According to a ranking of the best hospitals in the U.S., the best hospital for adult cancer is the University of *******************************, which had a score of *** out of 100, as of 2025. This statistic shows the top 10 hospitals for adult cancer in the United States based on the score given by U.S. News and World Report's annual hospital ranking.

This statistic shows the size of the 100 best hospitals in the United States in 2012, sorted by the number of beds per hospital. In 2012, ** out of the top 100 U.S. hospitals had between 100 and *** patient beds.

According to a ranking of the best hospitals in the U.S., the best hospital for adult cardiology, heart, and vascular surgery is the ******************** in New York, which had a score of *** out of 100, as of 2025. This statistic shows the top 10 hospitals for adult cardiology, heart, and vascular surgery in the United States based on the score given by U.S. News and World Report's annual hospital ranking.

Context

There are all sorts of reasons why you'd want to know a hospital's quality rating.

• Your mom is having her second hip replacement. Her first one went terribly and you're nervous about how she'll do. Which hospital would you suggest she have her surgery?
• You're selecting a health plan on your state's Exchange, but your top two choices partner with different hospitals. How will you decide which plan to pick?
• Your brother has Cystic Fibrosis and has to go to the ER frequently. He hates waiting. Which hospitals/states provide care in the timeliest manner?
• Your in-laws moved to Florida recently to retire, and have been trying to convince you to move there too. You're looking for any way possible to show them that your state is better. Does your state have better hospitals?

Every hospital in the United States of America that accepts publicly insured patients (Medicaid or MediCare) is required to submit quality data, quarterly, to the Centers for Medicare & Medicaid Services (CMS). There are very few hospitals that do not accept publicly insured patients, so this is quite a comprehensive list.

Content

This file contains general information about all hospitals that have been registered with Medicare, including their addresses, type of hospital, and ownership structure. It also contains information about the quality of each hospital, in the form of an overall rating (1-5, where 5 is the best possible rating & 1 is the worst), and whether the hospital scored above, same as, or below the national average for a variety of measures.

This data was updated by CMS on July 25, 2017. CMS' overall rating includes 60 of the 100 measures for which data is collected & reported on Hospital Compare website (https://www.medicare.gov/hospitalcompare/search.html). Each of the measures have different collection/reporting dates, so it is impossible to specify exactly which time period this dataset covers. For more information about the timeframes for each measure, see: https://www.medicare.gov/hospitalcompare/Data/Data-Updated.html# For more information about the data itself, APIs and a variety of formats, see: https://data.medicare.gov/Hospital-Compare

Acknowledgements

Attention: Works of the U.S. Government are in the public domain and permission is not required to reuse them. An attribution to the agency as the source is appreciated. Your materials, however, should not give the false impression of government endorsement of your commercial products or services. See 42 U.S.C. 1320b-10.

Inspiration

Which hospital types & hospital ownerships are most common?

Which hospital types & ownerships are associated with better than average ratings/mortality/readmission/etc?

What is the average hospital rating, by state?

Which hospital types & hospital ownerships are more likely to have not submitted proper data ("Not Available" & "Results are not available for this reporting period")?

Which parts of the country have the highest & lowest density of religious hospitals?

Public reporting of measures of hospital performance is an important component of quality improvement efforts in many countries. However, it can be challenging to provide an overall characterization of hospital performance because there are many measures of quality. In the United States, the Centers for Medicare and Medicaid Services reports over 100 measures that describe various domains of hospital quality, such as outcomes, the patient experience and whether established processes of care are followed. Although individual quality measures provide important insight, it is challenging to understand hospital performance as characterized by multiple quality measures. Accordingly, we developed a novel approach for characterizing hospital performance that highlights the similarities and differences between hospitals and identifies common patterns of hospital performance. Specifically, we built a semi-supervised machine learning algorithm and applied it to the publicly-available quality measures for 1,614 U.S. hospitals to graphically and quantitatively characterize hospital performance. In the resulting visualization, the varying density of hospitals demonstrates that there are key clusters of hospitals that share specific performance profiles, while there are other performance profiles that are rare. Several popular hospital rating systems aggregate some of the quality measures included in our study to produce a composite score; however, hospitals that were top-ranked by such systems were scattered across our visualization, indicating that these top-ranked hospitals actually excel in many different ways. Our application of a novel graph analytics method to data describing U.S. hospitals revealed nuanced differences in performance that are obscured in existing hospital rating systems.

This table shows the low, high, and average percents of discharges related to a referenced DRG (diagnosis-related group) as a share of the total discharges from the top 100 common DRGs for hospitals in the United States. The source of data for this table is FY2011 hospital charges file provided by the Centers for Medicare and Medicaid Services (CMS).

Success.ai’s Healthcare Industry Leads Data and B2B Contact Data for US Healthcare Professionals offers an extensive and verified database tailored to connect businesses with key executives and administrators in the healthcare industry across the United States. With over 170M verified profiles, including work emails and direct phone numbers, this dataset enables precise targeting of decision-makers in hospitals, clinics, and healthcare organizations.

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Abstract

Different countries have different health outcomes that are in part due to the way respective health systems perform. Regardless of the type of health system, individuals will have health and non-health expectations in terms of how the institution responds to their needs. In many countries, however, health systems do not perform effectively and this is in part due to lack of information on health system performance, and on the different service providers.

The aim of the WHO World Health Survey is to provide empirical data to the national health information systems so that there is a better monitoring of health of the people, responsiveness of health systems and measurement of health-related parameters.

