About this dataset

Looking for a dataset on hospitals in the United States? Look no further! This dataset contains information on all of the hospitals registered with Medicare in the US, including their addresses, phone numbers, hospital type, and more. With such a large amount of data, this dataset is perfect for anyone interested in studying the US healthcare system.

This dataset can also be used to study hospital ownership, emergency services

How to use the dataset

If you want to study the US healthcare system, this dataset is perfect for you. It contains information on all of the hospitals registered with Medicare, including their addresses, phone numbers, hospital type, and more. With such a large amount of data, this dataset is perfect for anyone interested in studying the US healthcare system.

This dataset can also be used to study hospital ownership, emergency services, and EHR usage. In addition, the hospital overall rating and various comparisons are included for safety of care, readmission rates

Research Ideas

1. Predicting readmission rates for different hospital conditions
2. Analyzing relationships between hospital ownership and quality of care
3. Studying the relationship between hospital type and patient experience

Acknowledgements

This dataset was originally published by Centers for Medicare and Medicaid Services and has been modified for this project

Columns

File: Hospital_General_Information.csv | Column name | Description | |:-------------------------------------------------------|:----------------------------------------------------------------------------------------------------------| | Hospital Name | The name of the hospital. (String) | | Hospital Name | The name of the hospital. (String) | | Address | The address of the hospital. (String) | | Address | The address of the hospital. (String) | | City | The city in which the hospital is located. (String) | | City | The city in which the hospital is located. (String) | | State | The state in which the hospital is located. (String) | | State | The state in which the hospital is located. (String) | | ZIP Code | The ZIP code of the hospital. (Integer) | | ZIP Code | The ZIP code of the hospital. (Integer) | | County Name | The county in which the hospital is located. (String) | | County Name | The county in which the hospital is located. (String) | | Phone Number | The phone number of the hospital. (String) | | Phone Number | The phone number of the hospital. (String) | | Hospital Type | The type of hospital. (String) | | Hospital Type | The type of hospital. (String) | | Hospital Ownership | The ownership of the hospital. (String) | | Hospital Ownership | The ownership of the hospital. (String) | | Emergency Services | Whether or not the...

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 Bed Availability Exchange HIE Market Outlook

According to our latest research, the Global Hospital Bed Availability Exchange HIE market size was valued at $1.2 billion in 2024 and is projected to reach $4.7 billion by 2033, expanding at a robust CAGR of 16.4% during the forecast period of 2025–2033. The primary driver behind this impressive growth trajectory is the increasing demand for real-time hospital bed management solutions, especially in the wake of global health crises and the ongoing digital transformation of healthcare infrastructure. As healthcare systems strive to optimize resource allocation and improve patient outcomes, the integration of Health Information Exchange (HIE) platforms for bed availability has become a critical component in enhancing operational efficiency and emergency response capabilities worldwide.

Regional Outlook

North America currently dominates the Hospital Bed Availability Exchange HIE market, accounting for the largest share of global revenue, with an estimated market value of $560 million in 2024. This leadership is attributed to the region’s mature healthcare IT ecosystem, widespread adoption of electronic health records (EHRs), and strong regulatory support for interoperability and data exchange. The United States, in particular, benefits from significant federal investments in healthcare digitization, stringent policies on patient safety and resource management, and a high concentration of leading software and service providers. Furthermore, the presence of robust hospital networks and advanced emergency response systems has accelerated the deployment of HIE solutions for bed availability, making North America the benchmark for best practices and innovation in this sector.

In contrast, the Asia Pacific region is projected to experience the fastest growth in the Hospital Bed Availability Exchange HIE market, with a forecasted CAGR exceeding 20.1% from 2025 to 2033. This remarkable expansion is fueled by substantial investments in healthcare infrastructure, rapid urbanization, and a growing emphasis on digital health transformation across major economies such as China, India, Japan, and South Korea. National initiatives aimed at expanding hospital capacity, improving patient management, and modernizing legacy systems are driving the adoption of cloud-based HIE platforms for real-time bed tracking and capacity planning. Additionally, the surge in demand for emergency response solutions during health emergencies has highlighted the critical need for integrated bed management systems, prompting regional governments and private stakeholders to accelerate technology adoption.

