India Total Health Expenditure: % of Gross Domestic Product data was reported at 4.685 % in 2014. This records an increase from the previous number of 4.529 % for 2013. India Total Health Expenditure: % of Gross Domestic Product data is updated yearly, averaging 4.289 % from Dec 1995 (Median) to 2014, with 20 observations. The data reached an all-time high of 4.685 % in 2014 and a record low of 3.897 % in 1996. India Total Health Expenditure: % of Gross Domestic Product data remains active status in CEIC and is reported by CEIC Data. The data is categorized under India Premium Database’s Health Sector – Table IN.HLD001: Health Expenditure.

This dataset is about countries per year in India. It has 64 rows. It features 4 columns: country, capital city, and health expenditure.

India Public Health Expenditure: % of Government Expenditure data was reported at 5.048 % in 2014. This records an increase from the previous number of 4.657 % for 2013. India Public Health Expenditure: % of Government Expenditure data is updated yearly, averaging 4.423 % from Dec 1995 (Median) to 2014, with 20 observations. The data reached an all-time high of 5.048 % in 2014 and a record low of 3.604 % in 2003. India Public Health Expenditure: % of Government Expenditure data remains active status in CEIC and is reported by CEIC Data. The data is categorized under India Premium Database’s Health Sector – Table IN.HLD001: Health Expenditure.

India Public Health Expenditure: % of Gross Domestic Product data was reported at 1.407 % in 2014. This records an increase from the previous number of 1.287 % for 2013. India Public Health Expenditure: % of Gross Domestic Product data is updated yearly, averaging 1.112 % from Dec 1995 (Median) to 2014, with 20 observations. The data reached an all-time high of 1.407 % in 2014 and a record low of 0.985 % in 2003. India Public Health Expenditure: % of Gross Domestic Product data remains active status in CEIC and is reported by CEIC Data. The data is categorized under India Premium Database’s Health Sector – Table IN.HLD001: Health Expenditure.

This dataset provides a comprehensive overview of India's population, economy, education, health, and other key indicators. The data covers the period from 2010 to 2021 and includes information on population demographics, GDP, inflation, employment, education attainment, healthcare spending, and more. Researchers and analysts can use this dataset to gain insights into India's economic and social development, as well as to compare it with other countries in the region and around the world. The dataset is sourced from various official and publicly available data sources, including the World Bank, the United Nations, and the Indian government. This dataset can be used to analyse the situation of india and can predict the future of india

This dataset is about countries per year in India. It has 1 row and is filtered where the date is 2021. It features 4 columns: country, health expenditure, and military expenditure.

This dataset is about countries per year in India. It has 1 row and is filtered where the date is 2021. It features 4 columns: country, health expenditure, and fertility rate.

IntroductionAs high out-of-pocket healthcare expenses pose heavy financial burden on the families, Government of India is considering a variety of financing and delivery options to universalize health care services. Hence, an estimate of the cost of delivering universal health care services is needed. MethodsWe developed a model to estimate recurrent and annual costs for providing health services through a mix of public and private providers in Chandigarh located in northern India. Necessary health services required to deliver good quality care were defined by the Indian Public Health Standards. National Sample Survey data was utilized to estimate disease burden. In addition, morbidity and treatment data was collected from two secondary and two tertiary care hospitals. The unit cost of treatment was estimated from the published literature. For diseases where data on treatment cost was not available, we collected data on standard treatment protocols and cost of care from local health providers. ResultsWe estimate that the cost of universal health care delivery through the existing mix of public and private health institutions would be INR 1713 (USD 38, 95%CI USD 18–73) per person per annum in India. This cost would be 24% higher, if branded drugs are used. Extrapolation of these costs to entire country indicates that Indian government needs to spend 3.8% (2.1%–6.8%) of the GDP for universalizing health care services. ConclusionThe cost of universal health care delivered through a combination of public and private providers is estimated to be INR 1713 per capita per year in India. Important issues such as delivery strategy for ensuring quality, reducing inequities in access, and managing the growth of health care demand need be explored.