The overall aims of the survey is to examine the way populations report their health, understand how people value health states, measure the performance of health systems in relation to responsiveness and gather information on modes and extents of payment for health encounters through a nationally representative population based community survey. In addition, it addresses various areas such as health care expenditures, adult mortality, birth history, various risk factors, assessment of main chronic health conditions and the coverage of health interventions, in specific additional modules.

The objectives of the survey programme are to: 1. develop a means of providing valid, reliable and comparable information, at low cost, to supplement the information provided by routine health information systems. 2. build the evidence base necessary for policy-makers to monitor if health systems are achieving the desired goals, and to assess if additional investment in health is achieving the desired outcomes. 3. provide policy-makers with the evidence they need to adjust their policies, strategies and programmes as necessary.

Geographic coverage

The survey sampling frame must cover 100% of the country's eligible population, meaning that the entire national territory must be included. This does not mean that every province or territory need be represented in the survey sample but, rather, that all must have a chance (known probability) of being included in the survey sample.

There may be exceptional circumstances that preclude 100% national coverage. Certain areas in certain countries may be impossible to include due to reasons such as accessibility or conflict. All such exceptions must be discussed with WHO sampling experts. If any region must be excluded, it must constitute a coherent area, such as a particular province or region. For example if ¾ of region D in country X is not accessible due to war, the entire region D will be excluded from analysis.

Analysis unit

Households and individuals

Universe

The WHS will include all male and female adults (18 years of age and older) who are not out of the country during the survey period. It should be noted that this includes the population who may be institutionalized for health reasons at the time of the survey: all persons who would have fit the definition of household member at the time of their institutionalisation are included in the eligible population.

If the randomly selected individual is institutionalized short-term (e.g. a 3-day stay at a hospital) the interviewer must return to the household when the individual will have come back to interview him/her. If the randomly selected individual is institutionalized long term (e.g. has been in a nursing home the last 8 years), the interviewer must travel to that institution to interview him/her.

The target population includes any adult, male or female age 18 or over living in private households. Populations in group quarters, on military reservations, or in other non-household living arrangements will not be eligible for the study. People who are in an institution due to a health condition (such as a hospital, hospice, nursing home, home for the aged, etc.) at the time of the visit to the household are interviewed either in the institution or upon their return to their household if this is within a period of two weeks from the first visit to the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

SAMPLING GUIDELINES FOR WHS

Surveys in the WHS program must employ a probability sampling design. This means that every single individual in the sampling frame has a known and non-zero chance of being selected into the survey sample. While a Single Stage Random Sample is ideal if feasible, it is recognized that most sites will carry out Multi-stage Cluster Sampling.

The WHS sampling frame should cover 100% of the eligible population in the surveyed country. This means that every eligible person in the country has a chance of being included in the survey sample. It also means that particular ethnic groups or geographical areas may not be excluded from the sampling frame.

The sample size of the WHS in each country is 5000 persons (exceptions considered on a by-country basis). An adequate number of persons must be drawn from the sampling frame to account for an estimated amount of non-response (refusal to participate, empty houses etc.). The highest estimate of potential non-response and empty households should be used to ensure that the desired sample size is reached at the end of the survey period. This is very important because if, at the end of data collection, the required sample size of 5000 has not been reached additional persons must be selected randomly into the survey sample from the sampling frame. This is both costly and technically complicated (if this situation is to occur, consult WHO sampling experts for assistance), and best avoided by proper planning before data collection begins.

All steps of sampling, including justification for stratification, cluster sizes, probabilities of selection, weights at each stage of selection, and the computer program used for randomization must be communicated to WHO

STRATIFICATION

Stratification is the process by which the population is divided into subgroups. Sampling will then be conducted separately in each subgroup. Strata or subgroups are chosen because evidence is available that they are related to the outcome (e.g. health, responsiveness, mortality, coverage etc.). The strata chosen will vary by country and reflect local conditions. Some examples of factors that can be stratified on are geography (e.g. North, Central, South), level of urbanization (e.g. urban, rural), socio-economic zones, provinces (especially if health administration is primarily under the jurisdiction of provincial authorities), or presence of health facility in area. Strata to be used must be identified by each country and the reasons for selection explicitly justified.

Stratification is strongly recommended at the first stage of sampling. Once the strata have been chosen and justified, all stages of selection will be conducted separately in each stratum. We recommend stratifying on 3-5 factors. It is optimum to have half as many strata (note the difference between stratifying variables, which may be such variables as gender, socio-economic status, province/region etc. and strata, which are the combination of variable categories, for example Male, High socio-economic status, Xingtao Province would be a stratum).

Strata should be as homogenous as possible within and as heterogeneous as possible between. This means that strata should be formulated in such a way that individuals belonging to a stratum should be as similar to each other with respect to key variables as possible and as different as possible from individuals belonging to a different stratum. This maximises the efficiency of stratification in reducing sampling variance.

MULTI-STAGE CLUSTER SELECTION

A cluster is a naturally occurring unit or grouping within the population (e.g. enumeration areas, cities, universities, provinces, hospitals etc.); it is a unit for which the administrative level has clear, nonoverlapping boundaries. Cluster sampling is useful because it avoids having to compile exhaustive lists of every single person in the population. Clusters should be as heterogeneous as possible within and as homogenous as possible between (note that this is the opposite criterion as that for strata). Clusters should be as small as possible (i.e. large administrative units such as Provinces or States are not good clusters) but not so small as to be homogenous.