Emerging economies in Latin America and Middle East & Africa present a unique set of opportunities and challenges for the Hospital Bed Availability Exchange HIE market. While these regions are witnessing increased awareness and gradual adoption of HIE solutions, they face hurdles such as fragmented healthcare systems, limited IT infrastructure, and budgetary constraints. However, localized demand for efficient patient flow management, driven by population growth and urbanization, is prompting governments to explore public-private partnerships and donor-funded projects. Policy reforms focused on healthcare digitalization and capacity building are gradually overcoming adoption barriers, setting the stage for accelerated market growth in the medium to long term.

Report Scope

This statistic shows the size of the ** best hospitals in the United States in 2011, sorted by the percentage of hospitals that fall into each bed size category. In 2011, ** percent out of the top 50 hospitals had between 100 and *** patient beds.

This statistic depicts a ranking of the top 10 largest U.S. for-profit hospitals based on the number of beds as of February 2024. At this point, the Methodist Hospital in San Antonio, Texas, was ranked first among such hospitals in the United States, with a total of 1,831 beds. The top three largest for-profit hospitals were all in Texas.

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?

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

Context

IHME has produced forecasts which show hospital bed use, need for intensive care beds, and ventilator use due to COVID-19 based on projected deaths for all 50 U.S. states. These projections are produced by models based on observed death rates from COVID-19 and include uncertainty intervals.

They incorporate information about social distancing and other protective measures and are being updated daily with new data. These forecasts were developed in order to provide hospitals, policymakers, and the public with crucial information about how expected need aligns with existing resources so that cities and states can best prepare.

All the column descriptors and details are attached in the PDF.

Acknowledgements

Institute for Health Metrics and Evaluation (IHME). United States COVID-19 Hospital Needs and Death Projections. Seattle, United States of America: Institute for Health Metrics and Evaluation (IHME), University of Washington, 2020

Why Not the Best VA or WNTBVA is a system for comparing Veterans Health Administration (VHA) hospital system performance with regional and U.S. national benchmarks. This report includes key quality measures available on CMS Hospital Compare and top hospital recognition programs from reporting agencies of hospital quality. These .ZIP files are no longer supported and are in an 'as-is' state. They were accurate at time of publication. This currently only has Q1

Why Not the Best VA or WNTBVA is a system for comparing Veterans Health Administration (VHA) hospital system performance with regional and U.S. national benchmarks. This report includes key quality measures available on CMS Hospital Compare and top hospital recognition programs from reporting agencies of hospital quality. These .ZIP files are no longer supported and are in an 'as-is' state. They were accurate at time of publication.

Medicare Spending per Beneficiary

Detailed Hospital Expense Breakdown

By Health [source]

About this dataset

This file allows healthcare executives and analysts to make informed decisions regarding how well continued improvements are being made over time so that they can understand how efficient they are fulfilling treatments while staying within budgetary constraints. Additionally, it’ll also help them map out trends amongst different hospitals and spot anomalies that could indicate areas where decisions should be reassessed as needed

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How to use the dataset

This dataset can provide valuable insights into how Medicare is spending per patient at specific hospitals in the United States. It can be used to gain a better understanding of the types of services covered under Medicare, and to what extent those services are being used. By comparing the average Medicare spending across different hospitals, users can also gain insight into potential disparities in care delivery or availability.

To use this dataset, first identify which hospital you are interested in analyzing. Then locate the row for that hospital in the dataset and review its associated values: value, footnote (optional), and start/end dates (optional). The Value column refers to how much Medicare spends on each particular patient; this is a numerical value represented as a decimal number up to 6 decimal places. The Footnote (optional) provides more information about any special circumstances that may need attention when interpreting the value data points. Finally, if Start Date and End Date fields are present they will specify over what timeframe these values were aggregated over.

