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).

After May 3, 2024, this dataset and webpage will no longer be updated because hospitals are no longer required to report data on COVID-19 hospital admissions, and hospital capacity and occupancy data, to HHS through CDC’s National Healthcare Safety Network. Data voluntarily reported to NHSN after May 1, 2024, will be available starting May 10, 2024, at COVID Data Tracker Hospitalizations. The following dataset provides state-aggregated data for hospital utilization in a timeseries format dating back to January 1, 2020. These are derived from reports with facility-level granularity across three main sources: (1) HHS TeleTracking, (2) reporting provided directly to HHS Protect by state/territorial health departments on behalf of their healthcare facilities and (3) National Healthcare Safety Network (before July 15). The file will be updated regularly and provides the latest values reported by each facility within the last four days for all time. This allows for a more comprehensive picture of the hospital utilization within a state by ensuring a hospital is represented, even if they miss a single day of reporting. No statistical analysis is applied to account for non-response and/or to account for missing data. The below table displays one value for each field (i.e., column). Sometimes, reports for a given facility will be provided to more than one reporting source: HHS TeleTracking, NHSN, and HHS Protect. When this occurs, to ensure that there are not duplicate reports, prioritization is applied to the numbers for each facility. On April 27, 2022 the following pediatric fields were added: all_pediatric_inpatient_bed_occupied all_pediatric_inpatient_bed_occupied_coverage all_pediatric_inpatient_beds all_pediatric_inpatient_beds_coverage previous_day_admission_pediatric_covid_confirmed_0_4 previous_day_admission_pediatric_covid_confirmed_0_4_coverage previous_day_admission_pediatric_covid_confirmed_12_17 previous_day_admission_pediatric_covid_confirmed_12_17_coverage previous_day_admission_pediatric_covid_confirmed_5_11 previous_day_admission_pediatric_covid_confirmed_5_11_coverage previous_day_admission_pediatric_covid_confirmed_unknown previous_day_admission_pediatric_covid_confirmed_unknown_coverage staffed_icu_pediatric_patients_confirmed_covid staffed_icu_pediatric_patients_confirmed_covid_coverage staffed_pediatric_icu_bed_occupancy staffed_pediatric_icu_bed_occupancy_coverage total_staffed_pediatric_icu_beds total_staffed_pediatric_icu_beds_coverage On January 19, 2022, the following fields have been added to this dataset: inpatient_beds_used_covid inpatient_beds_used_covid_coverage On September 17, 2021, this data set has had the following fields added: icu_patients_confirmed_influenza, icu_patients_confirmed_influenza_coverage, previous_day_admission_influenza_confirmed, previous_day_admission_influenza_confirmed_coverage, previous_day_deaths_covid_and_influenza, previous_day_deaths_covid_and_influenza_coverage, previous_day_deaths_influenza, previous_day_deaths_influenza_coverage, total_patients_hospitalized_confirmed_influenza, total_patients_hospitalized_confirmed_influenza_and_covid, total_patients_hospitalized_confirmed_influenza_and_covid_coverage, total_patients_hospitalized_confirmed_influenza_coverage On September 13, 2021, this data set has had the following fields added: on_hand_supply_therapeutic_a_casirivimab_imdevimab_courses, on_hand_supply_therapeutic_b_bamlanivimab_courses, on_hand_supply_therapeutic_c_bamlanivimab_etesevimab_courses, previous_week_therapeutic_a_casirivimab_imdevimab_courses_used, previous_week_therapeutic_b_bamlanivimab_courses_used, previous_week_therapeutic_c_bamlanivima

As of October 6, 2022, 11,641 confirmed COVID-19 patients were in hospital in the United Kingdom. The number of COVID patients in hospitals first peaked at over 21.6 thousand on April 12, 2020 and dropped as low as 772 on September 11, 2020. However, the number of patients reached a new peak in the winter of 2020/21 with over 39.2 thousand patients in hospital on January 18, 2021.

The total number of cases in the UK can be found here. For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

Content

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

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

For a given entry, the term “collection_week” signifies the start of the period that is aggregated. For example, a “collection_week” of 2020-11-20 means the average/sum/coverage of the elements captured from that given facility starting and including Friday, November 20, 2020, and ending and including reports for Thursday, November 26, 2020.

