According to a survey conducted in 2022, ** percent of respondents from healthcare organizations at a mature stage of AI adoption stated that natural language text was used in their AI applications. Structured data was the most common data type on which AI models were applied by healthcare organizations in early-stage AI adoption.

Background

Preventive and health-promoting policies can guide (place and space-specific) factors influencing human health, such as the physical and social environment. Required is data that can lead to a more nuanced decision-making process and identify both, existing and future challenges. Along with the rise of new technologies, and thus the multiple opportunities to use and process data, new options have emerged to measure and monitor factors that affect health. Thus, in recent years, several gateways for open data (including governmental and geospatial data) became available. At present, an increasing number of research institutions as well as (state and private) companies and citizens' initiatives provide data. However, there is a lack of overviews covering the range of such offerings regarding health. In particular, for geographically differentiated analyses, there are challenges related to data availability at different spatial levels and the growing number of data providers.

O...

Big Data Analytics In Healthcare Market size is estimated at USD 37.22 Billion in 2024 and is projected to reach USD 74.82 Billion by 2032, growing at a CAGR of 9.12% from 2026 to 2032.

Big Data Analytics In Healthcare Market: Definition/ Overview

Big Data Analytics in Healthcare, often referred to as health analytics, is the process of collecting, analyzing, and interpreting large volumes of complex health-related data to derive meaningful insights that can enhance healthcare delivery and decision-making. This field encompasses various data types, including electronic health records (EHRs), genomic data, and real-time patient information, allowing healthcare providers to identify patterns, predict outcomes, and improve patient care.

This formatted dataset (AnalysisDatabaseGBD) originates from raw data files from the Institute of Health Metrics and Evaluation (IHME) Global Burden of Disease Study (GBD2017) affiliated with the University of Washington. We are volunteer collaborators with IHME and not employed by IHME or the University of Washington.

The population weighted GBD2017 data are on male and female cohorts ages 15-69 years including noncommunicable diseases (NCDs), body mass index (BMI), cardiovascular disease (CVD), and other health outcomes and associated dietary, metabolic, and other risk factors. The purpose of creating this population-weighted, formatted database is to explore the univariate and multiple regression correlations of health outcomes with risk factors. Our research hypothesis is that we can successfully model NCDs, BMI, CVD, and other health outcomes with their attributable risks.

These Global Burden of disease data relate to the preprint: The EAT-Lancet Commission Planetary Health Diet compared with Institute of Health Metrics and Evaluation Global Burden of Disease Ecological Data Analysis. The data include the following: 1. Analysis database of population weighted GBD2017 data that includes over 40 health risk factors, noncommunicable disease deaths/100k/year of male and female cohorts ages 15-69 years from 195 countries (the primary outcome variable that includes over 100 types of noncommunicable diseases) and over 20 individual noncommunicable diseases (e.g., ischemic heart disease, colon cancer, etc). 2. A text file to import the analysis database into SAS 3. The SAS code to format the analysis database to be used for analytics 4. SAS code for deriving Tables 1, 2, 3 and Supplementary Tables 5 and 6 5. SAS code for deriving the multiple regression formula in Table 4. 6. SAS code for deriving the multiple regression formula in Table 5 7. SAS code for deriving the multiple regression formula in Supplementary Table 7
8. SAS code for deriving the multiple regression formula in Supplementary Table 8 9. The Excel files that accompanied the above SAS code to produce the tables

For questions, please email davidkcundiff@gmail.com. Thanks.

