The MZG is a registration with which all non-psychiatric hospitals in Belgium must make their (anonymised) administrative, medical and nursing data available to the Federal Public Service (FPS) Public Health. The aim of the MZG is to support the government's health policy by

• Determining the needs for hospital facilities;
• Describing the qualitative and quantitative accreditation standards of hospitals and their services;
• Organising the financing of hospitals;
• Determining policy for the practice of medicine;
• To outline epidemiological policy.

The MZG aims also to support the health policy of hospitals by providing national and individual feedback so that a hospital can compare itself with other hospitals and adapt its internal policy.

All reports can be found here (in French/Dutch).

Heterogenous Big dataset is presented in this proposed work: electrocardiogram (ECG) signal, blood pressure signal, oxygen saturation (SpO2) signal, and the text input. This work is an extension version for our relevant formulating of dataset that presented in [1] and a trustworthy and relevant medical dataset library (PhysioNet [2]) was used to acquire these signals. The dataset includes medical features from heterogenous sources (sensory data and non-sensory). Firstly, ECG sensor’s signals which contains QRS width, ST elevation, peak numbers, and cycle interval. Secondly: SpO2 level from SpO2 sensor’s signals. Third, blood pressure sensors’ signals which contain high (systolic) and low (diastolic) values and finally text input which consider non-sensory data. The text inputs were formulated based on doctors diagnosing procedures for heart chronic diseases. Python software environment was used, and the simulated big data is presented along with analyses.

Macks Psychology Group is a comprehensive mental health organization providing diagnostic testing, therapeutic services, and educational support to individuals of all ages. Led by a team of experienced providers, the group offers a range of services including psychological testing, individual and family therapy, speech and language therapy, and social skills groups. Their team of experts specializes in various areas, including autism spectrum disorder, attention deficit hyperactivity disorder (ADHD), anxiety, depression, and traumatic brain injury.

With a focus on providing personalized care, Macks Psychology Group utilizes evidence-based practices to address the unique needs of each individual. Their services are designed to promote emotional, social, and cognitive development, as well as academic and professional success. With locations in West Chester, Ohio, and Cincinnati, Ohio, Macks Psychology Group is dedicated to empowering individuals and families to reach their full potential.

The Research and Development Survey (RANDS) is a platform designed for conducting survey question evaluation and statistical research. RANDS is an ongoing series of surveys from probability-sampled commercial survey panels used for methodological research at the National Center for Health Statistics (NCHS). RANDS estimates are generated using an experimental approach that differs from the survey design approaches generally used by NCHS, including possible biases from different response patterns and sampling frames as well as increased variability from lower sample sizes. Use of the RANDS platform allows NCHS to produce more timely data than would be possible using traditional data collection methods. RANDS is not designed to replace NCHS’ higher quality, core data collections. Below are experimental estimates of reduced access to healthcare for three rounds of RANDS during COVID-19. Data collection for the three rounds of RANDS during COVID-19 occurred between June 9, 2020 and July 6, 2020, August 3, 2020 and August 20, 2020, and May 17, 2021 and June 30, 2021. Information needed to interpret these estimates can be found in the Technical Notes. RANDS during COVID-19 included questions about unmet care in the last 2 months during the coronavirus pandemic. Unmet needs for health care are often the result of cost-related barriers. The National Health Interview Survey, conducted by NCHS, is the source for high-quality data to monitor cost-related health care access problems in the United States. For example, in 2018, 7.3% of persons of all ages reported delaying medical care due to cost and 4.8% reported needing medical care but not getting it due to cost in the past year. However, cost is not the only reason someone might delay or not receive needed medical care. As a result of the coronavirus pandemic, people also may not get needed medical care due to cancelled appointments, cutbacks in transportation options, fear of going to the emergency room, or an altruistic desire to not be a burden on the health care system, among other reasons. The Household Pulse Survey (https://www.cdc.gov/nchs/covid19/pulse/reduced-access-to-care.htm), an online survey conducted in response to the COVID-19 pandemic by the Census Bureau in partnership with other federal agencies including NCHS, also reports estimates of reduced access to care during the pandemic (beginning in Phase 1, which started on April 23, 2020). The Household Pulse Survey reports the percentage of adults who delayed medical care in the last 4 weeks or who needed medical care at any time in the last 4 weeks for something other than coronavirus but did not get it because of the pandemic. The experimental estimates on this page are derived from RANDS during COVID-19 and show the percentage of U.S. adults who were unable to receive medical care (including urgent care, surgery, screening tests, ongoing treatment, regular checkups, prescriptions, dental care, vision care, and hearing care) in the last 2 months. Technical Notes: https://www.cdc.gov/nchs/covid19/rands/reduced-access-to-care.htm#limitations

Premier Pts is a company that specializes in providing real-time sports data and analytics to various clients. As a leading provider of sports data, Premier Pts offers a vast array of information, including team and player statistics, game scores, and betting odds.