Measurement error of consumption expenditure and their effect on estimates of catastrophic health spending in IndiaThe National Sample Survey (NSS), India data is publicly available data set and can be accessed on request. It can be downloaded upon registration at http://microdata.gov.in/nada43/index.php/home. We have used data from the consumption (2011-12) and health round (2014) of NSS. The datasets from these surveys have been used to obtain this dataset used in the analysis for a paper entitled "Implications of measurement error of consumption expenditure on estimates of catastrophic health expenditure in India" to be published in Humanities and Social Sciences Communications.

This dataset provides a summary of government spending on healthcare, presented as a share of a country's GDP, for the years 2000–2020. The summary contains data for selected European countries, including Poland, the US, China, and India.

Description:

This comprehensive dataset provides a historical overview of India's key statistical indicators across multiple domains. The data has been sourced from https://www.macrotrends.net, which aggregates information from reputable sources like the United Nations (UN), World Bank, and other authoritative organizations.

Contents:

1. Population: Demographic data including population size, growth rates, and age distribution.
2. Economy: Economic indicators such as GDP, GDP per capita, inflation rates, and employment figures.
3. Trade: Information on imports, exports, trade balances, and international trade partnerships.
4. Health: Health-related statistics encompassing life expectancy, disease prevalence, and healthcare infrastructure.
5. Education: Educational metrics including literacy rates, school enrollment, and education expenditure.
6. Development: Human development indices, poverty rates, and access to basic amenities.
7. Labor Force: Labor market statistics comprising employment rates, workforce composition, and wage trends.
8. Environment: Environmental data covering factors like carbon emissions, pollution, and natural resource usage.
9. Crime: Crime rates and trends, including various types of criminal activities.
10. Immigration: Information on immigration patterns, citizenship, and foreign-born populations.
11. Other: Miscellaneous data on various aspects of India's socio-economic landscape.

Disclaimer and Terms of Use:

The historical data provided in this dataset is intended solely for informational purposes and is not meant for trading purposes or as financial advice. Neither Macrotrends LLC nor any of our information providers will be liable for any damages relating to your use of the data provided. Users are encouraged to verify the data's accuracy and refer to the original sources for any critical decisions or analyses.

This data contains all the essential data in the form of % with respect to rural and urban Indian states . This dataset is highly accurate as this is taken from the Indian govt. it is updated till 2021 for all states and union territories. source of data is data.gov.in titled - ******All India and State/UT-wise Factsheets of National Family Health Survey******

it is advised to you pls search the data keywords you need by using (Ctrl+f) , as it will help to avoid time wastage. States/UTs

Different columns it contains are Area

Number of Households surveyed Number of Women age 15-49 years interviewed Number of Men age 15-54 years interviewed

Female population age 6 years and above who ever attended school (%)

Population below age 15 years (%)

Sex ratio of the total population (females per 1,000 males)

Sex ratio at birth for children born in the last five years (females per 1,000 males)

Children under age 5 years whose birth was registered with the civil authority (%)

Deaths in the last 3 years registered with the civil authority (%)

Population living in households with electricity (%)

Population living in households with an improved drinking-water source1 (%)

Population living in households that use an improved sanitation facility2 (%)

Households using clean fuel for cooking3 (%) Households using iodized salt (%)

Households with any usual member covered under a health insurance/financing scheme (%)

Children age 5 years who attended pre-primary school during the school year 2019-20 (%)

Women (age 15-49) who are literate4 (%)

Men (age 15-49) who are literate4 (%)

Women (age 15-49) with 10 or more years of schooling (%)

Men (age 15-49) with 10 or more years of schooling (%)

Women (age 15-49) who have ever used the internet (%)

Men (age 15-49) who have ever used the internet (%)

Women age 20-24 years married before age 18 years (%)

Men age 25-29 years married before age 21 years (%)

Total Fertility Rate (number of children per woman) Women age 15-19 years who were already mothers or pregnant at the time of the survey (%)

