To effectively utilise hospital beds, operating rooms (OR) and other treatment spaces, it is necessary to precisely plan patient admissions and treatments in advance. As patient treatment and recovery times are unequal and uncertain, this is not easy. In response a sophisticated flexible job-shop scheduling (FJSS) model is introduced, whereby patients, beds, hospital wards and health care activities are respectively treated as jobs, single machines, parallel machines and operations. Our approach is novel because an entire hospital is describable and schedulable in one integrated approach. The scheduling model can be used to recompute timings after deviations, delays, postponements and cancellations. It also includes advanced conditions such as activity and machine setup times, transfer times between activities, blocking limitations and no wait conditions, timing and occupancy restrictions, buffering for robustness, fixed activities and sequences, release times and strict deadlines. To solve the FJSS problem, constructive algorithms and hybrid meta-heuristics have been developed. Our numerical testing shows that the proposed solution techniques are capable of solving problems of real world size. This outcome further highlights the value of the scheduling model and its potential for integration into actual hospital information systems.

The "COVID-19 Reported Patient Impact and Hospital Capacity by Facility" dataset from the U.S. Department of Health & Human Services, filtered for Connecticut. View the full dataset and detailed metadata here: https://healthdata.gov/Hospital/COVID-19-Reported-Patient-Impact-and-Hospital-Capa/anag-cw7u

The following dataset provides facility-level data for hospital utilization aggregated on a weekly basis (Friday to Thursday). 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 2020-11-20 means the average/sum/coverage of the elements captured from that given facility starting and including Friday, November 20, 2020, and ending and including reports for Thursday, November 26, 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”.

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.

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 has been converted to a corrected data set. The corrections applied to this data set are to smooth out data anomalies caused by keyed in data errors. To help determine which records have had corrections made to it. An additional Boolean field called is_corrected has been added. To see the numbers as reported by the facilities, go to: https://healthdata.gov/Hospital/COVID-19-Reported-Patient-Impact-and-Hospital-Capa/uqq2-txqb

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 collected fields only. This reflects that these fields are only reported on Wednesdays in a given week.

On 9/20/2021, the following has been updated: The use of analytic dataset as a source.

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_

The China Dimensions Data Collection: Chinese County-Level Data on Hospitals and Epidemiology Stations, 1950-1985 consists of hospital and epidemiological station data for the administrative regions of China from 1950 to 1985. The data includes name and years of operation at a scale of one to one million (1:1M) at the county level. This data set is produced in collaboration with the University of Washington as part of the China in Time and Space (CITAS) project and the Center for International Earth Science Information Network (CIESIN).

This study was undertaken for the purpose of providing baseline national indicators of access to health care for an evaluation of a program of hospital-based primary care group practices funded by the Robert Wood Johnson Foundation. The main objective of that large-scale social experiment was to improve access to medical care for the population in areas served by the groups. The access framework and questionnaires designed for the study were developed to provide empirical indicators of the concept that could be used to monitor progress toward this objective. Five data collection instruments were used by the study: the Household Enumeration Folder, the Main Questionnaire, the Health Opinions Questionnaire, the Physician Supplement, and the Hospital/Extended Care Supplement. The Household Enumeration Folder collected basic demographic information on all household members and served as a screener for the episode of illness and minority oversamples. The Main Questionnaire collected information on disability, symptoms of illness, episodes of illness, socioeconomic and demographic characteristics, and access to health care: sources of medical care utilized, problems associated with access to sources of care (e.g., transportation, parking, waiting time for an appointment), satisfaction with medical services received, utilization of medical diagnostic procedures, dental care, and eye care, and insurance coverage and out-of-pocket expenditures for health care. Respondents' opinions concerning the medical care that they received were gauged by the Health Opinions Questionnaire. The Physician Supplement and the Hospital/Extended Care Supplement collected information on physicians contacted and facilities utilized in connection with reported episodes of illness. File 1, File 2, and File 3 constitute the data files for this collection. File 1 comprises data from the Household Enumeration Folder, the Main Questionnaire, and the Health Opinions Questionnaire, plus variables from secondary sources, such as characteristics, derived from the American Medical Association Physician Masterfile, of physicians named as caregivers by respondents, and medical shortage data, from various sources, for the respondent's county of residence. File 2 contains the data from the Physician Supplement, while File 3 provides the data collected by the Hospital/Extended Care Supplement.