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

• A “_coverage” append denotes how many times the facility reported that element during that collection week.
• A “_sum” append denotes the sum of the reports provided for that facility for that element during that collection week.
• A “_avg” append is the average of the reports provided for that facility for that element during that collection week. #
  The file will be updated weekly. No statistical analysis is applied to impute non-response. For averages, calculations are based on the number of values collected for a given hospital in that collection week. Suppression is applied to the file for sums and averages less than four (4). In these cases, the field will be replaced with “-999,999”.

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

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

Data Dictionary

Acknowledgements

The data provided by HealthData.gov. On this site, you can find data on a wide range of topics, including environmental health, medical devices, Medicare & Medicaid, social services, community health, mental health, and substance abuse.

Inspiration

-Covid-19 research

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.

This dataset details the percentage of COVID-19 positive patients in hospitals and ICUs for COVID-19 related reasons, and for reasons other than COVID-19. Data includes: * reporting date * percentage of COVID-19 positive patients in hospital admitted for COVID-19 * percentage of COVID-19 positive patients in hospital admitted for other reasons * percentage of COVID-19 positive patients in ICU admitted for COVID-19 * percentage of COVID-19 positive patients in ICU admitted for other reasons **Effective November 14, 2024 this page will no longer be updated. Information about COVID-19 and other respiratory viruses is available on Public Health Ontario’s interactive respiratory virus tool: https://www.publichealthontario.ca/en/Data-and-Analysis/Infectious-Disease/Respiratory-Virus-Tool ** Due to incomplete weekend and holiday reporting, data for hospital and ICU admissions are not updated on Sundays, Mondays and the day after holidays. This dataset is subject to change.

Hospital COVID patients (6th Jan 2020 onwards). Granular severity, ethnicity, co-morbidity. Serial physiology, blood biomarkers, images, treatments, ITU, outcome, pre/post admission healthcare use. Deeply phenotyped, longitudinal. Waves 1/ 2

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.

This dataset compiles daily snapshots of publicly reported data on 2019 Novel Coronavirus (COVID-19) testing in Ontario.

Learn how the Government of Ontario is helping to keep Ontarians safe during the 2019 Novel Coronavirus outbreak.

Data includes:

• date
• OH region
• current hospitalizations with COVID-19
• current patients in Intensive Care Units (ICUs) due to COVID-related critical Illness
• current patients in Intensive Care Units (ICUs) testing positive for COVID
• current patients in Intensive Care Units (ICUs) no longer testing positive for COVID
• current patients in Intensive Care Units (ICUs) on ventilators due to COVID-related critical illness
• current patients in Intensive Care Units (ICUs) on ventilators testing positive for COVID
• current patients in Intensive Care Units (ICUs) on ventilators no longer testing positive for COVID

**Effective November 14, 2024 this page will no longer be updated. Information about COVID-19 and other respiratory viruses is available on Public Health Ontario’s interactive respiratory virus tool: https://www.publichealthontario.ca/en/Data-and-Analysis/Infectious-Disease/Respiratory-Virus-Tool **

Additional notes

Data for the period of October 24, 2023 to March 24, 2024 excludes hospitals in the West region who were experiencing data availability issues.

Daily adult, pediatric, and neonatal patient ICU census data were impacted by technical issues between September 9 and October 20, 2023. As a result, when public reporting resumes on November 16, 2023, historical ICU data for this time period will be excluded.

As of August 3, 2023, the data in this file has been updated to reflect that there are now six Ontario Health (OH) regions.

This dataset is subject to change. Please review the daily epidemiologic summaries for information on variables, methodology, and technical considerations.

Context

Coronavirus disease (COVID-19) is an infectious disease caused by a newly discovered coronavirus. Most people infected with COVID-19 virus will experience mild to moderate respiratory illness and recover without requiring special treatment. Older people, and those with underlying medical problems like cardiovascular disease, diabetes, chronic respiratory disease, and cancer are more likely to develop serious illness. During the entire course of the pandemic, one of the main problems that healthcare providers have faced is the shortage of medical resources and a proper plan to efficiently distribute them. In these tough times, being able to predict what kind of resource an individual might require at the time of being tested positive or even before that will be of immense help to the authorities as they would be able to procure and arrange for the resources necessary to save the life of that patient.