Adolescent fertility rate for women age 15-19 years5 Neonatal mortality rate (per 1000 live births)

Infant mortality rate (per 1000 live births) Under-five mortality rate (per 1000 live births)

Current Use of Family Planning Methods (Currently Married Women Age 15-49 years) - Any method6 (%)

Current Use of Family Planning Methods (Currently Married Women Age 15-49 years) - Any modern method6 (%)

Current Use of Family Planning Methods (Currently Married Women Age 15-49 years) - Female sterilization (%)

Current Use of Family Planning Methods (Currently Married Women Age 15-49 years) - Male sterilization (%)

Current Use of Family Planning Methods (Currently Married Women Age 15-49 years) - IUD/PPIUD (%)

Current Use of Family Planning Methods (Currently Married Women Age 15-49 years) - Pill (%)  

Current Use of Family Planning Methods (Currently Married Women Age 15-49 years) - Condom (%)

Current Use of Family Planning Methods (Currently Married Women Age 15-49 years) - Injectables (%)

Total Unmet need for Family Planning (Currently Married Women Age 15-49 years)7 (%)

Unmet need for spacing (Currently Married Women Age 15-49 years)7 (%)

Health worker ever talked to female non-users about family planning (%)

Current users ever told about side effects of current method of family planning8 (%)

Mothers who had an antenatal check-up in the first trimester (for last birth in the 5 years before the survey) (%)

Mothers who had at least 4 antenatal care visits (for last birth in the 5 years before the survey) (%)

Mothers whose last birth was protected against neonatal tetanus (for last birth in the 5 years before the survey)9 (%)

Mothers who consumed iron folic acid for 100 days or more when they were pregnant (for last birth in the 5 years before the survey) (%)

Mothers who consumed iron folic acid for 180 days or more when they were pregnant (for last birth in the 5 years before the survey} (%)

Registered pregnancies for which the mother received a Mother and Child Protection (MCP) card (for last birth in the 5 years before the survey) (%)

Mothers who received postnatal care from a doctor/nurse/LHV/ANM/midwife/other health personnel within 2 days of delivery (for last birth in the 5 years before the survey) (%)

Average out-of-pocket expenditure per delivery in a public health facility (for last birth in the 5 years before the survey) (Rs.)

Children born at home who were taken to a health facility for a check-up within 24 hours of birth (for last birth in the 5 years before the survey} (%)

Children who received postnatal care from a doctor/nurse/LHV/ANM/midwife/ other health personnel within 2 days of delivery (for last birth in the 5 years before the survey) (%)

Institutional births (in the 5...

The dataset contains State wise Health Infrastructure - Doctors and Specialists from Handbook of Statistics on Indian States

Manipur(2015): Data for 2013-14 repeated. If the 'In Position' value exceeds the 'Required' or 'Sanctioned' values, then 'Vacant' and 'Shortfall' will indicate a surplus Gujarath(2015): Data for 2013 repeated Mizoram(2015):Sanctioned data for 2011 used Bihar(2015): In position data for 2013-14 and Sanctioned data for 2011 used

Note: 1. Four (4) specialist per CHC. 2. Total given in the Table are not strictly comparable as figures for some of the States were not available in 2005. For calculating the overall percentages of vacancy and shortfall, the States/UTs for which manpower position is not available, may be excluded. 3. For 2013, Specialists attending CHCs on hiring basis. 4. All India figures for Vacancy and Shortfall are the totals of State-wise Vacancy and Shortfall ignoring surplus in some States / UTs. 5. Data pertain to 31st March 6. Vacant = Sanctioned - In Position, Shortfall = Required - In Position

Snapshot img

Big Data Spending In Healthcare Sector Market Size 2025-2029

The big data spending in healthcare sector market size is valued to increase by USD 7.78 billion, at a CAGR of 10.2% from 2024 to 2029. Need to improve business efficiency will drive the big data spending in healthcare sector market.

