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:

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.

Healthcare fraud is considered a challenge for many societies. Health care funding that could be spent on medicine, care for the elderly, or emergency room visits is instead lost to fraudulent activities by materialistic practitioners or patients. With rising healthcare costs, healthcare fraud is a major contributor to these increasing healthcare costs.

Try out various unsupervised techniques to find the anomalies in the data.

Detailed Data File:

The following variables are included in the detailed Physician and Other Supplier data file (see Appendix A for a condensed version of variables included)).

npi – National Provider Identifier (NPI) for the performing provider on the claim. The provider NPI is the numeric identifier registered in NPPES.

nppes_provider_last_org_name – When the provider is registered in NPPES as an individual (entity type code=’I’), this is the provider’s last name. When the provider is registered as an organization (entity type code = ‘O’), this is the organization's name.

nppes_provider_first_name – When the provider is registered in NPPES as an individual (entity type code=’I’), this is the provider’s first name. When the provider is registered as an organization (entity type code = ‘O’), this will be blank.

nppes_provider_mi – When the provider is registered in NPPES as an individual (entity type code=’I’), this is the provider’s middle initial. When the provider is registered as an organization (entity type code= ‘O’), this will be blank.

nppes_credentials – When the provider is registered in NPPES as an individual (entity type code=’I’), these are the provider’s credentials. When the provider is registered as an organization (entity type code = ‘O’), this will be blank.

nppes_provider_gender – When the provider is registered in NPPES as an individual (entity type code=’I’), this is the provider’s gender. When the provider is registered as an organization (entity type code = ‘O’), this will be blank.

nppes_entity_code – Type of entity reported in NPPES. An entity code of ‘I’ identifies providers registered as individuals and an entity type code of ‘O’ identifies providers registered as organizations.

nppes_provider_street1 – The first line of the provider’s street address, as reported in NPPES.

nppes_provider_street – The second line of the provider’s street address, as reported in NPPES.

nppes_provider_city – The city where the provider is located, as reported in NPPES.

nppes_provider_zip – The provider’s zip code, as reported in NPPES.

nppes_provider_state – The state where the provider is located, as reported in NPPES. The fifty U.S. states and the District of Columbia are reported by the state postal abbreviation. The following values are used for all other areas:

'XX' = 'Unknown' 'AA' = 'Armed Forces Central/South America' 'AE' = 'Armed Forces Europe' 'AP' = 'Armed Forces Pacific' 'AS' = 'American Samoa' 'GU' = 'Guam' 'MP' = 'North Mariana Islands' 'PR' = 'Puerto Rico' 'VI' = 'Virgin Islands' 'ZZ' = 'Foreign Country'

nppes_provider_country – The country where the provider is located, as reported in NPPES. The country code will be ‘US’ for any state or U.S. possession. For foreign countries (i.e., state values of ‘ZZ’), the provider country values include the following: AE=United Arab Emirates IT=Italy AG=Antigua JO= Jordan AR=Argentina JP=Japan AU=Australia KR=Korea BO=Bolivia KW=Kuwait BR=Brazil KY=Cayman Islands CA=Canada LB=Lebanon CH=Switzerland MX=Mexico CN=China NL=Netherlands CO=Colombia NO=Norway DE= Germany NZ=New Zealand ES= Spain PA=Panama FR=France PK=Pakistan GB=Great Britain RW=Rwanda GR=Greece SA=Saudi Arabia HU= Hungary SY=Syria IL= Israel TH=Thailand IN=India TR=Turkey IS= Iceland VE=Venezuela

provider_type – Derived from the provider specialty code reported on the claim.

medicare_participation_indicator – Identifies whether the provider participates in Medicare and/or accepts the assigned assignment of Medicare allowed amounts.

place_of_service – Identifies whether the place of service submitted on the claims is a facility (value of ‘F’) or non-facility (value of ‘O’). Non-facility is generally an office setting; however other entities are included in non-facility.

hcpcs_code – HCPCS code used to identify the specific medical service furnished by the provider.

hcpcs_description – Description of the HCPCS code for the specific medical service furnished by the provider.

hcpcs_drug_indicator –Identifies whether the HCPCS code for the specific service furnished by the provider is an HCPCS listed on the Medicare Part B Drug Average Sales Price (ASP) File.

line_srvc_cnt – Number of services provided; note that the metrics used to count the number provided can vary from service to service.

bene_unique_cnt – Number of distinct Medicare beneficiaries rec...

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 2024 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 32 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. This statistic reveals the share of individuals who said select problems were the biggest facing the health care system in the United States in 2024.

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

The Big Data in 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 Sep 2025 about healthcare, uncertainty, health, World, and indexes.

