AHA Annual Survey Database for Fiscal Year 2020 is a comprehensive hospital database for health services research and market analysis. It is derived primarily from the AHA Annual Survey of Hospitals, which has been conducted by the American Hospital Association (AHA) or its subsidiary, Health Forum, since 1946. The survey responses are supplemented by data drawn from the American Hospital Association registration database, the US Census Bureau, hospital accrediting bodies, and other organizations. The database maintains hospital characteristics across time to allow researchers to conduct time-series analyses.

AHA Annual Survey Database™ for Fiscal Year 2022 is a comprehensive hospital database for peer comparisons, market analysis, and health services research. It is produced primarily from the AHA Annual Survey of Hospitals, which has been administered by the American Hospital Association (AHA) since 1946. The survey responses are supplemented by data drawn the U.S. Census Bureau, hospital accrediting bodies, and other organizations.

Regression results of patient experience measures.

Hospital characteristics by EHR status.

Data on community hospital beds in the United States, by state. 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.

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.

This hospitals GIS data represents the locations and selected attributes for hospitals included in the FY2005 edition of the American Hospital Association (AHA) Annual Survey Database and located in Vermont or within 25 miles of Vermont in Massachusetts, New Hampshire, or New York. Data fields detail hospital names, services, admissions, visits, beds, Medicare, health, society, structure, and location. Fields were added by the Vermont Dept. of Health (VDH) detailing hospital type and primary phone number. July 2021: Added webite hyperlinks and changed projection to WGS_1984_Web_Mercator_Auxiliary_Sphere for feeding into web maps.

A dataset of hospital bed capacity data for each of 306 U.S. hospital markets, including data for nine different models of COVID-19 infection scenarios. The data comes from a team of researchers at the Harvard Global Data Institute. They modeled various scenarios, in which 20%, 40% and 60% of the adult population would be infected with the novel coronavirus, many of whom would have no or few symptoms, and examined whether hospitals had the capacity to handle them if the cases came in over six months, 12 months and 18 months. Hospital bed figures were derived from recent surveys conducted by the American Hospital Association and data compiled by the American Hospital Directory. The data is divided into slightly more than 300 regions, also known as hospital referral regions.

The Healthcare Cost and Utilization Project (HCUP) State Inpatient Databases (SID) are a set of hospital databases that contain the universe of hospital inpatient discharge abstracts from data organizations in participating States. The data are translated into a uniform format to facilitate multi-State comparisons and analyses. The SID are based on data from short term, acute care, nonfederal hospitals. Some States include discharges from specialty facilities, such as acute psychiatric hospitals. The SID include all patients, regardless of payer and contain clinical and resource use information included in a typical discharge abstract, with safeguards to protect the privacy of individual patients, physicians, and hospitals (as required by data sources). Developed through a Federal-State-Industry partnership sponsored by the Agency for Healthcare Research and Quality (AHRQ), HCUP data inform decision making at the national, State, and community levels. The SID contain clinical and resource-use information that is included in a typical discharge abstract, with safeguards to protect the privacy of individual patients, physicians, and hospitals (as required by data sources). Data elements include but are not limited to: diagnoses, procedures, admission and discharge status, patient demographics (e.g., sex, age), total charges, length of stay, and expected payment source, including but not limited to Medicare, Medicaid, private insurance, self-pay, or those billed as ‘no charge’. In addition to the core set of uniform data elements common to all SID, some include State-specific data elements. The SID exclude data elements that could directly or indirectly identify individuals. For some States, hospital and county identifiers are included that permit linkage to the American Hospital Association Annual Survey File and county-level data from the Bureau of Health Professions' Area Resource File except in States that do not allow the release of hospital identifiers. Restricted access data files are available with a data use agreement and brief online security training.

The Healthcare Cost and Utilization Project (HCUP) State Emergency Department Databases (SEDD) contain the universe of emergency department visits in participating States. The data are translated into a uniform format to facilitate multi-State comparisons and analyses. The SEDD consist of data from hospital-based emergency department visits that do not result in an admission. The SEDD include all patients, regardless of the expected payer including but not limited to Medicare, Medicaid, private insurance, self-pay, or those billed as ‘no charge’. Developed through a Federal-State-Industry partnership sponsored by the Agency for Healthcare Research and Quality (AHRQ), HCUP data inform decision making at the national, State, and community levels.