In cluster sampling, a number of clusters are randomly selected from a list of clusters. Then, either all members of the chosen cluster or a random selection from among them are included in the sample. Multistage sampling is an extension of cluster sampling where a hierarchy of clusters are chosen going from larger to smaller.

In order to carry out multi-stage sampling, one needs to know only the population sizes of the sampling units. For the smallest sampling unit above the elementary unit however, a complete list of all elementary units (households) is needed; in order to be able to randomly select among all households in the TSU, a list of all those households is required. This information may be available from the most recent population census. If the last census was >3 years ago or the information furnished by it was of poor quality or unreliable, the survey staff will have the task of enumerating all households in the smallest randomly selected sampling unit. It is very important to budget for this step if it is necessary and ensure that all households are properly enumerated in order that a representative sample is obtained.

It is always best to have as many clusters in the PSU as possible. The reason for this is that the fewer the number of respondents in each PSU, the lower will be the clustering effect which

The data provided here include hospital-specific charges for the more than 3,000 U.S. hospitals that receive Medicare Inpatient Prospective Payment System (IPPS) payments for the top 100 most frequently billed discharges, paid under Medicare based on a rate per discharge using the Medicare Severity Diagnosis Related Group (MS-DRG) for Fiscal Year (FY) 2011, 2012, and 2013. These DRGs represent more than 7 million discharges or 60 percent of total Medicare IPPS discharges.

As of 2025, New York-Presbyterian hospital is the largest hospital in the United States with its eight campuses based in New York City. This was followed by AdventHealth Orlando in Florida stands as the second largest hospital in the United States, boasting an impressive 2,787 beds. Evolving landscape of U.S. hospitals Despite the decline in the total number of hospitals since 1980, the healthcare sector continues to grow in other ways. U.S. hospitals now employ about 7.5 million workers and generate a gross output of around 1,263 billion U.S. dollars. The Hospital Corporation of America, based in Nashville, Tennessee, leads the pack as the largest health system in the country, operating 222 hospitals as of February 2025. This reflects a trend towards consolidation and the rise of for-profit hospital chains, which gained prominence in the 1990s. Specialization and emergency care While bed count is one measure of hospital size, institutions also distinguish themselves through specialization and emergency care capabilities. For instance, the University of California at Los Angeles Medical Center performed 22,287 organ transplants between January 1988 and March 2025, making it the leading transplant center in the nation. In terms of emergency care, Parkland Health and Hospital System in Dallas recorded the highest number of emergency department visits in 2024, with 235,893 patients seeking urgent care.

Success.ai’s Healthcare Industry Leads Data empowers businesses and organizations to connect with key decision-makers and stakeholders in the global healthcare and pharmaceutical sectors. Leveraging over 170 million verified professional profiles and 30 million company profiles, this dataset includes detailed contact information, firmographic insights, and leadership data for hospitals, clinics, biotech firms, medical device manufacturers, pharmaceuticals, and other healthcare-related enterprises. Whether your goal is to pitch a new medical technology, partner with healthcare providers, or conduct market research, Success.ai ensures that your outreach and strategic planning are guided by reliable, continuously updated, and AI-validated data.

Why Choose Success.ai’s Healthcare Industry Leads Data?

1. Comprehensive Contact Information

   • Access verified work emails, phone numbers, and LinkedIn profiles of healthcare administrators, pharmaceutical executives, R&D directors, procurement officers, and medical staff.
   • AI-driven validation ensures 99% accuracy, reducing wasted efforts and fostering efficient communication.

2. Global Reach Across Healthcare Segments

   • Includes profiles from hospitals, private clinics, pharmaceutical companies, biotech startups, research institutions, and medical supply chain partners.
   • Covers North America, Europe, Asia-Pacific, South America, and the Middle East, enabling a global perspective on healthcare systems and opportunities.

3. Continuously Updated Datasets

   • Real-time updates reflect leadership changes, organizational shifts, and emerging trends in patient care, medical innovation, and regulatory compliance.

4. Ethical and Compliant

   • Adheres to GDPR, CCPA, and other global data privacy regulations, ensuring your data usage respects legal standards and patient confidentiality norms.

Data Highlights:

• 170M+ Verified Professional Profiles: Connect with healthcare and pharmaceutical professionals, decision-makers, and influencers worldwide.
• 50M Work Emails: AI-validated for direct, accurate communication and reduced bounce rates.
• 30M Company Profiles: Gain insights into organizational structures, operational scales, and specialization areas.
• 700M Global Professional Profiles: Enriched datasets to support market analysis, product development, and strategic planning.

Key Features of the Dataset:

1. Healthcare Decision-Maker Profiles

   • Identify and engage with CEOs, CIOs, CFOs, chief medical officers, hospital administrators, clinical directors, and procurement specialists.
   • Target professionals who influence equipment purchases, medical supply chain decisions, drug trial approvals, and healthcare delivery models.

2. Detailed Business Profiles

   • Access firmographic data, including company sizes, revenue ranges, key markets, and service lines for a holistic understanding of target organizations.
   • Leverage comprehensive insights to position your products, services, or solutions as tailored fits for specific operational needs.

3. Advanced Filters for Precision Targeting

   • Filter by region, specialty (oncology, cardiology, diagnostics, etc.), hospital size, pharmaceutical focus, or research areas.
   • Align campaigns with unique healthcare demands, reimbursement models, and regulatory environments.

