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.

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.

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.

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.

Attributes	Key Insights
Historical Size, 2024	USD 408.8 billion
Estimated Size, 2025	USD 421.8 billion
Projected Size, 2035	USD 578.0 billion
Value-based CAGR (2025 to 2035)	3.2%

Semi-Annual Market Update for the Global Hospital Consumables Market

Particular	Value CAGR
H1	3.9% (2024 to 2034)
H2	3.6% (2024 to 2034)
H1	3.2% (2025 to 2035)
H2	2.7% (2025 to 2035)

Analysis of Top Countries Manufacturing and Supplying Hospital Consumables

Countries	Value CAGR (2025 to 2035)
United States	1.5%
Germany	1.7%
China	5.4%
France	2.2%
India	5.8%
Spain	2.9%
Australia & New Zealand	3.1%
South Korea	4.2%

Hospital Consumables Industry Analysis by Top Investment Segments

By Product	Wound Care Products
Value Share (2025)	16.7%

By End User	Hospitals
Value Share (2025)	26.1%

The India EMR (Electronic Medical Records) market report segments the industry into By Component (Hardware, Software, Service), By Deployment Model (Cloud-based, On-premise), By Type (General EMR, Specialty EMR), By End User (Hospitals and Clinics, Ambulatory Surgical Centers, Other Users (Diagnostic Centers, Medical Academic Centers, and Other End Users)), and By Application (Cardiology, Neurology, Radiology, and more).

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

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

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.

78 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...

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.

I have been working on a Data Engineering Project to build an Healthcare Analytics System, where the data flows through ETL built on Azure DataBricks from Amazon DynamoDB (source system – NoSQL Database) to Amazon RedShift (destination system – Data Warehouse).

The problem was, to experiment on this pipeline I didn't have any data because the healthcare domain's data is not mostly available, much easily. Hence I decided to create this dataset of my own.

The dataset generation required knowledge of healthcare domain (which I didn't have much) and Data Modelling (which I fortunately had).

• To keep the dataset relevant to the context for the Trends Analytics and Deciphering Patterns, I've added substantially amount of fields (columns), i.e., 30 including the metadata columns.
• There're total 7,00,000 (7 lakhs) or 700,000 (700 thousand) records for you to work with which is pretty get for any beginner.

The dataset has been generated to be as comprehensive and explanatory as possible. For every record (row) or a document (json object) [ depending on the dataset file you'll use ], you'll have the data regarding the 5 entities which are explained in detail as follows:

TREATMENT

• id: Unique Identifier for treatment.
• start_date: The timestamp without time zone value when the treatment was initiated.
• completion_date: The timestamp without time zone value when the treatment was completed
• outcome_status: Status can be successful, partially-successful or others.
• outcome_date: The timestamp without time zone value when the outcome after the completion_date was declared.
• duration_in_days: Difference between start_date and completion_date
• cost: The cost is considered to be in INR.
• type: The kind of treatment provided which could be therapeutic, surgical, etc.

PROVIDER

• id: Unique Identifier for provider / practitioner / specialist in the hospital.
• full_name: First Name and Last Name of practitioner / specialist.
• speciality_id: The unique identifier of the specialty they have studies, in order to treat patients accordingly.
• speciality_name: Name of the specialisation.
• affiliated_hospital: The name of the Hospital they're working in.

LOCATION

• id: Unique Identifier for the hospitals location (where the provider has provided the treatment to their respective patients)
• country: India
• state: Maharashtra, Madhya Pradesh, Karnataka, etc.
• city: The 5 cities per states are chosen at random.

PATIENT

• id: Unique Identifier for the patients.
• full_name: First Name and Last Name of the Patient.
• gender: Male or Female.
• age: Numeric Value ranging from 18 to 80.

DISEASE

• id: Unique Identifier for the disease that the patient could be diagnosed with.
• speciality_id: Refers to the speciality of the provider's speciality_id that can treat this disease as they have specialized in it.
• name: Name of the disease
• type: Specifies type for the disease like acute, infectious, non-infectious, etc.
• severity: The severity could be moderate, severe, etc.
• transmission_mode: How the disease is generally transmitted.
• mortality_rate: A decimal value denoting the likelihood to live.

