This dataset contains synthetically generated real-time healthcare data intended for machine learning applications such as patient risk prediction, early diagnosis modeling, and health outcome analysis. The dataset simulates 1,000 patient records with 10 meaningful medical and demographic attributes including age, gender, heart rate, blood pressure, respiratory rate, oxygen saturation, glucose levels, smoking status, diabetes history, and a computed health risk score.

This dataset contains three healthcare datasets in Hindi and Punjabi, translated from English. The datasets cover medical diagnoses, disease names, and related healthcare information. The data has been carefully cleaned and formatted to ensure accuracy and usability for various applications, including machine learning, NLP, and healthcare analysis.

Diagnosis: Description of the medical condition or disease. Symptoms: List of symptoms associated with the diagnosis. Treatment: Common treatments or recommended procedures. Severity: Severity level of the disease (e.g., mild, moderate, severe). Risk Factors: Known risk factors associated with the condition. Language: Specifies the language of the dataset (Hindi, Punjabi, or English). The purpose of these datasets is to facilitate research and development in regional language processing, especially in the healthcare sector.

Column Descriptions: Original Data Columns: patient_id – Unique identifier for each patient. age – Age of the patient. gender – Gender of the patient (e.g., Male/Female/Other). Diagnosis – The diagnosed medical condition or disease. Remarks – Additional notes or comments from the doctor. doctor_id – Unique identifier for the doctor treating the patient. Patient History – Medical history of the patient, including previous conditions. age_group – Categorized age group (e.g., Child, Adult, Senior). gender_numeric – Numeric encoding for gender (e.g., 0 = Female, 1 = Male). symptoms – List of symptoms reported by the patient. treatment – Recommended treatment or medication. timespan – Duration of the illness or treatment period. Diagnosis Category – General category of the diagnosis (e.g., Cardiovascular, Neurological). Pseudonymized Data Columns: These columns replace personally identifiable information with anonymized versions for privacy compliance:

Pseudonymized_patient_id – An anonymized patient identifier. Pseudonymized_age – Anonymized age value. Pseudonymized_gender – Anonymized gender field. Pseudonymized_Diagnosis – Diagnosis field with anonymized identifiers. Pseudonymized_Remarks – Anonymized doctor notes. Pseudonymized_doctor_id – Anonymized doctor identifier. Pseudonymized_Patient History – Anonymized version of patient history. Pseudonymized_age_group – Anonymized version of age groups. Pseudonymized_gender_numeric – Anonymized numeric encoding of gender. Pseudonymized_symptoms – Anonymized symptom descriptions. Pseudonymized_treatment – Anonymized treatment descriptions. Pseudonymized_timespan – Anonymized illness/treatment duration. Pseudonymized_Diagnosis Category – Anonymized category of diagnosis.

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:

2025: All available figures are definite.

2024: Most available figures are definite. Figures are provisional for: - causes of death; - diagnoses known to the general practitioner; - supplied drugs; - AWBZ/Wlz-funded long term care; - youth 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; - average distance to facilities; - profitability and operating results at institutions;

2023: Most available figures are definite. Figures are provisional for: - hospital admissions by some diagnoses; - average period of hospitalisation; - physicians and nurses employed in care; - persons employed in health and welfare; - persons employed in healthcare;

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 and earlier: All available figures are definite.

Changes as of 18 December 2025: More recent figures have been added for: - crude birth rate; - live births to teenage mothers; - perinatal mortality at pregnancy duration at least 24 weeks; - diagnoses known to the general practitioner; - supplied drugs; - persons aged 80 or older; - AWBZ/Wlz-funded long term care; - youth care; - expenditures on health and welfare; - average distance to facilities; - profitability and operating results at institutions;

When will new figures be published? New figures will be published in July 2026.

A 10,000-patient database that contains in total 10,000 virtual patients, 36,143 admissions, and 10,726,505 lab observations.