Once all relevant data elements have been reviewed successively for all hospitals of interest then comparison analysis among them can be conducted based on Value, Footnote or Start/End dates as necessary to answer specific research questions or formulate conclusions about how Medicare is spending per patient at various hospitals nationwide

Research Ideas

• Developing a cost comparison tool for hospitals that allows patients to compare how much Medicare spends per patient across different hospitals.
• Creating an algorithm to help predict Medicare spending at different facilities over time and build strategies on how best to manage those costs.
• Identifying areas in which a hospital can save money by reducing unnecessary spending in order to reduce overall Medicare expenses

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: Medicare_hospital_spending_per_patient_Medicare_Spending_per_Beneficiary_Additional_Decimal_Places.csv | Column name | Description | |:---------------|:--------------------------------------------------------------------------------------| | Value | The amount of Medicare spending per patient for a given hospital or region. (Numeric) | | Footnote | Any additional notes or information related to the value. (Text) | | Start_Date | The start date of the period for which the value applies. (Date) | | End_Date | The end date of the period for which the value applies. (Date) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Health.

VA Community Care Comparison or VAC3 (formerly Why Not the Best VA) is a system for comparing Veterans Health Administration (VHA) hospital system performance with regional and U.S. national benchmarks. This report includes key quality measures available on CMS Hospital Compare and top hospital recognition programs from reporting agencies of hospital quality. VAC3 data tables are updated every quarter.

According to a ranking by Statista and Newsweek, the best hospital in the United States is the *********** in Rochester, Minnesota. Moreover, the *********** was also ranked as the best hospital in the world, among over 50,000 hospitals in 30 countries. **************** in Ohio and the ************* Hospital in Maryland were ranked as second and third best respectively in the U.S., while they were second and forth best respectively in the World.

