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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/
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.
table_headings.csv has explanatory names of all columns.
Data was collected from Hero Dayanand Medical College Heart Institute Unit of Dayanand Medical College and Hospital, Ludhiana, Punjab, India.
For any questions about the data or collaborations please contact ashish.sahani@iitrpr.ac.in
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TwitterSince 2005, the Diagnosis Related Groups (DRG) statistics have provided annual information on morbidity events and morbidity trends in inpatient care, as well as on the volume and structure of demand for services, over and above the existing official hospital statistics. In particular, type of illness, case-flat-rate hospital statistic (DRGs), operations and procedures as well as length of stay and department are collected.
The aggregated data are freely accessible.
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TwitterThe number of hospitals in the United States was forecast to continuously decrease between 2024 and 2029 by in total 13 hospitals (-0.23 percent). According to this forecast, in 2029, the number of hospitals will have decreased for the twelfth consecutive year to 5,548 hospitals. Depicted is the number of hospitals in the country or region at hand. As the OECD states, the rules according to which an institution can be registered as a hospital vary across countries.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of hospitals in countries like Canada and Mexico.
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TwitterFrom the Web site: The American Hospital Directory® provides data, statistics, and analytics about more than 7,000 hospitals nationwide. AHD.com® hospital information includes both public and private sources such as Medicare claims data, hospital cost reports, and commercial licensors. AHD® is not affiliated with the American Hospital Association (AHA) and is not a source for AHA Data. Our data are evidence-based and derived from the most definitive sources.
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Publication Hospital resources 2016–17: Australian hospital statistics can be found on the AIHW Website.
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This synthetic dataset simulates the end-to-end operations of a California-based hospital for Q1 2025. It includes over 126,000 rows across 9 fully integrated tables that capture patient visits, clinical procedures, diagnoses, lab tests, medication prescriptions, provider details, billing, claims, and denials — designed for data analytics, machine learning, and healthcare research.
📦 Tables Included: patients.csv – Patient demographics, insurance, DOB, gender
encounters.csv – Admission/discharge details, visit types, departments
diagnoses.csv – ICD-10 diagnosis codes linked to encounters
procedures.csv – CPT/ICD-10-PCS procedure codes per patient
medications.csv – Drug names, dosages, prescription data
lab_tests.csv – Test names, result values, normal ranges
claims_and_billing.csv – Financial charges, insurance claims, payments
providers.csv – Doctors, specializations, provider roles
denials.csv – Reasons for claim denial, status, appeal info
This dataset was custom-built to reflect real-world healthcare challenges including:
Messy and missing data (for cleaning exercises)
Insurance claim workflows and denial patterns
Analysis of repeat admissions and chronic disease trends
Medication brand usage, cost patterns, and outcomes
🧠 Ideal For: Healthcare Data Science Projects
Revenue Cycle Management (RCM) analytics
Power BI & Tableau Dashboards
Machine Learning modeling (readmission, denial prediction, etc.)
Python/SQL Data Cleaning Practice
This dataset is completely synthetic and safe for public use. It was generated using custom rules, distributions, and logic reflective of real hospital operations.
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TwitterIn 2023, there were over **** million hospital admissions in the United States. The number of hospitals in the U.S. has decreased in recent years, although the country faces an increasing elder population. Predictably, the elderly account for the largest share of hospital admissions in the U.S. Hospital stays Stays in hospitals are more common among females than males, with around *** percent of females reporting one or more hospital stays in the past year, compared to *** percent of males. Furthermore, **** percent of those aged 65 years and older had a hospitalization in the past year, compared to just *** percent of those aged 18 to 44 years. The average length of a stay in a U.S. hospital is *** days. Hospital beds In 2022, there were ******* hospital beds in the U.S. In the past few years, there has been a decrease in the number of hospital beds available. This is unsurprising given the decrease in the number of overall hospitals. In 2021, the occupancy rate of hospitals in the U.S. was ** percent.
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TwitterAfter 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. This report shows facilities currently in suspense regarding CoP requirements due to being in a work plan or other related reasons is shown if any facilities are currently in suspense. These CCNs will not be included in the tab listing all other hospitals or included in any summary counts while in suspense. 01/05/2024 – As of FAQ 6, the following optional fields have been added to this report: total_adult_patients_hospitalized_confirmed_influenza total_pediatric_patients_hospitalized_confirmed_influenza previous_day_admission_adult_influenza_confirmed previous_day_admission_pediatric_influenza_confirmed staffed_icu_adult_patients_confirmed_influenza staffed_icu_pediatric_patients_confirmed_influenza total_adult_patients_hospitalized_confirmed_rsv total_pediatric_patients_hospitalized_confirmed_rsv previous_day_admission_adult_rsv_confirmed previous_day_admission_pediatric_rsv_confirmed staffed_icu_adult_patients_confirmed_rsv staffed_icu_pediatric_patients_confirmed_rsv 6/17/2023 - With the new 28-day compliance reporting period, CoP reports will be posted every 4 weeks. 9/12/2021 - To view other COVID-19 Hospital Data Coverage datasets, follow this link to view summary page: https://healthdata.gov/stories/s/ws49-ddj5 As of FAQ3, the following field are federally inactive and will no longer be included in this report: previous_week_personnel_covid_vaccinated_doses_administered total_personnel_covid_vaccinated_doses_none total_personnel_covid_vaccinated_doses_one total_personnel_covid_vaccinated_doses_all total_personnel previous_week_patients_covid_vaccinated_doses_one previous_week_patients_covid_vaccinated_doses_all
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TwitterOpen Database License (ODbL) v1.0https://www.opendatacommons.org/licenses/odbl/1.0/
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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.