Market Insights

APAC dominated the market and accounted for a 31% growth during the 2025-2029.
By Service - Services segment was valued at USD 5.9 billion in 2023
By Type - Descriptive analytics segment accounted for the largest market revenue share in 2023

Market Size & Forecast

Market Opportunities: USD 108.28 million 
Market Future Opportunities 2024: USD 7783.80 million
CAGR from 2024 to 2029 : 10.2%

Market Summary

The healthcare sector's adoption of big data analytics is a global trend that continues to gain momentum, driven by the need to improve business efficiency, enhance patient care, and ensure regulatory compliance. Big data in healthcare refers to the large and complex data sets generated from various sources, including Electronic Health Records, medical devices, and patient-generated data. This data holds immense potential for identifying patterns, predicting outcomes, and driving evidence-based decision-making. One real-world scenario illustrating this is supply chain optimization. Hospitals and healthcare providers can leverage big data analytics to optimize their inventory management, reduce wastage, and ensure timely availability of essential medical supplies.
For instance, predictive analytics can help anticipate demand for specific medical equipment or supplies, enabling healthcare providers to maintain optimal stock levels and minimize the risk of stockouts or overstocking. However, the adoption of big data analytics in healthcare is not without challenges. Data privacy and security concerns related to patients' medical data are a significant concern, with potential risks ranging from data breaches to unauthorized access. Ensuring robust Data security measures and adhering to regulatory guidelines, such as the Health Insurance Portability and Accountability Act (HIPAA) in the US, is essential for maintaining trust and protecting sensitive patient information.
In conclusion, the use of big data analytics in healthcare is a transformative trend that offers numerous benefits, from improved operational efficiency to enhanced patient care and regulatory compliance. However, it also presents challenges related to data privacy and security, which must be addressed to fully realize the potential of this technology.

What will be the size of the Big Data Spending In Healthcare Sector Market during the forecast period?

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

The market continues to evolve, with recent research indicating a significant increase in investments. This growth is driven by the need for improved patient care, regulatory compliance, and cost savings. One trend shaping the market is the adoption of advanced analytics techniques to gain insights from large datasets. For instance, predictive analytics is being used to identify potential health risks and improve patient outcomes.
Additionally, data visualization software and data analytics platforms are essential tools for healthcare organizations to make data-driven decisions. Compliance is another critical area where big data is making a significant impact. With the increasing amount of patient data being generated, there is a growing need for data security and privacy. Data encryption methods and data anonymization techniques are being used to protect sensitive patient information. Budgeting is also a significant consideration for healthcare organizations investing in big data. Cost benefit analysis and statistical modeling are essential tools for evaluating the return on investment of big data initiatives.
As healthcare organizations continue to invest in big data, they must balance the benefits against the costs to ensure they are making informed decisions. In conclusion, the market is experiencing significant growth, driven by the need for improved patient care, regulatory compliance, and cost savings. The adoption of advanced analytics techniques, data visualization software, and data analytics platforms is essential for healthcare organizations to gain insights from large datasets and make data-driven decisions. Additionally, data security and privacy are critical considerations, with data encryption methods and data anonymization techniques being used to protect sensitive patient information.
Budgeting is also a significant consideration, with cost benefit analysis and statistical modeling essential tools for evaluating the return on investment of big data initiatives.

Unpacking the Big Data Spending In Healthcare Sector Market Landscape

In the dynamic healthcare sector, the adoption of big data technologies has become a st

This dataset contains anonymized information on hospitals across India sourced from public data by NIT Jalandhar and expanded through web scraping from an online maps platform. It includes location information, ratings, and the number of reviews. Ideal for anyone interested in analyzing healthcare access and distribution.

Each entry includes the hospital name, city, state, and geographic coordinates, with cluster-preserving techniques applied to anonymize sensitive location data while retaining each hospital’s effective influence. This means the coordinates are not exact, but the clustering of hospitals even when adjusted for their prominence remains the same on a state and national level.

Additionally, population densities for districts have been added, allowing for more granular insights.