These datasets are for a cohort of n=1540 anonymised hospitalised COVID-19 patients, and the data provide information on outcomes (i.e. patient death or discharge), demographics and biomarker measurements for two New York hospitals: State
University of New York (SUNY) Downstate Health Sciences University and Maimonides
Medical Center.

The file "demographics_both_hospitals.csv" contains the ultimate outcomes of hospitalisation (whether a patient was discharged or died), demographic information and known comorbidities for each of the patients.

The file "dynamics_clean_both_hospitals.csv" contains cleaned dynamic biomarker measurements for the n=1233 patients where this information was available and the data passed our various checks (see https://doi.org/10.1101/2021.11.12.21266248 for information of these checks and the cleaning process). Patients can be matched to demographic data via the "id" column.

Study approval and data collection

Study approval was obtained from the State University of New York (SUNY) Downstate Health Sciences University Institutional Review Board (IRB\#1595271-1) and Maimonides Medical Center Institutional Review Board/Research Committee (IRB\#2020-05-07). A retrospective query was performed among the patients who were admitted to SUNY Downstate Medical Center and Maimonides Medical Center with COVID-19-related symptoms, which was subsequently confirmed by RT PCR, from the beginning of February 2020 until the end of May 2020. Stratified randomization was used to select at least 500 patients who were discharged and 500 patients who died due to the complications of COVID-19. Patient outcome was recorded as a binary choice of “discharged” versus “COVID-19 related mortality”. Patients whose outcome was unknown were excluded. Demographic, clinical history and laboratory data was extracted from the hospital’s electronic health records.

Electronic health records (EHRs) are a rich source of information for medical research and public health monitoring. Information systems based on EHR data could also assist in patient care and hospital management. However, much of the data in EHRs is in the form of unstructured text, which is difficult to process for analysis. Natural language processing (NLP), a form of artificial intelligence, has the potential to enable automatic extraction of information from EHRs and several NLP tools adapted to the style of clinical writing have been developed for English and other major languages. In contrast, the development of NLP tools for less widely spoken languages such as Swedish has lagged behind. A major bottleneck in the development of NLP tools is the restricted access to EHRs due to legitimate patient privacy concerns. To overcome this issue we have generated a citizen science platform for collecting artificial Swedish EHRs with the help of Swedish physicians and medical students. These artificial EHRs describe imagined but plausible emergency care patients in a style that closely resembles EHRs used in emergency departments in Sweden. In the pilot phase, we collected a first batch of 50 artificial EHRs, which has passed review by an experienced Swedish emergency care physician. We make this dataset publicly available as OpenChart-SE corpus (version 1) under an open-source license for the NLP research community. The project is now open for general participation and Swedish physicians and medical students are invited to submit EHRs on the project website (https://github.com/Aitslab/openchart-se), where additional batches of quality-controlled EHRs will be released periodically.

Dataset content

OpenChart-SE, version 1 corpus (txt files and and dataset.csv)

The OpenChart-SE corpus, version 1, contains 50 artificial EHRs (note that the numbering starts with 5 as 1-4 were test cases that were not suitable for publication). The EHRs are available in two formats, structured as a .csv file and as separate textfiles for annotation. Note that flaws in the data were not cleaned up so that it simulates what could be encountered when working with data from different EHR systems. All charts have been checked for medical validity by a resident in Emergency Medicine at a Swedish hospital before publication.

Codebook.xlsx

The codebook contain information about each variable used. It is in XLSForm-format, which can be re-used in several different applications for data collection.

suppl_data_1_openchart-se_form.pdf

OpenChart-SE mock emergency care EHR form.

suppl_data_3_openchart-se_dataexploration.ipynb

This jupyter notebook contains the code and results from the analysis of the OpenChart-SE corpus.

More details about the project and information on the upcoming preprint accompanying the dataset can be found on the project website (https://github.com/Aitslab/openchart-se).

