In 2023, there were around *** hospital admissions per 1,000 population in the state of West Virginia. In comparison, Alaska had just ** hospital admissions per 1,000 population in the same year. Hospital admission rates in the United States have been decreasing in the last decades before dropping at the start of the pandemic.

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

This dataset contains the statewide number and (unadjusted) rate for all-cause, unplanned, 30-day inpatient readmissions in California hospitals. Data are categorized by age, sex, race/ethnicity, expected payer and county.

In 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.

Overview

Predict which patients are at high risk of readmission within 30 days of discharge. This synthetic dataset mimics real-world patterns to help reduce healthcare costs (estimated $17B annually in preventable readmissions).

Size: 30,000 records
Features: 11 clinical/demographic variables
Target: readmitted_30_days (Binary: Yes/No)

Files

• hospital_readmissions_30k.csv: Main dataset
• sample_submission.csv: Example submission file (for competitions)

Use Cases

• Build ML models to flag high-risk patients
• Analyze risk factors (e.g., diabetes, discharge destination)
• Healthcare operational planning

Column Descriptions

Dataset from Singapore Department of Statistics. For more information, visit https://data.gov.sg/datasets/d_e2acb8eaf4eaf1e0a1608af9a9cd2634/view

Dataset from Ministry of Health. For more information, visit https://data.gov.sg/datasets/d_dd32a9abff167b63efc11fb2f25cb341/view

Percentage of emergency admissions to any hospital in England occurring within 30 days of the last, previous discharge from hospital after admission: indirectly standardised by age, sex, method of admission and diagnosis/procedure. The indicator is broken down into the following demographic groups for reporting: ● All years and female only, male only and both male and female (persons). ● <16 years and female only, male only and both male and female (persons). ● 16+ years and female only, male only and both male and female (persons) ● 16-74 years and female only, male only and both male and female (persons) ● 75+ years and female only, male only and both male and female (persons) Results for each of these groups are also split by the following geographical and demographic breakdowns: ● Local authority of residence. ● Region. ● Area classification. ● NHS and private providers. ● NHS England regions. ● Deprivation (Index of Multiple Deprivation (IMD) Quintiles, 2019). ● Sustainability and Transformation Partnerships (STP) & Integrated Care Boards (ICB) from 2016/17. ● Clinical Commissioning Groups (CCG) & sub-Integrated Care Boards (sub-ICB). ● Treatment Functions. All annual trends are indirectly standardised against 2014/15.

Context

These data files contain information about hospitalisation and Intensive Care Unit (ICU) admission rates and current occupancy for COVID-19 by date and country. The data are updated weekly.

Source

• https://data.europa.eu/euodp/en/data/dataset/hospital-and-icu-admission-rates-and-occupancy-for-covid-19