The HCUP Summary Trend Tables include monthly information on hospital utilization derived from the HCUP State Inpatient Databases (SID) and HCUP State Emergency Department Databases (SEDD). Information on emergency department (ED) utilization is dependent on availability of HCUP data; not all HCUP Partners participate in the SEDD. The HCUP Summary Trend Tables include downloadable Microsoft® Excel tables with information on the following topics: Overview of monthly trends in inpatient and emergency department utilization All inpatient encounter types Inpatient stays by priority conditions -COVID-19 -Influenza -Other acute or viral respiratory infection Inpatient encounter type -Normal newborns -Deliveries -Non-elective inpatient stays, admitted through the ED -Non-elective inpatient stays, not admitted through the ED -Elective inpatient stays Inpatient service line -Maternal and neonatal conditions -Mental health and substance use disorders -Injuries -Surgeries -Other medical conditions Emergency department treat-and-release visits Emergency department treat-and-release visits by priority conditions -COVID-19 -Influenza -Other acute or viral respiratory infection Description of the data source, methodology, and clinical criteria

A 2025 survey found that over half of U.S. individuals indicated the cost of accessing treatment was the biggest problem facing the national healthcare system. This is much higher than the global average of 33 percent and is in line with the high cost of health care in the U.S. compared to other high-income countries. Bureaucracy along with a lack of staff were also considered to be pressing issues.

The Big Data Healthcare Market Report is Segmented by Component (Software, Services), Deployment (On-Premise, Cloud), Analytics Type (Descriptive Analytics, Predictive Analytics, Prescriptive Analytics), Application (Financial Analytics, and More), End User (Healthcare Providers, and More), and Geography (North America, Europe, Asia-Pacific, and More). The Market Forecasts are Provided in Terms of Value (USD).

Graph and download economic data for Economic Policy Uncertainty Index: Categorical Index: Health care (EPUHEALTHCARE) from Jan 1985 to Nov 2025 about healthcare, uncertainty, health, World, and indexes.

The California Health Care Quality Report Cards show the quality of health care for millions of Californians who get their care through medical groups and commercial insurance plans. Quality health care is getting the right care at the right time.

• The Health Plan Report Card data includes the 10 largest Health Maintenance Organizations (HMOs) and the 5 largest Preferred Provider Organizations (PPOs).
• The Medical Group - Commercial Report Card data includes 204 medical groups that have contracts with the HMOs listed in the Health Plan Report Card.
• The Medical Group - Medicare Report Card data includes 186 medical groups that have contracts with the Medicare Advantage plans listed in the Health Plan Report Card and serve Medicare Advantage members.
• The Counties data lists the counties served by each health plan and medical group included in the Report Cards.

Note: This data is maintained by CDII and was formerly created by the CA Office of the Patient Advocate. For more information please visit the CDII OPA webpage

My HealtheVet (www.myhealth.va.gov) is a Personal Health Record portal designed to improve the delivery of health care services to Veterans, to promote health and wellness, and to engage Veterans as more active participants in their health care. The My HealtheVet portal enables Veterans to create and maintain a web-based PHR that provides access to patient health education information and resources, a comprehensive personal health journal, and electronic services such as online VA prescription refill requests and Secure Messaging. Veterans can visit the My HealtheVet website and self-register to create an account, although registration is not required to view the professionally-sponsored health education resources, including topics of special interest to the Veteran population. Once registered, Veterans can create a customized PHR that is accessible from any computer with Internet access.

Find data on health care facilities in Massachusetts that are licensed or certified by the Department of Public Health.