The global medical equipment data collector market is experiencing robust growth, driven by the increasing adoption of electronic health records (EHRs), the rising prevalence of chronic diseases necessitating enhanced patient monitoring, and the expanding demand for real-time data analysis in healthcare settings. The market is segmented by application (hospitals and clinics) and type (handheld and fixed), with handheld devices gaining traction due to their portability and ease of use in various medical environments. Hospitals currently dominate the application segment, owing to their higher data collection needs and sophisticated infrastructure. However, the clinic segment is expected to witness significant growth due to increasing adoption of data-driven approaches in outpatient care. Technological advancements, such as the integration of advanced sensors and improved data security features, are further fueling market expansion. Competition is intense, with established players like Zebra Technologies and CipherLab alongside emerging companies like Ciontek and Supoin vying for market share through product innovation and strategic partnerships. The market’s geographical distribution shows a concentration in North America and Europe, primarily due to advanced healthcare infrastructure and high adoption rates. However, Asia-Pacific is projected to exhibit the fastest growth, fueled by rising healthcare expenditure and increasing digitalization efforts in developing economies. Regulatory changes related to data privacy and interoperability are likely to influence market dynamics in the coming years. The market is anticipated to maintain a steady CAGR, resulting in significant market expansion throughout the forecast period (2025-2033). The restraints to market growth primarily involve the high initial investment costs associated with implementing data collection systems, concerns regarding data security and patient privacy, and the need for substantial training and support for healthcare professionals in utilizing the technology effectively. However, these challenges are being progressively addressed through the development of cost-effective solutions, robust security protocols, and comprehensive training programs. Furthermore, the increasing availability of cloud-based solutions and data analytics platforms is simplifying data management and accessibility, driving wider adoption. The overall market outlook is positive, with continued growth driven by factors like increasing government initiatives promoting digital healthcare, advancements in wireless technologies, and the burgeoning need for improved operational efficiency in healthcare facilities.

A survey of *** hospitals across China in 2022 and 2023 found that three-quarters of Chinese hospitals already offered online diagnosis and treatment services. Among these hospitals, more than ** percent stored core data collected through their online operations within the institutions. Only **** percent of all hospitals permanently stored their core data on third-party platforms.

The global healthcare data collection and labeling market is experiencing robust growth, driven by the increasing adoption of artificial intelligence (AI) and machine learning (ML) in healthcare. The rising volume of patient data generated through electronic health records (EHRs), wearable devices, and medical imaging necessitates efficient and accurate data labeling for training sophisticated AI algorithms. This demand fuels the market's expansion. While precise market sizing figures require further details, a reasonable estimate, considering the current growth trajectory of related AI and healthcare sectors, would place the 2025 market value at approximately $2 billion, with a Compound Annual Growth Rate (CAGR) of 15-20% projected through 2033. Key drivers include the need for improved diagnostic accuracy, personalized medicine, and drug discovery, all heavily reliant on high-quality labeled datasets. Furthermore, regulatory compliance mandates around data privacy and security are indirectly driving the adoption of specialized data collection and labeling services, ensuring data integrity and patient confidentiality. The market is segmented based on data type (imaging, text, sensor data), labeling method (supervised, unsupervised, semi-supervised), service type (data annotation, data augmentation, model training), and end-user (hospitals, pharmaceutical companies, research institutions). Companies like Alegion, Appen, and iMerit are key players, offering a range of services to meet diverse healthcare data needs. However, challenges remain, including data heterogeneity, scalability concerns related to large datasets, and the potential for bias in labeled data. Addressing these challenges requires continuous innovation in data collection methodologies, advanced labeling techniques, and the development of robust quality control measures. Future market growth will hinge on the successful integration of advanced technologies like synthetic data generation and automated labeling tools, aiming to reduce costs and accelerate the development of AI-powered healthcare solutions.

The following dataset provides facility-level data for hospital utilization aggregated on a weekly basis (Friday to Thursday). 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 2020-11-20 means the average/sum/coverage of the elements captured from that given facility starting and including Friday, November 20, 2020, and ending and including reports for Thursday, November 26, 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”.

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.

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

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

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

Source: CDC National Healthcare Safety Network (NHSN).

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

This dataset represents weekly hospital respiratory data and metrics aggregated to national and state/territory levels reported to CDC’s National Health Safety Network (NHSN) beginning August 2020. Data for reporting dates through April 30, 2024 represent data reported during a previous mandated reporting period as specified by the HHS Secretary. Data for reporting dates May 1, 2024 – October 31, 2024 represent voluntarily reported data in the absence of a mandate. Data for reporting dates beginning November 1, 2024 represent data reported during a current mandated reporting period. All data and metrics capturing information on respiratory syncytial virus (RSV) were voluntarily reported until November 1, 2024. All data included in this dataset represent aggregated counts, and include metrics capturing information specific to hospital capacity, occupancy, hospitalizations, and new hospital admissions with corresponding metrics indicating reporting coverage for a given reporting week. NHSN monitors national and local trends in healthcare system stress and capacity for all acute care and critical access hospitals in the United States.