The SEDD contain clinical and resource use information included in a typical discharge abstract, with safeguards to protect the privacy of individual patients, physicians, and facilities (as required by data sources). Data elements include but are not limited to: diagnoses, procedures, admission and discharge status, patient demographics (e.g., sex, age, race), total charges, length of stay, and expected payment source, including but not limited to Medicare, Medicaid, private insurance, self-pay, or those billed as ‘no charge’. In addition to the core set of uniform data elements common to all SEDD, some include State-specific data elements. The SEDD exclude data elements that could directly or indirectly identify individuals. For some States, hospital and county identifiers are included that permit linkage to the American Hospital Association Annual Survey File and the Bureau of Health Professions' Area Resource File except in States that do not allow the release of hospital identifiers.

Restricted access data files are available with a data use agreement and brief online security training.

The State Ambulatory Surgery Databases (SASD) contain the universe of hospital-based ambulatory surgery encounters in participating States. Some States include ambulatory surgery encounters from free-standing facilities as well. Restricted access data files are available with a data use agreement and brief online security training.

The data are translated into a uniform format to facilitate multi-State comparisons and analyses. The SASD include all patients in participating settings, regardless of payer, e.g., persons covered by Medicare, Medicaid, private insurance, and the uninsured.

The SASD contain clinical and resource use information included in a typical discharge abstract, with safeguards to protect the privacy of individual patients, physicians, and facilities (as required by data sources).

Data elements include but are not limited to: diagnoses, procedures, admission and discharge status, patient demographics (e.g., gender, age), total charges, and expected payment source (e.g., Medicare, Medicaid, private insurance, self-pay; for some States, additional discrete payer categories, such as managed care). In addition to the core set of uniform data elements common to all SASD, some include State-specific data elements, such as the patient's race. The SASD exclude data elements that could directly or indirectly identify individuals.

For some States, hospital and county identifiers are included that permit linkage to the American Hospital Association Annual Survey File and the Area Resource File.

Content

Kaiser Health News evaluated the capacity of intensive care unit (ICU) beds around the nation by first identifying the number of ICU beds each hospital reported in its most recent financial cost report, filed annually to the Centers for Medicare & Medicaid Services. KHN included beds reported in the categories of intensive care unit, surgical intensive care unit, coronary care unit and burn intensive care unit.

KHN then totaled the ICU beds per county and matched the data with county population figures from the Census Bureau’s American Community Survey. KHN focused on the number of people 60 and older in each county because older people are considered the most likely group to require hospitalization, given their increased frailty and existing health conditions compared with younger people. For each county, KHN calculated the number of people 60 and older for each ICU bed. KHN also calculated the percentage of county population who were 60 or older.

KHN’s ICU bed tally does not include Veterans Affairs hospitals, which are sure to play a role in treating coronavirus victims, because VA hospitals do not file cost reports. The total number of the nation’s ICU beds in the cost reports is less than the number identified by the American Hospital Association’s annual survey of hospital beds, which is the other authoritative resource on hospital characteristics. Experts attributed the discrepancies to different definitions of what qualifies as an ICU bed and other factors, and told KHN both sources were equally credible.

Acknowledgements

Kaiser Health News

https://khn.org/news/as-coronavirus-spreads-widely-millions-of-older-americans-live-in-counties-with-no-icu-beds/ https://khn.org/news/as-coronavirus-spreads-widely-millions-of-older-americans-live-in-counties-with-no-icu-beds/

Fred Schulte: fschulte@kff.org, @fredschulte

Elizabeth Lucas: elucas@kff.org, @eklucas

Jordan Rau: jrau@kff.org, @JordanRau

Liz Szabo: lszabo@kff.org, @LizSzabo

Jay Hancock: jhancock@kff.org, @JayHancock1

Inspiration

Your data will be in front of the world's largest data science community. What questions do you want to see answered?

The State Emergency Department Databases (SEDD) contain the universe of emergency department visits in participating States. Restricted access data files are available with a data use agreement and brief online security training.

The data are translated into a uniform format to facilitate multi-State comparisons and analyses. The SEDD consist of data from hospital-based emergency departments and include all patients, regardless of payer, e.g., persons covered by Medicare, Medicaid, private insurance, and the uninsured.