4. AI-Driven Enrichment

   • Profiles enriched with actionable data enable personalized messaging, highlight value propositions, and enhance engagement outcomes with healthcare stakeholders.

Strategic Use Cases:

1. Sales and Business Development

   • Present medical devices, pharma products, or healthcare IT solutions to hospital administrators, chief medical officers, and procurement managers.
   • Build relationships with decision-makers who oversee budgeting, supplier selection, and patient care initiatives.

2. Market Research and Product Innovation

   • Analyze trends in patient treatments, drug pipelines, and healthcare infrastructure to inform R&D and product roadmaps.
   • Identify emerging specialties, new treatment modalities, and growth markets to focus marketing, sales, and investment efforts.

3. Strategic Partnerships and Alliances

   • Connect with R&D directors, biotech executives, or hospital groups to explore collaborations, clinical trials, and joint ventures.
   • Foster partnerships that accelerate product development, enhance patient outcomes, and drive long-term competitiveness.

4. Recruitment and Talent Acquisition

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   • Offer staffing, training, or professional development services to healthcare institutions aiming to improve service delivery and compliance.

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   • Access high-quality, verified data at...

Introduction

EHR Industry Statistics: Electronic Health Records (EHRs) are digital versions of patient paper charts, revolutionizing healthcare by providing instant, secure access to comprehensive medical information.

They include details like medical history, diagnoses, medications, and test results, consolidating data from various sources into one accessible record.

EHRs enhance patient care by supporting better coordination among healthcare providers, improving efficiency through reduced paperwork, and enabling patient engagement via access to their records.

Challenges include high implementation costs, interoperability issues between different systems, and concerns about data privacy.

Looking ahead, advancements aim to improve interoperability, enhance data analytics, and integrate with telemedicine for more efficient and personalized healthcare delivery.

As of February 2025, the Hospital Corporation of America, based in Nashville, Tennessee, was the largest health system in the United States, with a total of 222 hospitals. HCA Healthcare is also the largest U.S. health system when ranked by the number of beds and, as expected, by net patient revenue.Hospitals in the United StatesCurrently, there are approximately 6,120 hospitals in the United States. Looking over the past decades, this figure was constantly decreasing. For example, there were nearly 7,000 hospitals in 1980. There are some 5.3 million persons employed in U.S. hospitals in full-time. Contrary to the decrease in the number of hospitals, employment has been increasing steadily. According to the Bureau of Economic Analysis, U.S. hospitals generate a total gross output of around 1,075 billion U.S. dollars. The largest portion of U.S. hospitals are non-profit facilities. A smaller share includes private-owned for-profit hospitals. In most cases, these hospitals are part of hospital chains. For-profit hospitals developed especially in the 1990s, with the aim to gain profit for their shareholders. The Hospital Corporation of America, based in Nashville, Tennessee, is the U.S. for-profit hospital operator with the highest number of hospitals.

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

Success.ai’s Healthcare Industry Leads Data for the North American Healthcare Sector provides businesses with a comprehensive dataset designed to connect with healthcare organizations, decision-makers, and key stakeholders across the United States, Canada, and Mexico. Covering hospitals, pharmaceutical firms, biotechnology companies, and medical equipment providers, this dataset delivers verified contact information, firmographic details, and actionable business insights.

With access to over 170 million verified professional profiles and 30 million company profiles, Success.ai ensures your outreach, market research, and strategic initiatives are powered by accurate, continuously updated, and AI-validated data. Backed by our Best Price Guarantee, this solution is your key to success in the North American healthcare market.

Why Choose Success.ai’s Healthcare Industry Leads Data?

1. Verified Contact Data for Precision Targeting

   • Access verified work emails, phone numbers, and LinkedIn profiles of healthcare executives, clinical managers, procurement officers, and compliance leaders.
   • AI-driven validation ensures 99% accuracy, reducing bounce rates and improving engagement efficiency.

2. Comprehensive Coverage of North America’s Healthcare Sector

   • Includes profiles of organizations such as hospitals, private clinics, research facilities, biotech firms, and medical supply distributors.
   • Gain visibility into the unique healthcare dynamics of the United States, Canada, and Mexico, including regional trends, regulatory differences, and market opportunities.

3. Continuously Updated Datasets

   • Real-time updates reflect changes in leadership, organizational structures, service offerings, and market activities.
   • Ensure your outreach and strategy stay relevant and aligned with the rapidly evolving healthcare industry.

4. Ethical and Compliant

   • Adheres to GDPR, CCPA, and other global privacy regulations, ensuring responsible and compliant use of data for your campaigns.

Data Highlights:

• 170M+ Verified Professional Profiles: Engage with decision-makers and influencers across North America’s healthcare sector.
• 30M Company Profiles: Access detailed firmographic data, including organization sizes, revenue ranges, and geographic footprints.
• Decision-Maker Contacts: Connect with CEOs, CMOs, clinical directors, R&D leaders, and procurement managers shaping healthcare strategies.
• Operational Insights: Understand supply chains, service lines, and product pipelines within the healthcare ecosystem.

Key Features of the Dataset:

1. Healthcare Decision-Maker Profiles

   • Identify and connect with healthcare leaders driving innovation, procurement decisions, and patient care delivery.
   • Engage with professionals responsible for technology adoption, regulatory compliance, and resource management.

