In fiscal year 2022, Max Healthcare Group (MHIL) generated the highest revenue from each occupied hospital bed with about ****** Indian rupees. In comparison, Narayana Hrudayalaya hospitals generated the lowest revenue of over ****** rupees per occupied hospital bed.

In financial year 2022, the net worth of Apollo Hospitals Enterprise Limited (AHEL) was the highest valuing at over ** billion Indian rupees among the top hospitals in India. This was followed by Fortis Healthcare Ltd (FHL) with a net worth of above ** billion rupees.

The multispecialty hospital chain, ****************, had the highest net sales in the hospitals and medical services sector in India as of June 2025, with sales aggregating to over ** billion Indian rupees. The cardiac specialty hospital, Narayana Hrudayala, followed with around ** billion rupees worth of net sales in the given period.

The India hospital market size was valued at USD 98.98 Billion in 2024, driven by the rise in the prevalence of chronic and infectious diseases across India. The market size is anticipated to grow at a CAGR of 5.80% during the forecast period of 2025-2034 to achieve a value of USD 173.94 Billion by 2034.

This horizontal bar chart displays hospital beds (per 1,000 people) by country full name using the aggregation average, weighted by population in India. The data is filtered where the date is 2021. The data is about countries per year.

We offer a doctor database in India that is designed to help you generate highly qualified sales leads. With our Doctors Database, you can easily connect with the appropriate healthcare professionals in India and abroad. These doctor data are invaluable for promoting and selling products, services, and solutions within the healthcare industry, as well as for medical publications, travel-related services, insurance, banking, charity/donations, and memberships, among other areas. All India Doctors Database list consists of approximately 11 lakh doctors in India who are specialists in various fields of medicine. This database of doctors in India includes Doctors across various specialties/fields of medicine such as General Practitioners, Family Physicians, Obstetrics and Gynecology Cardiology, Gastroenterology, Endocrinology and Diabetes, Anesthesia, Pediatrics, Oncology, Emergency Medicine, Neurology, Nephrology, Haematology, Internal Medicine, Dermatology etc.

In 2019, there were an estimated ** thousand public and private sector hospitals in India with the highest number of hospitals in the northern state of Uttar Pradesh. Private sector hospitals outnumbered the public hospitals by a wide margin.

DISCLAIMER

Dataset consists of historical data of pre-pandemic period and doesn’t represent the current reality which may have changed due to the spikes in demand. This dataset has been generated in collaboration of efforts within CoronaWhy community.

Context

Last updated: April 26th 2020 Updates: April 14th 2020 - Added missing population data April 15th 2020 - Added Brazil statewise ICU hospital beds dataset April 21th 2020 - Added Italy, Spain statewise ICU hospital beds dataset, India statewise TOTAL hospital beds dataset April 26th 2020 - Added Sweden ICU(2019) and TOTAL(2018) beds datasets

Purpose of the dataset

I am trying to produce a dataset that will provide a foundation for policymakers to understand the realistic capacity of healthcare providers being able to deal with the spikes in demand for intensive care. As a way to help, I’ve prepared a dataset of beds across countries and states. Work in progress dataset that should and will be updated as more data becomes available and public on weekly basis.

Importance

This dataset is intended to be used as a baseline for understanding the typical bed capacity and coverage globally. This information is critical for understanding the impact of a high utilization event, like COVID-19.

Current challenges

Datasets are scattered across the web and are very hard to normalize, I did my best but help would be much appreciated.

