resources/publications/list-of-declared-hospitals), however, there is considerable cross-over between the NPHE and Commonwealth list. There are a small number of private hospitals in the Commonwealth list of hospitals that are treated as public hospitals by the AIHW in NPHE. For 2022-23, these hospitals are:Hawkesbury District Health Service (NSW)Northern Beaches Hospital (NSW)Mclaren Vale & Districts War Memorial Hospital Incoperated (SA)Joondalup Health Campus (WA)St John Of God Midland Public & Private Hospital (WA)CurrencyDate modified: September 2024Modification frequency: As neededData ExtentSpatial ExtentWest: 96.82°South: -43.74°East: 167.99°North: -9.14°Source InformationAustralian Institute of Health and Welfare’s (AIHW) National Public Hospital Establishment (NPHE) databaseAustralian Department of Health, Disability and Ageing: Geospatial Data HubLineage StatementA public hospital is defined as a hospital included in the Australian Institute of Health and Welfare’s (AIHW) National Public Hospital Establishment (NPHE) database, for the relevant financial year.Data DictionaryAttribute NameDescriptionOBJECTIDAutomatically Generated System IDhsib_idDepartment of Health, Disability and Ageing unique identifier for each hospitalhosp_nameHospital NamecategoryHospital typestreetStreetpcodePost CodesuburbSuburbstateState/TerritoryxcoordLongitudeycoordLatitudeContactContact: Department of Health, Disability and Ageing, geospatial@health.gov.au

Health care in Nigeria as in many other countries is confronted with growing demand for medical treatment and services. The medical records must appropriately have all of the patients’ medical history. Physicians must maintain flawless records, because this document serves a number of purposes. This study on hospital patient datable management system was design to transform the manual way of searching, sorting, keeping and accessing patient medical information (files) into electronic medical record (EMR) in order to solve the problem associate with manual method. The existing system (manual) has been studied and hence a computer based application was provided to replace this manual method. These computer based systems generate the patient report as the patientregister in and out of the hospital. This paper generally looks for a more accurate, reliable and efficient method ofcomputer to facilitate patient record’s keeping in General Hospitals to ensure efficient outcome that will lessen timeconsuming. The study proposed that the design of hospital patient database record will be a solution to the problembeing experienced by the current manual method of keeping patient medical record.

Context

This dataset contains locations of Hospitals for 50 US states, Washington D.C., US territories of Puerto Rico, Guam, American Samoa, Northern Mariana Islands, Palau, and Virgin Islands.

Content

This feature class/shapefile contains locations of Hospitals for 50 US states, Washington D.C., US territories of Puerto Rico, Guam, American Samoa, Northern Mariana Islands, Palau, and Virgin Islands. The dataset only includes hospital facilities based on data acquired from various state departments or federal sources which has been referenced in the SOURCE field. Hospital facilities which do not occur in these sources will be not present in the database. The source data was available in a variety of formats (pdfs, tables, webpages, etc.) which was cleaned and geocoded and then converted into a spatial database. The database does not contain nursing homes or health centers. Hospitals have been categorized into children, chronic disease, critical access, general acute care, long term care, military, psychiatric, rehabilitation, special, and women based on the range of the available values from the various sources after removing similarities.In this version any information contained in ADDRESS2 field found in earlier versions of this dataset has been merged with the ADDRESS field and the ADDRESS2 field has been deleted.In this update 75 additional records were added and the TRAUMA field was populated for 574 additional hospitals.

Acknowledgements

This dataset was downloaded on March 23, 2019 from: https://hifld-geoplatform.opendata.arcgis.com/datasets/a2817bf9632a43f5ad1c6b0c153b0fab_0

This dataset is provided by the Homeland Infrastructure Foundation-Level Data (HIFLD) without a license and for Public Use.

HIFLD Open GP - Public Health Shared By: jrayer_geoplatform Data Source: services1.arcgis.com

Users are advised to read the data set's metadata thoroughly to understand appropriate use and data limitations.

This data package contains the hospital bed availability and occupancy data by consultant main specialty and sector as well as data on inpatient and outpatient related hospital activity in England. It also contains information on Sub-Saharan public hospitals.