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 laboratory-confirmed COVID-19 (including both adult and pediatric admissions) in the entire jurisdiction since August 1, 2020.
• Cumulative COVID-19 Hospital Admissions Rate: Cumulative total number of admissions of patients with laboratory-confirmed COVID-19 (including both adult and pediatric admissions) in the entire jurisdiction since August 1, 2020 divided by 2019 intercensal population estimate for that jurisdiction multiplied by 100,000.
• New COVID-19 Hospital Admissions Rate (7-day average) percent change from prior week: Percent change in the 7-day average new admissions of patients with laboratory-confirmed COVID-19 per 100,000 population compared with the prior week.
• New COVID-19 Hospital Admissions (7-Day Total): 7-day total number of new admissions of patients with laboratory-confirmed COVID-19 (including both adult and pediatric admissions) in the entire jurisdiction.
• New COVID-19 Hospital Admissions Rate (7-Day Total): 7-day total number of new admissions of patients with laboratory-confirmed COVID-19 (including both adult and pediatric admissions) for the entire jurisdiction divided by 2019 intercensal population estimate for that jurisdiction multiplied by 100,000.
• Total Hospitalized COVID-19 Patients: 7-day total number of patients currently hospitalized with laboratory-confirmed COVID-19 (including both adult and pediatric patients) for the entire jurisdiction.
• Total Hospitalized COVID-19 Patients (7-Day Average): 7-day average of the number of patients currently hospitalized with laboratory-confirmed COVID-19 (including both adult and pediatric patients) for the entire jurisdiction.
• COVID-19 Inpatient Bed Occupancy (7-Day Average): Percentage of all staffed inpatient beds occupied by patients with laboratory-confirmed COVID-19 (including both adult and pediatric patients) within the entire jurisdiction is calculated as an average of valid daily values within the past 7 days (e.g., if only three valid values, the average of those three is taken). Averages are separately calculated for the daily numerators (patients hospitalized with confirmed COVID-19) and denominators (staffed inpatient beds). The average percentage can then be taken as the ratio of these two values for the entire jurisdiction.
• COVID-19 Inpatient Bed Occupancy absolute change from prior week: The absolute change in the percent of staffed inpatient beds occupied by patients with laboratory-confirmed COVID-19 represents the week-over-week absolute difference between the 7-day average occupancy of patients with confirmed COVID-19 in staffed inpatient beds in the past 7 days, compared with the prior week, in the entire jurisdiction.
• COVID-19 ICU Bed Occupancy (7-Day Average): Percentage of all staffed inpatient beds occupied by adult patients with confirmed COVID-19 within the entire jurisdiction is calculated as a 7-day average of valid daily values within the past 7 days (e.g., if only three valid values, the average of those three is taken). Averages are separately calculated for the daily numerators (adult patients hospitalized with confirmed COVID-19) and denominators (staffed adult ICU beds). The average percentage can then be taken as the ratio of these two values for the entire jurisdiction.
• COVID-19 ICU Bed Occupancy absolute change from prior week: The absolute change in the percent of staffed ICU beds occupied by patients with laboratory-confirmed COVID-19 represents the week-over-week absolute difference between the average occupancy of patients with confirmed COVID-19 in staffed adult ICU beds for the past 7 days, compared with the prior week, in the in the entire jurisdiction.

Note: October 27, 2023: Due to a data processing error, reported values for avg_percent_inpatient_beds_occupied_covid_confirmed will appear lower than previously reported values by an average difference of less than 1%. Therefore, previously reported values for avg_percent_inpatient_beds_occupied_covid_confirmed may have been overestimated and should be interpreted with caution.

October 27, 2023: Due to a data processing error, reported values for abs_chg_avg_percent_inpatient_beds_occupied_covid_confirmed will differ from previously reported values by an average absolute difference of less than 1%. Therefore, previously reported values for abs_chg_avg_percent_inpatient_beds_occupied_covid_confirmed should be interpreted with caution.

December 29, 2023: Hospitalization data reported to CDC’s National Healthcare Safety Network (NHSN) through December 23, 2023, should be interpreted with caution due to potential reporting delays that are impacted by Christmas and New Years holidays. As a result, metrics including new hospital admissions for COVID-19 and influenza and hospital occupancy may be underestimated for the week ending December 23, 2023.

January 5, 2024: Hospitalization data reported to CDC’s National Healthcare Safety Network (NHSN) through December 30, 2023 should be interpreted with caution due to potential reporting delays that are impacted by Christmas and New Years holidays. As a result, metrics including new hospital admissions for COVID-19 and influenza and hospital occupancy may be underestimated for the week ending December 30, 2023.

In 2022, almost ten percent of individuals over 15 years of age stayed overnight or longer in a hospital as an inpatient within the past 12 months. Rates of hospital admission increased with age in most of the age groups. The highest rate of hospital admission rate was recorded for Turkish men who aged 75 or older, with above ** percent.