Data sources / Acknowledgments

arcgis (USA) - https://services1.arcgis.com/Hp6G80Pky0om7QvQ/arcgis/rest/services/Hospitals_1/FeatureServer/0 KHN (USA) - https://khn.org/news/as-coronavirus-spreads-widely-millions-of-older-americans-live-in-counties-with-no-icu-beds/ datahub.io (World) - https://datahub.io/world-bank/sh.med.beds.zs eurostat - https://data.europa.eu/euodp/en/data/dataset/vswUL3c6yKoyahrvIRyew OECD - https://data.oecd.org/healtheqt/hospital-beds.htm WDI (World) - https://data.worldbank.org/indicator/SH.MED.BEDS.ZS NHP(India) - http://www.cbhidghs.nic.in/showfile.php?lid=1147 data.gov.sg (Singapore) - https://data.gov.sg/dataset/health-facilities?view_id=91b4feed-dcb9-4720-8cb0-ac2f04b7efd0&resource_id=dee5ccce-4dfb-467f-bcb4-dc025b56b977 dati.salute.gov.it (Italy)- http://www.dati.salute.gov.it/dati/dettaglioDataset.jsp?menu=dati&idPag=96 portal.icuregswe.org (Sweden) - https://portal.icuregswe.org/seiva/en/Rapport publications: Intensive Care Medicine Journal (Europe) - https://link.springer.com/article/10.1007/s00134-012-2627-8 Critical Care Medicine Journal (Asia) - https://www.researchgate.net/figure/Number-of-critical-care-beds-per-100-000-population_fig1_338520008 Medicina Intensiva (Spain) - https://www.medintensiva.org/en-pdf-S2173572713000878 news: https://lanuovaferrara.gelocal.it/italia-mondo/cronaca/2020/03/19/news/dietro-la-corsa-a-nuovi-posti-in-terapia-intensiva-gli-errori-del-passato-1.38611596 kaggle: germany - https://www.kaggle.com/manuelblechschmidt/icu-beds-in-germany brazil (IBGE) - https://www.kaggle.com/thiagobodruk/brazilianstates Manual population data search from wiki

Data columns

country,state,county,lat,lng,type,measure,beds,population,year,source,source_url - country - country of origin, if present - state - more granular location, if present - lat - latitude - lng - longtitude - type - [TOTAL, ICU, ACUTE(some data could include ICU beds too), PSYCHIATRIC, OTHER(merged ‘SPECIAL’, ‘CHRONIC DISEASE’, ‘CHILDREN’, ‘LONG TERM CARE’, ‘REHABILITATION’, ‘WOMEN’, ‘MILITARY’] - measure - type of measure (per 1000 inhabitants) - beds - number of beds per 1000 - population - population of location based on multiple sources and wikipedia - year - source year for beds and population data - source - source of data - source_url - URL of the original source

Files

for each of datasource: hospital_beds_per_source.csv

US only: US arcgis + khn (state/county granularity): hospital_beds_USA.csv

Global (state(region)/county granularity): hospital_beds_global_regional.csv

Global (country granularity): hospital_beds_global_v1.csv

Contributors

Igor Kiulian - extracting/normalizing/formatting/merging data Artur Kiulian - helped with Kaggle setup Augaly S. Kiedi - helped with country population data Kristoffer Jan Zieba - found Swedish data sources

Possible Improvements

Find and megre more detailed (state/county wise) or newer datasource

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.

The global sales of hospital consumables are estimated to be worth USD 421.8 billion in 2025 and anticipated to reach a value of USD 578.0 billion by 2035. Sales are projected to rise at a CAGR of 3.2% over the forecast period between 2025 and 2035. The revenue generated by hospital consumables in 2024 was USD 408.8 billion.

Semi-Annual Market Update for the Global Hospital Consumables Market

Analysis of Top Countries Manufacturing and Supplying Hospital Consumables

Hospital Consumables Industry Analysis by Top Investment Segments

The size of the Pediatric Hospitals Market market was valued at USD 169.44 billion in 2024 and is projected to reach USD 249.44 billion by 2033, with an expected CAGR of 5.68 % during the forecast period. Recent developments include: In May 2024, Cincinnati Children's Hospital Medical Center announced the acquisition of a medical building neighboring Eden Park that consists of three operational theaters. This acquisition addresses a notable surge in the need for pediatric surgical procedures. , In March 2024, Cincinnati Children's Hospital and Parkview Health announced the extension of their partnership to enhance the availability of top-tier pediatric healthcare in Fort Wayne and nearby areas. This collaboration integrates Parkview's existing pediatric hospital services, along with pediatric primary and specialty care, under the umbrella of Cincinnati Children's. , In November 2023, Rainbow Children's Hospital, a chain of pediatric and maternity hospitals in India, announced expansion of its capacity in the latter half of the fiscal year 2024 to open four new hospitals, adding a total of 270 beds, across Bengaluru, Hyderabad, and Chennai. .