2. Advanced Filters for Precision Targeting

   • Filter companies by sector (hospitals, biotech, pharma, medical devices), geographic location, revenue size, or workforce composition.
   • Tailor your outreach to align with the unique needs and priorities of North American healthcare organizations.

3. Market and Operational Insights

   • Analyze trends such as telemedicine adoption, value-based care initiatives, and investments in AI and automation.
   • Leverage these insights to position your solutions effectively within a rapidly transforming industry.

4. AI-Driven Enrichment

   • Profiles enriched with actionable data allow for personalized messaging, highlight your value propositions, and improve engagement outcomes with healthcare stakeholders.

Strategic Use Cases:

1. Sales and Lead Generation

   • Offer technology solutions, medical devices, or consulting services to healthcare organizations seeking operational improvements.
   • Build relationships with procurement managers, clinical directors, and decision-makers responsible for resource allocation.

2. Marketing and Demand Generation

   • Target marketing teams and outreach coordinators within healthcare organizations to promote software solutions, diagnostic tools, or patient engagement platforms.
   • Leverage verified contact data to launch impactful email and multi-channel marketing campaigns.

3. Regulatory Compliance and Risk Mitigation

   • Connect with compliance officers and legal teams responsible for adhering to healthcare regulations and standards.
   • Present solutions for streamlined reporting, risk management, and quality assurance processes.

4. Recruitment and Workforce Optimization

   • Engage HR professionals and hiring managers in recruiting healthcare talent, from clinical staff to administrative roles.
   • Provide staffing solutions, training platforms, or workforce management tools tailored to healthcare environments.

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1. Best Price Guarantee
   • Access premium-q...

Success.ai’s Healthcare Marketing Data provides businesses with a robust dataset of verified contact details, operational insights, and decision-maker profiles for healthcare companies worldwide. Covering hospitals, pharmaceutical firms, biotechnology companies, medical equipment manufacturers, and healthcare service providers, this dataset offers unparalleled visibility into the global healthcare industry.

With access to over 170 million verified professional profiles and 30 million company profiles, Success.ai ensures that your outreach, research, and business development initiatives are informed by reliable, continuously updated, and AI-validated data. Backed by our Best Price Guarantee, this solution empowers you to connect with key stakeholders driving healthcare innovation and delivery.

Why Choose Success.ai’s Healthcare Contact Data?

1. Verified Contact Data for Precision Outreach

   • Access verified work emails, direct phone numbers, and LinkedIn profiles of healthcare executives, procurement managers, compliance officers, and operational leads.
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   • Includes profiles of hospitals, clinics, pharmaceutical companies, biotech firms, and medical device manufacturers spanning all major markets.
   • Gain visibility into healthcare systems across North America, Europe, Asia-Pacific, South America, and the Middle East.

3. Continuously Updated Datasets

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4. Ethical and Compliant

   • Adheres to GDPR, CCPA, and other global privacy regulations, ensuring responsible use of data and compliance with industry standards.

Data Highlights:

• 170M+ Verified Professional Profiles: Engage with decision-makers, medical directors, R&D heads, and compliance officers across the global healthcare industry.
• 30M Company Profiles: Access comprehensive firmographic data, including operational scopes, revenue ranges, and geographic distributions.
• Leadership Contact Details: Connect with CEOs, CMOs, CTOs, and department heads shaping healthcare innovation and strategy.
• Operational Insights: Gain visibility into supply chains, product pipelines, and regulatory certifications.

Key Features of the Dataset:

1. Decision-Maker Profiles in Healthcare

   • Identify and engage with executives, clinical directors, procurement leads, and R&D professionals responsible for strategic decisions.
   • Target professionals influencing drug development, technology adoption, and patient care delivery.

2. Advanced Filters for Precision Targeting

   • Filter companies by industry segment (hospitals, biotech, pharma, medical devices), geographic location, company size, or operational focus.
   • Tailor campaigns to address specific needs such as regulatory compliance, cost efficiency, or technological modernization.

3. AI-Driven Enrichment

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Strategic Use Cases:

1. Sales and Lead Generation

   • Offer healthcare technology solutions, medical devices, or consulting services to hospitals, pharmaceutical firms, or biotech startups.
   • Build relationships with procurement managers and clinical leads responsible for vendor selection and resource allocation.

2. Market Research and Competitive Analysis

   • Analyze trends in patient care, drug pipelines, and healthcare delivery models to guide product development and marketing strategies.
   • Benchmark against competitors to identify market gaps, emerging trends, and untapped opportunities.

3. Regulatory Compliance and Risk Mitigation

   • Connect with compliance officers and regulatory managers overseeing adherence to healthcare laws and standards.
   • Present solutions that simplify compliance reporting, risk assessments, or quality assurance processes.

4. Recruitment and Talent Acquisition

   • Engage HR professionals and department heads seeking skilled healthcare professionals, from clinical staff to administrative personnel.
   • Offer staffing services, recruitment platforms, or workforce optimization solutions to support hiring goals.

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1. Best Price Guarantee

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According to cognitive market research-"Global Medical Disposables market size 2023 was XX Million. Medical Disposables Industry compound annual growth rate (CAGR) was XX% from 2024 till 2031."

  In 2023, the sterilisation supplies segment held the lead with XX% of total revenue. Infection control is critical in healthcare settings.
  The plastic resin segment dominated the market in 2023. Infection control is critical in healthcare settings.
  Hospitals dominated the market in 2023 because they provide a diverse range of medical services and treatments, including various medical specialties and procedures.
  North America accounted for XX% of the worldwide market in 2023 and is expected to maintain its dominance during the forecast period. North America boasts a highly developed and modern healthcare infrastructure, including hospitals, clinics, and medical facilities.

