This statistic displays the average daily census in hospitals in the United States from 1946 to 2019. In 2019, the daily average census reached some ******* people in hospitals located in the country. The majority of registered hospitals in the United States are considered community hospitals.

Graph and download economic data for Total Inpatient Days for Hospitals, All Establishments (INPAT622ALLEST176QNSA) from Q4 2004 to Q2 2025 about hospitals, establishments, and USA.

This dataset contains measures of the number and density of hospitals per United States Census Tract or ZIP Code Tabulation Area (ZCTA) in 2023. The dataset includes four separate files for four different geographic areas (GIS shapefiles from the United States Census Bureau). The four geographies include: Census Tract 2010 Census Tract 2020 ZIP Code Tabulation Area (ZCTA) 2010 ZIP Code Tabulation Area (ZCTA) 2020

Graph and download economic data for Total Discharges for Hospitals, All Establishments (DISC622ALLEST157QNSA) from Q1 2005 to Q2 2025 about discharges, hospitals, establishments, rate, and USA.

ObjectivesThe primary objective of this study was to identify clinical and socioeconomic predictors of hospital and ED use among children with medical complexity within 1 and 5 years of an initial discharge between 2010 and 2013. A secondary objective was to estimate marginal associations between important predictors and resource use.MethodsThis retrospective, population-cohort study of children with medical complexity in Alberta linked administrative health data with Canadian census data and used tree-based, gradient-boosted regression models to identify clinical and socioeconomic predictors of resource use. Separate analyses of cumulative numbers of hospital days and ED visits modeled the probability of any resource use and, when present, the amount of resource use. We used relative importance in each analysis to identify important predictors.ResultsThe analytic sample included 11 105 children with medical complexity. The best short- and long-term predictors of having a hospital stay and number of hospital days were initial length of stay and clinical classification. Initial length of stay, residence rurality, and other socioeconomic factors were top predictors of short-term ED use. The top predictors of ED use in the long term were almost exclusively socioeconomic, with rurality a top predictor of number of ED visits. Estimates of marginal associations between initial length of stay and resource use showed that average number of hospital days increases as initial length of stay increases up to approximately 90 days. Children with medical complexity living in rural areas had more ED visits on average than those living in urban or metropolitan areas.ConclusionsClinical factors are generally better predictors of hospital use whereas socioeconomic factors are more predictive of ED use among children with medical complexity in Alberta. The results confirm existing literature on the importance of socioeconomic factors with respect to health care use by children with medical complexity.

This dataset presents statistics for Health Care and Social Assistance: Ownership and Control of Government Hospitals for the U.S.

Distribution (N records, %) of variables related to health status and hospital stay with descriptive statistics (mean (SD), median (IQR)) of length of stay and number of side diagnoses and percentage (%) of transfer to inpatient setting = yes.

HR006 - Irish Psychiatric Units and Hospital Census. Published by Health Research Board. Available under the license Creative Commons Attribution 4.0 (CC-BY-4.0).Irish Psychiatric Units and Hospital Census...

Irish Psychiatric Units and Hospitals Census

Imports - Other Scientific, Medical & Hospital Eqp. (Census) in the United States decreased to 4991.18 USD Million in February from 4996.99 USD Million in January of 2024. This dataset includes a chart with historical data for the United States Imports of Other Scientific, Medical & Hospital E.

Distribution (N records, %) of demographic and social factors with descriptive statistics (mean (SD), median (IQR)) of length of stay and number of side diagnoses and percentage (%) of transfer to inpatient setting = yes.

Exports - Oth. Scientific, Medical & Hospital Eqp. (Census) in the United States decreased to 3912.64 USD Million in February from 3974.47 USD Million in January of 2024. This dataset includes a chart with historical data for the United States Exports of Oth. Scientific, Medical & Hospital Eq.

This series consists of microfiche and printouts showing hospital separations data for inpatients to New South Wales hospitals between 1976 and 1989. The data was collected and collated by the Demand and Performance Evaluation Reporting Unit which formed part of the Demand and Performance Evaluation Branch of the NSW Department of Health. The collated statistics were printed and transferred on to microfiche by the Australian Bureau of Statistics.

The statistics are displayed in tables and in some cases in graph format. The statistics relate to individual hospitals, to particular regions within NSW and in some cases the data sets relate to all hospitals in New South Wales. The data was collected in order to gauge demand for hospitals in New South Wales and for the purpose of future planning.

Some of the statistics provided include separations by diagnosis, separations by principal operation, average length of stay by principal diagnosis, morbidity statistics by operation and admissions by disease. Within these categories information regarding the age, sex, principal diagnosis, number of bed days, average length of stay, procedure, class or type of disease and usual residence of the patient can be provided.

The microfiche are grouped into 13 bundles and arranged in chronological and item number order.

Names of hospitals in Canada, their addresses, geographic coordinates (Longitude and Latitude) and an assigned hospital identifier

We developed an application (https://rush-covid19.herokuapp.com/) to aid US hospitals in planning their response to the ongoing COVID-19 pandemic. Our application forecasts hospital visits, admits, discharges, and needs for hospital beds, ventilators, and personal protective equipment by coupling COVID-19 predictions to models of time lags, patient carry-over, and length-of-stay. Users can choose from seven COVID-19 models, customize a large set of parameters, examine trends in testing and hospitalization, and download forecast data.