The company's data collection process involves scraping relevant information from various sports websites, online publications, and official sports organizations. With a strong focus on accuracy and timeliness, Premier Pts ensures that its data is up-to-date and reliable, making it a go-to source for sports enthusiasts, researchers, and businesses alike.

The CarePrecise U.S. HCP/HCO Collection Dataset includes deep data on all 6.7 million U.S. HIPAA-covered healthcare practitioners and organizations. Monthly full updates. Includes linkages between the individual practitioners and their practice groups, hospitals, and hospital systems. Licensing plans are available for basic (internal use), derivative products, and redistribution. Data updates are delivered quarterly or monthly to suit customer need; FTP push is available, standard delivery is via CDN. Single download for evaluation is available. CarePrecise is a leader in the fields of HCP/HCO data, supplying provider data to the industry since 2008. Note regarding pricing: The Collection price shown in Pricing is separate from email addresses. Email addresses are priced as low as $0.075 per, based on volume. Pricing shown is without derivative product (DP) licensing for use in web applications; DP license ranges in price from $1,900/year to $9,000/year on top of data purchase, based on application and overall exposure estimate. DP license is sold in two-year term and requires a license agreement.

This data package contains information about the Centers for Medicare and Medicaid Services (CMS) Place of Service Codes. It consists of Healthcare Claim for Adjustment, Status Category and Status Codes as well as Healthcare Insurance over Business Process Application Error, Payment Type and Report Type Codes. It also comprises of data about Healthcare Provider Characteristics, Remittance Advice Remark, Services Decision Reason, Inpatient Revenue Crosswalk Codes.

Introduction

Global Healthcare Analytics Market, valued at USD 36.4 billion in 2023, is projected to expand to approximately USD 249.3 billion by 2032, registering a CAGR of 24.6% during the forecast period. In 2023, North America dominated the market with over 40% share, generating revenue of USD 16.6 million.

This rapid growth highlights the increasing adoption of advanced analytics in healthcare, driven by the need to optimize service efficiency, reduce costs, and enhance patient outcomes.

Healthcare analytics integrates real-time and historical data, enabling professionals to predict trends, improve clinical care, enhance operational efficiency, and drive patient engagement. By leveraging vast datasets, healthcare enterprises gain actionable insights to deliver better services and foster long-term growth.

The market’s expansion is fueled by government initiatives and the growing adoption of big data analytics in healthcare. These trends are encouraging the widespread implementation of electronic health records (EHRs), which play a pivotal role in improving healthcare delivery. Additionally, the push to reduce unnecessary expenditures positively influences market growth.

The rising costs of healthcare are prompting a shift toward solutions that enhance operational efficiency, broaden service offerings, reduce costs, and improve treatment outcomes. The transition from paper-based systems to EHRs has created massive datasets that enhance physician practices and increase demand for healthcare analytics. Furthermore, technological advancements and growing investor interest in analytical tools are bolstering the market's expansion.

Evaluating Health Home Care Quality

CMS Core Set and Health Home SPA Measures

By Health Data New York [source]

About this dataset

This dataset provides comprehensive measures to evaluate the quality of medical services provided to Medicaid beneficiaries by Health Homes, including the Centers for Medicare & Medicaid Services (CMS) Core Set and Health Home State Plan Amendment (SPA). This allows us to gain insight into how well these health homes are performing in terms of delivering high-quality care. Our data sources include the Medicaid Data Mart, QARR Member Level Files, and New York State Delivery System Inform Incentive Program (DSRIP) Data Warehouse. With this data set you can explore essential indicators such as rates for indicators within scope of Core Set Measures, sub domains, domains and measure descriptions; age categories used; denominators of each measure; level of significance for each indicator; and more! By understanding more about Health Home Quality Measures from this resource you can help make informed decisions about evidence based health practices while also promoting better patient outcomes

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How to use the dataset

This dataset contains measures that evaluate the quality of care delivered by Health Homes for the Centers for Medicare & Medicaid Services (CMS). With this dataset, you can get an overview of how a health home is performing in terms of quality. You can use this data to compare different health homes and their respective service offerings.

