About this dataset

Looking for a dataset on hospitals in the United States? Look no further! This dataset contains information on all of the hospitals registered with Medicare in the US, including their addresses, phone numbers, hospital type, and more. With such a large amount of data, this dataset is perfect for anyone interested in studying the US healthcare system.

This dataset can also be used to study hospital ownership, emergency services

How to use the dataset

If you want to study the US healthcare system, this dataset is perfect for you. It contains information on all of the hospitals registered with Medicare, including their addresses, phone numbers, hospital type, and more. With such a large amount of data, this dataset is perfect for anyone interested in studying the US healthcare system.

This dataset can also be used to study hospital ownership, emergency services, and EHR usage. In addition, the hospital overall rating and various comparisons are included for safety of care, readmission rates

Research Ideas

1. Predicting readmission rates for different hospital conditions
2. Analyzing relationships between hospital ownership and quality of care
3. Studying the relationship between hospital type and patient experience

Acknowledgements

This dataset was originally published by Centers for Medicare and Medicaid Services and has been modified for this project

Columns

File: Hospital_General_Information.csv | Column name | Description | |:-------------------------------------------------------|:----------------------------------------------------------------------------------------------------------| | Hospital Name | The name of the hospital. (String) | | Hospital Name | The name of the hospital. (String) | | Address | The address of the hospital. (String) | | Address | The address of the hospital. (String) | | City | The city in which the hospital is located. (String) | | City | The city in which the hospital is located. (String) | | State | The state in which the hospital is located. (String) | | State | The state in which the hospital is located. (String) | | ZIP Code | The ZIP code of the hospital. (Integer) | | ZIP Code | The ZIP code of the hospital. (Integer) | | County Name | The county in which the hospital is located. (String) | | County Name | The county in which the hospital is located. (String) | | Phone Number | The phone number of the hospital. (String) | | Phone Number | The phone number of the hospital. (String) | | Hospital Type | The type of hospital. (String) | | Hospital Type | The type of hospital. (String) | | Hospital Ownership | The ownership of the hospital. (String) | | Hospital Ownership | The ownership of the hospital. (String) | | Emergency Services | Whether or not the...

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 M

The dataset contains hours worked by hospital employee classification and by hospital cost center groupings, as well as adjusted patient days for all licensed, comparable hospitals in California. State mental hospitals and psychiatric health facilities are excluded.

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.

• hhs_ids
• previous_day_admission_adult_covid_confirmed_7_day_coverage
• previous_day_admission_pediatric_covid_confirmed_7_day_coverage
• previous_day_admission_adult_covid_suspected_7_day_coverage
• previous_day_admission_pediatric_covid_suspected_7_day_coverage
• previous_week_personnel_covid_vaccinated_doses_administered_7_day_sum
• total_personnel_covid_vaccinated_doses_none_7_day_sum
• total_personnel_covid_vaccinated_doses_one_7_day_sum
• total_personnel_covid_vaccinated_doses_all_7_day_sum
• previous_week_patients_covid_vaccinated_doses_one_7_day_sum
• previous_week_patients_covid_vaccinated_doses_all_7_day_sum

On May 8, 2021, this data set is the originally reported numbers by the facility. This data set may contain data anomalies due to data key entries.

On May 13, 2021 Changed vaccination fields from sum to max or min fields. This reflects the maximum or minimum number reported for that metric in a given week.

On June 7, 2021 Changed vaccination fields from max or min fields to Wednesday reported only. This reflects that the number reported for that metric is only reported on Wednesdays in a given week.

On January 19, 2022, the following fields have been added to this dataset:

• inpatient_beds_used_covid_7_day_avg
• inpatient_beds_used_covid_7_day_sum
• inpatient_beds_used_covid_7_day_coverage

On April 28, 2022, the following pediatric fields have been added to this dataset:

• all_pediatric_inpatient_bed_occupied_7_day_avg
• all_pediatric_inpatient_bed_occupied_7_day_coverage
• all_pediatric_inpatient_bed_occupied_7_day_sum
• all_pediatric_inpatient_beds_7_day_avg
• all_pediatric_inpatient_beds_7_day_coverage
• all_pediatric_inpatient_beds_7_day_sum
• previous_day_admission_pediatric_covid_confirmed_0_4_7_day_sum
• previous_day_admission_pediatric_covid_confirmed_12_17_7_day_sum
• previous_day_admission_pediatric_covid_confirmed_5_11_7_day_sum
• previous_day_admission_pediatric_covid_confirmed_unknown_7_day_sum
• staffed_icu_pediatric_patients_confirmed_covid_7_day_avg
• staffed_icu_pediatric_patients_confirmed_covid_7_day_coverage
• staffed_icu_pediatric_patients_confirmed_covid_7_day_sum
• staffed_pediatric_icu_bed_occupancy_7_day_avg
• staffed_pediatric_icu_bed_occupancy_7_day_coverage
• staffed_pediatric_icu_bed_occupancy_7_day_sum
• total_staffed_pediatric_icu_beds_7_day_avg
• total_staffed_pediatric_icu_beds_7_day_coverage
• total_staffed_pediatric_icu_beds_7_day_sum

Due to changes in reporting requirements, after June 19, 2023, a collection week is defined as starting on a Sunday and ending on the next Saturday.

The number of hospitals in the United States was forecast to continuously decrease between 2024 and 2029 by in total 13 hospitals (-0.23 percent). According to this forecast, in 2029, the number of hospitals will have decreased for the twelfth consecutive year to 5,548 hospitals. Depicted is the number of hospitals in the country or region at hand. As the OECD states, the rules according to which an institution can be registered as a hospital vary across countries.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of hospitals in countries like Canada and Mexico.

US Healthcare Readmissions and Mortality

Evaluating Hospital Performance

By Health [source]

About this dataset

This dataset contains detailed information about 30-day readmission and mortality rates of U.S. hospitals. It is an essential tool for stakeholders aiming to identify opportunities for improving healthcare quality and performance across the country. Providers benefit by having access to comprehensive data regarding readmission, mortality rate, score, measure start/end dates, compared average to national as well as other pertinent metrics like zip codes, phone numbers and county names. Use this data set to conduct evaluations of how hospitals are meeting industry standards from a quality and outcomes perspective in order to make more informed decisions when designing patient care strategies and policies

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How to use the dataset

This dataset provides data on 30-day readmission and mortality rates of U.S. hospitals, useful in understanding the quality of healthcare being provided. This data can provide insight into the effectiveness of treatments, patient care, and staff performance at different healthcare facilities throughout the country.

In order to use this dataset effectively, it is important to understand each column and how best to interpret them. The ‘Hospital Name’ column displays the name of the facility; ‘Address’ lists a street address for the hospital; ‘City’ indicates its geographic location; ‘State’ specifies a two-letter abbreviation for that state; ‘ZIP Code’ provides each facility's 5 digit zip code address; 'County Name' specifies what county that particular hospital resides in; 'Phone number' lists a phone contact for any given facility ;'Measure Name' identifies which measure is being recorded (for instance: Elective Delivery Before 39 Weeks); 'Score' value reflects an average score based on patient feedback surveys taken over time frame listed under ' Measure Start Date.' Then there are also columns tracking both lower estimates ('Lower Estimate') as well as higher estimates ('Higher Estimate'); these create variability that can be tracked by researchers seeking further answers or formulating future studies on this topic or field.; Lastly there is one more measure oissociated with this set: ' Footnote,' which may highlight any addional important details pertinent to analysis such as numbers outlying National averages etc..

This data set can be used by hospitals, research facilities and other interested parties in providing inciteful information when making decisions about patient care standards throughout America . It can help find patterns about readmitis/mortality along county lines or answer questions about preformance fluctuations between different hospital locations over an extended amount of time. So if you are ever curious about 30 days readmitted within US Hospitals don't hesitate to dive into this insightful dataset!

Research Ideas

• Comparing hospitals on a regional or national basis to measure the quality of care provided for readmission and mortality rates.
• Analyzing the effects of technological advancements such as telemedicine, virtual visits, and AI on readmission and mortality rates at different hospitals.
• Using measures such as Lower Estimate Higher Estimate scores to identify systematic problems in readmissions or mortality rate management at hospitals and informing public health care policy

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: Readmissions_and_Deaths_-_Hospital.csv | Column name | Description | |:-------------------------|:---------------------------------------------------------------------------------------------------| | Hospital Name ...

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.