The main goal of this project is to build a machine learning model that, given a Covid-19 patient's current symptom, status, and medical history, will predict whether the patient is in high risk or not.

content

The dataset was provided by the Mexican government (link). This dataset contains an enormous number of anonymized patient-related information including pre-conditions. The raw dataset consists of 21 unique features and 1,048,576 unique patients. In the Boolean features, 1 means "yes" and 2 means "no". values as 97 and 99 are missing data.

• sex: 1 for female and 2 for male.
• age: of the patient.
• classification: covid test findings. Values 1-3 mean that the patient was diagnosed with covid in different degrees. 4 or higher means that the patient is not a carrier of covid or that the test is inconclusive.
• patient type: type of care the patient received in the unit. 1 for returned home and 2 for hospitalization.
• pneumonia: whether the patient already have air sacs inflammation or not.
• pregnancy: whether the patient is pregnant or not.
• diabetes: whether the patient has diabetes or not.
• copd: Indicates whether the patient has Chronic obstructive pulmonary disease or not.
• asthma: whether the patient has asthma or not.
• inmsupr: whether the patient is immunosuppressed or not.
• hypertension: whether the patient has hypertension or not.
• cardiovascular: whether the patient has heart or blood vessels related disease.
• renal chronic: whether the patient has chronic renal disease or not.
• other disease: whether the patient has other disease or not.
• obesity: whether the patient is obese or not.
• tobacco: whether the patient is a tobacco user.
• usmr: Indicates whether the patient treated medical units of the first, second or third level.
• medical unit: type of institution of the National Health System that provided the care.
• intubed: whether the patient was connected to the ventilator.
• icu: Indicates whether the patient had been admitted to an Intensive Care Unit.
• date died: If the patient died indicate the date of death, and 9999-99-99 otherwise.

Note: As of July 21, 2021, this dataset no longer updates. A. SUMMARY Data on daily hospital bed use and available capacity at San Francisco acute care hospitals from April 2020 onward. Long Term Care facilities (like Laguna Honda and Kentfield) are not included in this data as acute care patients cannot be admitted to these facilities. B. HOW THE DATASET IS CREATED This hospital capacity information is based on data that all SF acute care hospitals report to the San Francisco Department of Public Health. C. UPDATE PROCESS Updates automatically at 05:00 Pacific Time each day. Redundant runs are scheduled at 07:00 and 09:00 in case of pipeline failure. This data is on a 4-day lag to account for the time needed to complete and validate data from all SF acute care hospitals. D. HOW TO USE THIS DATASET This data provides visibility into current occupancy levels and use of San Francisco acute care hospitals and potential ability to accommodate anticipated surges of COVID patients. Data includes current census of COVID-19 patients (including both confirmed cases and suspected COVID patients) and other patients in acute care hospitals, shown in the “Status” column. The “Status” column also includes all available beds. This daily census information is stratified by type of bed (acute care, intensive care, and surge) in the “Bed Type” column. Acute care beds treat patients with illnesses and injuries including recovery from surgeries. Intensive care (ICU) beds are for sicker patients in need of critical and life support services that can include the use of a ventilator. Surge beds are the additional beds that can be made available to handle an influx of COVID-19 patients; surge beds are differentiated between acute care surge beds and ICU surge beds. Note: The current census of COVID patients shown here may not always match the hospitalizations data (https://data.sfgov.org/COVID-19/COVID-19-Hospitalizations/nxjg-bhem), as that data includes all hospitals and long term care facilities. As described above, those long term care facilities are not included here as they don’t have the capacity to take in additional acute care patients and therefore aren’t included in capacity measures.

OMOP dataset: Hospital COVID patients: severity, acuity, therapies, outcomes Dataset number 2.0

Coronavirus disease 2019 (COVID-19) was identified in January 2020. Currently, there have been more than 6 million cases & more than 1.5 million deaths worldwide. Some individuals experience severe manifestations of infection, including viral pneumonia, adult respiratory distress syndrome (ARDS) & death. There is a pressing need for tools to stratify patients, to identify those at greatest risk. Acuity scores are composite scores which help identify patients who are more unwell to support & prioritise clinical care. There are no validated acuity scores for COVID-19 & it is unclear whether standard tools are accurate enough to provide this support. This secondary care COVID OMOP dataset contains granular demographic, morbidity, serial acuity and outcome data to inform risk prediction tools in COVID-19.