Abstract (en): The purpose of the Health Interview Survey is to obtain information about the amount and distribution of illness, its effects in terms of disability and chronic impairments, and the kinds of health services people receive. There are five types of records in this core survey, each in a separate data file. The variables in the Household File (Part 1) in this collection include type of living quarters, size of family, and geographic region. The Person File (Part 2) variables include sex, age, race, marital status, veteran status, education, income, occupation, and limits on activity. The Condition File (Part 3) contains variables on the incidence of illness or injury within the past year. The Hospital Episode File (Part 4) contains variables on the incidence of hospitalizations and presence of chronic conditions. The Doctor Visit File (Part 5) includes variables regarding frequency of doctor visits, type of doctor seen, and reasons for each visit. A sixth, seventh, eighth, and ninth file have been provided. The Disability Supplement File (Part 6) contains variables on the need for help, services, and environment modifications. The H1 Supplement File (Part 7) includes basic demographic variables, medical information, health variables, doctor visits, medical insurance, work days lost, and activity level variables. The Special Aids Supplement File (Part 8)includes basic demographic variables, special aids onset and amount needed, medical information, health variables, and doctor visits. The Influenza Supplement File (Part 9) includes basic demographic variables, flu, grippe, or fever onset, work and school days lost, hospital visits, length of stay, and cost of care. These data contain multiple weight variables for each part. Users should refer to the User Guide for further information regarding the weights and their derivation. Additionally, users may need to weight the data prior to analysis. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Created online analysis version with question text.. Civilian noninstitutionalized population of the United States from 1,900 geographically defined Primary Sampling Units. A multistage probability sample was used in selecting housing units. 2010-12-14 SAS, SPSS, and Stata setup files have been added. Some corresponding documentation has been updated and pre-existing data files have been replaced as well as the Codebook files that can now be found updated with the other documentation. Funding insitution(s): United States Department of Health and Human Services. Centers for Disease Control and Prevention. National Center for Health Statistics. face-to-face interviewThese data files contain weights, which must be used in any analysis.Per agreement with the National Center for Health Statistics (NCHS), ICPSR distributes the data files and text of the technical documentation for this collection as prepared by NCHS.

Technical notes and documentation on the common data model of the project CONCEPT-DM2.

This publication corresponds to the Common Data Model (CDM) specification of the CONCEPT-DM2 project for the implementation of a federated network analysis of the healthcare pathway of type 2 diabetes.

Aims of the CONCEPT-DM2 project:

General aim: To analyse chronic care effectiveness and efficiency of care pathways in diabetes, assuming the relevance of care pathways as independent factors of health outcomes using data from real life world (RWD) from five Spanish Regional Health Systems.

Main specific aims:

• To characterize the care pathways in patients with diabetes through the whole care system in terms of process indicators and pharmacologic recommendations
• To compare these observed care pathways with the theoretical clinical pathways derived from the clinical practice guidelines
• To assess if the adherence to clinical guidelines influence on important health outcomes, such as cardiovascular hospitalizations.
• To compare the traditional analytical methods with process mining methods in terms of modeling quality, prediction performance and information provided.

Study Design: It is a population-based retrospective observational study centered on all T2D patients diagnosed in five Regional Health Services within the Spanish National Health Service. We will include all the contacts of these patients with the health services using the electronic medical record systems including Primary Care data, Specialized Care data, Hospitalizations, Urgent Care data, Pharmacy Claims, and also other registers such as the mortality and the population register.

Cohort definition: All patients with code of Type 2 Diabetes in the clinical health records

• Inclusion criteria: patients that, at 01/01/2017 or during the follow-up from 01/01/2017 to 31/12/2022 had active health card (active TIS - tarjeta sanitaria activa) and code of type 2 diabetes (T2D, DM2 in spanish) in the clinical records of primary care (CIAP2 T90 in case of using CIAP code system)
• Exclusion criteria:
  • patients with no contact with the health system from 01/01/2017 to 31/12/2022
  • patients that had a T1D (DM1) code opened after the T2D code during the follow-up.
• Study period. From 01/01/2017 to 31/12/2022

Files included in this publication:

• Datamodel_CONCEPT_DM2_diagram.png
• Common data model specification (Datamodel_CONCEPT_DM2_v.0.1.0.xlsx)
• Synthetic datasets (Datamodel_CONCEPT_DM2_sample_data)
  • sample_data1_dm_patient.csv
  • sample_data2_dm_param.csv
  • sample_data3_dm_patient.csv
  • sample_data4_dm_param.csv
  • sample_data5_dm_patient.csv
  • sample_data6_dm_param.csv
  • sample_data7_dm_param.csv
  • sample_data8_dm_param.csv
• Datamodel_CONCEPT_DM2_explanation.pptx

Abstract

This study examines various dimensions of primary health care delivery in Uganda, using a baseline survey of public and private dispensaries, the most common lower level health facilities in the country.