This is the current Medical Service Study Area. California Medical Service Study Areas are created by the California Department of Health Care Access and Information (HCAI).Check the Data Dictionary for field descriptions.Search for the Medical Service Study Area data on the CHHS Open Data Portal.Checkout the California Healthcare Atlas for more Medical Service Study Area information.This is an update to the MSSA geometries and demographics to reflect the new 2020 Census tract data. The Medical Service Study Area (MSSA) polygon layer represents the best fit mapping of all new 2020 California census tract boundaries to the original 2010 census tract boundaries used in the construction of the original 2010 MSSA file. Each of the state's new 9,129 census tracts was assigned to one of the previously established medical service study areas (excluding tracts with no land area), as identified in this data layer. The MSSA Census tract data is aggregated by HCAI, to create this MSSA data layer. This represents the final re-mapping of 2020 Census tracts to the original 2010 MSSA geometries. The 2010 MSSA were based on U.S. Census 2010 data and public meetings held throughout California.Source of update: American Community Survey 5-year 2006-2010 data for poverty. For source tables refer to InfoUSA update procedural documentation. The 2010 MSSA Detail layer was developed to update fields affected by population change. The American Community Survey 5-year 2006-2010 population data pertaining to total, in households, race, ethnicity, age, and poverty was used in the update. The 2010 MSSA Census Tract Detail map layer was developed to support geographic information systems (GIS) applications, representing 2010 census tract geography that is the foundation of 2010 medical service study area (MSSA) boundaries. ***This version is the finalized MSSA reconfiguration boundaries based on the US Census Bureau 2010 Census. In 1976 Garamendi Rural Health Services Act, required the development of a geographic framework for determining which parts of the state were rural and which were urban, and for determining which parts of counties and cities had adequate health care resources and which were "medically underserved". Thus, sub-city and sub-county geographic units called "medical service study areas [MSSAs]" were developed, using combinations of census-defined geographic units, established following General Rules promulgated by a statutory commission. After each subsequent census the MSSAs were revised. In the scheduled revisions that followed the 1990 census, community meetings of stakeholders (including county officials, and representatives of hospitals and community health centers) were held in larger metropolitan areas. The meetings were designed to develop consensus as how to draw the sub-city units so as to best display health care disparities. The importance of involving stakeholders was heightened in 1992 when the United States Department of Health and Human Services' Health and Resources Administration entered a formal agreement to recognize the state-determined MSSAs as "rational service areas" for federal recognition of "health professional shortage areas" and "medically underserved areas". After the 2000 census, two innovations transformed the process, and set the stage for GIS to emerge as a major factor in health care resource planning in California. First, the Office of Statewide Health Planning and Development [OSHPD], which organizes the community stakeholder meetings and provides the staff to administer the MSSAs, entered into an Enterprise GIS contract. Second, OSHPD authorized at least one community meeting to be held in each of the 58 counties, a significant number of which were wholly rural or frontier counties. For populous Los Angeles County, 11 community meetings were held. As a result, health resource data in California are collected and organized by 541 geographic units. The boundaries of these units were established by community healthcare experts, with the objective of maximizing their usefulness for needs assessment purposes. The most dramatic consequence was introducing a data simultaneously displayed in a GIS format. A two-person team, incorporating healthcare policy and GIS expertise, conducted the series of meetings, and supervised the development of the 2000-census configuration of the MSSAs.MSSA Configuration Guidelines (General Rules):- Each MSSA is composed of one or more complete census tracts.- As a general rule, MSSAs are deemed to be "rational service areas [RSAs]" for purposes of designating health professional shortage areas [HPSAs], medically underserved areas [MUAs] or medically underserved populations [MUPs].- MSSAs will not cross county lines.- To the extent practicable, all census-defined places within the MSSA are within 30 minutes travel time to the largest population center within the MSSA, except in those circumstances where meeting this criterion would require splitting a census tract.- To the extent practicable, areas that, standing alone, would meet both the definition of an MSSA and a Rural MSSA, should not be a part of an Urban MSSA.- Any Urban MSSA whose population exceeds 200,000 shall be divided into two or more Urban MSSA Subdivisions.- Urban MSSA Subdivisions should be within a population range of 75,000 to 125,000, but may not be smaller than five square miles in area. If removing any census tract on the perimeter of the Urban MSSA Subdivision would cause the area to fall below five square miles in area, then the population of the Urban MSSA may exceed 125,000. - To the extent practicable, Urban MSSA Subdivisions should reflect recognized community and neighborhood boundaries and take into account such demographic information as income level and ethnicity. Rural Definitions: A rural MSSA is an MSSA adopted by the Commission, which has a population density of less than 250 persons per square mile, and which has no census defined place within the area with a population in excess of 50,000. Only the population that is located within the MSSA is counted in determining the population of the census defined place. A frontier MSSA is a rural MSSA adopted by the Commission which has a population density of less than 11 persons per square mile. Any MSSA which is not a rural or frontier MSSA is an urban MSSA. Last updated December 6th 2024.

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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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.
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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

Electric Hospital Patient Transport Shuttle Market Outlook

According to our latest research, the Global Electric Hospital Patient Transport Shuttle market size was valued at $1.2 billion in 2024 and is projected to reach $3.6 billion by 2033, expanding at a robust CAGR of 12.8% during the forecast period of 2025 to 2033. The primary growth driver for this market is the increasing adoption of automation and electrification in healthcare logistics, aimed at enhancing patient mobility, safety, and operational efficiency within medical facilities. The rising focus on patient-centric care, coupled with a growing need to reduce manual labor and prevent cross-contamination, is further accelerating the transition from traditional manual transport methods to advanced electric hospital patient transport shuttles globally.