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

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

Source: CDC National Healthcare Safety Network (NHSN).

• Data source description (updated November 15, 2024): As of October 9, 2024, Hospital Respiratory Data (HRD; formerly Respiratory Pathogen, Hospital Capacity, and Supply data or 'COVID-19 hospital data') are reported to HHS through CDC's National Healthcare Safety Network (NHSN) based on updated requirements from the Centers for Medicare and Medicaid Services (CMS). These data were voluntarily reported to NHSN May 1, 2024 until November 1, 2024, at which time CMS began requiring acute care and critical access hospitals to electronically report information via NHSN about COVID-19, influenza, and RSV, hospital bed census and capacity. Hospital bed capacity and occupancy data for all patients and for patients with COVID-19 or influenza for collection dates prior to May 1, 2024, represent data reported during a previously mandated reporting period as specified by the HHS Secretary, and data for collection dates May 1, 2024 – October 31, 2024 represent data reported voluntarily to NHSN. All RSV data through October 31, 2024 represent voluntarily reported data; as such, all voluntarily reported data included in this dataset represent reporting hospitals only for a given week and might not be complete or representative of all hospitals during the specified reporting periods.
• NHSN monitors national and local trends in healthcare system stress and capacity for all acute care and critical access hospitals in the United States. Data reported by hospitals to NHSN represent aggregated counts and include metrics capturing information specific to hospital capacity, occupancy, hospitalizations, and admissions. Find more information about reporting to NHSN: https://www.cdc.gov/nhsn/psc/hospital-respiratory-reporting.html.
• Data quality: While CDC reviews reported data for completeness and errors and corrects those found, some reporting errors might still exist within the data. CDC and partners work with reporters to correct these errors and update the data in subsequent weeks. Data reported as of December 1, 2020 are subject to thorough, routine data quality review procedures, including identifying and excluding invalid values from metric calculations and application of error correction methodology; data prior to this date may have anomalies that are not yet resolved. Data prior to August 1, 2020, are unavailable. As a result of data quality implementation and submission of any backfilled data, data and metrics might fluctuate or change week-over-week after initial posting.
• Inclusion criteria and metric calculations:
• Facility types and status: Many hospital subtypes, including acute care and critical access hospitals, are included in the metric calculations displayed on this page. Psychiatric, rehabilitation, and religious non-medical hospital types are excluded from calculations. Number of reporting hospitals is determined based on the NHSN unique hospital identifier and not aggregated to the CMS certification number (CCN). Only hospitals indicated as active reporters in NHSN are included.
• For occupancy metrics through week ending October 5, 2024: hospitals that reported those data at least one day during a given week are included in the metric calculation, which are displayed as weekly averages.
• For occupancy metrics beginning week ending October 12, 2024: hospitals that reported those data for Wednesday during a given week are included in the metric calculation, which are displayed as single day (i.e. Wednesday) values.
• For new hospital admissions metrics through week ending October 5, 2024: hospitals that reported those data at least one day during a given week are included in the metric calculation, which are displayed as weekly totals. Under previous reporting requirements, new hospital admissions data were reported daily to NHSN, as the number of new hospital admissions for the previous day.
• For new hospital admissions metrics beginning week ending October 12, 2024: hospitals that reported those data for an entire reporting week are included in the metric calculation, which are displayed as weekly totals. Under current reporting requirements, new admissions data are reported to represent the number of new admissions occurring on a given reporting date (rather than previous day) or during a given reporting week.
• Find full details on NHSN Hospital Respiratory Data (HRD) reporting guidance, including additional information on bed type definitions at https://www.cdc.gov/nhsn/psc/hospital-respiratory-reporting.html.

