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.

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

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).

Dataset

This dataset was created by Leonardo Vieira

Contents

Demographic trends play a major role in shaping the healthcare landscape, as economic factors and an aging population contribute to fast-rising healthcare spending. While consumers are spending more on healthcare services in the US, healthcare providers are confronting complex challenges related to labor, competition and tech advances. COVID-19 exposed healthcare and social assistance providers to unprecedented financial and operating pressures, with the lasting impacts still shaping every corner of the sector in 2025. Providers continue to grapple with workforce shortages intensified by the pandemic, resulting in ongoing staffing and recruitment challenges that pressure wage growth and new strategies to recruit and retain. At the same time, consolidation activity is reshaping the landscape, with more patients than ever receiving care from massive, integrated health systems rather than independent ones. Meanwhile, social assistance providers are finding it difficult to meet rising demand for services like food banks and emergency shelters. Despite this challenging operating environment, revenue has been expanding at a CAGR of 4.0% to an estimated $4.3 trillion over the past five years, with revenue rising an expected 2.3% in 2025. Healthcare and social assistance providers are struggling to address staffing challenges. The pandemic exacerbated existing staffing shortages, as the physical and mental toll of the pandemic pushed some to leave the sector entirely. Persistent labor shortages jeopardize healthcare and social assistance providers' ability to address demand, creating widespread staff burnout, high turnover rates and wage inflation. While the health sector labor market began stabilizing in 2024, alleviating wage pressures, an undersized workforce still leaves hundreds of thousands of jobs open. Staff shortages have been a driver of AI adoption in the health sector, as organizations adopt tech solutions to maintain care quality and efficiency with fewer personnel. Automating time- and cost-intensive administrative task helps organizations cope with labor shortages, but also enhances operating efficiency and patient outcomes amid workforce gaps. Demographic trends will remain the driving force behind rising healthcare spending moving forward. However, increasing demand and elevated costs will pressure healthcare and social assistance providers to shift how they operate. For example, investments in digital tools, including AI, and telehealth will accelerate because of their ability to lower costs, increase capacity and improve patient outcomes. As this occurs, cybersecurity will become a core priority, as health systems must mitigate the impact of increasingly disruptive and sophisticated cyberattacks. The sector will also face significant challenges from Medicaid cuts resulting from the OBBBA, as estimates suggest that nearly 17.0 million people will lose health coverage between 2026 and 2034. This substantial loss of coverage is likely to strain providers, particularly those serving large Medicaid and uninsured populations, creating new financial pressures. These dynamics will reinforce and accelerate the ongoing consolidation activity, as providers increasingly seek mergers or acquisitions to access resources, achieve operating efficiencies and ensure stability. In all, sector revenue will grow at a CAGR 3.4% to reach an estimated $5.0 trillion over the next five years.

The big data in healthcare market size is estimated to grow from USD 78 billion in 2024 to USD 540 billion by 2035, representing a CAGR of 19.20% till 2035

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.

Dataset

This dataset was created by Md.Hamid Hosen

Released under MIT

Contents

Snapshot img

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?

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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

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

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.

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.

This dataset contains data for the Healthcare Payments Data (HPD) Snapshot visualization. The Enrollment data file contains counts of claims and encounter data collected for California's statewide HPD Program. It includes counts of enrollment records, service records from medical and pharmacy claims, and the number of individuals represented across these records. Aggregate counts are grouped by payer type (Commercial, Medi-Cal, or Medicare), product type, and year. The Medical data file contains counts of medical procedures from medical claims and encounter data in HPD. Procedures are categorized using claim line procedure codes and grouped by year, type of setting (e.g., outpatient, laboratory, ambulance), and payer type. The Pharmacy data file contains counts of drug prescriptions from pharmacy claims and encounter data in HPD. Prescriptions are categorized by name and drug class using the reported National Drug Code (NDC) and grouped by year, payer type, and whether the drug dispensed is branded or a generic.

Graph and download economic data for Expenditures: Healthcare by Age: from Age 35 to 44 (CXUHEALTHLB0404M) from 1984 to 2023 about healthcare, age, health, expenditures, and USA.

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.

As of February 2022, 71 percent of healthcare leaders surveyed globally said they have confidence in the actionable insights their hospital/healthcare facility is able to extract from available data. Overall, healthcare leaders had high confidence in the data utilization process of their organization and the value that data can bring to their work.