Regional Outlook

North America currently holds the largest share of the global electric hospital patient transport shuttle market, accounting for approximately 38% of the total market value in 2024. This dominance is attributed to the region's mature healthcare infrastructure, high adoption of technological advancements, and supportive regulatory frameworks that encourage the integration of automation in hospital operations. The United States, in particular, is a frontrunner due to significant investments in healthcare modernization, a strong presence of leading manufacturers, and active government initiatives focused on patient safety and operational efficiency. The region's well-established reimbursement policies and stringent standards for patient handling further reinforce the demand for electric transport shuttles, making North America a benchmark for innovation and best practices in this sector.

The Asia Pacific region is expected to be the fastest-growing market, with a projected CAGR of 15.6% from 2025 to 2033. This rapid expansion is driven by escalating healthcare investments, growing hospital construction projects, and increasing awareness of the benefits of automated patient transport solutions. Countries such as China, India, and Japan are witnessing a surge in healthcare infrastructure upgrades, which includes the adoption of electric and automated mobility solutions to address rising patient volumes and workforce shortages. Strategic collaborations between local and international players, coupled with favorable government policies supporting healthcare digitization and electrification, are fueling the market’s growth trajectory in this region.

Emerging economies in Latin America and the Middle East & Africa are gradually embracing electric hospital patient transport shuttles, though adoption is still at a nascent stage compared to developed regions. Challenges such as limited healthcare budgets, infrastructural constraints, and lack of skilled personnel hinder widespread uptake. However, increasing government initiatives to modernize healthcare facilities, rising incidence of chronic diseases necessitating efficient patient handling, and growing partnerships with global manufacturers are expected to gradually boost demand in these regions. Localized demand patterns, influenced by population demographics and policy reforms, will play a crucial role in shaping the market landscape across these emerging markets.

Report Scope

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COVID-19 US Daily Data

State level daily COVID-19 data for United States, provided by Johns Hopkins University (JHU) Center for Systems Science and Engineering (CSSE). If you want to use the updated version of the data, you can use our daily updated data with the help of api key by entering it via Altadata.

Overview

In this data product, you may find the latest and historical daily data on the COVID-19 pandemic for United States with the states level breakdown.

The COVID‑19 pandemic, also known as the coronavirus pandemic, is an ongoing global pandemic of coronavirus disease 2019 (COVID‑19), caused by severe acute respiratory syndrome coronavirus 2 (SARS‑CoV‑2). The outbreak was first identified in December 2019 in Wuhan, China. The World Health Organization declared the outbreak a Public Health Emergency of International Concern on 30 January 2020 and a pandemic on 11 March. As of 12 August 2020, more than 20.2 million cases of COVID‑19 have been reported in more than 188 countries and territories, resulting in more than 741,000 deaths; more than 12.5 million people have recovered.

The Johns Hopkins Coronavirus Resource Center is a continuously updated source of COVID-19 data and expert guidance. They aggregate and analyze the best data available on COVID-19 - including cases, as well as testing, contact tracing and vaccine efforts - to help the public, policymakers and healthcare professionals worldwide respond to the pandemic.

Methodology

• Cases and Death counts include confirmed and probable (where reported)
• Recovered cases are estimates based on local media reports, and state and local reporting when available, and therefore may be substantially lower than the true number. US state-level recovered cases are from COVID Tracking Project.
• Active cases = total cases - total recovered - total deaths
• Incidence Rate = cases per 100,000 persons
• Case-Fatality Ratio (%) = Number recorded deaths / Number cases
• US Testing Rate = total test results per 100,000 persons. The "total test results" are equal to "Total test results (Positive + Negative)" from COVID Tracking Project.
• US Hospitalization Rate (%) = Total number hospitalized / Number cases. The "Total number hospitalized" is the "Hospitalized – Cumulative" count from COVID Tracking Project. The "hospitalization rate" and "Total number hospitalized" are only presented for those states which provide cumulative hospital data.
• States Population data is retrieved from U.S. Census Bureau on top of the JHU CSSE's COVID-19 data

Data Source

• Johns Hopkins University Center for Systems Science and Engineering (JHU CSSE)
• U.S. Census Bureau, Population Division