The survey was designed and implemented by the World Bank in collaboration with the Makerere Institute for Social Research and the Ugandan Ministries of Health and of Finance, Planning and Economic Development. It was carried out in October - December 2000 and covered 155 local health facilities and seven district administrations in ten districts. In addition, 1617 patients exiting health facilities were interviewed. Three types of dispensaries (both with and without maternity units) were included: those run by the government, by private for-profit providers, and by private nonprofit providers, mainly religious.

This research is a Quantitative Service Delivery Survey (QSDS). It collected microlevel data on service provision and analyzed health service delivery from a public expenditure perspective with a view to informing expenditure and budget decision-making, as well as sector policy.

Objectives of the study included: 1) Measuring and explaining the variation in cost-efficiency across health units in Uganda, with a focus on the flow and use of resources at the facility level; 2) Diagnosing problems with facility performance, including the extent of drug leakage, as well as staff performance and availability;
3) Providing information on pricing and user fee policies and assessing the types of service actually provided; 4) Shedding light on the quality of service across the three categories of service provider - government, for-profit, and nonprofit; 5) Examining the patterns of remuneration, pay structure, and oversight and monitoring and their effects on health unit performance; 6) Assessing the private-public partnership, particularly the program of financial aid to nonprofits.

Geographic coverage

The study districts were Mpigi, Mukono, and Masaka in the central region; Mbale, Iganga, and Soroti in the east; Arua and Apac in the north; and Mbarara and Bushenyi in the west.

Analysis unit

• local dispensary with or without maternity unit

Universe

The survey covered government, for-profit and nonprofit private dispensaries with or without maternity units in ten Ugandan districts.

Kind of data

Sample survey data [ssd]

Sampling procedure

The survey covered government, for-profit and nonprofit private dispensaries with or without maternity units in ten Ugandan districts.

The sample design was governed by three principles. First, to ensure a degree of homogeneity across sampled facilities, attention was restricted to dispensaries, with and without maternity units (that is, to the health center III level). Second, subject to security constraints, the sample was intended to capture regional differences. Finally, the sample had to include facilities in the main ownership categories: government, private for-profit, and private nonprofit (religious organizations and NGOs). The sample of government and nonprofit facilities was based on the Ministry of Health facility register for 1999. Since no nationwide census of for-profit facilities was available, these facilities were chosen by asking sampled government facilities to identify the closest private dispensary.

Of the 155 health facilities surveyed, 81 were government facilities, 30 were private for-profit facilities, and 44 were nonprofit facilities. An exit poll of clients covered 1,617 individuals.

The final sample consisted of 155 primary health care facilities drawn from ten districts in the central, eastern, northern, and western regions of the country. It included government, private for-profit, and private nonprofit facilities. The nonprofit sector includes facilities owned and operated by religious organizations and NGOs. Approximately one third of the surveyed facilities were dispensaries without maternity units; the rest provided maternity care. The facilities varied considerably in size, from units run by a single individual to facilities with as many as 19 staff members.

Ministry of Health facility register for 1999 was used to design the sampling frame. Ten districts were randomly selected. From the selected districts, a sample of government and private nonprofit facilities and a reserve list of replacement facilities were randomly drawn. Because of the unreliability of the register for private for-profit facilities, it was decided that for-profit facilities would be identified on the basis of information from the government facilities sampled. The administrative records for facilities in the original sample were first reviewed at the district headquarters, where some facilities that did not meet selection criteria and data collection requirements were dropped from the sample. These were replaced by facilities from the reserve list. Overall, 30 facilities were replaced.