This report shows data completeness information on data submitted by hospitals for the previous week, from Friday to Thursday. The U.S. Department of Health and Human Services requires all hospitals licensed to provide 24-hour care to report certain data necessary to the all-of-America COVID-19 response. The report includes the following information for each hospital:
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TwitterThis dataset contains annual Excel pivot tables that display summaries of the patients treated in each Emergency Department (ED). The Emergency Department data is sourced from two databases, the ED Treat-and-Release Database and the Inpatient Database (i.e. patients treated in the ED and then formally admitted to the hospital). The summary data include number of visits, expected payer, discharge disposition, age groups, sex, preferred language spoken, race groups, principal diagnosis groups, and principal external cause of injury/morbidity groups. The data can also be summarized statewide or for a specific hospital county, ED service level, teaching/rural status, and/or type of control.
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This publication reports on Admitted Patient Care activity in England for the financial year 2024-25 This report includes but is not limited to analysis of hospital episodes by patient demographics, diagnoses, external causes/injuries, operations, bed days, admission method, time waited, specialty, provider level analysis and Adult Critical Care (ACC). It describes NHS Admitted Patient Care Activity, Adult Critical Care activity and performance in hospitals in England. The purpose of this publication is to inform and support strategic and policy-led processes for the benefit of patient care and may also be of interest to researchers, journalists and members of the public interested in NHS hospital activity in England. The data source for this publication is Hospital Episode Statistics (HES). It contains final data and replaces the provisional data that are released each month. HES contains records of all admissions, appointments and attendances at NHS-commissioned hospital services in England. The HES data used in this publication are called 'Finished Consultant Episodes', and each episode relates to a period of care for a patient under a single consultant at a single hospital. Therefore, this report counts the number of episodes of care for admitted patients rather than the number of patients. This publication shows the number of episodes during the period, with breakdowns including by patient's age, gender, diagnosis, procedure involved and by provider. Please send queries or feedback via email to enquiries@nhsdigital.nhs.uk. Author: Secondary Care Open Data and Publications, NHS England. Lead Analyst: Karl Eichler
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TwitterAs of 9/12/2024, we have resumed reporting on COVID-19 hospitalization data using a San Francisco specific dataset. These new data differ slightly from previous hospitalization data sources but the overall patterns and trends in hospitalizations remain consistent. You can access the previous data here.
A. SUMMARY This dataset includes information on COVID+ hospital admissions for San Francisco residents into San Francisco hospitals. Specifically, the dataset includes the count and rate of COVID+ hospital admissions per 100,000. The data are reported by week.
B. HOW THE DATASET IS CREATED Hospital admission data is reported to the San Francisco Department of Public Health (SFDPH) via the COVID Hospital Data Repository (CHDR), a system created via health officer order C19-16. The data includes all San Francisco hospitals except for the San Francisco VA Medical Center.
San Francisco population estimates are pulled from a view based on the San Francisco Population and Demographic Census dataset. These population estimates are from the 2018-2022 5-year American Community Survey (ACS).
C. UPDATE PROCESS Data updates weekly on Wednesday with data for the past Wednesday-Tuesday (one week lag). Data may change as more current information becomes available.
D. HOW TO USE THIS DATASET New admissions are the count of COVID+ hospital admissions among San Francisco residents to San Francisco hospitals by week.
The admission rate per 100,000 is calculated by multiplying the count of admissions each week by 100,000 and dividing by the population estimate.
E. CHANGE LOG
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MyHospitals provides performance information for public and private hospitals in Australia. You can also compare the performance of these hospitals and find information about hospitals near you.
The annual average number of beds available to be used by an admitted patient was grouped into the following categories: fewer than 50, 50-100, 100-200, 200-500 and more than 500. These data are as reported by states and territories to the NPHED, and are referred to in statistical publications (including Australian hospital statistics) as 'average available beds'. The average number of available beds presented may differ from counts published elsewhere. For example, counts based on bed numbers at a specified date such as 30 June may differ from the average available beds over the reporting period. Comparability of bed numbers can be affected by the range and types of patients treated by a hospital. For example, hospitals may have different proportions of beds available for general versus special purposes (such as beds or cots used exclusively for intensive care). Bed counts also include chairs for same-day admissions.