Related Data Products

• COVID-19 Worldwide Daily Data

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Data Dictionary

• Reported Date (reported_date): Covid-19 Report Date
• Province State (province_state): State name
• Population (population): Estimated state populations as of July 2019, as per U.S. Census Bureau Population Division
• Latitude (lat): Dot locations, not representative of a specific address
• Longitude (lng): Dot locations longitude, not representative of a specific address
• Confirmed Case (confirmed): Confirmed cases include presumptive positive cases and probable cases
• Active cases (active): Active cases = total confirmed - total recovered - total deaths
• Deaths (deaths): Death cases counts
• Recovered (recovered): Recovered cases counts
• Hospitalization Rate (hospitalization_rate): Total number of people hospitalized * 100...

The North American wireless healthcare market, valued at $63.76 million in 2025, is projected to experience robust growth, exhibiting a Compound Annual Growth Rate (CAGR) of 21.82% from 2025 to 2033. This expansion is driven by several key factors. The increasing adoption of telehealth services, fueled by the convenience and accessibility they offer, particularly for remote patient monitoring and virtual consultations, is a major catalyst. Furthermore, the rising prevalence of chronic diseases necessitates continuous health monitoring, creating significant demand for wireless medical devices and connected healthcare solutions. Technological advancements, such as the development of miniaturized sensors and improved wireless communication technologies (5G and beyond), are further enhancing the capabilities and affordability of these systems. The market is segmented by component (hardware, software, services), application (hospitals & nursing homes, home care, pharmaceuticals), and geography (United States, Canada). Major players like GE Healthcare, Siemens Healthineers, and technology giants such as AT&T, Cisco, and Qualcomm are actively involved, driving innovation and competition within this rapidly evolving landscape. The United States, with its advanced healthcare infrastructure and high technology adoption rate, is expected to dominate the market, followed by Canada. Regulatory support for telehealth and data privacy regulations will also influence market trajectory. The growth trajectory of the North American wireless healthcare market is influenced by several factors beyond the technological advancements. Increasing healthcare costs and the need for cost-effective solutions are pushing the adoption of wireless technologies. The aging population in North America presents a significant opportunity for remote patient monitoring and home healthcare solutions. However, challenges remain, including concerns about data security and interoperability of different wireless systems, alongside the need for robust infrastructure to support widespread adoption. The market will likely see increased focus on data analytics and AI-driven insights to optimize healthcare delivery and improve patient outcomes. Addressing these challenges through strategic partnerships, robust cybersecurity measures, and standardization efforts will be crucial for sustaining the market's impressive growth trajectory. Recent developments include: June 2023: Cardinal Health, a United States-based healthcare services company, announced that it had signed an official agreement to transfer its Outcomes business to BlackRock Long Term Private Capital and GTCR portfolio firm Transaction Data Systems (TDS) in exchange for a small equity investment in the newly formed organization. The purchase is going to generate one of the largest networks of 40,000 retail, chain, and grocery pharmacies in the country, as well as a broad, integrated portfolio of pharmacy software for workflow with involvement from patients and clinical solutions designed to serve patients, pharmacies, payers, and pharmaceutical company ecosystems., March 2023: Atrium Health and Best Buy Health announced a partnership in creating new hospital-at-home services to improve patients' experiences of obtaining hospital-level care at home. The partnership aims to empower healthcare professionals to offer patients high-quality treatment while easing the financial and mental stresses on patients and caregivers.. Key drivers for this market are: Increasing Adoption of Connected Devices in Healthcare, Growing Technological Advancements; Growing Presence of Digital Health Startups and Increased Investments in Healthcare Technology. Potential restraints include: Increasing Adoption of Connected Devices in Healthcare, Growing Technological Advancements; Growing Presence of Digital Health Startups and Increased Investments in Healthcare Technology. Notable trends are: Presence of Digital Health Startups and Increased Investments in Healthcare Technology to Drive the Market Growth.