THE IMPACTS OF WORKING REMOTELY AND IN AN OFFICE

Abstract: Working from home nowadays, particularly after COVID-19 hit the world, became the preferable choice for many employees because it gives flexibility and saves more time, according to them. However, many studies revealed that working from home caused a negative effect on many employees’ mental and physical health, such as isolation and back pain. The careless and unplanned way of living while working remotely, such as lack of socialization and equipment for a healthy home office, is the cause for that negative effect. In this paper, we explore the reasons that lead to the negative impact of working remotely on mental and physical health and investigate whether employees are aware of the negative and the positive effects of working either from home or in an office. Our investigation involved a questionnaire handed to hundred employees and revealed that the majority of them were aware of the negative and the positive impacts of working remotely and in an office and suggest, therefore, a mixed-mode of working to obtain the best advantages of both modes.

Keywords: COVID-19; working from home; working in an office; questionnaire; advantages; disadvantages; negative impact; positive impact; mental health; physical health; work experience

1. Introduction

Who would not like to wake up late and avoid the traffic every morning? I always had dreamed of that, and I guess you too. Working from home, which provides these advantages, has become the preferred choice for many employees and employers for the sake of getting more flexibility, increasing productivity, and saving time and money (Ipsen et al., 2021). I have noticed, especially during the COVID-19 pandemic, that many people switched willingly to work from home, expecting their life would totally improve. On the other hand, many people do not have the office work option. For instance, people work in the human resources, marketing, and customer service sectors (Iacurci, 2021). They work remotely until a hundred percent effective covid vaccine is developed. However, many studies, such as "Survey reveals the mental and physical health impacts of home working during Covid-19" by RSPH (2021), revealed that people who work from home are likely to suffer from mental and physical disorders.

In fact, the reason for the negative impact is not the work from home. Rather, it is the unmanaged lifestyle that comes with working from home. Of course, many other jobs still need people to be physically present, such as working in hospitals and beauty centers. However, Iacurci (2021) suggests that people will work remotely even after the pandemic finishes and the economy reopens. While many people are switching to work from home, and many others hoping so, it might be an opportunity for them to know the negative impact of working remotely, such as isolation and back pain, due to lack of socialization and equipment for a healthy home office. I am not willing to tell people what they should do in order to work healthily from home because this is not my study field. However, because I have experienced that negative impact, I will only give hints about the consequences, which could happen if they did not take care of themselves when working from home. Thus, this research investigated hundred people who have already worked before, regardless of gender identity, whether they are aware of the negative and the positive impacts of working from home in order to take care of themselves.

2. Literature review

Reviewing Worker's and Employees' Opinions in Working from Home

Before the COVID-19 pandemic, people could choose between working from home and in an office. However, many people are forced or got the opportunity to work from home to reduce the number of new daily infections during the pandemic. Thus, it was an opportunity for researchers to do research on a large number of people to figure out how working from home experience affected them. Also, after the pandemic is over, what would they prefer if they could choose between working remotely or being physically in an office.

In the study, "Six key advantages and disadvantages of working from home in Europe during COVID-19," Ipsen et al. (2021) investigated employees who have experience with working from home during the pandemic in 29 European countries. They used first the six key advantages and disadvantages approach, which involves the employees' opinions in working from home. Although the employees mentioned 16 disadvantages and 11 advantages, its results indicate that "the majority (55%) of employees were mostly positive about WFH" (p. 11). However, they assumed that maybe there are other circumstances that make the employees prefer working remotely over in an office. Hence, Ipsen et al. (2021) used the six factors approach, which involved the employe...

The summation contains updated data which reflect all corrections made by HCAI audit staff and hospital representatives. Each file consists of one rolling 4th quarter file for the respective calendar year of data. Comparison of the previously released data files with the revised data files may not have a material effect on statewide aggregations, but may have a significant effect on the data for individual hospitals.

