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

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

Data are provisional and subject to change.

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

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

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

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.

Healthcare data breaches in the United States are a constantly increasing risk with the potential for significant damage to affected parties. The largest recorded U.S. data breach in the healthcare sector as of November 2024, was recorded in July 2024, at Change Healthcare, Inc., a health insurance provider in the United States, when criminal hackers stole personal data affecting *** million individuals.

The size of the US Health Information Exchange Industry market was valued at USD 0.66 Million in 2023 and is projected to reach USD 1.47 Million by 2032, with an expected CAGR of 12.12% during the forecast period. The U.S. HIE market has been enjoying a robust growth trajectory for years now and has received substantial impetus due to the requirements to improve care and outcome, occasioned by rising demand for healthcare providers to have their requirements of liquid sharing of data. HIE enables the electronic exchange of health information across various organizations and systems. This enables them to have broad access to patient information by healthcare professionals and reduces redundancies while enhancing care coordination. Key drivers in the market are driven by governments pushing interoperability and the use of EHRs seen within the 21st Century Cures Act, underlining the improvement of shared data. More attention is paid to value-based care models and population health management for health providers involved in better decision-making and improving patient care through HIE solutions. The geographic regions further illustrate an extensive array of public and private HIEs throughout the US; the fact that significant investment is occurring within both the public and private sectors speaks to the rapidly evolving market. Increased emphasis on advanced technologies such as cloud computing, artificial intelligence, and blockchain is being given to enable security and interoperability improvements for data systems as more healthcare organizations become conscious of the need for interconnected systems. Actually, the U.S. health information exchange industry is better poised to continue its growth in and around the future of healthcare delivery, one that is changing and further becoming efficient by its integration of collaboration among healthcare stakeholders. Recent developments include: In October 2022, Mpowered Health launched its xChange, the United States consumer-mediated healthcare data exchange. The exchange enables health plans, health systems, and other healthcare organizations to request and obtain medical records from consumers with their consent., In March 2022, mpro5 Inc announced its launch into the United States market with a strategy of enabling the collection and leverage of real-time data to simplify the most complex operational challenges in healthcare and hospitals.. Key drivers for this market are: Increasing Demand for Electronic Health Records Resulting in the Expansion of the Market, Government Support via Various Programs and Incentives; Reduction in Healthcare Cost and Improved Efficacy. Potential restraints include: Huge Initial Infrastructural Investment and Slow Return on Investment, Data Privacy and Security Concerns. Notable trends are: The Decentralized/Federated Model is Expected to Hold a Notable Market Share Over the Forecast Period.

Adoption of electronic medical record (EMR) systems has proliferated in healthcare settings over the past decade. Federal funding and legislation requiring the uptake of EMR systems in recent years have led more than 96.0% of hospitals and physicians to go digital as of 2023, compared to less than 10.0% in 2010. An evolving healthcare landscape is enhancing the role of EMR providers in the health sector. While demographic and economic factors drive the use of EMRs, valuable cost-savings, security and coordination show the strength of digital records over paper-based ones. In all, industry-wide revenue has been falling at a CAGR of 0.7% over the past five years – totaling an estimated $19.4 billion in 2024 – when revenue will increase an expected 3.4%. The COVID-19 pandemic accelerated the modernization of the entire health sector out of necessity. A shutdown of in-person medical appointments in place of telemedicine changed how healthcare providers and patients accessed and used medical data. The pandemic also highlighted the importance of interoperability – the ability of various systems to exchange data among providers seamlessly. While EMR systems have nearly reached saturation, how healthcare providers and patients use them will continue to change and evolve. Consolidation characterizing the health sector could change the competitive landscape for EMR providers. Larger health systems will consolidate their EHRs, as using one standard system has advantages over multiple separate ones. Consolidating EHR systems poses opportunities for some companies, but others could struggle to acquire new clients. How EHR providers leverage telemedicine, AI and other tech advances will determine competitiveness. Rising healthcare expenditure will support electronic medical record system providers, leading revenue to expand at a CAGR of 4.2% to an estimated $23.9 billion.