Snapshot img

Artificial Intelligence (AI) Market In Healthcare Size 2025-2029

The artificial intelligence (AI) market in healthcare size is valued to increase USD 30.23 billion, at a CAGR of 33.1% from 2024 to 2029. Push for digitization in healthcare will drive the artificial intelligence (AI) market in healthcare.

Major Market Trends & Insights

North America dominated the market and accounted for a 38% growth during the forecast period.
By Application - Medical imaging and diagnostics segment was valued at USD 1.52 billion in 2023
By Component - Software segment accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: USD 961.16 million
Market Future Opportunities: USD 30230.60 million
CAGR : 33.1%
North America: Largest market in 2023

Market Summary

The market is a dynamic and rapidly evolving sector, driven by advancements in core technologies such as machine learning and natural language processing. These technologies are revolutionizing healthcare delivery through applications like predictive analytics, medical imaging, and virtual nursing assistants. According to recent reports, the global AI in healthcare market is expected to reach a significant market share by 2027, growing at a steady pace due to increasing adoption rates and the need for digitization in healthcare. For instance, AI-based tools are increasingly being used to improve elderly care, with applications ranging from fall detection to medication management.
However, challenges such as physician and provider skepticism, data privacy concerns, and regulatory issues persist. Despite these challenges, the opportunities for AI in healthcare are vast, with potential applications in disease diagnosis, treatment planning, and population health management.

What will be the Size of the Artificial Intelligence (AI) Market In Healthcare during the forecast period?

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How is the Artificial Intelligence (AI) In Healthcare Market Segmented and what are the key trends of market segmentation?

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

Application

  Medical imaging and diagnostics
  Drug discovery
  Virtual assistants
  Operations management
  Others


Component

  Software
  Hardware
  Services


End-user

  Hospitals and clinics
  Research institutes and academies


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    UK


  APAC

    China
    India
    Japan


  South America

    Brazil


  Rest of World (ROW)

By Application Insights

The medical imaging and diagnostics segment is estimated to witness significant growth during the forecast period.

Artificial Intelligence (AI) is revolutionizing the healthcare sector by enhancing various applications, from treatment optimization and diagnostics to patient engagement and fraud detection. Natural language processing and machine learning algorithms enable AI-powered virtual assistants to assist in clinical decision support, while computer vision systems analyze medical images for disease prediction and radiation therapy planning. Genomic data analysis and drug discovery platforms leverage AI to uncover new insights and accelerate research. Data mining techniques and predictive modeling are crucial for risk stratification and clinical trial optimization, while deep learning models improve healthcare chatbots and robotic surgery systems' precision.

The market for AI in healthcare is expanding rapidly, with remote patient monitoring and AI-powered diagnostics witnessing significant growth. According to recent studies, the market for AI in healthcare is projected to reach 61.2 billion USD by 2026, representing a 41.5% increase from its current size. Additionally, the adoption of AI in healthcare is expected to grow by 38.2% in the next five years. AI's impact on healthcare is multifaceted, from improving patient outcomes and reducing costs to enhancing operational efficiency and enabling personalized medicine. Wearable sensor data and electronic health records are essential data sources for AI applications in healthcare, while healthcare data interoperability and big data analytics are crucial for driving innovation and improving patient care.

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The Medical imaging and diagnostics segment was valued at USD 1.52 billion in 2019 and showed a gradual increase during the forecast period.

AI's role in healthcare is continuously evolving, with ongoing developments in precision oncology, disease prediction models, and drug repurposing. AI-powered fraud detection systems and biometric authentica

Please note that this list does not include all certifying healthcare providers. The list only includes those providers who have consented to be publicly listed. Please click the ‘Data’ tab to view the list of consenting providers. You can search the data by any field.

Graph and download economic data for Expenditures: Healthcare by Income Before Taxes: Total Complete Income Reporters (CXUHEALTHLB02A2M) from 1984 to 2003 about healthcare, health, tax, expenditures, income, and USA.

The Clinical Data Analytics in Healthcare market is booming, projected to reach $50 billion by 2033. Discover key drivers, trends, and restraints shaping this rapidly evolving landscape, including the roles of AI, machine learning, and leading companies like Cerner and IBM.