NAPSG Foundation simply changed the symbology based on bed utilization and filter set to Hospital Type is not 'Psychiatric Hospital'. NAPSG Foundation is not the host of this dataset, see notes below for more details.Also - it is not clear how often the bed utilization rate updates, but it is now presumed to be 4x per year. THIS IS NOT A REAL-TIME DATASET.Definitive Healthcare is the leading provider of data, intelligence, and analytics on healthcare organizations and practitioners. In this service, Definitive Healthcare provides intelligence on the numbers of licensed beds, staffed beds, ICU beds, and the bed utilization rate for the hospitals in the United States. Please see the following for more details about each metric, data was last updated on 17 March 2020:Number of Licensed beds: is the maximum number of beds for which a hospital holds a license to operate; however, many hospitals do not operate all the beds for which they are licensed. This number is obtained through DHC Primary Research. Licensed beds for Health Systems are equal to the total number of licensed beds of individual Hospitals within a given Health System. Number of Staffed Bed: is defined as an "adult bed, pediatric bed, birthing room, or newborn ICU bed (excluding newborn bassinets) maintained in a patient care area for lodging patients in acute, long term, or domiciliary areas of the hospital." Beds in labor room, birthing room, post-anesthesia, postoperative recovery rooms, outpatient areas, emergency rooms, ancillary departments, nurses and other staff residences, and other such areas which are regularly maintained and utilized for only a portion of the stay of patients (primarily for special procedures or not for inpatient lodging) are not termed a bed for these purposes. Definitive Healthcare sources Staffed Bed data from the Medicare Cost Report or Proprietary Research as needed. As with all Medicare Cost Report metrics, this number is self-reported by providers. Staffed beds for Health Systems are equal to the total number of staffed beds of individual Hospitals within a given Health System. Total number of staffed beds in the US should exclude Hospital Systems to avoid double counting. ICU beds are likely to follow the same logic as a subset of Staffed beds. Number of ICU Beds - ICU (Intensive Care Unit) Beds: are qualified based on definitions by CMS, Section 2202.7, 22-8.2. These beds include ICU beds, burn ICU beds, surgical ICU beds, premature ICU beds, neonatal ICU beds, pediatric ICU beds, psychiatric ICU beds, trauma ICU beds, and Detox ICU beds. Bed Utilization Rate: is calculated based on metrics from the Medicare Cost Report: Bed Utilization Rate = Total Patient Days (excluding nursery days)/Bed Days AvailablePotential Increase in Bed Capacity: This metric is computed by subtracting “Number of Staffed Beds from Number of Licensed beds” (Licensed Beds – Staffed Beds). This would provide insights into scenario planning for when staff can be shifted around to increase available bed capacity as needed. Hospital Definition: Definitive Healthcare defines a hospital as a healthcare institution providing inpatient, therapeutic, or rehabilitation services under the supervision of physicians. In order for a facility to be considered a hospital it must provide inpatient care. Hospital types are defined by the last four digits of the hospital’s Medicare Provider Number. If the hospital does not have a Medicare Provider Number, Definitive Healthcare determines the Hospital type by proprietary research. Hospital Types:· Short Term Acute Care Hospital (STAC)o Provides inpatient care and other services for surgery, acute medical conditions, or injurieso Patients care can be provided overnight, and average length of stay is less than 25 days· Critical Access Hospital (CAH)o 25 or fewer acute care inpatient bedso Located more than 35 miles from another hospitalo Annual average length of stay is 96 hours or less for acute care patientso Must provide 24/7 emergency care serviceso Designation by CMS to reduce financial vulnerability of rural hospitals and improve access to healthcare· Religious Non-Medical Health Care Institutionso Provide nonmedical health care items and services to people who need hospital or skilled nursing facility care, but for whom that care would be inconsistent with their religious beliefs· Long Term Acute Care Hospitalso Average length of stay is more than 25 dayso Patients are receiving acute care - services often include respiratory therapy, head trauma treatment, and pain management· Rehabilitation Hospitalso Specializes in improving or restoring patients' functional abilities through therapies· Children’s Hospitalso Majority of inpatients under 18 years old· Psychiatric Hospitalso Provides inpatient services for diagnosis and treatment of mental illness 24/7o Under the supervision of a physician· Veteran's Affairs (VA) Hospital o Responsible for the care of war veterans and other retired military personnelo Administered by the U.S. VA, and funded by the federal government· Department of Defense (DoD) Hospitalo Provides care for military service people (Army, Navy, Air Force, Marines, and Coast Guard), their dependents, and retirees (not all military service retirees are eligible for VA services) For more information please visit - https://www.definitivehc.com/ - or contact sales@definitivehc.com

A. SUMMARY This dataset includes hospital diversion events declared by San Francisco hospitals.

B. HOW THE DATASET IS CREATED San Francisco hospitals can declare ambulance diversion status, which diverts all ambulance transports away from the hospital of interest except certain specialty calls. This dataset contains number of diversion hours for each hospital. Each record includes the hospital name, the date and time diversion status started, the date and time diversion status ended, and duration of diversion status.