PIONEER geography The West Midlands (WM) has a population of 5.9 million & includes a diverse ethnic & socio-economic mix. There is a higher than average percentage of minority ethnic groups. WM has a large number of elderly residents but is the youngest population in the UK. Each day >100,000 people are treated in hospital, see their GP or are cared for by the NHS. The West Midlands was one of the hardest hit regions for COVID admissions in both wave 1 & 2.

EHR. University Hospitals Birmingham NHS Foundation Trust (UHB) 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 & 100 ITU beds. 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”. UHB has cared for >5000 COVID admissions to date. This is a subset of data in OMOP format.

Scope: All COVID swab confirmed hospitalised patients to UHB from January – August 2020. The dataset includes highly granular patient demographics & co-morbidities taken from ICD-10 & SNOMED-CT codes. Serial, structured data pertaining to care process (timings, staff grades, specialty review, wards), presenting complaint, acuity, all physiology readings (pulse, blood pressure, respiratory rate, oxygen saturations), all blood results, microbiology, all prescribed & administered treatments (fluids, antibiotics, inotropes, vasopressors, organ support), all outcomes.

Available supplementary data: Health data preceding & following admission event. Matched “non-COVID” controls; ambulance, 111, 999 data, synthetic data. Further OMOP data available as an additional service.

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.

Deprecated report. This report was created early in the response to the COVID-19 pandemic. Increased reporting and quality in hospital data have rendered the estimated datasets obsolete.

The following dataset provides state-aggregated data for estimated patient impact and hospital utilization.

The source data for estimation is derived from reports with facility-level granularity across two main sources: (1) HHS TeleTracking, and (2) reporting provided directly to HHS Protect by state/territorial health departments on behalf of their healthcare facilities.

Estimates Basis: These files are representative estimates for each state and are updated weekly. These projections are based on the information we have from those who reported. As more hospitals report more frequently our projections become more accurate. The actual data for these data points are updated every day, once a day on healthdata.gov and these are the downloadable data sets.

NOTE: This dataset is historical-only as of 5/10/2023. All data currently in the dataset will remain, but new data will not be added. The recommended alternative dataset for similar data beyond that date is  https://healthdata.gov/Hospital/COVID-19-Reported-Patient-Impact-and-Hospital-Capa/anag-cw7u. (This is not a City of Chicago site. Please direct any questions or comments through the contact information on the site.)

During the COVID-19 pandemic, the Chicago Department of Public Health (CDPH) required EMS Region XI (Chicago area) hospitals to report hospital capacity and patient impact metrics related to COVID-19 to CDPH through the statewide EMResource system. This requirement has been lifted as of May 9, 2023, in alignment with the expiration of the national and statewide COVID-19 public health emergency declarations on May 11, 2023. However, all hospitals will still be required by the U.S. Department of Health and Human Services (HHS) to report COVID-19 hospital capacity and utilization metrics into the HHS Protect system through the CDC’s National Healthcare Safety Network until April 30, 2024. Facility-level data from the HHS Protect system can be found at healthdata.gov.

Until May 9, 2023, all Chicago (EMS Region XI) hospitals (n=28) were required to report bed and ventilator capacity, availability, and occupancy to the Chicago Department of Public Health (CDPH) daily. A list of reporting hospitals is included below. All data represent hospital status as of 11:59 pm for that calendar day. Counts include Chicago residents and non-residents.

ICU bed counts include both adult and pediatric ICU beds. Neonatal ICU beds are not included. Capacity refers to all staffed adult and pediatric ICU beds. Availability refers to all available/vacant adult and pediatric ICU beds. Hospitals began reporting COVID-19 confirmed and suspected (PUI) cases in ICU on 03/19/2020. Hospitals began reporting ICU surge capacity as part of total capacity on 5/18/2020.

Acute non-ICU bed counts include burn unit, emergency department, medical/surgery (ward), other, pediatrics (pediatric ward) and psychiatry beds. Burn beds include those approved by the American Burn Association or self-designated. Capacity refers to all staffed acute non-ICU beds. An additional 500 acute/non-ICU beds were added at the McCormick Place Treatment Facility on 4/15/2020. These beds are not included in the total capacity count. The McCormick Place Treatment Facility closed on 05/08/2020. Availability refers to all available/vacant acute non-ICU beds. Hospitals began reporting COVID-19 confirmed and suspected (PUI) cases in acute non-ICU beds on 04/03/2020.