The sample was designed in such a way that the proportion of facilities drawn from different regions and ownership categories broadly mirrors that of the universe of facilities. Because no nationwide census of for-profit health facilities is available, it is difficult to assess the extent to which the sample is representative of this category. A census of health care facilities in selected districts, carried out in the context of the Delivery of Improved Services for Health (DISH) project supported by the U.S. Agency for International Development (USAID), suggests that about 63 percent of all facilities operate on a for-profit basis, while government and nonprofit providers run 26 and 11 percent of facilities, respectively. This would suggest an undersampling of private providers in the survey. It is not clear, however, whether the DISH districts are representative of other districts in Uganda in terms of the market for health care.

For the exit poll, 10 interviews per facility were carried out in approximately 85 percent of the facilities. In the remaining facilities the target of 10 interviews was not met, as a result of low activity levels.

Sampling deviation

In the first stage in the sampling process, eight districts (out of 45) had to be dropped from the sample frame due to security concerns. These districts were Bundibugyo, Gulu, Kabarole, Kasese, Kibaale, Kitgum, Kotido, and Moroto.

Mode of data collection

Face-to-face [f2f]

Research instrument

The following survey instruments are available:

• District Health Team Questionnaire;
• District Facility Data Sheets;
• Uganda Health Facility Survey Questionnaire;
• Facility Data Sheets;
• Facility Patient Exit Poll Questionnaire.

The survey collected data at three levels: district administration, health facility, and client. In this way it was possible to capture central elements of the relationships between the provider organization, the frontline facility, and the user. In addition, comparison of data from different levels (triangulation) permitted cross-validation of information.

At the district level, a District Health Team Questionnaire was administered to the district director of health services (DDHS), who was interviewed on the role of the DDHS office in health service delivery. Specifically, the questionnaire collected data on health infrastructure, staff training, support and supervision arrangements, and sources of financing.

The District Facility Data Sheet was used at the district level to collect more detailed information on the sampled health units for fiscal 1999-2000, including data on staffing and the related salary structures, vaccine supplies and immunization activity, and basic and supplementary supplies of drugs to the facilities. In addition, patient data, including monthly returns from facilities on total numbers of outpatients, inpatients, immunizations, and deliveries, were reviewed for the period April-June 2000.

At the facility level, the Uganda Health Facility Survey Questionnaire collected a broad range of information related to the facility and its activities. The questionnaire, which was administered to the in-charge, covered characteristics of the facility (location, type, level, ownership, catchment area, organization, and services); inputs (staff, drugs, vaccines, medical and nonmedical consumables, and capital inputs); outputs (facility utilization and referrals); financing (user charges, cost of services by category, expenditures, and financial and in-kind support); and institutional support (supervision, reporting, performance assessment, and procurement). Each health facility questionnaire was supplemented by a Facility Data Sheet (FDS). The FDS was designed to obtain data from the health unit records on staffing and the related salary structure; daily patient records for fiscal 1999-2000; the type of patients using the facility; vaccinations offered; and drug supply and use at the facility.

Finally, at the facility level, an exit poll was used to interview about 10 patients per facility on the cost of treatment, drugs received, perceived quality of services, and reasons for using that unit instead of alternative sources of health care.

Cleaning operations

Detailed information about data editing procedures is available in "Data Cleaning Guide for PETS/QSDS Surveys" in external resources.

STATA cleaning do-files and the data quality reports on the datasets can also be found in external resources.

