In 2022, the occupancy rate of hospitals in the U.S. stood at ** percent. In the recorded time period, the highest occupancy rate was **** percent back in 1969. Hospital occupancy rate has mostly decreased since then, even though the number of hospital beds has also decreased. In 2020, during the COVID pandemic, occupancy rate reached a historical low of **** percent. The last time this occurred was in 1996. Number of hospitals In 2022, there were around ***** hospitals in operation in the U.S., compared to ***** hospitals in the year 1995. There has been a decline in the number of hospitals in the U.S. starting as far back as the 1970s, despite a growing overall population and increasing elderly population. Most hospitals in the U.S. are non-profit, while a smaller proportion are for-profit or state/government hospitals. Economic impact Hospitals contribute to an economy in many ways. In 2020, this total contribution in the U.S. was around *** trillion dollars. At that time, hospitals contributed over *** trillion dollars in wages and salaries. As of 2022, there were an estimated **** million people employed in hospitals across the United States.

In 2021, the occupancy rate of curative (acute) care beds in hospitals in Canada was almost 87 percent. In the year 2000, Canada had an acute care bed occupancy rate of around 89 percent. High occupancy rates may be a sign that a health system is under pressure and that there may be a risk of bed shortages and higher rates of infection. This statistic shows the occupancy rates of acute care beds in hospitals in select countries worldwide in 2010 and 2021.

Hospital bed occupancy rates by type of bed.......

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.

Bed Occupancy Rate: Emergency Department: Banten: Tangerang Regency data was reported at 0.806 % in 09 Oct 2022. This stayed constant from the previous number of 0.806 % for 08 Oct 2022. Bed Occupancy Rate: Emergency Department: Banten: Tangerang Regency data is updated daily, averaging 2.963 % from Aug 2021 (Median) to 09 Oct 2022, with 331 observations. The data reached an all-time high of 24.832 % in 16 Aug 2021 and a record low of 0.000 % in 05 Jul 2022. Bed Occupancy Rate: Emergency Department: Banten: Tangerang Regency data remains active status in CEIC and is reported by Ministry of Health. The data is categorized under Indonesia Premium Database’s Health Sector – Table ID.HLA020: Hospital Bed Occupancy Rate: Emergency Department: by Regency/Municipality (Discontinued).

Germany Patients: Average Bed Occupancy Rate data was reported at 71.200 % in 2023. This records an increase from the previous number of 69.000 % for 2022. Germany Patients: Average Bed Occupancy Rate data is updated yearly, averaging 77.500 % from Dec 1991 (Median) to 2023, with 33 observations. The data reached an all-time high of 84.100 % in 1991 and a record low of 67.300 % in 2020. Germany Patients: Average Bed Occupancy Rate data remains active status in CEIC and is reported by Statistisches Bundesamt. The data is categorized under Global Database’s Germany – Table DE.G057: Health Care Statistics.

As of the end of December 2023, **** percent of the hospital beds in Japan were occupied. During the same month, approximately **** million people were hospitalized across the country per day.

This statistic depicts the the occupancy rate of hospital beds in Massachusetts during fiscal years 2013 to 2020, sorted by type of hospital. In 20208, the occupancy rate for teaching hospitals was around 71 percent.

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.

Bed Occupancy Rate: Emergency Department: Central Java: Kudus Regency data was reported at 0.000 % in 09 Oct 2022. This stayed constant from the previous number of 0.000 % for 08 Oct 2022. Bed Occupancy Rate: Emergency Department: Central Java: Kudus Regency data is updated daily, averaging 0.000 % from Aug 2021 (Median) to 09 Oct 2022, with 361 observations. The data reached an all-time high of 3.922 % in 25 Feb 2022 and a record low of 0.000 % in 09 Oct 2022. Bed Occupancy Rate: Emergency Department: Central Java: Kudus Regency data remains active status in CEIC and is reported by Ministry of Health. The data is categorized under Indonesia Premium Database’s Health Sector – Table ID.HLA020: Hospital Bed Occupancy Rate: Emergency Department: by Regency/Municipality (Discontinued).

This dataset provides detailed forecasts of hospital bed occupancy rates by department and time, incorporating expected admissions, discharges, and resource utilization metrics. It enables healthcare administrators to anticipate capacity needs, optimize resource allocation, and improve patient care delivery through data-driven planning. The dataset supports both operational and strategic decision-making in hospital management.

The occupancy rate for Apollo Hospitals in fiscal year 2024 was ** percent. This was a decrease compared to financial year 2014 when it was ** percent. The occupancy rate for a hospital depends on the maturity of hospitals as mature hospitals tend to get more references for treatments compared to lesser matured hospitals. In addition to this, the occupancy rate also depends on the availability of required hospitals, skills of the doctors, standard of the hospitals, and the quality of results and treatment provided by hospitals.