This Public Health Portfolio (Directly Funded Research - Programme and Training Awards) dataset contains NIHR directly funded research awards where the funding is allocated to an award holder or host organisation to carry out a specific piece of research or complete a training award. The NIHR also invests significantly in centres of excellence, collaborations, services and facilities to support research in England. Collectively these form NIHR infrastructure support. NIHR infrastructure supported projects are available in the Public Health Portfolio (Infrastructure Support) dataset which you can find here.NIHR directly funded research awards (Programmes and Training Awards) that were funded between January 2006 and the present extraction date are eligible for inclusion in this dataset. An agreed inclusion/exclusion criteria is used to categorise awards as public health awards (see below). Following inclusion in the dataset, public health awards are second level coded to one of the four Public Health Outcomes Framework domains. These domains are: (1) wider determinants (2) health improvement (3) health protection (4) healthcare and premature mortality.More information on the Public Health Outcomes Framework domains can be found here.This dataset is updated quarterly to include new NIHR awards categorised as public health awards. Please note that for those Public Health Research Programme projects showing an Award Budget of £0.00, the project is undertaken by an on-call team for example, PHIRST, Public Health Review Team, or Knowledge Mobilisation Team, as part of an ongoing programme of work.Inclusion CriteriaThe NIHR Public Health Overview project team worked with colleagues across NIHR public health research to define the inclusion criteria for NIHR public health research. NIHR directly funded research awards are categorised as public health if they are determined to be ‘investigations of interventions in, or studies of, populations that are anticipated to have an effect on health or on health inequity at a population level.’ This definition of public health is intentionally broad to capture the wide range of NIHR public health research across prevention, health improvement, health protection, and healthcare services (both within and outside of NHS settings). This dataset does not reflect the NIHR’s total investment in public health research. The intention is to showcase a subset of the wider NIHR public health portfolio. This dataset includes NIHR directly funded research awards categorised as public health awards. This dataset does not include public health awards or projects funded by any of the three NIHR Research Schools or NIHR Health Protection Research Units.DisclaimersUsers of this dataset should acknowledge the broad definition of public health that has been used to develop the inclusion criteria for this dataset. Please note that this dataset is currently subject to a limited data quality review. We are working to improve our data collection methodologies. Please also note that some awards may also appear in other NIHR curated datasets. Further InformationFurther information on the individual awards shown in the dataset can be found on the NIHR’s Funding & Awards website here. Further information on individual NIHR Research Programme’s decision making processes for funding health and social care research can be found here.Further information on NIHR’s investment in public health research can be found as follows:The NIHR is one of the main funders of public health research in the UK. Public health research falls within the remit of a range of NIHR Directly Funded Research (Programmes and Training Awards), and NIHR Infrastructure Support. NIHR School for Public Health here.NIHR Public Health Policy Research Unit here. NIHR Health Protection Research Units here.NIHR Public Health Research Programme Health Determinants Research Collaborations (HDRC) here.NIHR Public Health Research Programme Public Health Intervention Responsive Studies Teams (PHIRST) here.

The dataset contains a ratio of the number of patient encounters (i.e., Inpatient Hospitalizations and Emergency Department visits) with a behavioral health diagnosis, per healthcare provider license with a specialty in behavioral health. The ratio is categorized based on the values of the Numerator to Denominator. Larger ratios may indicate a greater need for providers specializing in behavioral health. Smaller ratios may indicate a lower need for providers specializing in behavioral health. The dataset also contains the numbers of the top ten behavioral health diagnoses, by diagnosis category, that were present during the encounters. The table is broken down by county, and it is limited to hospital Inpatient and Emergency Department settings.

Point geometry with attributes displaying all health care related businesses in East Baton Rouge Parish, Louisiana.

A list of all Home Health Agencies that have been registered with Medicare. The list includes addresses, phone numbers, and quality measure ratings for each agency.

This dataset describes the number and density of health care services in each census tract in the United States. The data includes counts, per capita densities, and area densities per tract for many types of businesses in the health care sector, including doctors, dentists, mental health providers, nursing homes, and pharmacies.

Discover the booming medical database software market! Learn about its $15 billion valuation in 2025, projected 12% CAGR to 2033, key drivers, regional trends, and leading companies. Explore EHR, HIM systems impacting healthcare.

Dataset

This dataset was created by Hussain Afzaal 03

Released under Apache 2.0

Contents

Graph and download economic data for Personal consumption expenditures: Services: Health care (DHLCRC1Q027SBEA) from Q1 1959 to Q3 2025 about health, PCE, consumption expenditures, consumption, personal, services, GDP, and USA.

As of 2019, there were ******* small and medium sized ambulatory health care service businesses. Businesses in this category consist of doctor's offices, laboratory and diagnostic services, blood banks, and other outpatient facilities.