State and Local Public Health Departments in the United States Governmental public health departments are responsible for creating and maintaining conditions that keep people healthy. A local health department may be locally governed, part of a region or district, be an office or an administrative unit of the state health department, or a hybrid of these. Furthermore, each community has a unique "public health system" comprising individuals and public and private entities that are engaged in activities that affect the public's health. (Excerpted from the Operational Definition of a functional local health department, National Association of County and City Health Officials, November 2005) Please reference http://www.naccho.org/topics/infrastructure/accreditation/upload/OperationalDefinitionBrochure-2.pdf for more information. Facilities involved in direct patient care are intended to be excluded from this dataset; however, some of the entities represented in this dataset serve as both administrative and clinical locations. This dataset only includes the headquarters of Public Health Departments, not their satellite offices. Some health departments encompass multiple counties; therefore, not every county will be represented by an individual record. Also, some areas will appear to have over representation depending on the structure of the health departments in that particular region. Town health officers are included in Vermont and boards of health are included in Massachusetts. Both of these types of entities are elected or appointed to a term of office during which they make and enforce policies and regulations related to the protection of public health. Visiting nurses are represented in this dataset if they are contracted through the local government to fulfill the duties and responsibilities of the local health organization. Since many town health officers in Vermont work out of their personal homes, TechniGraphics represented these entities at the town hall. This is denoted in the [DIRECTIONS] field. Effort was made by TechniGraphics to verify whether or not each health department tracks statistics on communicable diseases. Records with "-DOD" appended to the end of the [NAME] value are located on a military base, as defined by the Defense Installation Spatial Data Infrastructure (DISDI) military installations and military range boundaries. "#" and "*" characters were automatically removed from standard HSIP fields populated by TechniGraphics. Double spaces were replaced by single spaces in these same fields. At the request of NGA, text fields in this dataset have been set to all upper case to facilitate consistent database engine search results. At the request of NGA, all diacritics (e.g., the German umlaut or the Spanish tilde) have been replaced with their closest equivalent English character to facilitate use with database systems that may not support diacritics. The currentness of this dataset is indicated by the [CONTDATE] field. Based on this field, the oldest record dates from 11/18/2009 and the newest record dates from 01/08/2010.

Users can access data related to quality of health care for each state and the District of Columbia. Background 2010 State Snapshots is a database maintained by the Agency for Healthcare Research and Quality (AHRQ). Data is based on information collected from the National Healthcare Quality Report (NHQR). 2010 State Snapshots database offers an in-depth analysis of the quality of care – by type of condition, level of care, treatment setting, race and income, and insurance status. User functionality Users can search for state data by using the interactive map and cl icking on the state. Users are given data for the most recent year (2010) and baseline data year which varies by state. Each state has information on the state dashboard which includes information on types of care, settings of care, and care by clinical area. Users also have the option to focus on diabetes care, asthma care, healthy people 2010 goals, clinical preventative services, disparities in care, payer, and variation in overtime. Data is presented in tables or visual charts. Data is available for download using excel and XML format. Data Notes Detailed information about the data is available under the “Methods” section. The website does not indicate when new data will become available.

Note: This web page provides data on health facilities only. To file a complaint against a facility, please see: https://www.cdph.ca.gov/Programs/CHCQ/LCP/Pages/FileAComplaint.aspx

The California Department of Public Health (CDPH), Center for Health Care Quality, Licensing and Certification (L&C) Program licenses more than 30 types of healthcare facilities. The Electronic Licensing Management System (ELMS) is a California Department of Public Health data system created to manage state licensing-related data. This file lists the bed types and bed type capacities that are associated with California healthcare facilities that are operational and have a current license issued by the CDPH and/or a current U.S. Department of Health and Human Services’ Centers for Medicare and Medicaid Services (CMS) certification. This file can be linked by FACID to the Healthcare Facility Locations (Detailed) Open Data file for facility-related attributes, including geo-coding. The L&C Open Data facility beds file is updated monthly. To link the CDPH facility IDs with those from other Departments, like HCAI, please reference the "Licensed Facility Cross-Walk" Open Data table at https://data.chhs.ca.gov/dataset/licensed-facility-crosswalk. A list of healthcare facilities with addresses can be found at: https://data.chhs.ca.gov/dataset/healthcare-facility-locations.