Current scenario of the Medical disposables market

Key drivers of the Medical Disposables market

An increase in hospital-acquired infections (HAIs) around the globe is fuelling market demand. 

HAI is becoming an increasingly serious issue in healthcare facilities around the world. According to the WHO, 10 out of every 100 hospital patients in developing countries and seven in industrialised nations suffer from HAIs. Infectious agents provide an elevated risk to health practitioners and professionals, and patients who are exposed are more likely to catch hospital-acquired diseases. According to statistical research published by the Centers for Disease Control and Prevention (CDC), such HAIs cause around 1.7 million diseases and more than 99,000 deaths in American hospitals. https://www.who.int/news/item/06-05-2022-who-launches-first-ever-global-report-on-infection-prevention-and-control#:~:text=Today%2C%20out%20of%20every%20100,will%20die%20from%20their%20HAI.

HAIs were listed among the top five leading causes of mortality. Surgical infections, urinary tract infections, lung infections, and bloodstream infections are some of the most common hospital-acquired infection types. According to the American Hospital Association, post-surgical bloodstream infections have grown by 8%, while urinary tract infections have increased by 3.6% as a result of catheter placement during surgery. As a result, the usage of medical disposables may play an important role in preventing cross-contamination and reducing the danger of infection spread, driving the market to higher standards. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9501203/

Rising Diabetes Rates Will Drive Up market demand

Rapid urbanization and the growing trend towards sedentary lifestyles in both developed and emerging nations are the main causes of the increased prevalence of diabetes. According to the International Diabetes Federation, 537 million adults globally were predicted to have diabetes in 2021; 51.6% of those people were estimated to reside in China, India, the United States, Brazil, and Mexico. The rapid acceptance of these systems worldwide can be attributed to the large rise in the number of patients with type-1 or insulin-dependent type 2 diabetes. Type 1 diabetes affects around 149.5 out of every 1,000 children and teenagers worldwide, accounting for 9.8% of the total population. https://idf.org/about-diabetes/diabetes-facts-figures/ Long-term diabetes may result in foot ulcers, longer hospital stays, and a positive impact on the market during the forecast period. Despite the healthcare industry's careful efforts, many people contract hospital-acquired infections while being treated there. The Centres for Disease Control and Prevention (CDC) estimate that over 3% of patients in the United States contract hospital-acquired infections each year.

Constraints for the global Medical Disposables market

The increase in waste production limits market expansion.

The sustainable healthcare business requires effective biomedical waste management. Efficiency in processing the vast amount of generated biomedical waste became a work for the entire world, as well as a fight to manage the exuberant amount of garbage, with the outbreak of the COVID-19 pandemic, when hospitals and care centres were inundated with patients. Despite the fact that the bulk of medical waste poses no harm to human...

Market Overview:

The global real time location systems (RTLS) for healthcare market size reached USD 3.0 Billion in 2024. Looking forward, IMARC Group expects the market to reach USD 11.6 Billion by 2033, exhibiting a growth rate (CAGR) of 15.34% during 2025-2033. The rising need for asset tracking and management, the increasing demand for remote patient monitoring, and various technological advancements represent some of the key factors driving the market.

Real-time location systems (RTLS) are a set of technologies used to automatically identify and track the location of assets, people or objects in real time. They use wireless communication technologies such as radio frequency identification (RFID), Wireless Fidelity (Wi-Fi), Bluetooth low energy, global positioning system (GPS), and ultrasound to track and locate assets or individuals. It is used in the healthcare industry to improve patient safety, staff efficiency, and asset management. It is also utilized to track medical equipment, monitor patient movements, and ensure that staff members are adhering to proper hygiene protocols. In addition, it is used to optimize workflows, increase efficiency, reduce wait times, and enhance patient satisfaction. Besides this, the data collected by these systems can also be used for analytics, such as identifying bottlenecks, optimizing resource allocation, and improving overall operational efficiency. Owing to these benefits, RTLS systems are widely adopted in the healthcare industry across the globe.

Real Time Location Systems (RTLS) for Healthcare Market Trends:

The market is primarily driven by the increasing need for asset tracking and management. Healthcare organizations are adopting solutions to manage medical equipment, drugs, and supplies. RTLS can provide real-time information on the location and status of assets, improving inventory control and reducing equipment loss. In addition, the growing focus on patient safety represents another major growth-inducing factor. RTLS can improve patient safety by providing real-time information on patient location and movement, reducing the risk of adverse events, and improving response times in emergencies. Besides this, various technological advancements, such as the integration of The Internet of Things (IoT) in healthcare to provide real-time location data for patients and assets, are also contributing to the market growth. Moreover, various government agencies are implementing policies mandating healthcare institutes to maintain accurate records of patient and equipment movements. RTLS can help healthcare facilities comply with these requirements by providing real-time location data. This, coupled with the increasing demand for remote patient monitoring due to the sudden outbreak of the coronavirus disease (COVID-19) pandemic, is accelerating the product adoption rate. Furthermore, the developing healthcare infrastructure and rising healthcare expenditure are also creating a favorable market outlook across the globe.