The SEDD contain clinical and resource use information included in a typical discharge abstract, with safeguards to protect the privacy of individual patients, physicians, and facilities (as required by data sources). Data elements include but are not limited to: diagnoses, procedures, admission and discharge status, patient demographics (e.g., gender, age), total charges, length of stay, and expected payment source (e.g., Medicare, Medicaid, private insurance, self-pay; for some States, additional discrete payer categories, such as managed care). In addition to the core set of uniform data elements common to all SEDD, some include State-specific data elements, such as the patient's race. The SEDD exclude data elements that could directly or indirectly identify individuals. For some States, hospital and county identifiers are included that permit linkage to the American Hospital Association Annual Survey File and the Area Resource File.

The State Emergency Department Databases (SEDD) contain the universe of emergency department visits in participating States. The data are translated into a uniform format to facilitate multi-State comparisons and analyses. The SEDD consist of data from hospital-based emergency departments and include all patients, regardless of payer, e.g., persons covered by Medicare, Medicaid, private insurance, and the uninsured. The SEDD contain clinical and resource use information included in a typical discharge abstract, with safeguards to protect the privacy of individual patients, physicians, and facilities (as required by data sources). Data elements include but are not limited to: diagnoses, procedures, admission and discharge status, patient demographics (e.g., gender, age), total charges, length of stay, and expected payment source (e.g., Medicare, Medicaid, private insurance, self-pay; for some States, additional discrete payer categories, such as managed care). In addition to the core set of uniform data elements common to all SEDD, some include State-specific data elements, such as the patient's race. The SEDD exclude data elements that could directly or indirectly identify individuals. For some States, hospital and county identifiers are included that permit linkage to the American Hospital Association Annual Survey File and the Area Resource File.

Context

Health care in the United States is provided by many distinct organizations. Health care facilities are largely owned and operated by private sector businesses. 58% of US community hospitals are non-profit, 21% are government owned, and 21% are for-profit. According to the World Health Organization (WHO), the United States spent more on healthcare per capita ($9,403), and more on health care as percentage of its GDP (17.1%), than any other nation in 2014. Many different datasets are needed to portray different aspects of healthcare in US like disease prevalences, pharmaceuticals and drugs, Nutritional data of different food products available in US. Such data is collected by surveys (or otherwise) conducted by Centre of Disease Control and Prevention (CDC), Foods and Drugs Administration, Center of Medicare and Medicaid Services and Agency for Healthcare Research and Quality (AHRQ). These datasets can be used to properly review demographics and diseases, determining start ratings of healthcare providers, different drugs and their compositions as well as package informations for different diseases and for food quality. We often want such information and finding and scraping such data can be a huge hurdle. So, Here an attempt is made to make available all US healthcare data at one place to download from in csv files.

Content

• Nhanes Survey (National Health and Nutrition Examination Survey) - The National Health and Nutrition Examination Survey (NHANES) is a program of studies designed to assess the health and nutritional status of adults and children in the United States. The survey is unique in that it combines interviews and physical examinations. NHANES is a major program of the National Center for Health Statistics (NCHS). NCHS is part of the Centers for Disease Control and Prevention (CDC) and has the responsibility for producing vital and health statistics for the Nation. The NHANES interview includes demographic, socioeconomic, dietary, and health-related questions. The examination component consists of medical, dental, and physiological measurements, as well as laboratory tests administered by highly trained medical personnel. The diseases, medical conditions, and health indicators to be studied include: Anemia, Cardiovascular disease, Diabetes, Environmental exposures, Eye diseases, Hearing loss, Infectious diseases, Kidney disease, Nutrition, Obesity, Oral health, Osteoporosis, Physical fitness and physical functioning, Reproductive history and sexual behavior, Respiratory disease (asthma, chronic bronchitis, emphysema), Sexually transmitted diseases, Vision. 10000 individuals are surveyed to represent US statistics. Five files in this datasets represent current recent Nhanes data -
  Nhanes_2005_2006.csv
  Nhanes_2007_2008.csv
  Nhanes_2009_2010.csv
  Nhanes_2011_2012.csv
  Nhanes_2013_2014.csv
  
  • Data fields' description -
    • Nhanes_2005_2006.csv - Demographic, Dietary, Examinations, Laboratory
    • Nhanes_2007_2008.csv - Demographic, Dietary, Examinations, Laboratory
    • Nhanes_2009_2010.csv - Demographic, Dietary, Examinations, Laboratory
    • Nhanes_2011_2012.csv - Demographic, Dietary, [Examinations](http://https://wwwn.cdc.gov/nchs/nhanes/search/variab...