Ventilator counts prior to 04/24/2020 include all full-functioning mechanical ventilators, with ventilators with bilevel positive airway pressure (BiPAP), anesthesia machines, and portable/transport ventilators counted as surge. Beginning 04/24/2020, ventilator counts include all full-functioning mechanical ventilators, BiPAP, anesthesia machines and portable/transport ventilators. Ventilators are counted regardless of ability to staff. Hospitals began reporting COVID-19 confirmed and suspected (PUI) cases on ventilators on 03/19/2020. CDPH has access to additional ventilators from the EAMC (Emergency Asset Management Center) cache. These ventilators are included in the total capacity count.

Chicago (EMS Region 11) hospitals: Advocate Illinois Masonic Medical Center, Advocate Trinity Hospital, AMITA Resurrection Medical Center Chicago, AMITA Saint Joseph Hospital Chicago, AMITA Saints Mary & Elizabeth Medical Center, Ann & Robert H Lurie Children's Hospital, Comer Children's Hospital, Community First Medical Center, Holy Cross Hospital, Jackson Park Hospital & Medical Center, John H. Stroger Jr. Hospital of Cook County, Loretto Hospital, Mercy Hospital and Medical Center, , Mount Sinai Hospital, Northwestern Memorial Hospital, Norwegian American Hospital, Roseland Community Hospital, Rush University Medical Center, Saint Anthony Hospital, Saint Bernard Hospital, South Shore Hospital, Swedish Hospital, Thorek Memorial Hospital, Thorek Hospital Andersonville. University of Chicago Medical Center, University of Illinois Hospital & Health Sciences System, Weiss Memorial Hospital.

Chicago (EMS Region 11) specialty hospitals: Provident Hospital/Cook County, RML Specialty Hospital, Chicago, Montrose Behavioral Health (previously Lakeshore Hospital.) Shirley Ryan AbilityLab (previously RIC), Jesse Brown VA Medical Center, Kindred Chicago – North, Hartgrove Hospital, Kindred Chicago – Lakeshore, Kindred Chicago – Central, Shriners Hospital for Children – Chicago, LaRabida Hospital.

Data Source: Hospitals reporting to CDPH via EMResource (Juvare)

These datasets are for a cohort of n=1540 anonymised hospitalised COVID-19 patients, and the data provide information on outcomes (i.e. patient death or discharge), demographics and biomarker measurements for two New York hospitals: State
University of New York (SUNY) Downstate Health Sciences University and Maimonides
Medical Center.

The file "demographics_both_hospitals.csv" contains the ultimate outcomes of hospitalisation (whether a patient was discharged or died), demographic information and known comorbidities for each of the patients.

The file "dynamics_clean_both_hospitals.csv" contains cleaned dynamic biomarker measurements for the n=1233 patients where this information was available and the data passed our various checks (see https://doi.org/10.1101/2021.11.12.21266248 for information of these checks and the cleaning process). Patients can be matched to demographic data via the "id" column.

Study approval and data collection

Study approval was obtained from the State University of New York (SUNY) Downstate Health Sciences University Institutional Review Board (IRB\#1595271-1) and Maimonides Medical Center Institutional Review Board/Research Committee (IRB\#2020-05-07). A retrospective query was performed among the patients who were admitted to SUNY Downstate Medical Center and Maimonides Medical Center with COVID-19-related symptoms, which was subsequently confirmed by RT PCR, from the beginning of February 2020 until the end of May 2020. Stratified randomization was used to select at least 500 patients who were discharged and 500 patients who died due to the complications of COVID-19. Patient outcome was recorded as a binary choice of “discharged” versus “COVID-19 related mortality”. Patients whose outcome was unknown were excluded. Demographic, clinical history and laboratory data was extracted from the hospital’s electronic health records.

In the United States between February 12 and March 16, 2020, the percentage of COVID-19 patients hospitalized with the disease increased with age. Findings estimated that up to 70 percent of adults aged 85 years and older were hospitalized.