Key Market Segmentation:

IMARC Group provides an analysis of the key trends in each segment of the global real time location systems (RTLS) for healthcare market, along with forecasts at the global, regional, and country levels from 2025-2033. Our report has categorized the market based on offering, applications and end use.

Offering Insights:

• Software
• Hardware
• Service
  

The report has provided a detailed breakup and analysis of the real time location systems (RTLS) for healthcare market based on the offering. This includes software, hardware and service. According to the report, hardware accounted for the majority of the market share.

Applications Insights:

• Asset Tracking
• Patient Safety
• Personnel Tracking
• Environmental Monitoring
• Others
  

The report has provided a detailed breakup and analysis of the real time location systems (RTLS) for healthcare market based on the applications. This includes asset tracking, patient safety, personnel tracking, environmental monitoring, and others. According to the report, asset tracking represented the largest segment.

End Use Insights:

• Hospitals
• Clinics
• Emergency Medical Services
• Eldercare Facilities
• Diagnostic Labs
  

A detailed breakup and analysis of the real time location systems (RTLS) for healthcare market based on the end use has also been provided in the report. This includes hospitals, clinics, emergency medical services, eldercare facilities, and diagnostic labs. According to the report, hospitals accounted for the largest market share.

Regional Insights:

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• North America
  • United States
  • Canada
• Europe
  • Germany
  • France
  • United Kingdom
  • Italy
  • Spain
  • Russia
  • Others
• Asia Pacific
  • China
  • Japan
  • India
  • South Korea
  • Australia
  • Indonesia
  • Others
• Latin America
  • Brazil
  • Mexico
  • Others
• Middle East and Africa
  

The report has also provided a comprehensive analysis of all the major regional markets, which include North America (the United States and Canada); Europe (Germany, France, the United Kingdom, Italy, Spain, Russia, and others); Asia Pacific (China, Japan, India, South Korea, Australia, Indonesia, and others); Latin America (Brazil, Mexico, and others); and the Middle East and Africa. According to the report, Asia Pacific was the largest market for real time location systems (RTLS) for healthcare. Some of the factors driving the Asia Pacific real time location systems (RTLS) for healthcare market included the growing preference for better patient care, the rising need for asset management, the implementation of government initiatives, etc.

Competitive Landscape:

The report has also provided a comprehensive analysis of the competitive landscape in the global real time location systems (RTLS) for healthcare market. Competitive analysis such as market structure, market share by key players, player positioning, top winning strategies, competitive dashboard, and company evaluation quadrant has been covered in the report. Also, detailed profiles

In 2023, Singapore dominated the ranking of the world's health and health systems, followed by Japan and South Korea. The health index score is calculated by evaluating various indicators that assess the health of the population, and access to the services required to sustain good health, including health outcomes, health systems, sickness and risk factors, and mortality rates. The health and health system index score of the top ten countries with the best healthcare system in the world ranged between 82 and 86.9, measured on a scale of zero to 100.

Global Health Security Index  Numerous health and health system indexes have been developed to assess various attributes and aspects of a nation's healthcare system. One such measure is the Global Health Security (GHS) index. This index evaluates the ability of 195 nations to identify, assess, and mitigate biological hazards in addition to political and socioeconomic concerns, the quality of their healthcare systems, and their compliance with international finance and standards. In 2021, the United States was ranked at the top of the GHS index, but due to multiple reasons, the U.S. government failed to effectively manage the COVID-19 pandemic. The GHS Index evaluates capability and identifies preparation gaps; nevertheless, it cannot predict a nation's resource allocation in case of a public health emergency.

Universal Health Coverage Index  Another health index that is used globally by the members of the United Nations (UN) is the universal health care (UHC) service coverage index. The UHC index monitors the country's progress related to the sustainable developmental goal (SDG) number three. The UHC service coverage index tracks 14 indicators related to reproductive, maternal, newborn, and child health, infectious diseases, non-communicable diseases, service capacity, and access to care. The main target of universal health coverage is to ensure that no one is denied access to essential medical services due to financial hardships. In 2021, the UHC index scores ranged from as low as 21 to a high score of 91 across 194 countries. 

Abstract

Different countries have different health outcomes that are in part due to the way respective health systems perform. Regardless of the type of health system, individuals will have health and non-health expectations in terms of how the institution responds to their needs. In many countries, however, health systems do not perform effectively and this is in part due to lack of information on health system performance, and on the different service providers.

The aim of the WHO World Health Survey is to provide empirical data to the national health information systems so that there is a better monitoring of health of the people, responsiveness of health systems and measurement of health-related parameters.

The overall aims of the survey is to examine the way populations report their health, understand how people value health states, measure the performance of health systems in relation to responsiveness and gather information on modes and extents of payment for health encounters through a nationally representative population based community survey. In addition, it addresses various areas such as health care expenditures, adult mortality, birth history, various risk factors, assessment of main chronic health conditions and the coverage of health interventions, in specific additional modules.

The objectives of the survey programme are to: 1. develop a means of providing valid, reliable and comparable information, at low cost, to supplement the information provided by routine health information systems. 2. build the evidence base necessary for policy-makers to monitor if health systems are achieving the desired goals, and to assess if additional investment in health is achieving the desired outcomes. 3. provide policy-makers with the evidence they need to adjust their policies, strategies and programmes as necessary.

Geographic coverage

The survey sampling frame must cover 100% of the country's eligible population, meaning that the entire national territory must be included. This does not mean that every province or territory need be represented in the survey sample but, rather, that all must have a chance (known probability) of being included in the survey sample.