Home Health Care Agency Ratings

Quality Measurements, Types of Services and More

By US Open Data Portal, data.gov [source]

About this dataset

This dataset provides a list of all Home Health Agencies registered with Medicare. Contained within this dataset is information on each agency's address, phone number, type of ownership, quality measure ratings and other associated data points. With this valuable insight into the operations of each Home Health Care Agency, you can make informed decisions about your care needs. Learn more about the services offered at each agency and how they are rated according to their quality measure ratings. From dedicated nursing care services to speech pathology to medical social services - get all the information you need with this comprehensive look at U.S.-based Home Health Care Agencies!

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For more datasets, click here.

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How to use the dataset

Are you looking to learn more about Home Health Care Agencies registered with Medicare? This dataset can provide quality measure ratings, addresses, phone numbers, types of services offered and other information that may be helpful when researching Home Health Care Agencies.

This guide will explain how to use the data in this dataset to gain a better understanding of Home Health Care Agencies registered with Medicare.

First, you will need to become familiar with the columns in the dataset. A list of all columns and their associated descriptions is provided above for your reference. Once you understand each column’s purpose, it will be easier for you to decide what metrics or variables are most important for your own research.

Next, use this data to compare various facets between different Home Health Care Agencies such as type of ownership, services offered and quality measure ratings like star rating or CMS certification number (from 0-5 stars). Collecting information from multiple sources such as public reviews or customer feedback can help supplement these numerical metrics in order to paint a more accurate picture about each agency's performance and customer satisfaction level.

Finally once you have collected enough data points on one particular agency or a comparison between multiple agencies then conduct more analysis using statistical methods like correlation matrices in order to determine any patterns that exist within the data set which may reveal valuable insights into topic of research at hand

Research Ideas

• Using the data to compare quality of care ratings between agencies, so people can make better informed decisions about which agency to hire for home health services.
• Analyzing the costs associated with different types of home health care services, such as nursing care and physical therapy, in order to determine where money could be saved in health care budgets.
• Evaluating the performance of certain agencies by analyzing the number of episodes billed to Medicare compared to their national averages, allowing agencies with lower numbers of billing episodes to be identified and monitored more closely if necessary

Acknowledgements

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

License

Unknown License - Please check the dataset description for more information.

Columns

File: csv-1.csv | Column name | Description | |:----------------------------------------...

The global health information exchange market, worth USD 4.23 billion in 2024, is expected to surpass USD 14.25 billion by 2034, with a CAGR of 12.9% from 2025 to 2034.

This dataset was generated using Simio simulation software. The simulations model patient flow in healthcare settings, capturing key metrics such as queue times, length of stay (LOS) for patients, and nurse utilization rates. Each CSV file contains time-series data, with measured variables including patient waiting times, resource utilization percentages, and service durations.## File Overview**CheckBloodPressure.csv** - (9 KB): Contains blood pressure Server records of patients.**CheckPatientType.csv** - (19 KB): Identifies the type of each patient (e.g., 1 or 3).**Fill_Information.csv** - (2 KB): Fill information records for new patients.**MedicalRecord1.csv** - (10 KB): Medical record dataset for patient type 1.**MedicalRecord2.csv** - (4 KB): Medical record dataset for patient type 2.**MedicalRecord3.csv** - (2 KB): Medical record dataset for patient type 3.**MedicalRecord4.csv** - (13 KB): Medical record dataset for patient type 4.**OutPatientDepartment.csv** - (18 KB): Data related to the satisfaction and length of stay of an given patient.**Triage.csv** - (13 KB): Data related to the triage process.**README.txt** - (4 KB): Documentation of the dataset, including structure, metadata, and usage.## Common Fields Across Files**Patient ID** (Integer): Unique identifier for each patient.**Patient Type** (Integer): Classification of patient (e.g., 1, 4).**Medical Records Arrival Time** (DateTime): Timestamp of the patient's first arrival in the medical record department.**Exiting Time** (DateTime): Timestamp when the patient exits a Server.**Waiting Time (min)** (Real): Total waiting time before being attended to.**Resource Used** (String): Resource (e.g., Operator) allocated to the patient.**Utilization %** (Real): Utilization rate of the resource as a percentage.**Queue Count Before Processing** (Integer): Number of patients in the queue before processing begins.**Queue Count After Processing** (Integer): Number of patients in the queue after processing ends.**Queue Difference** (Integer): Difference between the before and after queue counts.**Length of Stay (min)** (Real): Total time spent in the simulation by the patient.**LOS without Queues (min)** (Real): Length of stay excluding any queuing time.**Satisfaction %** (Real): Patient satisfaction rating based on their experience.**New Patient?** (String): Indicates if this is a new patient or a returning one.