9884 Global export shipment records of Hospital Good with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

Market Summary: • The global Clinical Decision Support Systems market size in 2023 was XX Million. Clinical Decision Support Systems Industry's compound annual growth rate (CAGR) was XX% from 2024 to 2031. • The rise in demand for quality healthcare solutions and for the provision of effective clinical decision-making and fewer chances of error, Clinical decision-support systems are being used are the key drivers for this market. • Clinical decision support systems proved to be quite helpful in providing patient resources and services throughout the global COVID-19 pandemic. • R&D projects supported by substantial corporate investments are expected to yield several market enhancements. If more systems made use of cloud computing and interoperability platforms to enable effective operation and seamless data flow, the adoption of CDSS would rise. • North America is the dominant region in this market.

Market Dynamics:

Key Drivers:

Rise in demand for quality healthcare solutions drives the market for Clinical decision support system

The population is aging quickly, treatment plans and SOPs for healthcare are insufficient, and resources are becoming more limited, among other problems. For Instance, According to UNICEF, 80% of the elderly will reside in low- and middle-income nations in 2050. Moreover, The population is aging far more quickly than it did in the past. (Source:https://www.who.int/news-room/fact-sheets/detail/ageing-and-health) Hence, To satisfy this need, hospitals and healthcare organizations are more open to switching to frameworks that are driven by technology. To handle time-consuming paperwork and staff members who handle both medical and administrative duties, hospitals and other healthcare institutions are merging systems. They simplify the process for various departments to get clinical and administrative data about a patient. Today, the majority of the delivery of healthcare services and lacking of interoperability with medical devices. Interoperability standards and data exchange frameworks are used to allow the exchange of data effectively in the healthcare industry. They make it possible to share data between several systems, regardless of the vendor or program. Hence, to promote the use of superior, interoperable solutions, hospital chains need to share data with their branch offices. For Instance, According to the Agency for Healthcare and Quality, Clinical decision support is the provision of correct information and assists in making decisions regarding the treatment of a patient. Clinical decision assistance can successfully enhance patient outcomes and result in medical services of a higher quality.(Source: https://www.wolterskluwer.com/en/news/evercare-group-adopts-uptodate-advanced-across-hospitals-in-africa-and-india)Treatment and diagnosis guidelines are provided by the CDSS system. It improves the quality of care by retrieving information from the knowledge base and using it to inform treatment decisions. For Instance, UpToDate Advanced has been selected by the Evercare Group, a top impact-driven healthcare network in emerging economies, by Wolters Kluwer Health, a global provider of reliable clinical technology and evidence-based solutions, to improve patient care and quality across the organization. It also gives the practitioner clinical guidelines to follow during the course of therapy Hence, The creation of CDSS has been recognized as an essential tool for overcoming challenges in delivering high-quality care to patients by improving and optimizing the delivery of healthcare

Clinical decision-support systems are utilized to provide efficient clinical decision-making with fewer errors that drive the market growth.

Writing, dispensing, or administering a prescription can all result in medication errors. Negative effects could result from it, for as prescribing medicine to a patient who has a documented allergy to that drug. For Instance, according to the National Institute of Health, The annual cost of medical errors is estimated to be $20 billion. (Source:https://www.ncbi.nlm.nih.gov/books/NBK499956/)Every year, medical errors in hospitals and clinics cause about 100,000 deaths. Such drug errors and unfavorable results might be prevented by systems with integrated CDSS. By matching the patient's electronic healt...

78 Global export shipment records of Hospital,good with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

Introduction

Introducing the Indian English Scripted Monologue Speech Dataset for the Healthcare Domain, a voice dataset built to accelerate the development and deployment of English language automatic speech recognition (ASR) systems, with a sharp focus on real-world healthcare interactions.

Speech Data

This dataset includes over 6,000 high-quality scripted audio prompts recorded in Indian English, representing typical voice interactions found in the healthcare industry. The data is tailored for use in voice technology systems that power virtual assistants, patient-facing AI tools, and intelligent customer service platforms.

Topic Coverage

The prompts span a broad range of healthcare-specific interactions, such as:

Contextual Depth

To maximize authenticity, the prompts integrate linguistic elements and healthcare-specific terms such as:

These elements make the dataset exceptionally suited for training AI systems to understand and respond to natural healthcare-related speech patterns.