There may be exceptional circumstances that preclude 100% national coverage. Certain areas in certain countries may be impossible to include due to reasons such as accessibility or conflict. All such exceptions must be discussed with WHO sampling experts. If any region must be excluded, it must constitute a coherent area, such as a particular province or region. For example if ¾ of region D in country X is not accessible due to war, the entire region D will be excluded from analysis.

Analysis unit

Households and individuals

Universe

The WHS will include all male and female adults (18 years of age and older) who are not out of the country during the survey period. It should be noted that this includes the population who may be institutionalized for health reasons at the time of the survey: all persons who would have fit the definition of household member at the time of their institutionalisation are included in the eligible population.

If the randomly selected individual is institutionalized short-term (e.g. a 3-day stay at a hospital) the interviewer must return to the household when the individual will have come back to interview him/her. If the randomly selected individual is institutionalized long term (e.g. has been in a nursing home the last 8 years), the interviewer must travel to that institution to interview him/her.

The target population includes any adult, male or female age 18 or over living in private households. Populations in group quarters, on military reservations, or in other non-household living arrangements will not be eligible for the study. People who are in an institution due to a health condition (such as a hospital, hospice, nursing home, home for the aged, etc.) at the time of the visit to the household are interviewed either in the institution or upon their return to their household if this is within a period of two weeks from the first visit to the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

SAMPLING GUIDELINES FOR WHS

Surveys in the WHS program must employ a probability sampling design. This means that every single individual in the sampling frame has a known and non-zero chance of being selected into the survey sample. While a Single Stage Random Sample is ideal if feasible, it is recognized that most sites will carry out Multi-stage Cluster Sampling.

The WHS sampling frame should cover 100% of the eligible population in the surveyed country. This means that every eligible person in the country has a chance of being included in the survey sample. It also means that particular ethnic groups or geographical areas may not be excluded from the sampling frame.

The sample size of the WHS in each country is 5000 persons (exceptions considered on a by-country basis). An adequate number of persons must be drawn from the sampling frame to account for an estimated amount of non-response (refusal to participate, empty houses etc.). The highest estimate of potential non-response and empty households should be used to ensure that the desired sample size is reached at the end of the survey period. This is very important because if, at the end of data collection, the required sample size of 5000 has not been reached additional persons must be selected randomly into the survey sample from the sampling frame. This is both costly and technically complicated (if this situation is to occur, consult WHO sampling experts for assistance), and best avoided by proper planning before data collection begins.

All steps of sampling, including justification for stratification, cluster sizes, probabilities of selection, weights at each stage of selection, and the computer program used for randomization must be communicated to WHO

STRATIFICATION

Stratification is the process by which the population is divided into subgroups. Sampling will then be conducted separately in each subgroup. Strata or subgroups are chosen because evidence is available that they are related to the outcome (e.g. health, responsiveness, mortality, coverage etc.). The strata chosen will vary by country and reflect local conditions. Some examples of factors that can be stratified on are geography (e.g. North, Central, South), level of urbanization (e.g. urban, rural), socio-economic zones, provinces (especially if health administration is primarily under the jurisdiction of provincial authorities), or presence of health facility in area. Strata to be used must be identified by each country and the reasons for selection explicitly justified.

Stratification is strongly recommended at the first stage of sampling. Once the strata have been chosen and justified, all stages of selection will be conducted separately in each stratum. We recommend stratifying on 3-5 factors. It is optimum to have half as many strata (note the difference between stratifying variables, which may be such variables as gender, socio-economic status, province/region etc. and strata, which are the combination of variable categories, for example Male, High socio-economic status, Xingtao Province would be a stratum).

Strata should be as homogenous as possible within and as heterogeneous as possible between. This means that strata should be formulated in such a way that individuals belonging to a stratum should be as similar to each other with respect to key variables as possible and as different as possible from individuals belonging to a different stratum. This maximises the efficiency of stratification in reducing sampling variance.

MULTI-STAGE CLUSTER SELECTION

A cluster is a naturally occurring unit or grouping within the population (e.g. enumeration areas, cities, universities, provinces, hospitals etc.); it is a unit for which the administrative level has clear, nonoverlapping boundaries. Cluster sampling is useful because it avoids having to compile exhaustive lists of every single person in the population. Clusters should be as heterogeneous as possible within and as homogenous as possible between (note that this is the opposite criterion as that for strata). Clusters should be as small as possible (i.e. large administrative units such as Provinces or States are not good clusters) but not so small as to be homogenous.

In cluster sampling, a number of clusters are randomly selected from a list of clusters. Then, either all members of the chosen cluster or a random selection from among them are included in the sample. Multistage sampling is an extension of cluster sampling where a hierarchy of clusters are chosen going from larger to smaller.

In order to carry out multi-stage sampling, one needs to know only the population sizes of the sampling units. For the smallest sampling unit above the elementary unit however, a complete list of all elementary units (households) is needed; in order to be able to randomly select among all households in the TSU, a list of all those households is required. This information may be available from the most recent population census. If the last census was >3 years ago or the information furnished by it was of poor quality or unreliable, the survey staff will have the task of enumerating all households in the smallest randomly selected sampling unit. It is very important to budget for this step if it is necessary and ensure that all households are properly enumerated in order that a representative sample is obtained.

It is always best to have as many clusters in the PSU as possible. The reason for this is that the fewer the number of respondents in each PSU, the lower will be the clustering effect which