The data and scripts contained herein are used to generate Figure 1 of the associated manuscript, which presents general forms of the models used by our application and presents results for each model across time.

If hospitalization becomes inevitable in the course of a chronic disease, discharge from acute hospital care in older persons is often associated with temporary or persistent frailty, functional limitations and the need for help with daily activities. Thus, acute hospitalization represents a particularly vulnerable phase of transient dependency on social support and health care. This study examines how social and regional inequality affect the decision for an institutionalization after acute hospital discharge in Switzerland. The current analysis uses routinely collected inpatient data from all Swiss acute hospitals that was linked on the individual level with Swiss census data. The study sample included 60,209 patients 75 years old and older living still at a private home and being hospitalized due to a chronic health condition in 199 hospitals between 2010 and 2016. Random intercept multilevel logistic regression was used to assess the impact of social and regional factors on the odds of a nursing home admission after hospital discharge. Results show that 7.8% of all patients were admitted directly to a nursing home after hospital discharge. We found significant effects of education level (compulsory vs. tertiary education OR = 1.16 (95% CI: 1.03–1.30), insurance class (compulsory vs. private insurance OR = 1.24 (95% CI: 1.09–1.41), living alone vs. living with others (OR = 1.64; 95% CI: 1.53–1.76) and language regions (French vs. German speaking part: OR = 0.54; 95% CI: 0.37–0.80) on the odds of nursing home admission in a model adjusted for age, gender, nationality, health status, year of hospitalization and hospital-level variance. The language regions moderated the effect of education and insurance class but not of living alone. This study shows that acute hospital discharge in older age is a critical moment of transient dependency especially for socially disadvantaged patients. Social and health care should work coordinated together to avoid unnecessary institutionalizations.

This dataset represents weekly hospital respiratory data and metrics aggregated to national and state/territory levels reported to CDC’s National Health Safety Network (NHSN) beginning November 2024. Data and metrics included in this dataset are NOT updated or adjusted week-over-week after initial publication, and therefore represent data received at the time of publication for a given reporting week. All data included in this dataset represent aggregated counts, and include metrics capturing information specific to hospital capacity, occupancy, hospitalizations, and new hospital admissions with corresponding metrics indicating reporting coverage for a given reporting week. NHSN monitors national and local trends in healthcare system stress and capacity for all acute care and critical access hospitals in the United States.

For more information on the reporting mandate per the Centers for Medicare and Medicaid Services (CMS) requirements, visit: Updates to the Condition of Participation (CoP) Requirements for Hospitals and Critical Access Hospitals (CAHs) To Report Acute Respiratory Illnesses.

For more information regarding NHSN’s collection of these data, including full reporting guidance, visit: NHSN Hospital Respiratory Data.

Source: CDC National Healthcare Safety Network (NHSN).

• Data source description  (updated November 15, 2024): As of October 9, 2024, Hospital Respiratory Data (HRD; formerly Respiratory Pathogen, Hospital Capacity, and Supply data or 'COVID-19 hospital data') are reported to HHS through CDC's National Healthcare Safety Network (NHSN) based on updated requirements from the Centers for Medicare and Medicaid Services (CMS). These data were voluntarily reported to NHSN May 1, 2024 until November 1, 2024, at which time CMS began requiring acute care and critical access hospitals to electronically report information via NHSN about COVID-19, influenza, and RSV, hospital bed census and capacity. Hospital bed capacity and occupancy data for all patients and for patients with COVID-19 or influenza for collection dates prior to May 1, 2024, represent data reported during a previously mandated reporting period as specified by the HHS Secretary, and data for collection dates May 1, 2024 – October 31, 2024 represent data reported voluntarily to NHSN. All RSV data through October 31, 2024 represent voluntarily reported data; as such, all voluntarily reported data included in this dataset represent reporting hospitals only for a given week and might not be complete or representative of all hospitals during the specified reporting periods.
• NHSN monitors national and local trends in healthcare system stress and capacity for all acute care and critical access 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. Find more information about reporting to NHSN: https://www.cdc.gov/nhsn/psc/hospital-respiratory-reporting.html.
• Data quality: While CDC reviews reported data for completeness and errors and corrects those found, some reporting errors might still exist within the data. CDC and partners work with reporters to correct these errors and update the data in subsequent weeks. Data reported as of December 1, 2020 are subject to thorough, routine data quality review procedures, including identifying and excluding invalid values from metric calculations and application of error correction methodology; data prior to this date may have anomalies that are not yet resolved. Data prior to August 1, 2020, are unavailab

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

These data contain the Age-Adjusted Colorado Census Tract Rate of Diabetes-Related Hospital Discharges (2015-2019) and Inpatient Hospitalizations per 100,000 persons based on the ICD-10 Code of E10-E14. The rates are calculated using the geocoded billing address of discharged individuals found in the dataset with the selected ICD-10 Codes and 2015-2019 Population Estimates from the American Community Survey. These data are from the Colorado Hospital Association's Hospital Discharge Dataset and are published annually by the Colorado Department of Public Health and Environment.

Graph and download economic data for Rate of Preventable Hospital Admissions (5-year estimate) in Nome Census Area, AK (DISCONTINUED) (DMPCRATE002180) from 2008 to 2015 about Nome Census Area, AK; preventable; admissions; hospitals; AK; 5-year; rate; and USA.