The data used to create this dataset was collected from Medicaid Data Mart, QARR Member Level Files, and New York State Delivery System Incentive Program (DSRIP) Data Warehouse sources.

In order to use this dataset effectively, you should start by looking at the columns provided. These include: Measurement Year; Health Home Name; Domain; Sub Domain; Measure Description; Age Category; Denominator; Rate; Level of Significance; Indicator. Each column provides valuable insight into how a particular health home is performing in various measurements of healthcare quality.

When examining this data, it is important to remember that many variables are included in any given measure and that changes may have occurred over time due to varying factors such as population or financial resources available for healthcare delivery. Furthermore, changes in policy may also affect performance over time so it is important to take these things into account when evaluating the performance of any given health home from one year to the next or when comparing different health homes on a specific measure or set of indicators over time

Research Ideas

• Using this dataset, state governments can evaluate the effectiveness of their health home programs by comparing the performance across different domains and subdomains.
• Healthcare providers and organizations can use this data to identify areas for improvement in quality of care provided by health homes and strategies to reduce disparities between individuals receiving care from health homes.
• Researchers can use this dataset to analyze how variations in cultural context, geography, demographics or other factors impact delivery of quality health home services across different locations

Acknowledgements

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

License

See the dataset description for more information.

Columns

File: health-home-quality-measures-beginning-2013-1.csv | Column name | Description | |:--------------------------|:----------------------------------------------------| | Measurement Year | The year in which the data was collected. (Integer) | | Health Home Name | The name of the health home. (String) | | Domain | The domain of the measure. (String) | | Sub Domain | The sub domain of the measure. (String) | | Measure Description | A description of the measure. (String) | | Age Category | The age category of the patient. (String) | | Denominator | The denominator of the measure. (Integer) | | Rate | The rate of the measure. (Float) | | Level of Significance | The level of significance of the measure. (String) | | Indicator | The indicator of the measure. (String) |

Acknowledgements

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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 Healthcare Data Collection And Labeling Market size was valued at USD 665.3 million in 2023 and is projected to reach USD 3525.73 million by 2032, exhibiting a CAGR of 26.9 % during the forecasts period. Health care data acquisition and annotation market entails the process of acquiring, sorting and tagging, health care data for different uses including, studies, diagnosis, and enhancing patient care. This data is very helpful for training up machine learning algorithms in the field of health care services including diagnosis of diseases, treatment, drug prescription and in research on the spread of diseases. Current trends depict a rising need for superior quality labeled dataset to enhance the performance of the health-care AI systems. Some of the key uses of this imaging technique are; diagnosis, electronic personal health record, and molecular biology for drug development. Growing adoption of healthcare data across medical fields and the usage of AI and digital records open a pathway in the market for better-annotated datasets.

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 Healthcare Operational Data Flows (HODF): Acute Data Set provides an automated patient-based daily data collection to support NHS delivery plans for the recovery of elective care and emergency and urgent care.

The AI Training Dataset In Healthcare Market size was valued at USD 341.8 million in 2023 and is projected to reach USD 1464.13 million by 2032, exhibiting a CAGR of 23.1 % during the forecasts period. The growth is attributed to the rising adoption of AI in healthcare, increasing demand for accurate and reliable training datasets, government initiatives to promote AI in healthcare, and technological advancements in data collection and annotation. These factors are contributing to the expansion of the AI Training Dataset In Healthcare Market. Healthcare AI training data sets are vital for building effective algorithms, and enhancing patient care and diagnosis in the industry. These datasets include large volumes of Electronic Health Records, images such as X-ray and MRI scans, and genomics data which are thoroughly labeled. They help the AI systems to identify trends, forecast and even help in developing unique approaches to treating the disease. However, patient privacy and ethical use of a patient’s information is of the utmost importance, thus requiring high levels of anonymization and compliance with laws such as HIPAA. Ongoing expansion and variety of datasets are crucial to address existing bias and improve the efficiency of AI for different populations and diseases to provide safer solutions for global people’s health.