Transcription

Every audio recording is accompanied by a verbatim, manually verified transcription.

📌 Overview

"Living in India 2025" is a synthetic yet realistic dataset that explores the cost of living and quality of life across 200 Indian cities. It combines key indicators such as average rent, food cost, internet speed, healthcare rating, safety score, and happiness index to help analysts, students, and data enthusiasts perform in-depth comparisons and uncover meaningful insights. 📊 What’s Inside

The dataset contains 200 rows (one per city) and the following columns:

City – Name of the Indian city.

Average Rent (₹) – Estimated monthly rent for a standard apartment.

Food Cost (₹) – Average monthly food expenses per person.

Internet Speed (Mbps) – Typical broadband download speed.

Healthcare Rating (1-10) – Quality and accessibility of healthcare services.

Safety Score (1-10) – Perceived safety level in the city.

Happiness Index (1-10) – Overall life satisfaction rating.

💡 Potential Insights You Can Explore

Which Indian cities provide the best happiness for the least money?

How safety and happiness correlate across regions.

Which cities are most digital-nomad-friendly based on internet speed and cost.

Regional patterns in healthcare quality vs cost of living.

🛠 Ideal For

Exploratory Data Analysis (EDA)

Data Visualization Projects

Regression & Correlation Studies

Geospatial Mapping

Urban Economics & Policy Research

In the past five years, the healthcare sector's growth has supported hospital bed manufacturers' revenue. Population growth, rising obesity rates, and an increase in older adults have heightened demand for healthcare services. Healthcare providers have accordingly been expanding facilities, especially in underserved areas, leading to greater demand for hospital beds. While international trade of hospital beds has seen historic levels of volatility, exports remain elevated after skyrocketing at the height of the pandemic. Revenue has been climbing at a CAGR of 2.1% to an estimated $2.8 billion over the five years through 2024. Revenue has swelled by 2.3% in 2024 alone. Product innovation has been a critical driver for hospital bed manufacturers. Companies have integrated advanced technologies into their products to differentiate from competitors, enhancing features like integrated monitoring systems, new therapeutic capabilities and pressure redistribution. These advancements aim to boost patient care and operational efficiency. Hospitals increasingly seek beds with real-time monitoring capabilities, allowing them to quickly respond to patient needs and make informed decisions. Manufacturers drive sales by tapping into hospitals' pressure to provide the best care available to their patients by bringing new, more effective hospital beds to market. Still, price competition between manufacturers of standardized acute care beds remains intense. The healthcare sector will continue to consolidate as demand climbs and economies of scale become a larger priority. This trend will especially benefit larger hospital bed manufacturers through established relationships with major buyers. As healthcare spending rises amid population growth, aging demographics and expanded insurance coverage, demand for hospital beds is expected to remain strong. Crowded hospitals will support at-home care, supported by Medicare for compatible needs, further driving hospital bed sales. Emerging markets like China and India offer promising growth opportunities for hospital bed manufacturers because of improving healthcare infrastructure and rising expenditures. Companies will likely invest in these regions, taking advantage of a slipping US dollar to enhance export potential. Revenue is set to rise at a CAGR of 2.3% to an estimated $3.1 billion through the end of 2029.

Hospital-at-Home Orchestration Market Outlook

According to our latest research, the Global Hospital-at-Home Orchestration market size was valued at $4.8 billion in 2024 and is projected to reach $18.7 billion by 2033, expanding at a robust CAGR of 16.2% during 2024–2033. The primary driver fueling the expansion of this market is the increasing global demand for cost-effective, patient-centric healthcare delivery models, which can significantly reduce hospital readmissions and optimize resource utilization. As healthcare systems worldwide face mounting pressure to address rising chronic disease prevalence and aging populations, the adoption of hospital-at-home orchestration solutions is gaining momentum, offering a scalable and technologically advanced alternative to traditional inpatient care.

Regional Outlook

North America currently dominates the hospital-at-home orchestration market, holding the largest share of global revenue, estimated at over 38% in 2024. This commanding position is attributed to the region's mature healthcare infrastructure, high penetration of advanced digital health technologies, and proactive policy frameworks supporting value-based care models. The United States, in particular, has witnessed substantial investments from both public and private sectors, fostering rapid deployment and scaling of hospital-at-home programs. Favorable reimbursement policies, robust telehealth adoption, and a strong ecosystem of technology vendors and service providers further solidify North America's leadership in the global landscape, making it a benchmark for innovation and best practices in the field.