Archived datasets updated during the mandatory hospital reporting period from August 1, 2020, to April 30, 2024:

1. https://data.cdc.gov/Public-Health-Surveillance/Weekly-United-States-COVID-19-Hospitalization-Metr/akn2-qxic/about_data
2. https://data.cdc.gov/Public-Health-Surveillance/Weekly-United-States-COVID-19-Hospitalization-Metr/82ci-krud/about_data
3. https://data.cdc.gov/Public-Health-Surveillance/Respiratory-Virus-Response-RVR-United-States-Hospi/9t9r-e5a3/about_data
4. https://data.cdc.gov/Public-Health-Surveillance/Weekly-United-States-COVID-19-Hospitalization-Metr/7dk4-g6vg/about_data
5. https://data.cdc.gov/Public-Health-Surveillance/United-States-COVID-19-Hospitalization-Metrics-by-/39z2-9zu6/about_data

Archived datasets updated during the voluntary hospital reporting period from May 1, 2024, to October 31, 2024:

1. https://data.cdc.gov/Public-Health-Surveillance/Weekly-United-States-COVID-19-Hospitalization-Metr/akn2-qxic/about_data
2. https://data.cdc.gov/Public-Health-Surveillance/Weekly-United-States-Hospitalization-Metrics-by-Ju/ype6-idgy

Note: June 13th, 2025: Data for American Samoa (AS) for the June 1st, 2025 through June 7th, 2025 reporting period are not available for the Weekly NHSN Hospital Respiratory Data report released on June 13th, 2025.

June 6th, 2025: Data for American Samoa (AS) for the May 25th, 2025 through May 31th, 2025 reporting period are not available for the Weekly NHSN Hospital Respiratory Data report released on June 6th, 2025.

May 30th, 2025: Data for American Samoa (AS) for the May 18th, 2025 through May 24th, 2025 reporting period are not available for the Weekly NHSN Hospital Respiratory Data report released on May 30th, 2025.

May 23rd, 2025: Data for American Samoa (AS) for the May 11th, 2025 through May 17th, 2025 reporting period are not available for the Weekly NHSN Hospital Respiratory Data report released on May 23rd, 2025.

April 25th, 2025: Data for American Samoa (AS) for the April 13th, 2025 through April 19th, 2025 reporting period are not available for the Weekly NHSN Hospital Respiratory Data report released on April 25th, 2025.

April 18th, 2025: Data for American Samoa (AS) for the April 6th, 2025 through April 12th, 2025 reporting period are not available for the Weekly NHSN Hospital Respiratory Data report released on April 18th, 2025.

April 11th, 2025: Data for American Samoa (AS) for the March 30th, 2025 through April 5th, 2025 reporting period are not available for the Weekly NHSN Hospital Respiratory Data report released on April 11th, 2025.

March 28th, 2025: Data for Guam (GU) for the March 16th, 2025 through March 22nd, 2025 reporting period are not available for the Weekly NHSN Hospital Respiratory Data report released on March 28th, 2025.

March 21st, 2025: Data for the Commonwealth of the Northern Mariana Islands (CNMI) for the March 9th, 2025 through March 15th, 2025 reporting period are not available for the Weekly NHSN Hospital Respiratory Data report released on March 21st, 2025.

March 14th, 2025: Data for American Samoa (AS) and the Commonwealth of the Northern Mariana Islands (CNMI) for the March 2nd, 2025 through March 8th, 2025 reporting period are not available for the Weekly NHSN Hospital Respiratory Data report

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, hosp

[IMPORTANT NOTE: Sample file posted on Datarade is not the complete dataset, as Datarade permits only a single CSV file. Visit https://www.careprecise.com/healthcare-provider-data-sample.htm for more complete samples.] Updated every month, CarePrecise developed the AHD to provide a comprehensive database of U.S. hospital information. Extracted from the CarePrecise master provider database with information all of the 6.3 million HIPAA-covered US healthcare providers and additional sources, the Authoritative Hospital Database (AHD) contains records for all HIPAA-covered hospitals. In this database of hospitals we include bed counts, patient satisfaction data, hospital system ownership, hospital charges and cases by Zip Code®, and more. Most records include a cabinet-level or director-level contact. A PlaceKey is provided where available.

The AHD includes bed counts for 95% of hospitals, full contact information on 85%, and fax numbers for 62%. We include detailed patient satisfaction data, employee counts, and medical procedure volumes.

The AHD integrates directly with our extended provider data product to bring you the physicians and practice groups affiliated with the hospitals. This combination of data is the only commercially available hospital dataset of this depth.