According to Cognitive Market Research, the global healthcare data storage market size is USD 5.4 billion in 2024 and will expand at a compound annual growth rate (CAGR) of 14.3% from 2024 to 2031. Market Dynamics of Healthcare Data Storage Market

Key Drivers for Healthcare Data Storage Market

Increasing amount of healthcare records- Healthcare data storage market is in high demand due to the increasing amount of healthcare data. Electronic health records (EHRs), medical imaging, wearable electronics, and health applications all contribute to the daily deluge of data generated and amassed by healthcare institutions. This data includes a wide range of information, including patients’ medical records, diagnostic pictures, treatment programs, health indicators in real-time, and more. Moreover, healthcare data storage systems are necessary for efficient management of such vast data sets because they can manage high volumes, provide fast retrieval, and keep data secure. Further, state-of-the-art storage systems are required for compliance with data retention and security regulations. Thus, in order to facilitate better patient care and operational efficiency, the ever-increasing volume of healthcare data is driving the use of advanced data storage technologies.
The market is being propelled by the demand for efficient and rapid access to patient data in order to enhance clinical decision-making and patient care.

Key Restraints for Healthcare Data Storage Market

Healthcare data storage market growth is hindered due to the high costs of implementation and upkeep.
The market expansion is being impeded by concerns about data breaches and data accessibility.

Introduction of the Healthcare Data Storage Market

Healthcare data storage describes the infrastructure and procedures put in place to keep and handle massive volumes of patient records safely. Complying with regulatory requirements while ensuring data integrity, confidentiality, and accessibility is essential for healthcare data storage solutions. The rising amount of digital data produced by healthcare companies, the convenience and speed with which cloud storage solutions can be implemented, and the increasing popularity of hybrid data storage solutions are the primary elements propelling the expansion of this market. Security concerns over cloud-based image processing and analytics, however, are limiting the company’s growth. Concerns about the security of cloud-based image processing and analytics are expected to dampen the worldwide healthcare data storage industry. Additionally, advancements in artificial intelligence, big data analytics, and cloud computing have greatly improved the efficiency and capacity of the healthcare data storage market.

The DocGraph Teaming data set shows how healthcare providers in the United States team together to provide care to Medicare patients. The dataset is a simple graph data structure, using the National Provider Identifier as keys. We have not heard of a larger, publicly available graph data set that uses real identities. This file contains the links to both the data sets (which are many Gigabytes even as zip files) as well as the documentation for the data.Note: On Oct 5 2015, this data set was redacted due to signifigant issues with its content vs documentation. On Dec 15 2015, this data, along with updated documentation, was replaced. http://www.docgraph.com/teamingdatav2/

The dataset includes information on the healthcare systems that contributed their electronic health records (EHR) to the international consortium for the epidemiological and clinical purposes for COVID-19. The data contains information on the geographical location, number of beds and patient (adult and/or pediatric).

The data package contains NPI related datasets. The NPI number of all the covered health care professionals, the deactivated NPI's and dfferent codes used within the NPI dataset

The quality data in this table have been collected from health plans using standardized Health Effectiveness Data and Information Set (HEDIS) and are maintained and provided by the Utah Department of Health, Office of Health Care Statistics. The data span the years from 2010 through 2017. Each year of data represents patient care that occurred in the previous calendar year. This dataset is a sample of the full HEDIS data which is available for purchase from the Office of Health Care Statistics. To request a copy of the data in its entirety, please fill out a data request form located at: http://health.utah.gov/hda/order/view.php?id=10441

The Washington State Department of Health presents this information as a service to the public. True and correct copies of legal disciplinary actions taken after July 1998 are available on our Provider Credential Search site. These records are considered certified by the Department of Health. This includes information on health care providers. Please contact our Customer Service Center at 360-236-4700 for information about actions before July 1998. The information on this site comes directly from our database and is updated daily at 10:00 a.m.. This data is a primary source for verification of credentials and is extracted from the primary database at 2:00 a.m. daily. News releases about disciplinary actions taken against Washington State healthcare providers, agencies or facilities are on the agency's Newsroom webpage. Disclaimer The absence of information in the Provider Credential Search system doesn't imply any recommendation, endorsement or guarantee of competence of any healthcare professional. The presence of information in this system doesn't imply a provider isn't competent or qualified to practice. The reader is encouraged to carefully evaluate any information found in this data set.