On 11/14/2025, we launched updated hospitalization reporting using data from the National Healthcare Safety Network (NHSN). The new dataset includes hospital admissions for respiratory viruses including COVID-19, flu, and RSV. You can access the new dataset 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 11/14/2025 COVID-19 hosipital admissions is tracked in a new dataset 7/18/2025 - Dataset update is paused to assess data quality and completeness. 9/12/2024 - We updated the data source for our COVID-19 hospitalization data to 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.

Heat map of standardised any admission rates per hospital for different patient groups: Focus of infection*.

Emergency hospital admission rates for all conditions and all ages. Data is available from Health and Social Care Information Centre Indicator Portal and Hospital episode statistics legacy website containing content from the London Health Observatory]. Indirectly age and sex standardised rates.

As 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

Heat map of standardised any admission rates per hospital for different age groups*.

The acute-care pathway (from the emergency department (ED) through acute medical units or ambulatory care and on to wards) is the most visible aspect of the hospital health-care system to most patients. Acute hospital admissions are increasing yearly and overcrowded emergency departments and high bed occupancy rates are associated with a range of adverse patient outcomes. Predicted growth in demand for acute care driven by an ageing population and increasing multimorbidity is likely to exacerbate these problems in the absence of innovation to improve the processes of care.

Key targets for Emergency Medicine services are changing, moving away from previous 4-hour targets. This will likely impact the assessment of patients admitted to hospital through Emergency Departments.

This data set provides highly granular patient level information, showing the day-to-day variation in case mix and acuity. The data includes detailed demography, co-morbidity, symptoms, longitudinal acuity scores, physiology and laboratory results, all investigations, prescriptions, diagnoses and outcomes. It could be used to develop new pathways or understand the prevalence or severity of specific disease presentations.

PIONEER geography: The West Midlands (WM) has a population of 5.9 million & includes a diverse ethnic & socio-economic mix.

Electronic Health Record: University Hospital Birmingham is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & an expanded 250 ITU bed capacity during COVID. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”.

Scope: All patients with a medical emergency admitted to hospital, flowing through the acute medical unit. Longitudinal & individually linked, so that the preceding & subsequent health journey can be mapped & healthcare utilisation prior to & after admission understood. The dataset includes patient demographics, co-morbidities taken from ICD-10 & SNOMED-CT codes. Serial, structured data pertaining to process of care (timings, admissions, wards and readmissions), physiology readings (NEWS2 score and clinical frailty scale), Charlson comorbidity index and time dimensions.

Available supplementary data: Matched controls; ambulance data, OMOP data, synthetic data.

Available supplementary support: Analytics, Model build, validation & refinement; A.I.; Data partner support for ETL (extract, transform & load) process, Clinical expertise, Patient & end-user access, Purchaser access, Regulatory requirements, Data-driven trials, “fast screen” services.

A. SUMMARY This dataset includes weekly respiratory disease hospital admissions for Influenza, RSV, and COVID-19 into San Francisco hospitals. Columns in the dataset include a count and rate of hospital admissions per 100,000 people. 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) from the United States Center for Disease Control’s (CDC) National Healthcare Safety Network (NHSN) program. 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 2019-2023 5-year American Community Survey (ACS). C. UPDATE PROCESS The dataset is updated every Friday and includes data from the previous Sunday through Saturday. For example, the update on Friday, October 17th will include data through Saturday, October 11th. Data may change as more current information becomes available. D. HOW TO USE THIS DATASET Weekly data represent a count of confirmed admissions of Influenza, RSV, and COVID-19 patients 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.

The number of admissions has increased year-on-year from 2000 to 2020. Due to the COVID-19 pandemic, hospital admission dropped in 2020/21. In 2024/25 there were around **** million admissions* to NHS hospitals in England, showing that admission numbers have reached and exceeded pre-pandemic levels. Of these, *** million were emergency admissions.

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 2021. It contains final data and replaces the provisional data that are released each month[1]. 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 2020 to March 2021 period.