The Medical Expenditure Panel Survey, which began in 1996, is a set of large-scale surveys of families and individuals, their medical providers (doctors, hospitals, pharmacies, etc.), and employers across the United States. MEPS collects data on the specific health services that Americans use, how frequently they use them, the cost of these services, and how they are paid for, as well as data on the cost, scope, and breadth of health insurance held by and available to U.S. workers.The files in this deposit were downloaded from the AHRQ website by Julia Dennett, Yale University, and Toby Chaiken, J-PAL North America, and archived by Travis Donahoe, Harvard University. Additional information edited by Michael Darisse and Lars Vilhuber, Cornell University and American Economic Association.

The AHA Annual Survey Information Technology hospital database contains current information on healthcare technology adoption and indicators in response to the Health Information Technology for Economic and Clinical Health (HITECH) Act. The AHA Annual Survey IT database is used to report hospital statistics on adoption of HER systems.

BackgroundData on Long COVID and its associations with burnout, anxiety and depression among healthcare workers (HCW) in the United States (U. S.) is limited.MethodsThis study utilized cross-sectional data from the final survey conducted in July 2023, which was part of a longitudinal cohort study assessing COVID-19-related burnout and wellbeing among healthcare workers (HCWs) in a large tertiary academic healthcare system in the Chicago area. The survey included questions on self-reported Long COVID status, as well as the Oldenburg Burnout Inventory (OLBI) to measure burnout and the Patient-Reported Outcomes Measurement Information System (PROMIS) computer adaptive tests (CAT) to assess anxiety and depression. A total of 1,979 HCWs participated in the survey, yielding a response rate of 56.1%.ResultsThe analysis included 1,678 respondents with complete data, of whom 1,171 (70%) self-reported having had COVID-19. Of these, 90 (7.7%) reported Long COVID, with 53% indicating that their most bothersome symptoms persisted for more than 6 months, while 50% reported no longer experiencing those symptoms at the time of the survey. Multivariable linear regression analyses revealed that Long COVID was significantly associated with higher OLBI scores (β = 2.20, p = 0.004), PROMIS anxiety scores (β = 2.64, p = 0.001) and PROMIS depression scores (β = 1.98, p = 0.011) compared to those who had COVID-19 but not Long COVID. Similar patterns of associations were observed when comparing the Long COVID group to those who never had COVID-19. No significant differences were found between those who never had COVID-19 and those who had COVID-19 without developing Long COVID.ConclusionLong COVID was associated with higher levels of burnout, depression, and anxiety among healthcare workers compared to those who had COVID-19 alone or were never infected, despite its lower prevalence during the endemic phase. These findings underscore the need for continued prevention efforts and targeted support strategies in healthcare settings.

According to a survey conducted in the U.S. in 2019, most C-suite executives deploy advanced technologies in their healthcare organization or at least consider it. Artificial Intelligence (AI) and Advanced Analytics were the most implemented and considered technologies, whereas Blockchain or Distributed Ledger Technology (DLT) were the least. Nonetheless, ** percent of the respondents deployed or sought to implement Blockchain or DLT. The use of Artificial intelligence in hospitals The global market size for AI in healthcare was forecast to grow exponentially in the years to come. Artificial intelligence is reinventing modern healthcare through machines that can predict, comprehend, learn, and act. AI will save time and cost in hospital settings by performing tasks that are typically done by humans. Unsurprisingly, a growing number of hospital structures are adopting Artificial intelligence to simplify the lives of patients and healthcare professionals. In a survey conducted in the U.S. in 2019, one out of two C-suite executives declared that AI was already deployed in their healthcare organization. Technology implementation in hospitals User-oriented technologies are at the core of the emerging market of smart hospitals. To keep up with the digital revolution of healthcare, hospitals need to embrace technology use. Patients and professionals have growing needs and demands, especially in terms of efficiency, convenience, and comfortableness of healthcare delivery.