METADATA:

• added_at: The timestamp without timezone value at which that entire records was added to the database / dataset. This value does not change.
• modified_at: The timestamp without timezone value at which that document was updated. Either a part of the record / document or the entire data of it could be updated. Hence this value can be changed.

The only good AND bad thing about this dataset is that, it is a fairly clean dataset.

If you're simply willing to run statistics and draw some actionable insights, this dataset is good enough to get started. If you're looking to take it a complete assignment right from the data gathering, cleaning, transforming and organizing, I'm sorry to disappoint you.

I hope to see awesome analytics from this community. Get your curious minds to work, spin up your notebooks. Cheers!

AimTo describe self-reported practices and assess knowledge and attitudes regarding hand hygiene among healthcare workers in a rural Indian teaching hospital.SettingA rural teaching hospital and its associated medical and nursing colleges in the district of Ujjain, India.MethodThe study population consisted of physicians, nurses, teaching staff, clinical instructors and nursing students. Self-administered questionnaires based on the World Health Organization Guidelines on Hand Hygiene in Healthcare were used.ResultsOut of 489 healthcare workers, 259 participated in the study (response rate = 53%). The proportion of healthcare workers that reported to ‘always’ practice hand hygiene in the selected situations varied from 40–96% amongst categories. Reported barriers to maintaining good hand hygiene were mainly related to high workload, scarcity of resources, lack of scientific information and the perception that priority is not given to hand hygiene, either on an individual or institutional level. Previous training on the topic had a statistically significant association with self-reported practice (p = 0.001). Ninety three per cent of the respondents were willing to attend training on hand hygiene in the near future.ConclusionSelf-reported knowledge and adherence varied between situations, but hand hygiene practices have the potential to improve if the identified constraints could be reduced. Future training should focus on enhancing healthcare workers’ knowledge and understanding regarding the importance of persistent practice in all situations.

Context

I wanted to analyze the road accidents and hospital coverage of them. While I wanted to do for India, in lack of geo tagged accidents data for India, I decided to go for UK(being developed countries, they have highest level of digitisation and availability of free data). I could get the hospitals data for United Kingdom.

Content

This contains the details of all the hospitals in Great Britain. Further original file did not contain geocoding of the hospitals. This data is created by geocoding the town name of the accident, using geocode of gepandas.tool.

Acknowledgements

Original hospital data can be found at below: https://data.gov.uk/dataset/f4420d1c-043a-42bc-afbc-4c0f7d3f1620/hospitals

Inspiration

Inspired from Geospatial microcourse by Alexis, I completed at Kaggle: https://www.kaggle.com/learn/geospatial-analysis

Used to do proximity analysis, to find the nearest hospital

Context

This public dataset contains data concerning the public and private insurance companies provided by IRDAI(Insurance Regulatory and Development Authority of India) from 2013-2022. This is a multi-index data and can be a great practice to hone manipulation of pandas multi-index dataframes. Mainly, the business of the companies (total premiums and number of policies), subscription information(number of people subscribed), Claims incurred and the Network hospitals enrolled by Third Party Administrators are attributes focused by the dataset.

Content

The Excel file contains the following data | Table No.| Contents| | --- | --- | |**A**|**III.A: HEALTH INSURANCE BUSINESS OF GENERAL AND HEALTH INSURERS**| |62| Health Insurance - Number of Policies, Number of Persons Covered and Gross Premium| |63| Personal Accident Insurance - Number of Policies, Number of Persons Covered and Gross Premium| |64| Overseas Travel Insurance - Number of Policies, Number of Persons Covered and Gross Premium| |65| Domestic Travel Insurance - Number of Policies, Number of Persons Covered and Gross Premium| |66| Health Insurance - Net Premium Earned, Incurred Claims and Incurred Claims Ratio| |67| Personal Accident Insurance - Net Premium Earned, Incurred Claims and Incurred Claims Ratio| |68| Overseas Travel Insurance - Net Earned Premium, Incurred Claims and Incurred Claims Ratio| |69| Domestic Travel Insurance - Net Earned Premium, Incurred Claims and Incurred Claims Ratio| |70| Details of Claims Development and Aging - Health Insurance Business| |71| State-wise Health Insurance Business| |72| State-wise Individual Health Insurance Business| |73| State-wise Personal Accident Insurance Business| |74| State-wise Overseas Insurance Business| |75| State-wise Domestic Insurance Business| |76| State-wise Claims Settlement under Health Insurance Business| |**B**|**III.B: HEALTH INSURANCE BUSINESS OF LIFE INSURERS**| |77| Health Insurance Business in respect of Products offered by Life Insurers - New Busienss| |78| Health Insurance Business in respect of Products offered by Life insurers - Renewal Business| |79| Health Insurance Business in respect of Riders attached to Life Insurance Products - New Business| |80| Health Insurance Business in respect of Riders attached to Life Insurance Products - Renewal Business| |**C**|**III.C: OTHERS**| |81| Network Hospital Enrolled by TPAs| |82| State-wise Details on Number of Network Providers |