Asia Pacific is emerging as the fastest-growing region in the hospital-at-home orchestration market, projected to register a remarkable CAGR of 19.5% during the forecast period. This growth is underpinned by a confluence of factors, including rapidly increasing healthcare expenditures, a burgeoning middle class, and accelerated digital transformation across key markets such as China, Japan, and India. Governments and private healthcare entities are investing heavily in telemedicine infrastructure and remote monitoring technologies to address gaps in access to quality care, particularly in rural and underserved areas. The region’s dynamic start-up ecosystem, combined with strategic partnerships and international collaborations, is driving innovation and expanding the reach of hospital-at-home solutions at an unprecedented pace.

In contrast, emerging economies in Latin America, the Middle East, and Africa are experiencing a more gradual adoption curve for hospital-at-home orchestration solutions. These regions face unique challenges, including limited healthcare infrastructure, varying degrees of digital literacy, and regulatory complexities that can hinder market penetration. Nonetheless, growing awareness of the benefits of decentralized care, coupled with targeted policy reforms and pilot programs, is gradually paving the way for broader acceptance. Localized solutions tailored to specific demographic and epidemiological needs are gaining traction, with international aid and technology transfer initiatives playing a pivotal role in overcoming initial barriers to adoption.

Report Scope

Introduction

Introducing the Tamil Scripted Monologue Speech Dataset for the Healthcare Domain, a voice dataset built to accelerate the development and deployment of Tamil language automatic speech recognition (ASR) systems, with a sharp focus on real-world healthcare interactions.

Speech Data

This dataset includes over 6,000 high-quality scripted audio prompts recorded in Tamil, representing typical voice interactions found in the healthcare industry. The data is tailored for use in voice technology systems that power virtual assistants, patient-facing AI tools, and intelligent customer service platforms.

Topic Coverage

The prompts span a broad range of healthcare-specific interactions, such as:

Contextual Depth

To maximize authenticity, the prompts integrate linguistic elements and healthcare-specific terms such as:

These elements make the dataset exceptionally suited for training AI systems to understand and respond to natural healthcare-related speech patterns.

Transcription

Every audio recording is accompanied by a verbatim, manually verified transcription.

IntroductionThe objectives of this study were: 1) to describe the socio-demographics and classify the chief complaints and reasons to encounter facilities of patients presenting to public healthcare facilities; 2) to explore differences in these complaints and: International Classification of Primary Care-3 (ICPC-3) groups across socio-demographic and health system levels.MethodsThis is a cross-sectional study conducted in three districts of Odisha, India. Within each district, the district hospital (DH), one Sub-district hospital (SDH) (if available), two Community health centers (CHCs), and two Primary health care centers (PHCs) were selected. Thus, a total of three DHs, three SDHs, six CHCs, and six PHCs were covered. Two tertiary healthcare facilities were also included. Patients aged 18 years and older, attending the Outpatient Departments (OPD) of sampled health facilities were chosen as study participants through systematic random sampling.ResultsA total of 3044 patients were interviewed. In general, 65% of the sample reported symptoms as their chief complaint for reason of encounter, whereas 35% reported disease and diagnosis. The most common reasons to encounter health facilities were fever, hypertension, abdominal pain, chest pain, arthritis, skin disease, cough, diabetes, and injury. Among the symptoms, the highest number of patients reported the general category (29%), followed by the digestive system (16%). In the disease category, the circulatory system has the highest proportion, followed by the musculatory system. In symptom categories, general, digestive, and musculatory systems were the key systems for the reasons of encounter in outpatient departments irrespective of different groups of the population. In terms of different tiers of health systems, the top three reasons to visit OPD were dominated by the circulatory system, respiratory system, and musculatory system.ConclusionThis is the first Indian study using the ICPC-3 classification for all three levels of health care. Irrespective of age, socio-economic variables, and tiers of healthcare, the top three groups to visit public health facilities according to the ICPC-3 classification were consistent i.e., general, digestive, and circulatory. Implementation of standard management and referral guidelines for common diseases under these groups will improve the quality and burden at public health facilities in India.