ABSTRACT Objective: to analyze demographic Brazilian medical data from the national public healthcare system (SUS), which provides free universal health coverage for the entire population, and discuss the problems revealed, with particular focus on surgical care. Methods: data was obtained from public healthcare databases including the Medical Demography, the Brazilian Federal Council of Medicine, the Brazilian Institute of Geography and Statistics, and the National Database of Healthcare Establishments. Density and distribution of the medical workforce and healthcare facilities were calculated, and the geographic regions were analyzed using the public private inequality index. Results: Brazil has an average of two physicians for every 1,000 inhabitants, who are unequally distributed throughout the country. There are 22,276 board certified general surgeons in Brazil (11.49 for every 100,000 people). The country currently has 257 medical schools, with 25,159 vacancies for medical students each year, with only around 13,500 vacancies for residency. The public private inequality index is 3.90 for the country, and ranges from 1.63 in the Rio de Janeiro up to 12.06 in Bahia. Conclusions: A significant part of the local population still faces many difficulties in accessing surgical care, particularly in the north and northeast of the country, where there are fewer hospitals and surgeons. Physicians and surgeons are particularly scarce in the public health system nationwide, and better incentives are needed to ensure an equal public and private workforce.

This dataset is being provided under creative commons License (Attribution-Non-Commercial-Share Alike 4.0 International (CC BY-NC-SA 4.0)) https://creativecommons.org/licenses/by-nc-sa/4.0/

Context

This data was collected from patients admitted over a period of two years (1 April 2017 to 31 March 2019) at Hero DMC Heart Institute, Unit of Dayanand Medical College and Hospital, Ludhiana, Punjab, India. This is a tertiary care medical college and hospital. During the study period, the cardiology unit had 14,845 admissions corresponding to 12,238 patients. 1921 patients who had multiple admissions.

Specifically, data were related to patients ; date of admission; date of discharge; demographics, such as age, sex, locality (rural or urban); type of admission (emergency or outpatient); patient history, including smoking, alcohol, diabetes mellitus (DM), hypertension (HTN), prior coronary artery disease (CAD), prior cardiomyopathy (CMP), and chronic kidney disease (CKD); and lab parameters corresponding to hemoglobin (HB), total lymphocyte count (TLC), platelets, glucose, urea, creatinine, brain natriuretic peptide (BNP), raised cardiac enzymes (RCE) and ejection fraction (EF). Other comorbidities and features (28 features), including heart failure, STEMI, and pulmonary embolism, were recorded and analyzed.

Shock was defined as systolic blood pressure < 90 mmHg, and when the cause for shock was any reason other than cardiac. Patients in shock due to cardiac reasons were classified into cardiogenic shock. Patients in shock due to multifactorial pathophysiology (cardiac and non-cardiac) were considered for both categories. The outcomes indicating whether the patient was discharged or expired in the hospital were also recorded.

Further details about this dataset can be found here: https://doi.org/10.3390/diagnostics12020241

If you use this dataset in academic research all publications arising out of it must cite the following paper: Bollepalli, S.C.; Sahani, A.K.; Aslam, N.; Mohan, B.; Kulkarni, K.; Goyal, A.; Singh, B.; Singh, G.; Mittal, A.; Tandon, R.; Chhabra, S.T.; Wander, G.S.; Armoundas, A.A. An Optimized Machine Learning Model Accurately Predicts In-Hospital Outcomes at Admission to a Cardiac Unit. Diagnostics 2022, 12, 241. https://doi.org/10.3390/diagnostics12020241

If you intend to use this data for commercial purpose explicit written permission is required from data providers.

Content

table_headings.csv has explanatory names of all columns.

Acknowledgements

Data was collected from Hero Dayanand Medical College Heart Institute Unit of Dayanand Medical College and Hospital, Ludhiana, Punjab, India.