There are many initiatives attempting to harmonize data collection across human clinical studies using common data elements (CDEs). The increased use of CDEs in large prior studies can guide researchers planning new studies. For that purpose, we analyzed the All of Us (AoU) program, an ongoing US study intending to enroll one million participants and serve as a platform for numerous observational analyses. AoU adopted the OMOP Common Data Model to standardize both research (Case Report Form [CRF]) and real-world (imported from Electronic Health Records [EHRs]) data. AoU standardized specific data elements and values by including CDEs from terminologies such as LOINC and SNOMED CT. For this study, we defined all elements from established terminologies as CDEs and all custom concepts created in the Participant Provided Information (PPI) terminology as unique data elements (UDEs). We found 1 033 research elements, 4 592 element-value combinations and 932 distinct values. Most elements were UDEs (869, 84.1%), while most CDEs were from LOINC (103 elements, 10.0%) or SNOMED CT (60, 5.8%). Of the LOINC CDEs, 87 (53.1% of 164 CDEs) originated from previous data collection initiatives, such as PhenX (17 CDEs) and PROMIS (15 CDEs). On a CRF level, The Basics (12 of 21 elements, 57.1%) and Lifestyle (10 of 14, 71.4%) were the only CRFs with multiple CDEs. On a value level, 61.7% of distinct values are from an established terminology. AoU demonstrates the use of the OMOP model for integrating research and routine healthcare data (64 elements in both contexts), which allows for monitoring lifestyle and health changes outside the research setting. The increased inclusion of CDEs in large studies (like AoU) is important in facilitating the use of existing tools and improving the ease of understanding and analyzing the data collected, which is more challenging when using study specific formats.

In order to facilitate public review and access, enrollment data published on the Open Data Portal is provided as promptly as possible after the end of each month or year, as applicable to the data set. Due to eligibility policies and operational processes, enrollment can vary slightly after publication. Please be aware of the point-in-time nature of the published data when comparing to other data published or shared by the Department of Social Services, as this data may vary slightly. As a general practice, for monthly data sets published on the Open Data Portal, DSS will continue to refresh the monthly enrollment data for three months, after which time it will remain static. For example, when March data is published the data in January and February will be refreshed. When April data is published, February and March data will be refreshed, but January will not change. This allows the Department to account for the most common enrollment variations in published data while also ensuring that data remains as stable as possible over time. In the event of a significant change in enrollment data, the Department may republish reports and will notate such republication dates and reasons accordingly. In March 2020, Connecticut opted to add a new Medicaid coverage group: the COVID-19 Testing Coverage for the Uninsured. Enrollment data on this limited-benefit Medicaid coverage group is being incorporated into Medicaid data effective January 1, 2021. Enrollment data for this coverage group prior to January 1, 2021, was listed under State Funded Medical. Effective January 1, 2021, this coverage group have been separated: (1) the COVID-19 Testing Coverage for the Uninsured is now G06-I and is now listed as a limited benefit plan that rolls up into “Program Name” of Medicaid and “Medical Benefit Plan” of HUSKY Limited Benefit; (2) the emergency medical coverage has been separated into G06-II as a limited benefit plan that rolls up into “Program Name” of Emergency Medical and “Medical Benefit Plan” of Other Medical. An historical accounting of enrollment of the specific coverage group starting in calendar year 2020 will also be published separately. This data represents number of active recipients who received benefits under a medical benefit plan in that calendar year and month. A recipient may have received benefits from multiple plans in the same month; if so that recipient will be included in multiple categories in this dataset (counted more than once.) 2021 is a partial year. For privacy considerations, a count of zero is used for counts less than five. NOTE: On April 22, 2019 the methodology for determining HUSKY A Newborn recipients changed, which caused an increase of recipients for that benefit starting in October 2016. We now count recipients recorded in the ImpaCT system as well as in the HIX system for that assistance type, instead using HIX exclusively. Also, corrections in the ImpaCT system for January and February 2019 caused the addition of around 2000 and 3000 recipients respectively, and the counts for many types of assistance (e.g. SNAP) were adjusted upward for those 2 months. Also, the methodology for determining the address of the recipients changed: 1. The address of a recipient in the ImpaCT system is now correctly determined specific to that month instead of using the address of the most recent month. This resulted in some shuffling of the recipients among townships starting in October 2016. 2. If, in a given month, a recipient has benefit records in both the HIX system and in the ImpaCT system, the address of the recipient is now calculated as follows to resolve conflicts: Use the residential address in ImpaCT if it exists, else use the mailing address in ImpaCT if it exists, else use the address in HIX. This resulted in a reduction in counts for most townships starting in March 2017 because a single address is now used instead of two when the systems do not agree.\ NOTE: On February 14 2019, the enrollment

