The number of hospitals in Germany has been decreasing every year, amounting to 1,874 in 2023. By comparison, there were 2,242 hospitals in 2000. Hospital reform While there can be many reasons for a hospital closing, in Germany most of them have to do with the so-called ongoing hospital reform (Krankenhausreform) or the hospital structure law (Krankenhausstrukturgesetz). A summary of explanations from the government involves falling rural inhabitant numbers, which may lead to rural hospitals closing, and an aging population in general, therefore making it a necessity to check hospitals for being up-to-date in terms of equipment, staff and availability. Future remains to be seen On a positive note, statutory health insurance spending on hospital treatments in Germany has been increasing during the last decade, covering more than 88 million euros in 2022. The long-term effects of the hospital reform remain to be seen, as opening a new hospital after a closing, deciding on locations in rural areas, refurbishing working hospitals and hiring staff takes time and resources.

Data on hospital admission, average length of stay, outpatient visits, and outpatient surgery in the United States, by type of ownership and size of hospital. Data are from Health, United States. SOURCE: American Hospital Association (AHA) Annual Survey of Hospitals, Hospital Statistics. Search, visualize, and download these and other estimates from over 120 health topics with the NCHS Data Query System (DQS), available from: https://www.cdc.gov/nchs/dataquery/index.htm.

Graph and download economic data for Consumer Price Index for All Urban Consumers: Hospital and Related Services in U.S. City Average (CUSR0000SEMD) from Jan 1978 to May 2025 about hospitals, urban, consumer, services, CPI, inflation, price index, indexes, price, and USA.

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 as of the initial date of reporting for each weekly metric. 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 15, 2023: Due to incomplete or missing hospital data received for the June 4, 2023, through June 10, 2023, reporting period, the COVID-19 Hospital Admissions Level could not be calculated for CNMI and AS and will be reported as “NA” or “Not Available” in the COVID-19 Hospital Admissions Level data released on June 15, 2023.

July 10, 2023: Due to incomplete or missing hospital data received for the June 25, 2023, through July 1, 2023, reporting period, the COVID-19 Hospital Admissions Level could not be calculated for CNMI and AS and will be reported as “NA” or “Not Available” in the COVID-19 Hospital Admissions Level data released on July 10, 2023.

July 17, 2023: Due to incomplete or missing hospital data received for the July 2, 2023, through July 8, 2023, reporting

In 2023, nearly ** percent of the Italians with at least one hospitalization in the three months preceding the interview reported to be very much and quite satisfied with sanitary facilities in hospitals in the country. This statistic records differences in this percentage according to age group and gender of the individuals who were hospitalized. This statistic displays the share of individuals satisfied with sanitary facilities in hospitals in Italy in 2023, by age and gender.

In 2021, 42 percent of healthcare organizations in the European Union were currently using AI technologies for disease diagnosis, while a further 19 percent had plans to employ this technology within the next 3 years. Furthermore, 33 percent of healthcare organizations surveyed planned to use patient monitoring AI tools in the next 3 years How much impact does AI have on saving time in healthcare? An online survey from several European countries concluded that the implementation of AI could free up significant portions of time in healthcare – with nearly half of the hours worked by medical equipment preparers and one-third of the hours of medical assistants. While, according to another survey, physicians in Europe could spend almost ** percent more time with patients instead of administrative tasks with the help of AI. The same held true for nurses, whose time with patients would increase by *** percent thanks to AI, according to estimates. Attitudes and opinions regarding AI in healthcare In 2021, a quarter of respondents surveyed in the European Union reported trusting AI-enabled decisions in patient monitoring, higher than any other AI applications. Meanwhile, only * percent trusted AI-enabled decisions in disease diagnostics, with the majority preferring to combine it with expert judgment from healthcare professionals. Overall, the opinions of EU respondents on the impact of AI in healthcare were positive, with the majority agreeing that the use of AI could result in improvement in the quality of diagnosis decisions and treatment

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). All annual trends are indirectly standardised against 2013/14.

The publication relates to activity at consultant led outpatient services in Health and Social Care hospitals in Northern Ireland. Data includes the number of new and review attendances, missed appointments (DNAs), appointments cancelled by patients (CNAs) and appointments cancelled by hospitals, split by HSC Trust, hospital and specialty.

Source agency: Health, Social Service and Public Safety (Northern Ireland)

Designation: National Statistics

Language: English

Alternative title: Northern Ireland Outpatient Statistics

In 2024, the annual average consumer price index value of hospital services was measured at 156.8. This statistic shows the Consumer Price Index (CPI) for hospital services in the United Kingdom (UK) as an annual average from 2003 to 2024, where the year 2015 equals 100.

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. Updates to this report will be discontinued on July 29, 2021. 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.