Background: Particulate matter (PM) is one among the crucial air pollutants and has the potential to cause a wide range of health effects. Indian cities ranked top places in the World Health Organization list of most polluted cities by PM. Objectives: Present study aims to assess the trends, short- and long-term health effects of PM in major Indian cities. Methods: PM-induced hospital admissions and mortality are quantified using AirQ+ software. Results: Annual PM concentration in most of the cities is higher than the National Ambient Air Quality Standards of India. Trend analysis showed peak PM concentration during post-monsoon and winter seasons. The respiratory and cardiovascular hospital admissions in the male (female) population are estimated to be 31,307 (28,009) and 5460 (4882) cases, respectively. PM2.5 has accounted for a total of 1,27,014 deaths in 2017. Conclusion: Cities with high PM concentration and exposed population are more susceptible to mortality and hospital admissions.

In 2022, Uttar Pradesh, one of the largest states of India, possessed the highest number of district hospitals in India with *** hospitals across more than ** districts of the state. Madhya Pradesh followed with a total of over ** district hospitals during the same time period.

Background:Newspapers in India often report incidents of cardiac arrest. Media reports are a source for raising awareness of cardiac arrest and Cardiopulmonary Resuscitation (CPR) among the public. Data on Out-of-Hospital Cardiac Arrests(OHCA) is limited in India. Methods: The study aims at evaluating the reports of OHCA as reported in print media (particularly newspapers in India) with Utstein template-portrayal of cardiac arrest, demographics, patient and resuscitation characteristics. Methods: Study design: This is an observational study of a cohort of cardiac arrests reported in selected Indian English newspapers. Setting:There has been an attempt in securing national data on OHCA.There is no single source or agency in India through which all media reports can be collected. Only English newspapers with websites and data available for public domain were accessed for reports of cardiac arrest.
Participants: Subjects from reports of cardiac arrest in various locations in India.
Sources of data: Newspaper reports from English language Indian newspapers with wider circulation. They were retrieved from the archives during 2001-2019 from the websites of : The Hindu, The New Indian Express, The Times of India, Hans India and The Pioneer. All the articles are screened for eligibility. Initially, those reports with a search word "cardiac arrest" were retrieved. Articles eligible for inclusion included persons sustaining cardiac arrest at several Indian locations. Reports with no reference to a possible cardiac arrest were excluded. Articles in different newspapers of the same cardiac arrest victim were searched for any additional information, and only the best report was included. Those due to obvious possible non-cardiac causes such as trauma were excluded. Data related to Utstein variables were extracted from these reports. Data of OHCA subjects were selected for analysis.
Results: 1779 reports reviewed and 1703 selected after excluding 76. Of these, 279 reports did not specify whether it was an In-Hospital Cardiac Arrest (IHCA)or OHCA. Of the remaining 1424 reports,377 reports were IHCA, and 1047 were OHCA. 1047 OHCA cases selected for analysis. The study noted male preponderance and a median age of 51--60 years. OHCA commonly occurred in residential locations, followed by public buildings, other places and street/highways. Prior risk factors, heart disease, symptoms were reported in some reports. Of 15 subjects who received CPR, 11 were reported to have survived. Though demographic data is reported in the majority, there is poor reporting of clinical and resuscitation details. Limitations:The study may not reflect the total number of OHCA reported as accessing the information from newspapers in different languages from different States was a limiting factor. Conclusions: The study gives a glimpse of OHCA in India and emphasizes the need for elaborate reporting of data on cardiac arrest. The crucial role of media is recognized.