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

Indonesia Bed Occupancy Rate: Emergency Department: Central Kalimantan: Katingan Regency data was reported at 0.000 % in 09 Oct 2022. This stayed constant from the previous number of 0.000 % for 08 Oct 2022. Indonesia Bed Occupancy Rate: Emergency Department: Central Kalimantan: Katingan Regency data is updated daily, averaging 0.000 % from Sep 2021 (Median) to 09 Oct 2022, with 344 observations. The data reached an all-time high of 0.000 % in 09 Oct 2022 and a record low of 0.000 % in 09 Oct 2022. Indonesia Bed Occupancy Rate: Emergency Department: Central Kalimantan: Katingan Regency data remains active status in CEIC and is reported by Ministry of Health. The data is categorized under Indonesia Premium Database’s Health Sector – Table ID.HLA020: Hospital Bed Occupancy Rate: Emergency Department: by Regency/Municipality (Discontinued).

This dataset provides information on the total number of available and occupied bed days for facility wards open "overnight" and "day only" by NHS (National Health Service) Trust by Sector.

This data covers a time period during the coronavirus (COVID-19) pandemic, which has affected NHS services. During the pandemic, hospital services in Wales were reorganised due to enhanced infection prevention and control measures, and the need to treat COVID and non-COVID patients separately. Subsequently, planned operations were significantly reduced and non-urgent emergency admissions decreased. As a result, hospitals experienced lower occupancy rates in 2020-21 than in previous years. This table presents summary information, from the QueSt1 return, provided by the NHS Wales Informatics Service (NWIS), on bed use in Wales. Data presented in this statistical release are an annual average and illustrate yearly changing occupancy rates and bed availability. Therefore, these data won’t reflect changing levels of activity throughout the year. The data do not present data on average length of stay, turnover interval and bed use factor. These indicators are calculated using data on deaths and discharges which is no longer collected via the QS1 return, and need to be derived from the Patient Episode Database for Wales (PEDW) for 2012-13 onwards. When carrying out more detailed analysis of the deaths and discharges data from PEDW in preparation for the 2012-13 release, data quality issues arose in relation to assessment unit (AU) activity reporting in QS1 and in PEDW and how this should be treated in the data. It was identified that there is inconsistency in the reporting of assessment units, with some LHBs reporting AU activity within their beds data, and others omitting them. This inconsistency in the reporting of AU activity is also likely to affect historic data. Please find information on changes to the data published on NHS beds, as per the given weblink.

The Hospital Inpatient Occupancy data set includes various service-related metrics and indicators related to the calculated occupancy rate of the hospitals found in Nova Scotia Health and the IWK. This data includes measures for the calculated occupancy rates for the following areas: Acute Inpatient, Mental Health and Addictions Inpatient and Total Inpatient.

The global market for hospital shared accompanying beds is experiencing robust growth, driven by increasing hospital occupancy rates, a rising elderly population requiring more inpatient care, and a greater emphasis on improving patient comfort and experience. The market is segmented by application (small and medium hospitals, large hospitals) and type (accompanying chair type, bedside table type, card slot type). While precise market sizing data was not provided, a reasonable estimation based on similar medical equipment markets suggests a 2025 market value of approximately $500 million, growing at a Compound Annual Growth Rate (CAGR) of 7% from 2025 to 2033. This growth is fueled by technological advancements leading to more comfortable, versatile, and space-saving designs, coupled with increasing adoption of shared rooms to manage healthcare costs effectively. The accompanying chair type currently holds the largest market share due to its versatility and affordability, but bedside table and card slot types are expected to witness significant growth as hospitals adopt more modern and integrated patient care systems. Geographic distribution reveals that North America and Europe currently dominate the market due to higher healthcare spending and established healthcare infrastructure. However, rapid growth is anticipated in the Asia-Pacific region, particularly in countries like China and India, driven by increasing healthcare investments and growing healthcare awareness. Restraints to market growth include the high initial cost of these beds and the potential for logistical complexities in managing shared accommodation within hospitals. Nevertheless, the long-term benefits in terms of improved patient experience and cost efficiency are expected to outweigh these limitations, ensuring continued expansion of this niche market. Companies like AIPEI SHARING, MEI PEI TECHNOLOGY, and Mengyuan are key players, actively contributing to innovation and market expansion through their product offerings.

In 2021, King Abdullah Medical City in Makkah had a bed occupancy rate of around ** per 100 beds. In that year, almost ** percent of inpatients at the Ministry of Health (MOH) hospitals in Makkah in Saudi Arabia were Saudi nationals.

Bed Occupancy Rate: Emergency Department: East Java: Probolinggo Municipality data was reported at 0.000 % in 09 Oct 2022. This records a decrease from the previous number of 16.667 % for 08 Oct 2022. Bed Occupancy Rate: Emergency Department: East Java: Probolinggo Municipality data is updated daily, averaging 0.000 % from Aug 2021 (Median) to 09 Oct 2022, with 370 observations. The data reached an all-time high of 100.000 % in 22 Feb 2022 and a record low of 0.000 % in 09 Oct 2022. Bed Occupancy Rate: Emergency Department: East Java: Probolinggo Municipality data remains active status in CEIC and is reported by Ministry of Health. The data is categorized under Indonesia Premium Database’s Health Sector – Table ID.HLA020: Hospital Bed Occupancy Rate: Emergency Department: by Regency/Municipality (Discontinued).