NEW: Hospital NPI to CCN Rollup A CarePrecise Exclusive. Using advanced record-linkage technology, the AHD now includes a new file that makes it possible to mine the vast hospital information available in the National Provider Identifier registry database. Hospitals may have dozens of NPI records, each with its own information about a unit, listing facility type and/or medical specialties practiced, as well as separate contact names. To wield the power of this new feature, you'll need the CarePrecise Master Bundle, which contains all of the publicly available NPI registry data. These data are available in other CarePrecise data products.

Counts are approximate due to ongoing updates. Please review the current AHD information here: https://www.careprecise.com/detail_authoritative_hospital_database.htm

The AHD is sold as-is and no warranty is offered regarding accuracy, timeliness, completeness, or fitness for any purpose.

The National Hospital Ambulatory Medical Care Survey (NHAMCS) has been fielded annually since 1992 to collect data on the utilization and provision of ambulatory care services in hospital emergency and outpatient departments. Data collection from hospital-based ambulatory surgery centers began in 2009. And between 2010 and 2012 NHAMCS gathered data on visits to freestanding ambulatory surgery centers. In 2018, the survey began focusing on just the ambulatory visits made to emergency departments. Each emergency department is randomly assigned to a 4-week reporting period. During this period, data for a systematic random sample of visits are recorded by Census interviewers using a computerized Patient Record Form. Data are obtained on patient characteristics such as age, sex, race, and ethnicity, and visit characteristics such as patient’s reason for visit, provider’s diagnosis, services ordered or provided, and treatments, including medication therapy. In addition, data about the facility are collected as part of a survey induction interview.

This data is the Saving young lives: Triage and treatment using the pediatric rapid sepsis trigger (PRST) tool study. Data collected for this study occurred from April 2020 to April 2022. Objective(s): This is a pre-post intervention study involving pediatric patients presenting to the study hospitals in seek of medical care for an acute illness. The purpose of this study was to develop a prediction model and to perform clinical validation of a digital triage tool to guide triage and treatment of children at health facilities in LMICs with severe infections/suspected sepsis. The study involved three phases: (I) Baseline Period, (II) Interphase Period, (III) Intervention Period. The study hospitals include 2 sites in Kenya (1 control site, 1 experimental site) and 2 in Uganda (1 control site, 1 experimental site). Data Description: Predictor variables were collected at the time of triage by trained study nurses using a custom-built mobile application. All data entered into the mobile application was stored an encrypted database. Data was uploaded directly from the mobile device to a Research Electronic Data Capture (REDCap) database hosted at the BC Children’s Hospital Research Institute (Vancouver, Canada). Outcomes were obtained from facility records or telephone follow-up at 7-10 days and the data was collected electronically. Time-specific outcomes were tracked using an RFID tagging system with study personnel as backup. Limitations: There is missing data and some variables were not collected at all sites. Ethics Declaration: This study was approved by the Makerere University Higher Degrees research and Ethics Committee (No. 743), the Uganda National Institute of Science and Technology (HS 528ES), and the University of British Columbia / Children & Women’s Health Centre of British Columbia Research Ethics Board (H19-02398 & H20-00484). NOTE for restricted files: If you are not yet a CoLab member, please complete our membership application survey to gain access to restricted files within 2 business days. Some files may remain restricted to CoLab members. These files are deemed more sensitive by the file owner and are meant to be shared on a case-by-case basis. Please contact the CoLab coordinator at sepsiscolab@bcchr.ca or visit our website.

This survey, which is part of a continuing sample of hospital discharge records, supplies medical and demographic information used to calculate statistics on hospital utilization. The data collection consists of data abstracted from the face sheets of the medical records for sampled inpatients discharged from a national sample of nonfederal short-stay hospitals. The variables include information on the patient's demographic characteristics (sex, age, date of birth, race, marital status), dates of admission and discharge, status at discharge, diagnoses, procedures performed, source of payment, and hospital characteristics, such as bedsize, ownership, and region of the country.

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.

Anonymized Electronic Patient Record (EPR) Dataset from a UK-based Hospital. The dataset contains 1,007,727 rows of data collected between 28th February 2016 and 21st August 2017. Metadata is provided to explain the column headers. This dataset can only be used for research and must not be used for commercial purposes (CC BY-NC). The dataset is in .xlsx format and is ~49MBs in size. Date: 28th February 2016 to 21st August 2017 (data collection) Date Submitted: 2022-02-16