Data is current as of December 2015. Data sourced from: http://www.myhospitals.gov.au/about-the-data/download-data
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Hospital Episodes Statistics (HES) is a data warehouse containing records of all patients admitted to NHS hospitals in England. It contains details of inpatient care and outpatient appointments. Hospital episode statistics (HES) statistics are produced and published on a monthly basis. The data are provisional and should therefore be treated as an estimate until the final National Statistics annual publications.
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This is a report on admitted patient care activity in English NHS hospitals and English NHS-commissioned activity in the independent sector. This annual publication covers the financial year ending March 2020. It contains final data and replaces the provisional data that are released each month. The data are taken from the Hospital Episodes Statistics (HES) data warehouse. HES contains records of all admissions, appointments and attendances for patients at NHS hospitals in England. The HES data used in this publication are called 'Finished Consultant Episodes', and each episode relates to a period of care for a patient under a single consultant at a single hospital. Therefore this report counts the number of episodes of care for admitted patients rather than the number of patients. This publication shows the number of episodes during the period, with a number of breakdowns including by patient's age, gender, diagnosis, procedure involved and by provider. Hospital Adult Critical Care (ACC) data are now included within this report, following the discontinuation of the 'Hospital Adult Critical Care Activity' publication. The ACC data tables are not a designated National Statistic and they remain separate from the APC data tables. The ACC data used in this publication draws on records submitted by providers as an attachment to the admitted patient care record. These data show the number of adult critical care records during the period, with a number of breakdowns including admission details, discharge details, patient demographics and clinical information. The purpose of this publication is to inform and support strategic and policy-led processes for the benefit of patient care. This document will also be of interest to researchers, journalists and members of the public interested in NHS hospital activity in England. Supplementary analysis has been produced, by NHS Digital, containing experimental statistics using the Paediatric Critical Care Minimum Data Set (PCCMDS) data, collected by NHS Digital, against activity published in NHS Reference Costs. This analysis seeks to assist users of the data in understanding the data quality of reported paediatric critical care data. Also included within this release, is supplementary analysis that has been produced in addition to the Retrospective Review of Surgery for Urogynaecological Prolapse and Stress Urinary Incontinence using Tape or Mesh: Hospital Episode Statistics (HES), Experimental Statistics, April 2008 - March 2017. It contains a count of Finished Consultant Episodes (FCEs) where a procedure for urogynaecological prolapse or stress urinary incontinence using tape or mesh has been recorded during the April 2019 to March 2020 period.
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The Hospital Statistics publication shows activity data analysed by Programme of Care. Specialty tables covering both inpatients and outpatients along with a Key Points document showing comparisons over the previous five years are also published.
Source agency: Health, Social Service and Public Safety (Northern Ireland)
Designation: National Statistics
Language: English
Alternative title: Hospital Statistics
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The Hospital Statistics publication shows activity data analysed by Programme of Care. Specialty tables covering both inpatients and outpatients along with a Key Points document showing comparisons over the previous five years are also published. Source agency: Health, Social Service and Public Safety (Northern Ireland) Designation: National Statistics Language: English Alternative title: Hospital Statistics
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Data on hospital admission, average length of stay, outpatient visits, and outpatient surgery in the United States, by type of ownership and size of hospital. Data are from Health, United States. SOURCE: American Hospital Association (AHA) Annual Survey of Hospitals, Hospital Statistics. Search, visualize, and download these and other estimates from a wide range of health topics with the NCHS Data Query System (DQS), available from: https://www.cdc.gov/nchs/dataquery/index.htm.
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TwitterThis dataset contains annual Excel pivot tables that display summaries of the inpatients treated in each hospital. The summary data include discharges, discharge days, average length of stay, age groups, race groups, sex, expected payer, type of care, do not resuscitate orders, admission source, admission type, discharge disposition, principal diagnosis groups, principal procedure groups, and principal external cause of injury/morbidity groups. The data can also be summarized statewide or for a specific hospital county, bed size grouping, and/or type of control.
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This dataset presents the location and detailed overview of Australia's public and private hospitals. In 2012–13, there were about 9.4 million separations from hospitals, including: 5.2 million same-day acute separations; 3.7 million overnight acute separations; about 450,000 sub-acute and non-acute separations. There were also 7.9 million non-admitted patient emergency services and more than 46 million outpatient services provided by public hospitals. For further information about this dataset, visit the data source:Australian Institute of Health and Welfare.
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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/
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.
table_headings.csv has explanatory names of all columns.
Data was collected from Hero Dayanand Medical College Heart Institute Unit of Dayanand Medical College and Hospital, Ludhiana, Punjab, India.
For any questions about the data or collaborations please contact ashish.sahani@iitrpr.ac.in