28 November 2019. Following user and stakeholder consultation, we made several revisions to our data processing and methodology and revised all figures from September 2015 to December 2018 in the General Practice Workforce December 2018 publication. Following later changes to the September 2015 to December 2016 full-time equivalent (FTE) GP locum figures, we revised all affected locum figures in the General Practice Workforce September 2019 publication. The figures in this publication are no longer valid as they were calculated using the previous methodology and therefore have now been superseded. More information and the revised figures can be found on the General Practice Workforce September 2019 publication page at https://digital.nhs.uk/data-and-information/publications/statistical/general-and-personal-medical-services/final-30-september-2019. This report presents data about GPs, Nurses, Direct Patient Care and Admin/Non-Clinical staff working in General Practice in England, along with information on their patients, practice and the services they provide. This is a quarterly publication and includes final data from September 2015 to September 2018. Final December 2018 data will be available in February 2019. CHANGE NOTICE: From the June 2018 collection, the source for GP Registrars (foundation and specialty registrar trainees on placements in General Practice) changed. The new data source is the Health Education England (HEE) Trainee Information System (TIS). This has improved the quality of our Registrar data and removes the need for a provisional data release. This publication contains June 2018 and September 2018 data based on the change in data source, with information prior to June 2018 using the previous source of ESR data. CONSULTATION: Further information on the new data source for GP registrars and other proposed improvements to the General Practice Workforce publication are discussed in our "Methodological Change Notice" available under Resources. We are continually looking to improve the quality of the data in this series to make them more useful for our users and we welcome any feedback on these proposed changes to gp-data@nhs.net, by the 20th January 2018. Various data breakdowns are available in the accompanying Excel and CSV files, including time series and breakdowns by categories such as age and gender. Data is also presented regionally and at practice level in the accompanying CSVs. This publication also features an online interactive dashboard which allows users to explore the underlying data in a variety of ways. This can be accessed by clicking on the dashboard icon below. Links to other publications presenting healthcare workforce information can be found under Related Links.

This work uses data generated via the Zooniverse.org platform. All research publications using data derived from Zooniverse approved projects are required to acknowledge the Zooniverse and the Project Builder platform. Please use the text: "This publication uses data generated via the Zooniverse.org platform." We would like to thank the Zooniverse team and all the Zooniverse volunteers who donated their time freely and generously. This work uses data provided by patients and collected by the NHS as part of their care and support. We thank all the people of Oxfordshire who contribute to the Infections in Oxfordshire Research Database. Research Database Team: L Butcher, H Boseley, C Crichton, DW Crook, DW Eyre, O Freeman, J Gearing (community), R Harrington, K Jeffery, M Landray, A Pal, TEA Peto, TP Quan, J Robinson (community), J Sellors, B Shine, AS Walker, D Waller. Patient and Public Panel: G Blower, C Mancey, P McLoughlin, B Nichols. This work was supported by the National Institute for Health Research Health Protection Research Unit (NIHR HPRU) in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford in partnership with Public Health England (PHE) (NIHR200915), and by the NIHR Oxford Biomedical Research Centre. 5526 real-world time series with labels for the location of all abrupt changes in level, variability, trend, presence/absence of data points, and irregular outliers. The time series were produced from a range of electronic health record data extracts from a large UK hospital group. Values in each data field were aggregated by day/week/month, and numeric summary values calculated for each timepoint from the (often non-numeric) data by applying simple functions (e.g. number of values present, percentage of missing values, number of distinct values, median value). Labels were produced by visual inspection of time series plots from ~2000 volunteers, via the Health Record Hiccups project on the Zooniverse platform (https://www.zooniverse.org/projects/phuongquan/health-record-hiccups). Volunteers drew a vertical line on the image wherever they saw a change point (green line if they were certain, yellow line if they were unsure). Consensus labels per image were calculated using density based clustering with noise (R v3.6.3, dbscan v1.1-5), and converted back to a date.