Graph and download economic data for Per Capita Personal Consumption Expenditures: Services: Health Care for South Dakota (SDPCEPCHLTHCARE) from 1997 to 2024 about SD, healthcare, health, PCE, consumption expenditures, per capita, consumption, personal, services, and USA.

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.

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.

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.

The U.S. market for medical devices is the largest in the world. At an estimated $83 billion in 2006, this market represents nearly half the global total and is growing at 6 percent annually ? about double the rate of U.S. GDP. With the advent of microprocessors, miniaturization of electronic circuits, wired and wireless digital networking, and new materials and manufacturing processes, older generations of mechanical and analog electromechanical devices used in patient diagnosis, monitoring, and treatment have largely been replaced by devices and systems based on information technologies across the diverse array of contemporary medical devices...

This repository contains two 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.

The purpose of these datasets is to facilitate research and development in regional language processing, especially in the healthcare sector.

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.

Graph and download economic data for Personal consumption expenditures: Services: Health care (DHLCRC1Q027SBEA) from Q1 1959 to Q2 2025 about health, PCE, consumption expenditures, consumption, personal, services, GDP, and USA.

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.

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Smart Healthcare Market Size 2025-2029

The smart healthcare market size is forecast to increase by USD 151.3 billion, at a CAGR of 10.1% between 2024 and 2029.

The market represents a significant and continually evolving sector, characterized by the integration of technology into healthcare delivery and management. This market encompasses various applications, including telehealth, remote patient monitoring, electronic health records, and medical equipment with advanced capabilities. One of the primary drivers fueling the growth of the market is the increasing demand for remote health monitoring. This trend is particularly relevant in today's world, where social distancing measures have become a necessity. Remote patient monitoring enables healthcare providers to assess and manage patients' health conditions from a distance, reducing the need for in-person visits and minimizing potential exposure to infectious diseases.
Despite the numerous benefits, the market faces challenges, primarily due to the high costs associated with implementing and maintaining these advanced technologies. Nevertheless, the potential for improved patient outcomes, increased efficiency, and enhanced patient satisfaction makes the investment worthwhile for many healthcare organizations. Comparing the growth rates of different applications within the market, telehealth has experienced a remarkable surge in adoption. In 2020, the number of telehealth visits in the US increased by approximately 50% compared to the previous year. This trend is expected to continue, with telehealth expected to account for 25% of all healthcare visits by 2025.
In conclusion, the market represents a dynamic and evolving sector, characterized by the integration of technology into healthcare delivery and management. The market faces challenges, such as high costs, but also offers significant benefits, including improved remote patient outcomes, increased efficiency, and enhanced patient satisfaction. Applications like telehealth are experiencing rapid growth, with telehealth visits expected to account for a quarter of all healthcare visits by 2025.

Major Market Trends & Insights

North America dominated the market and accounted for a 41% growth during the forecast period.
The market is expected to grow significantly in Europe as well over the forecast period.
By the Distribution Channel, the Offline sub-segment was valued at USD 128.50 billion in 2023
By the Solution, the Telemedicine sub-segment accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: USD 135.06 billion
Future Opportunities: USD 151.30 billion 
CAGR : 10.1%
North America: Largest market in 2023

What will be the Size of the Smart Healthcare Market during the forecast period?

Get Key Insights on Market Forecast (PDF) Request Free Sample

The market encompasses various technologies and services that enhance preventive healthcare measures, facilitate health information privacy, and promote value-based healthcare. According to recent estimates, over 30% of the global healthcare expenditure is allocated to chronic disease management. This sector is anticipated to expand by approximately 15% annually, driven by the integration of advanced technologies such as remote diagnostics tools, genomic data analysis, and patient portal systems. Moreover, the adoption of personalized treatment plans, medical device cybersecurity, and clinical decision support systems has significantly improved patient outcomes and reduced healthcare costs. For instance, the implementation of telehealth infrastructure and wearable sensor data has led to a 10% decrease in hospital readmissions and a 20% increase in patient engagement.
Additionally, the digital health ecosystem, including mobile health apps, health information technology, and connected medical devices, has streamlined clinical trial data collection and the drug development process. In contrast, the healthcare industry continues to face challenges in patient safety protocols, medical device regulation, and pharmaceutical informatics. Despite these hurdles, the market's growth is propelled by the potential for enhanced patient experiences, improved clinical decision making, and increased efficiency in healthcare delivery.

How is this Smart Healthcare Industry segmented?

The smart healthcare industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Distribution Channel

  Offline
  Online


Solution

  Telemedicine
  mHealth
  EHR
  Smart pills
  Others


End-user

  Hospitals
  Home healthcare
  Specialty clinics
  Diagnostic centers


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    Spain
    UK


  Middle East a

Limit state-purchased health care cost growth to 2% less than the projected national health expenditures average every year through 2019.