If you're a researcher, policymaker, or healthcare analyst, you can use this to gain insights into the accessibility of healthcare services in India.

India Private Health Expenditure: % of Gross Domestic Product data was reported at 3.278 % in 2014. This records an increase from the previous number of 3.242 % for 2013. India Private Health Expenditure: % of Gross Domestic Product data is updated yearly, averaging 3.166 % from Dec 1995 (Median) to 2014, with 20 observations. The data reached an all-time high of 3.419 % in 2001 and a record low of 2.890 % in 1996. India Private Health Expenditure: % of Gross Domestic Product data remains active status in CEIC and is reported by CEIC Data. The data is categorized under India Premium Database’s Health Sector – Table IN.HLD001: Health Expenditure.

This dataset provides detailed budget allocation insights for urban and rural households in India, capturing present living standards. The data includes various spending areas such as housing, food, transportation, healthcare, education, and discretionary expenses. The dataset is designed to help researchers, policymakers, and individuals understand spending habits and optimize budget planning.

Context: The dataset is derived from various government reports, surveys, and market research studies that provide a snapshot of the current economic conditions and living standards in India. It includes average income levels, typical expenses, and common savings patterns for both urban and rural households.

Sources:

National Sample Survey Office (NSSO) Ministry of Statistics and Programme Implementation (MoSPI) Various market research reports and publications Inspiration: The inspiration behind this dataset is to provide a clear and detailed picture of how households in different regions of India allocate their budgets. This can be a valuable resource for economists, social scientists, financial advisors, and anyone interested in understanding the financial behavior of Indian households.

State Finances: BE: Madhya Pradesh: Expenditure: Medical and Public Health data was reported at 208,529.000 INR mn in 2025. This records an increase from the previous number of 155,965.000 INR mn for 2024. State Finances: BE: Madhya Pradesh: Expenditure: Medical and Public Health data is updated yearly, averaging 155,965.000 INR mn from Mar 2023 (Median) to 2025, with 3 observations. The data reached an all-time high of 208,529.000 INR mn in 2025 and a record low of 133,236.000 INR mn in 2023. State Finances: BE: Madhya Pradesh: Expenditure: Medical and Public Health data remains active status in CEIC and is reported by Reserve Bank of India. The data is categorized under India Premium Database’s Government and Public Finance – Table IN.FE028: State Finances: Expenditure: Medical and Public Health, Family Welfare and Water Supply and Sanitation: Budget Estimates.

India Health Expenditure per Capita data was reported at 74.995 USD in 2014. This records an increase from the previous number of 68.533 USD for 2013. India Health Expenditure per Capita data is updated yearly, averaging 29.072 USD from Dec 1995 (Median) to 2014, with 20 observations. The data reached an all-time high of 74.995 USD in 2014 and a record low of 15.822 USD in 1995. India Health Expenditure per Capita data remains active status in CEIC and is reported by CEIC Data. The data is categorized under India Premium Database’s Health Sector – Table IN.HLD001: Health Expenditure.

This dataset simulates heart disease risk factors for 3,000 individuals in India, drawing on real-world patterns observed in medical studies and publicly available datasets. It is designed to closely mirror Indian demographic, lifestyle, and clinical variables that influence cardiovascular health.

The primary objective of this dataset is to support:

Predictive modeling for heart disease.

Exploration of risk factors like age, blood pressure, cholesterol, obesity, and smoking.

Research in health equity, environmental exposure, and lifestyle correlations.

It includes 25 features per individual, covering demographic, clinical, environmental, and behavioral variables. The target variable (target) indicates whether the individual has been diagnosed with heart disease.

🧠 Use Cases Training and testing machine learning models (binary classification).

Exploratory data analysis (EDA) in public health.

Academic research and data science competitions.

Feature engineering and healthcare visualizations.

⚠️ Disclaimer This is a synthetically generated dataset based on the distribution of real-world Indian heart health data. It is not sourced from real patients but is designed to be statistically realistic and anonymized. It should be used for educational and analytical purposes only—not for clinical decision-making.