Who is at higher risk from COVID-19? The same study also found that coronavirus patients aged 85 and older were at the highest risk of death. There are other risk factors besides age that can lead to serious illness. People with pre-existing medical conditions, such as diabetes, heart disease, and lung disease, can develop more severe symptoms. In the U.S. between January and May 2020, case fatality rates among confirmed COVID-19 patients were higher for those with underlying health conditions.

How long should you self-isolate? As of August 24, 2020, more than 16 million people worldwide had recovered from COVID-19 disease, which includes patients in health care settings and those isolating at home. The criteria for discharging patients from isolation varies by country, but asymptomatic carriers of the virus can generally be released ten days after their positive case was confirmed. For patients showing signs of the illness, they must isolate for at least ten days after symptom onset and also remain in isolation for a short period after the symptoms have disappeared.

Context

The COVID-19 pandemic has placed an unprecedented strain on health systems, with rapidly increasing demand for healthcare in hospitals and intensive care units (ICUs) worldwide. As the pandemic escalates, determining the resulting needs for healthcare resources (beds, staff, equipment) has become a key priority for many countries. Projecting future demand requires estimates of how long patients with COVID-19 need different levels of hospital care. While healthcare management has various use cases for using data science, patient length of stay is one critical parameter to observe and predict if one wants to improve the efficiency of the healthcare management in a hospital.

This parameter helps hospitals to identify patients of high LOS risk (patients who will stay longer) at the time of admission. Once identified, patients with high LOS risk can have their treatment plan optimized to miminize LOS and lower the chance of staff/visitor infection. Also, prior knowledge of LOS can aid in logistics such as room and bed allocation planning.

The problem is to manage the functioning of Hospitals in a professional and optimal manner. #
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Content

The task is to accurately predict the Length of Stay for each patient on case by case basis so that the Hospitals can use this information for optimal resource allocation and better functioning. The length of stay is divided into 11 different classes ranging from 0-10 days to more than 100 days.

Data : host_train.csv – File containing features related to patient, hospital and Length of stay on case basis

For each record in the dataset the following is provided::

• case_id
• Hospital
• Hospital_type
• Hospital_city
• Hospital_region
• Available-Extra-Rooms-in-Hospital | Number of Extra rooms available in the Hospital
• Department | Department overlooking the case | ['radiotherapy' 'anesthesia' 'gynecology' 'TB & Chest disease' 'surgery']
• Ward_Type | ['R' 'S' 'Q' 'P' 'T' 'U']
• Ward_Facility | ['F' 'E' 'D' 'B' 'A' 'C']
• Bed_Grade | Condition of Bed in the Ward
• patientid
• City_Code_Patient | City Code for the patient
• Type of Admission | Admission Type registered by the Hospital | ['Emergency' 'Trauma' 'Urgent']
• Illness_Severity | Severity of the illness recorded at the time of admission | ['Extreme' 'Moderate' 'Minor']
• Patient_Visitors
• Age | Age category | ['51-60' '71-80' '31-40' '41-50' '81-90' '61-70' '21-30' '11-20' '0-10' '91-100']
• Admission_Deposit |Deposit at the Admission Time
• Stay_Days | Stay Days by the patient (target) | ['0-10' '41-50' '31-40' '11-20' '51-60' '21-30' '71-80' 'More than 100 Days' '81-90' '61-70' '91-100']

Starter Kernels

• EDA and Random Forest Benchmark

Inspiration

• Predict the Length of Stay for each patient
• Interpret best model(s) and mine influence factors in LOS risk

More References

• COVID-19 length of hospital stay
• A retrospective cohort study in a Fangcang shelter hospital