C. UPDATE PROCESS The data is updated monthly by San Francisco Emergency Medical Services Agency.

D. HOW TO USE THIS DATASET Hospitals are allowed to go on diversion for a maximum of 2 hours before they must re-declare diversion. If 4 or more hospitals go on diversion at the same time, diversion is suspended across all hospitals which means that no hospitals can go on diversion for the next 4 hours. The exception is San Francisco General (SFG) hospital. SFG can declare Trauma Override (functionally identical to hospital diversion) while diversion is suspended since it is San Francisco’s only trauma center. Please refer to the Hospital Suspensions dataset for more information on diversion suspension.

"The dataset contains hours worked by hospital employee classification and by hospital cost center groupings."

"The dataset contains hours worked by hospital employee classification and by hospital cost center groupings, as well as adjusted patient days for all licensed, comparable hospitals in California. State mental hospitals and psychiatric health facilities are excluded."

Source: http://hcai.ca.gov/HID/DataFlow/ Last updated at https://data.chhs.ca.gov : 2022-04-22 License: https://data.chhs.ca.gov/pages/terms

https://data.world/chhs/2417ea23-4b40-4953-b98d-6e26bc895a70

Image: https://www.aha.org/news/headline/2020-07-29-report-examines-clinical-staffing-considerations-when-implementing-crisis

The summation contains updated data which reflect all corrections made by HCAI audit staff and hospital representatives. Each file consists of one rolling 4th quarter file for the respective calendar year of data. Comparison of the previously released data files with the revised data files may not have a material effect on statewide aggregations, but may have a significant effect on the data for individual hospitals.

VA All Employee Survey (AES) data from the 2022 & 2023 AES administrations. Scores are provided at the station level and up, and the occupation level within hospitals.

Department of State Hospitals (DSH)-wide Violence Data Annual Rates of Assault from 2010-2020 for the following groups: Patient Assault (A2), Staff Assault (A4). A2 - Patient physical assaults are committed by another patient. Formally defined as “Aggressive Act to Another Patient - Physical: Hitting, pushing, kicking or similar acts directed against another individual to cause potential or actual injury.” This does not include verbal assault, which is coded as “A1.” A4 – Staff physical assaults are committed by a patient. Formally defined as “Aggressive Act to Staff - Physical: Hitting, pushing, kicking, or similar acts directed against a staff person that could cause potential or actual injury.” This does not include verbal assault, which is coded as “A3.” Please Note: 1.Please note that it is an update to the previously published dataset with additional datasets. 2.Violence Rates value (in previous publication) can be calculated as a number per 1000 Patient Days. This number is easily interpreted and enables more accurate comparisons across time. 3.Prior to January 1, 2016 DSH-Atascadero coded an assault as Patient on Staff (A4) only when physical contact was made between patient and staff. All other Department of State Hospitals (DSH)- facilities code an assault as Patient on Staff (A4) either when physical contact was made or when physical contact was attempted. On January 1, 2016 Department of State Hospitals (DSH)--Atascadero began coding assaults in the same manner as all other Department of State Hospitals (DSH)- facilities. 4.Prior to January 1, 2016 Violence incidents were not captured specifically as Physical Contact made or Physical Contact Attempted.

A. SUMMARY This dataset includes system-wide hospital diversion suspension information.

B. HOW THE DATASET IS CREATED This dataset contains the date and time diversion suspension began and ended. Hospitals can voluntarily declare diversion, which diverts all ambulance transports away from the given hospital excepting specialty care transports. However, when 4 or more hospitals go on diversion at the same time, diversion is suspended system-wide for 4 hours. This is called diversion suspension.

C. UPDATE PROCESS The data is updated monthly by San Francisco Emergency Medical Services Agency.

D. HOW TO USE THIS DATASET When 4 or more San Francisco hospitals declare diversion status, diversion gets suspended for all hospitals. Diversion suspension lasts for 4 hours until it ends and hospitals can declare diversion again. Please refer to the Hospital Diversions dataset for more information on diversion.