Note: This web page provides data on health facilities only. To file a complaint against a facility, please see: https://www.cdph.ca.gov/Programs/CHCQ/LCP/Pages/FileAComplaint.aspx

The California Department of Public Health (CDPH), Center for Health Care Quality, Licensing and Certification (L&C) Program licenses more than 30 types of healthcare facilities. The Electronic Licensing Management System (ELMS) is a CDPH data system created to manage state licensing-related data and enforcement actions.

The “Health Facilities’ State Enforcement Actions” dataset includes summary information for state enforcement actions (state citations or administrative penalties) issued to California healthcare facilities. This file, a sub-set of the ELMS system data, includes state enforcement actions that have been issued from July 1, 1997 through June 30, 2024. Data are presented for each citation/penalty, and include information about the type of enforcement action, violation category, penalty amount, violation date, appeal status, and facility.

The “LTC Citation Narrative” dataset contains the full text of citations that were issued to long-term care (LTC) facilities between January 1, 2012 – December 31, 2017. DO NOT DOWNLOAD in Excel as this file has large blocks of text which may truncate. For example, Excel 2007 and later display, and allow up to, 32,767 characters in each cell, whereas earlier versions of Excel allow 32,767 characters, but only display the first 1,024 characters. Please refer to instructions in “E_Citation_Access_DB_How_To_Docs”, about how to download and view data.

These files enable providers and the public to identify facility non-compliance and quality issues. By making this information available, quality issues can be identified and addressed. Please refer to the background paper, “About Health Facilities’ State Enforcement Actions” for information regarding California state enforcement actions before using these data. Data dictionaries and data summary charts are also available.

Note: The Data Dictionary at the bottom of the dataset incorrectly lists the data column formats as all Text. For proper format labels, please go here.

These data are the part of the two National Health Surveys in the Republic of Serbia, conducted in 2006 and 2013, funded by the Ministry of Health. The survey was conducted in accordance with the methodology and instruments of the European Health Interview Survey wave 2. Both surveys were conducted as cross sectional studies. Population presented in the research included adults, aged 19 and more. The researches excluded people living on the territory of Kosovo and Metohija, as well as people with residence addresses in Special institutions (retirement homes, prisons, psychiatric clinics). Data on basic characteristics of the interviewees, health condition of the interviewees, using hospital and non-hospital health care services and prevention check-ups and unachieved need for health care was obtained through a face-to-face interview carried out at home, while information at the level of the household was obtained by means of a household questionnaire. The questions were validated instruments based on the standard questionnaires from similar types of surveys.

The Open Database of Healthcare Facilities (ODHF) is a collection of open data containing the names, types, and locations of health facilities across Canada. It is released under the Open Government License - Canada. The ODHF compiles open, publicly available, and directly-provided data on health facilities across Canada. Data sources include regional health authorities, provincial, territorial and municipal governments, and public health and professional healthcare bodies. This database aims to provide enhanced access to a harmonized listing of health facilities across Canada by making them available as open data. This database is a component of the Linkable Open Data Environment (LODE).