🇺🇸 United States English After May 3, 2024, this dataset and webpage will no longer be updated because hospitals are no longer required to report data on COVID-19 hospital admissions, and hospital capacity and occupancy data, to HHS through CDC’s National Healthcare Safety Network. Data voluntarily reported to NHSN after May 1, 2024, will be available starting May 10, 2024, at COVID Data Tracker Hospitalizations. The following dataset provides facility-level data for hospital utilization aggregated on a weekly basis (Sunday to Saturday). These are derived from reports with facility-level granularity across two main sources: (1) HHS TeleTracking, and (2) reporting provided directly to HHS Protect by state/territorial health departments on behalf of their healthcare facilities. The hospital population includes all hospitals registered with Centers for Medicare & Medicaid Services (CMS) as of June 1, 2020. It includes non-CMS hospitals that have reported since July 15, 2020. It does not include psychiatric, rehabilitation, Indian Health Service (IHS) facilities, U.S. Department of Veterans Affairs (VA) facilities, Defense Health Agency (DHA) facilities, and religious non-medical facilities. For a given entry, the term “collection_week” signifies the start of the period that is aggregated. For example, a “collection_week” of 2020-11-15 means the average/sum/coverage of the elements captured from that given facility starting and including Sunday, November 15, 2020, and ending and including reports for Saturday, November 21, 2020. Reported elements include an append of either “_coverage”, “_sum”, or “_avg”. A “_coverage” append denotes how many times the facility reported that element during that collection week. A “_sum” append denotes the sum of the reports provided for that facility for that element during that collection week. A “_avg” append is the average of the reports provided for that facility for that element during that collection week. The file will be updated weekly. No statistical analysis is applied to impute non-response. For averages, calculations are based on the number of values collected for a given hospital in that collection week. Suppression is applied to the file for sums and averages less than four (4). In these cases, the field will be replaced with “-999,999”. A story page was created to display both corrected and raw datasets and can be accessed at this link: https://healthdata.gov/stories/s/nhgk-5gpv This data is preliminary and subject to change as more data become available. Data is available starting on July 31, 2020. Sometimes, reports for a given facility will be provided to both HHS TeleTracking and HHS Protect. When this occurs, to ensure that there are not duplicate reports, deduplication is applied according to prioritization rules within HHS Protect. For influenza fields listed in the file, the current HHS guidance marks these fields as optional. As a result, coverage of these elements are varied. For recent updates to the dataset, scroll to the bottom of the dataset description. On May 3, 2021, the following fields have been added to this data set.

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.

Note: May 3,2024: Due to incomplete or missing hospital data received for the April 21,2024 through April 27, 2024 reporting period, the COVID-19 Hospital Admissions Level could not be calculated for CNMI and will be reported as “NA” or “Not Available” in the COVID-19 Hospital Admissions Level data released on May 3, 2024.

This dataset represents COVID-19 hospitalization data and metrics aggregated to county or county-equivalent, for all counties or county-equivalents (including territories) in the United States. 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

• County-level hospital data are derived using calculations performed at the Health Service Area (HSA) level. An HSA is defined by CDC’s National Center for Health Statistics as a geographic area containing at least one county which is self-contained with respect to the population’s provision of routine hospital care. Every county in the United States is assigned to an HSA, and each HSA must contain at least one hospital. Therefore, use of HSAs in the calculation of local hospital metrics allows for more accurate characterization of the relationship between health care utilization and health status at the local level.
• Data presented at the county-level represent admissions, hospital inpatient and ICU bed capacity and occupancy among hospitals within the selected HSA. Therefore, admissions, capacity, and occupancy are not limited to residents of the selected HSA.
• For all county-level hospital metrics listed below the values are calculated first for the entire HSA, and then the HSA-level value is then applied to each county within the HSA.
• For all county-level hospital metrics listed below the values are calculated first for the entire HSA, and then the HSA-level value is then applied to each county within the HSA.