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 San Francisco Department of Public Health.

C. UPDATE PROCESS Updated daily, dataset uploaded manually by staff

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).

Data shown here include all San Francisco hospitals and will be updated daily with a two-day lag as information is collected and verified. Data may change as more current information becomes available.

On 11/14/2025, we launched updated hospitalization reporting using data from the National Healthcare Safety Network (NHSN). The new dataset includes hospital admissions for respiratory viruses including COVID-19, flu, and RSV. You can access the new dataset here.

A. SUMMARY This dataset includes information on COVID+ hospital admissions for San Francisco residents into San Francisco hospitals. Specifically, the dataset includes the count and rate of COVID+ hospital admissions per 100,000. The data are reported by week.

B. HOW THE DATASET IS CREATED Hospital admission data is reported to the San Francisco Department of Public Health (SFDPH) via the COVID Hospital Data Repository (CHDR), a system created via health officer order C19-16. The data includes all San Francisco hospitals except for the San Francisco VA Medical Center.

San Francisco population estimates are pulled from a view based on the San Francisco Population and Demographic Census dataset. These population estimates are from the 2018-2022 5-year American Community Survey (ACS).

C. UPDATE PROCESS Data updates weekly on Wednesday with data for the past Wednesday-Tuesday (one week lag). Data may change as more current information becomes available.

D. HOW TO USE THIS DATASET New admissions are the count of COVID+ hospital admissions among San Francisco residents to San Francisco hospitals by week.

The admission rate per 100,000 is calculated by multiplying the count of admissions each week by 100,000 and dividing by the population estimate.

E. CHANGE LOG

• 11/14/2025 COVID-19 hosipital admissions is tracked in a new dataset
• 7/18/2025 - Dataset update is paused to assess data quality and completeness.
• 9/12/2024 - We updated the data source for our COVID-19 hospitalization data to a San Francisco specific dataset. These new data differ slightly from previous hospitalization data sources but the overall patterns and trends in hospitalizations remain consistent. You can access the previous data here.

This dataset contains locations and attributes of hospitals licensed by the state of Wisconsin. The data are used for planning, management and analysis by Wisconsin Department of Health Services staff and by other government agencies. For additional attributes, please use the Wisconsin Hospitals Extended Attributes .csv file. The FACILITY_INTERNAL_ID field can be used to join the hospitals dataset with this .csv file.For more information please visit the Wisconsin Department of Health Services website: https://www.dhs.wisconsin.gov/guide/hospital.htm

Use real hospital operational data to proactively forecast how many patients might come back, letting you: 1. Allocate resources more efficiently 2. Investigate departments with higher readmissions 3, Improve discharge planning and post-discharge follow-ups

📌 1. num_patients_admitted (Integer)

What it is: Total number of patients admitted to the hospital on a particular day.

How to record it: Hospital reception or electronic health record (EHR) system logs this every day.

Why it matters: More patients → More workload → Higher chance of readmissions due to strain on care.

📌 2. avg_length_of_stay (Float, in days)

What it is: The average duration (in days) patients stay in the hospital that day.

How to record it: For each discharged patient, calculate how many days they stayed → average them all.

Why it matters: Longer stays may mean severe illness, but too-short stays may result in readmission due to premature discharge.

📌 3. avg_lab_result_score (Float, 0–100 scale)

What it is: Simplified index of average critical lab values (like blood sugar, creatinine, hemoglobin) for all patients that day.

How to record it: Extract key lab metrics for all patients, normalize to 0–100, and take the average.

Why it matters: Poor lab results = higher health risk = higher readmission chances.

📌 4. hospital_resource_utilization (Float, 0–1)

What it is: Percentage of hospital beds, ICUs, and staff used on that day.

How to record it: Resource tracking system → (resources used / total resources) = utilization ratio.

Why it matters: A busy hospital might discharge patients early or provide less attention → more readmissions.

🎯 Target: readmission_rate (Float, 0–1)

What it is: Fraction of patients discharged today who come back within 30 days.

Example: If 100 patients are discharged and 8 come back in 30 days → readmission rate = 0.08

How to record it: Match discharge records with re-admission logs from the same patient within 30 days.