Inspiration

For any questions about the data or collaborations please contact ashish.sahani@iitrpr.ac.in

The Nationwide Emergency Department Sample (NEDS) was created to enable analyses of emergency department (ED) utilization patterns and support public health professionals, administrators, policymakers, and clinicians in their decision-making regarding this critical source of care. The NEDS can be weighted to produce national estimates. The NEDS is the largest all-payer ED database in the United States. It was constructed using records from both the HCUP State Emergency Department Databases (SEDD) and the State Inpatient Databases (SID), both also described in healthdata.gov. The SEDD capture information on ED visits that do not result in an admission (i.e., treat-and-release visits and transfers to another hospital). The SID contain information on patients initially seen in the emergency room and then admitted to the same hospital. The NEDS contains 25-30 million (unweighted) records for ED visits for over 950 hospitals and approximates a 20-percent stratified sample of U.S. hospital-based EDs. The NEDS contains information about geographic characteristics, hospital characteristics, patient characteristics, and the nature of visits (e.g., common reasons for ED visits, including injuries). The NEDS 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 ED charge information for over 75% of patients, regardless of payer, including patients covered by Medicaid, private insurance, and the uninsured. The NEDS excludes data elements that could directly or indirectly identify individuals, hospitals, or states.

This is a list of the 11 acute care hospitals, four skilled nursing facilities, six large diagnostic and treatment centers and community-based clinics that make up the New York City Health and Hospitals Corporation, NYC's public hospital system. HHC is a $6.7 billion integrated healthcare delivery system which serves 1.3 million New Yorkers every year and more than 450,000 are uninsured. It provides medical, mental health and substance abuse services. Update Frequency: As needed

This data includes activity and performance data from the emergency departments at the local hospitals (Rural Category Three Public Health Services) in the Southwest region of Victoria.

This dataset is not being updated as hospitals are no longer mandated to report COVID Hospitalizations to CDPH.

Data is from the California COVID-19 State Dashboard at https://covid19.ca.gov/state-dashboard/

Note: Hospitalization counts include all patients diagnosed with COVID-19 during their stay. This does not necessarily mean they were hospitalized because of COVID-19 complications or that they experienced COVID-19 symptoms.

Note: Cumulative totals are not available due to the fact that hospitals report the total number of patients each day (as opposed to new patients).

The database for the SPSS statistical program is used as support for the data analysis carried out for the evaluation of the pilot study.

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.

Indonesia Average Monthly Expenditure per Capita: Goods and Services: Health: Public Hospital data was reported at 7,713.000 IDR in 2018. This records an increase from the previous number of 6,543.000 IDR for 2017. Indonesia Average Monthly Expenditure per Capita: Goods and Services: Health: Public Hospital data is updated yearly, averaging 3,244.500 IDR from Dec 2003 (Median) to 2018, with 16 observations. The data reached an all-time high of 7,713.000 IDR in 2018 and a record low of 439.000 IDR in 2003. Indonesia Average Monthly Expenditure per Capita: Goods and Services: Health: Public Hospital data remains active status in CEIC and is reported by Central Bureau of Statistics. The data is categorized under Indonesia Premium Database’s Domestic Trade and Household Survey – Table ID.HC001: Average Monthly Expenditure per Capita.

The Nationwide Emergency Department Sample (NEDS) was created to enable analyses of emergency department (ED) utilization patterns and support public health professionals, administrators, policymakers, and clinicians in their decision-making regarding this critical source of care. The NEDS can be weighted to produce national estimates. Restricted access data files are available with a data use agreement and brief online security training. The NEDS is the largest all-payer ED database in the United States. It was constructed using records from both the HCUP State Emergency Department Databases (SEDD) and the State Inpatient Databases (SID), both also described in healthdata.gov. The SEDD capture information on ED visits that do not result in an admission (i.e., treat-and-release visits and transfers to another hospital). The SID contain information on patients initially seen in the emergency room and then admitted to the same hospital. The NEDS contains 25-30 million (unweighted) records for ED visits for over 950 hospitals and approximates a 20-percent stratified sample of U.S. hospital-based EDs. The NEDS contains information about geographic characteristics, hospital characteristics, patient characteristics, and the nature of visits (e.g., common reasons for ED visits, including injuries). The NEDS 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 ED charge information for over 85% of patients, regardless of payer, including patients covered by Medicaid, private insurance, and the uninsured. The NEDS excludes data elements that could directly or indirectly identify individuals, hospitals, or states.

the database shows the number of patient to the emergency department in various hospitals of general directorate of health services at the ministry of defense throughout the months of 2022 the summary data includes three elements : name of the hospital months of the year 2022 number of patients total number of patients