• Time period: data for the previous MMWR week (Sunday-Saturday) 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 hospital admissions (count): Total number of admissions of patients with laboratory-confirmed COVID-19 in the previous week (including both adult and pediatric admissions) in the entire jurisdiction
• New Hospital Admissions Rate Value (Admissions per 100k): Total number of new admissions of patients with laboratory-confirmed COVID-19 in the past week (including both adult and pediatric admissions) for the entire jurisdiction divided by 2019 intercensal population estimate for that jurisdiction multiplied by 100,000. (Note: This metric is used to determine each county’s COVID-19 Hospital Admissions Level for a given week).
• New COVID-19 Hospital Admissions Rate Level: qualitative value of new COVID-19 hospital admissions rate level [Low, Medium, High, Insufficient Data]
• New hospital admissions percent change from prior week: Percent change in the current weekly total new admissions of patients with laboratory-confirmed COVID-19 per 100,000 population compared with the prior week.
• New hospital admissions percent change from prior week level: Qualitative value of percent change in hospital admissions rate from prior week [Substantial decrease, Moderate decrease, Stable, Moderate increase, Substantial increase, Insufficient data]
• COVID-19 Inpatient Bed Occupancy Value: Percentage of all staffed inpatient beds occupied by patients with laboratory-confirmed COVID-19 (including both adult and pediatric patients) within the in the entire jurisdiction is calculated as an average of valid daily values within the past week (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 Level: Qualitative value of inpatient beds occupied by COVID-19 patients level [Minimal, Low, Moderate, Substantial, High, Insufficient data]
• COVID-19 Inpatient Bed Occupancy percent 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 average occupancy of patients with confirmed COVID-19 in staffed inpatient beds in the past week, compared with the prior week, in the entire jurisdiction.
• COVID-19 ICU Bed Occupancy Value: Percentage of all staffed inpatient beds occupied by adult patients with confirmed COVID-19 within the entire jurisdiction is calculated as an average of valid daily values within the past week (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 Level: Qualitative value of ICU beds occupied by COVID-19 patients level [Minimal, Low, Moderate, Substantial, High, Insufficient data]
• COVID-19 ICU Bed Occupancy percent 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 week, compared with the prior week, in the in the entire jurisdiction.
• For all metrics, if there are no data in the specified locality for a given week, the metric value is displayed as “insufficient data”.

Notes: June 1, 2023: Due to incomplete or missing hospital data received for the May 21, 2023, through May 27, 2023, reporting period, the COVID-19 Hospital Admissions Level could not be calculated for the Commonwealth of the Northern Mariana Islands (CNMI) and will be reported as “NA” or “Not Available” in the COVID-19 Hospital Admissions Level data released on June 1, 2023.

June 8, 2023: Due to incomplete or missing hospital data received for the May 28, 2023, through June 3, 2023, reporting period, the COVID-19 Hospital Admissions Level could not be calculated for CNMI and American Samoa (AS) and will be reported as “NA” or “Not Available” in the COVID-19 Hospital Admissions Level data released on June 8, 2023.

June 15, 2023: Due to incomplete or missing hospital data received for the June 4, 2023, through June 10, 2023, reporting period,

As of 9/12/2024, we will begin reporting on hospitalization data again using a new San Francisco specific dataset. Updated data can be accessed here.

On 5/1/2024, hospitalization data reporting will change from mandatory to optional for all hospitals nationwide. We will be pausing the refresh of the underlying data beginning 5/2/2024.

A. SUMMARY Count of COVID+ patients admitted to the hospital. Patients who are hospitalized and test positive for COVID-19 may be admitted to an acute care bed (a regular hospital bed), or an intensive care unit (ICU) bed. This data shows the daily total count of COVID+ patients in these two bed types, and the data reflects totals from all San Francisco Hospitals.

B. HOW THE DATASET IS CREATED Hospital information is based on admission data reported to the National Healthcare Safety Network (NHSN) and provided by the California Department of Public Health (CDPH).

C. UPDATE PROCESS Updates automatically every week.

D. HOW TO USE THIS DATASET Each record represents how many people were hospitalized on the date recorded in either an ICU bed or acute care bed (shown as Med/Surg under DPHCategory field).

The dataset shown here includes all San Francisco hospitals and updates weekly with data for the past Sunday-Saturday as information is collected and verified. Data may change as more current information becomes available.

E. CHANGE LOG

• 9/12/2024 -Hospitalization data are now being tracked through a new source and are available here.
• 5/1/2024 - hospitalization data reporting to the National Healthcare Safety Network (NHSN) changed from mandatory to optional for all hospitals nationwide. We will be pausing the refresh of the underlying data beginning 5/2/2024.
• 12/14/2023 – added column “hospitalreportingpct” to indicate the percentage of hospitals who submitted data on each report date.
• 8/7/2023 - In response to the end of the federal public health emergency on 5/11/2023 the California Hospital Association (CHA) stopped the collection and dissemination of COVID-19 hospitalization data. In alignment with the California Department of Public Health (CDPH), hospitalization data from 5/11/2023 onward are being pulled from the National Healthcare Safety Network (NHSN). The NHSN data is updated weekly and does not include information on COVID suspected (PUI) patients.
• 4/9/2021 - dataset updated daily with a four-day data lag.