The projected impacts of climate change on the health of Pacific island communities are unavoidable thus making adaptation essential. Informed and timely responses are crucial to improve communities’ resilience to the challenges posed by climate change.23 However, developing countries such as the PICs lack the financial resources to effectively adapt to the demands of a changing climate. Furthermore, the information required to accurately map and analyze the threats of sea level rise to medical infrastructure, such as elevation data, is not available for PICs. As a result, distance from the coast can be used as an alternative in assessments of vulnerability to sea level rise.24 This study identifies the locations of critical medical infrastructure such as hospitals, medical centers, and nurse aid posts in 14 PICs. Their vulnerability to sea level rise is investigated through an assessment of their distance from the coast at 4 zones: 0 to 50 m, 50 to 100 m, 100 to 200 m and 200 to 500 m. The aim of this assessment is to provide a baseline analysis, which can inform early steps in adaptation efforts.

United Healthcare Transparency in Coverage Dataset

Unlock the power of healthcare pricing transparency with our comprehensive United Healthcare Transparency in Coverage dataset. This invaluable resource provides unparalleled insights into healthcare costs, enabling data-driven decision-making for insurers, employers, researchers, and policymakers.

Key Features:

• Extensive Coverage: Access detailed pricing information for a wide range of medical procedures and services across the United States, covering approximately 76,000 employers.
• Granular Data: Analyze costs at the provider, plan, and employer levels, allowing for in-depth comparisons and trend analysis.
• Massive Scale: Over 400TB of data generated monthly, providing a wealth of information for comprehensive analysis.
• Historical Perspective: Track pricing changes over time to identify patterns and forecast future trends.
• Regular Updates: Stay current with the latest pricing information, ensuring your analyses are always based on the most recent data.

Detailed Data Points:

For each of the 76,000 employers, the dataset includes: 1. In-network negotiated rates for covered items and services 2. Historical out-of-network allowed amounts and billed charges 3. Cost-sharing information for specific items and services 4. Pricing data for medical procedures and services across providers, plans, and employers

Use Cases

For Insurers: - Benchmark your rates against competitors - Optimize network design and provider contracting - Develop more competitive and cost-effective insurance products

For Employers: - Make informed decisions about health plan offerings - Negotiate better rates with insurers and providers - Implement cost-saving strategies for employee healthcare

For Researchers: - Conduct in-depth studies on healthcare pricing variations - Analyze the impact of policy changes on healthcare costs - Investigate regional differences in healthcare pricing

For Policymakers: - Develop evidence-based healthcare policies - Monitor the effectiveness of price transparency initiatives - Identify areas for potential cost-saving interventions

Data Delivery

Our flexible data delivery options ensure you receive the information you need in the most convenient format:

• Custom Extracts: We can provide targeted datasets focusing on specific regions, procedures, or time periods.
• Regular Reports: Receive scheduled updates tailored to your specific requirements.

Why Choose Our Dataset?