This statistical release makes available the most recent monthly data on NHS-funded maternity services in England, using data submitted to the Maternity Services Data Set (MSDS). This is the latest report from the newest version of the data set, MSDS.v.2, which has been in place since April 2019. The new data set was a significant change which added support for key policy initiatives such as continuity of carer, as well as increased flexibility through the introduction of new clinical coding. This was a major change, so data quality and coverage initially reduced from the levels seen in earlier publications. MSDS.v.2 data completeness improved over time, and we are looking at ways of supporting further improvements. This publication also includes the National Maternity Dashboard, which can be accessed via the link below. Data derived from SNOMED codes is used in some measures such as those for birthweight, and others will follow in later publications. SNOMED data is also included in some of the published Clinical Quality Improvement Metrics (CQIMs), where rules have been applied to ensure measure rates are calculated only where data quality is high enough. System suppliers are at different stages of development and delivery to trusts. In some cases, this has limited the aspects of data that can be submitted in the MSDS. Since last month, this publication contains a new Clinical Quality Improvement Metric (CQIM) called CQIMReadmissions. This new metric reports the number of babies born in hospital then discharged home, who were then readmitted to hospital while still under 30 days old. This is supported by five new data quality metrics to ensure we only publish CQIMReadmissions figures where the underlying data is of sufficient completeness and quality. The new data quality metrics are CQIMDQ46 to CQIMDQ50. Further information about this new readmissions metric can found in this publication’s Data Quality Statement. This new data can be found in the Measures file available for download and in the CQIM and CQIM+ pages in the National Maternity Dashboard, and further information on the new metrics can be found in the accompanying Metadata file. To help Trusts understand to what extent they met the Clinical Negligence Scheme for Trusts (CNST) Maternity Incentive Scheme (MIS) Data Quality Criteria for Safety Action 2, we have been producing a CNST Scorecard Dashboard showing trust performance against this criteria. This dashboard has been updated following the release of CNST Y6 criteria, and can be accessed via the link below. The percentages presented in this report are based on rounded figures and therefore may not total to 100%.

Financial overview and grant giving statistics of Healthcare Information And Management Systems Soci

Graph and download economic data for All Employees: Health Care: Hospitals in Harrisburg-Carlisle, PA (MSA) (SMU42254206562200001A) from 1990 to 2024 about Harrisburg, hospitals, health, PA, employment, and USA.

As of October 2021, the total number of general hospitals with departments handling rheumatology in Japan amounted to almost 1.4 thousand. More than 19 percent of all the general hospitals in the country had departments of rheumatology during the measured time period.

China Number of Bed in Hospital & Health Center: Shanghai: Nanhui data was reported at 5,423.000 Unit in 2008. This records an increase from the previous number of 4,954.000 Unit for 2007. China Number of Bed in Hospital & Health Center: Shanghai: Nanhui data is updated yearly, averaging 4,414.000 Unit from Dec 2001 (Median) to 2008, with 8 observations. The data reached an all-time high of 5,423.000 Unit in 2008 and a record low of 2,937.000 Unit in 2001. China Number of Bed in Hospital & Health Center: Shanghai: Nanhui data remains active status in CEIC and is reported by National Bureau of Statistics. The data is categorized under China Premium Database’s Socio-Demographic – Table CN.GV: Number of Bed in Hospital & Health Center: County Level Region.

The Next-Generation Sequencing (NGS) Informatics market has rapidly evolved over the past decade, becoming an integral component in genomics research, personalized medicine, and various biomedical applications. This market encompasses software and analytics tools that handle the vast data generated from NGS technolo

Graph and download economic data for Expenditures: Healthcare by Income Before Taxes: Less Than $15,000 (CXUHEALTHLB0218M) from 2015 to 2023 about healthcare, health, tax, expenditures, income, and USA.

After May 3, 2024, this dataset and webpage will no longer be updated because hospitals are no longer required to report data on COVID-19 hospital admissions, and hospital capacity and occupancy data, to HHS through CDC’s National Healthcare Safety Network. Data voluntarily reported to NHSN after May 1, 2024, will be available starting May 10, 2024, at COVID Data Tracker Hospitalizations.

This time series dataset includes viral COVID-19 laboratory test [Polymerase chain reaction (PCR)] results from over 1,000 U.S. laboratories and testing locations including commercial and reference laboratories, public health laboratories, hospital laboratories, and other testing locations. Data are reported to state and jurisdictional health departments in accordance with applicable state or local law and in accordance with the Coronavirus Aid, Relief, and Economic Security (CARES) Act (CARES Act Section 18115).

Data are provisional and subject to change.

Data presented here is representative of diagnostic specimens being tested - not individual people - and excludes serology tests where possible. Data presented might not represent the most current counts for the most recent 3 days due to the time it takes to report testing information. The data may also not include results from all potential testing sites within the jurisdiction (e.g., non-laboratory or point of care test sites) and therefore reflect the majority, but not all, of COVID-19 testing being conducted in the United States.

Sources: CDC COVID-19 Electronic Laboratory Reporting (CELR), Commercial Laboratories, State Public Health Labs, In-House Hospital Labs

Data for each state is sourced from either data submitted directly by the state health department via COVID-19 electronic laboratory reporting (CELR), or a combination of commercial labs, public health labs, and in-house hospital labs. Data is taken from CELR for states that either submit line level data or submit aggregate counts which do not include serology tests.