Healthcare Data Monetization Market size was valued at USD 566.27 Million in 2024 and is projected to reach USD 2082.09 Million by 2032, growing at a CAGR of 17.50% during the forecasted period 2025 to 2032.

The healthcare data monetization market is driven by the increasing volume of healthcare data, advancements in big data analytics, and the growing adoption of AI and machine learning for data-driven decision-making. The rising demand for personalized medicine, value-based care, and real-world evidence is encouraging healthcare providers, payers, and pharmaceutical companies to leverage data for improved patient outcomes and operational efficiency. Additionally, regulatory support for data interoperability, the expansion of health information exchanges (HIEs), and the adoption of blockchain for secure data transactions are fueling market growth. The shift toward digital health solutions and the growing interest in data-driven research and commercialization further accelerate the market expansion.

Healthcare Provider/Professional Data contains the data of individual providers and facilities, including their information about opening hours, insurance networks, specialties, NPI, etcetera. In addition to discovering data sources, merging data, running analytics, and receiving decision-making guidance, the bigger problem is responding to marketplace business and patient care demands in a timely manner. Pharmacy contains the location details of pharmacies and has attributes such as addresses, opening hours, facilities, etcetera.

A. Usecase/Applications possible with the data:

a. Provider network data systems (PNDS) - The primary goal of the PNDS is to collect data needed to evaluate provider networks, which include physicians, hospitals, labs, home health agencies, durable medical equipment providers, and so on, for all types of Health Insurers. Such information can be used to:

b. Find health care providers in my network - Use this directory to easily find other providers in my network.

c. Comprehensive services assessment - Determine whether insurers have contracted with a sufficient number of primary care practitioners, clinical specialists, and service facilities (hospitals, labs, etc.) within the insurer's service area.

d. Capacity analysis - Calculate the potential capacity of a managed care plan’s primary care providers.

e. Locate pharmacies in your local areas.

f. Support Employee Benefits Decisions - Having access to network data can help you make better decisions about which providers to use for Employee Medical Benefits.

g. Know about the facilities available across different pharmacies.

How does it work?

• Analyze sample data
• Customize parameters to suit your needs
• Add to your projects
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The National Hospital Care Survey (NHCS) collects data on patient care in hospital-based settings to describe patterns of health care delivery and utilization in the United States. Settings currently include inpatient and emergency departments (ED). From this collection, the NHCS contributes data that may inform emerging national health threats such as the current opioid public health emergency. The 2022 - 2024 NHCS are not yet fully operational so it is important to note that the data presented here are preliminary and not nationally representative. The data are from 24 hospitals submitting inpatient and 23 hospitals submitting ED Uniform Bill (UB)-04 administrative claims from October 1, 2022–September 30, 2024. Even though the data are not nationally representative, they can provide insight into the use of opioids and other overdose drugs. The NHCS data is submitted from various types of hospitals (e.g., general/acute, children’s, etc.) and can show results from a variety of indicators related to drug use, such as overall drug use, comorbidities, and drug and polydrug overdose. NHCS data can also be used to report on patient conditions within the hospital over time.

The State Snapshots provide graphical representations of State-specific health care quality information, including strengths, weaknesses, and opportunities for improvement. The goal is to help State officials and their public- and private-sector partners better understand health care quality and disparities in their State. State-level information used to create the State Snapshots is based on data collected for the National Healthcare Quality Report (NHQR). The State Snapshots include summary measures of quality of care and States' performances relative to all States, the region, and best performing States by overall health care quality, types of care (preventive, acute, and chronic), settings of care (hospitals, ambulatory care, nursing home, and home health), and clinical conditions (cancer, diabetes, heart disease, maternal and child health, and respiratory diseases). Special focus areas on diabetes, asthma, Healthy People 2010, clinical preventive services, disparities, payer, and variation over time are also featured. The Agency for Healthcare Research and Quality (AHRQ) has released the State Snapshots each year in conjunction with the 2004 NHQR through the 2009 NHQR.