1. Expertise: Our team has extensive experience in healthcare data retrieval and analysis, ensuring high-quality, reliable data.
2. Customization: We can tailor the dataset to meet your specific needs, whether you're interested in particular companies, regions, or procedures.
3. Scalability: Our infrastructure is designed to handle the massive scale of this dataset (400TB+ monthly), allowing us to provide comprehensive coverage without compromise.
4. Support: Our dedicated team is available to assist with data interpretation and technical support.

Harness the power of healthcare pricing transparency to drive your business forward. Contact us today to discuss how our United Healthcare Transparency in Coverage dataset can meet your specific needs and unlock valuable insights for your organization.

This table provides an overview of the key figures on health and care available on StatLine. All figures are taken from other tables on StatLine, either directly or through a simple conversion. In the original tables, breakdowns by characteristics of individuals or other variables are possible. The period after the year of review before data become available differs between the data series. The number of exam passes/graduates in year t is the number of persons who obtained a diploma in school/study year starting in t-1 and ending in t.

Data available from: 2001

Status of the figures:

2024: Most available figures are definite. Figures are provisional for: - causes of death; - youth care; - persons employed in health and welfare; - persons employed in healthcare; - Mbo health care graduates; - Hbo nursing graduates / medicine graduates (university).

2023: Most available figures are definite. Figures are provisional for: - perinatal mortality at pregnancy duration at least 24 weeks; - diagnoses known to the general practitioner; - hospital admissions by some diagnoses; - average period of hospitalisation; - supplied drugs; - AWBZ/Wlz-funded long term care; - physicians and nurses employed in care; - persons employed in health and welfare; - average distance to facilities; - profitability and operating results at institutions. Figures are revised provisional for: - expenditures on health and welfare.

2022: Most available figures are definite. Figures are revised provisional for: - expenditures on health and welfare.

2021: Most available figures are definite, Figures are revised provisional for: - expenditures on health and welfare.f

2020 and earlier: All available figures are definite.

Changes as of 4 July 2025: More recent figures have been added for: - causes of death; - life expectancy; - life expectancy in perceived good health; - self-perceived health; - hospital admissions by some diagnoses; - sickness absence; - average period of hospitalisation; - contacts with health professionals; - youth care; - smoking, heavy drinkers, physical activity; - overweight; - high blood pressure; - physicians and nurses employed in care; - persons employed in health and welfare; - persons employed in healthcare; - Mbo health care graduates; - Hbo nursing graduates / medicine graduates (university); - expenditures on health and welfare; - profitability and operating results at institutions.

Changes as of 18 december 2024: - Distance to facilities: the figures withdrawn on 5 June have been replaced (unchanged). - Youth care: the previously published final results for 2021 and 2022 have been adjusted due to improvements in the processing. - Due to a revision of the statistics Expenditure on health and welfare 2021, figures for expenditure on health and welfare care have been replaced from 2021 onwards. - Due to the revision of the National Accounts, the figures on persons employed in health and welfare have been replaced for all years. - AWBZ/Wlz-funded long term care: from 2015, the series Wlz residential care including total package at home has been replaced by total Wlz care. This series fits better with the chosen demarcation of indications for Wlz care.

When will new figures be published? New figures will be published in December 2025.

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.

According to two surveys of users in the United States conducted in September 2021 and January 2023, privacy concerns about health data used and stored by mobile applications have been diminishing among respondents. In September 2021, 64 percent of respondents reported feeling "very" or "somewhat" concerned about their health data privacy when using mobile apps, while in January 2023 56 percent of respondents reported feeling the same. Among the examined demographics, Millennials reported the highest change in opinion, with a difference of 13 points.

According to a survey of healthcare leaders in the U.S. in 2025, the top reason for switching RCM software vendors was negative client support experiences, with almost ** percent citing this reason. Another common reason for changing RCM providers was data security concerns.

This dataset is made available under Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0). See LICENSE.pdf for details.

Dataset description

Parquet file, with:

35694 rows

154 columns

The file is indexed on [participant]_[month], such that 34_12 means month 12 from participant 34. All participant IDs have been replaced with randomly generated integers and the conversion table deleted.

Column names and explanations are included as a separate tab-delimited file. Detailed descriptions of feature engineering are available from the linked publications.

File contains aggregated, derived feature matrix describing person-generated health data (PGHD) captured as part of the DiSCover Project (https://clinicaltrials.gov/ct2/show/NCT03421223). This matrix focuses on individual changes in depression status over time, as measured by PHQ-9.

The DiSCover Project is a 1-year long longitudinal study consisting of 10,036 individuals in the United States, who wore consumer-grade wearable devices throughout the study and completed monthly surveys about their mental health and/or lifestyle changes, between January 2018 and January 2020.

The data subset used in this work comprises the following:

Wearable PGHD: step and sleep data from the participants’ consumer-grade wearable devices (Fitbit) worn throughout the study

Screener survey: prior to the study, participants self-reported socio-demographic information, as well as comorbidities

Lifestyle and medication changes (LMC) survey: every month, participants were requested to complete a brief survey reporting changes in their lifestyle and medication over the past month

Patient Health Questionnaire (PHQ-9) score: every 3 months, participants were requested to complete the PHQ-9, a 9-item questionnaire that has proven to be reliable and valid to measure depression severity

From these input sources we define a range of input features, both static (defined once, remain constant for all samples from a given participant throughout the study, e.g. demographic features) and dynamic (varying with time for a given participant, e.g. behavioral features derived from consumer-grade wearables).

The dataset contains a total of 35,694 rows for each month of data collection from the participants. We can generate 3-month long, non-overlapping, independent samples to capture changes in depression status over time with PGHD. We use the notation ‘SM0’ (sample month 0), ‘SM1’, ‘SM2’ and ‘SM3’ to refer to relative time points within each sample. Each 3-month sample consists of: PHQ-9 survey responses at SM0 and SM3, one set of screener survey responses, LMC survey responses at SM3 (as well as SM1, SM2, if available), and wearable PGHD for SM3 (and SM1, SM2, if available). The wearable PGHD includes data collected from 8 to 14 days prior to the PHQ-9 label generation date at SM3. Doing this generates a total of 10,866 samples from 4,036 unique participants.

Change in Health Care: Hospitals Payroll Employment in Texas was -0.19446 Thous. of Persons in March of 2025, according to the United States Federal Reserve. Historically, Change in Health Care: Hospitals Payroll Employment in Texas reached a record high of 4.36772 in July of 2016 and a record low of -16.42816 in April of 2020. Trading Economics provides the current actual value, an historical data chart and related indicators for Change in Health Care: Hospitals Payroll Employment in Texas - last updated from the United States Federal Reserve on July of 2025.

This Dataverse contains replication data and scripts for the analysis in Stein DT, Sudharsanan N, Dewi S, Manne-Goehler J, Witoelar F, Geldsetzer P. Change in clinical knowledge of diabetes among primary healthcare providers in Indonesia: repeated cross-sectional survey of 5105 primary healthcare facilities. BMJ Open Diabetes Research and Care. 2020 Oct 1;8(1):e001415. There are 3 do files and 6 raw data files in this dataverse.

This study examined how students’ academic performance changed after undergoing a transition to online learning during the coronavirus disease 2019 (COVID-19) pandemic, based on the test results of 16 integrated courses conducted in 3 semesters at Hanyang This study was conducted at Hanyang University College of Medicine (HYUCM), a private medical school in Seoul, South Korea. The average number of students per year is about 100. In HYUCM, the transition to online teaching was first implemented after COVID-19. Almost all face-to-face classroom lectures were replaced by online recorded videos, while fewer than 5% of classes were conducted as live online lectures. The major examinations’ raw scores were collected for each student. Because the total score was different for each examination, percent-correct scores were used in subsequent analyses. For courses that conducted more than 1 major examination, student achievement was calculated as an average of the percent-correct scores obtained from the examinations.

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.