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.

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.

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?

Context

Every year, all U.S. hospitals that accept payments from Medicare and Medicaid must submit quality data to The Centers for Medicare and Medicaid Services (CMS). CMS' Hospital Compare program is a consumer-oriented website that provides information on "the quality of care hospitals are providing to their patients." CMS releases this quality data publicly in order to encourage hospitals to improve their quality and to help consumer make better decisions about which providers they visit.

"Hospital Compare provides data on over 4,000 Medicare-certified hospitals, including acute care hospitals, critical access hospitals (CAHs), children’s hospitals, Veterans Health Administration (VHA) Medical Centers, and hospital outpatient departments"

The Centers for Medicare & Medicaid Services (CMS) uses a five-star quality rating system to measure the experiences Medicare beneficiaries have with their health plan and health care system — the Star Rating Program. Health plans are rated on a scale of 1 to 5 stars, with 5 being the highest.

Content

• Includes the most recent Hospital General Information.csv data for each archive year found on CMS' archive site. Years: 2016-2020

| Column Name | Data Type | Description | | --- | --- | -- | | Facility ID | Char(6) | Facility Medicare ID | | Facility Name | Char(72) | Name of the facility | | Address | Char(51) | Facility street address | | City | Char(20) | Facility City | | State | Char(2) | Facility State | | ZIP Code | Num(8) | Facility ZIP Code | | County Name | Char(25) | Facility County | | Phone Number | Char(14) | Facility Phone Number | | Hospital Type | Char(34) | What type of facility is it? | | Hospital Ownership | Char(43) | What type of ownership does the facility have? | | Emergency Services | Char(3)) | Does the facility have emergency services Yes/No? | | Meets criteria for promoting interoperability of EHRs | Char(1) | Does facility meet government EHR standard Yes/No? | | Hospital overall rating | Char(13) | Hospital Overall Star Rating 1=Worst; 5=Best. Aggregate measure of all other measures | | Hospital overall rating footnote | Num(8) | | | Mortality national comparison | Char(28) | Facility overall performance on mortality measures compared to other facilities | | Mortality national comparison footnote | Num(8) | | | Safety of care national comparison | Char(28) | Facility overall performance on safety measures compared to other facilities | | Safety of care national comparison footnote | Num(8) | | | Readmission national comparison | Char(28) | Facility overall performance on readmission measures compared to other facilities | | Readmission national comparison footnote | Num(8) | | | Patient experience national comparison | Char(28) | Facility overall performance on pat. exp. measures compared to other facilities | | Patient experience national comparison footnote | Char(8) | | | Effectiveness of care national comparison | Char(28) | Facility overall performance on effect. of care measures compared to other facilities | | Effectiveness of care national comparison footnote | Char(8) | | | Timeliness of care national comparison | Char(28) | Facility overall performance on timeliness of care measures compared to other facilities | | Timeliness of care national comparison footnote| Char(8) | | | Efficient use of medical imaging national comparison | Char(28) | Facility overall performance on efficient use measures compared to other facilities | | Efficient use of medical imaging national comparison footnote | Char(8) | | | Year | Char(4) | cms data release year |

Acknowledgements

A similar dataset called Hospital General Information was previously uploaded to Kaggle. However, that dataset only includes data from one year (2017). I was inspired by this dataset to go a little further and try to add a time dimension. This dataset includes a union of Hospital General Information for the years 2016-2020. The python script used to collect and union all the datasets can be found on my [github[(https://github.com/abrambeyer/cms_hospital_general_info_file_downloader). Thanks to this dataset owner for the inspiration.

Thanks to CMS for releasing this dataset publicly to help consumers find better hospitals and make better-informed decisions.

***All Hospital Compare websites are publically accessible. As works of the U.S. government, Hospital Compare data are in the public domain and permission is not required to reuse them. An attribution to the agency as the source is appreciated. Your ...

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.

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.

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.

Hospitals play a critical role in healthcare, offering specialized treatments and emergency services essential for public health, regardless of economic fluctuations or individuals' financial situations. Rising incomes and broader access to insurance have fueled demand for care in recent years, supporting hospitals' post-pandemic recovery initiated by federal policies and funding. The recovery for many hospitals was also promoted by mergers that lessened financial strains, especially in rural hospitals. This trend toward consolidation has resulted in fewer enterprises relative to establishments, enhancing hospitals' bargaining power regarding input costs and insurance reimbursements. With this improved position, hospitals are expected to see revenue climb at a CAGR of 2.0%, reaching $1.5 trillion by 2025, with a 3.2% increase in 2025 alone. Competition, economic conditions and regulatory changes will impact hospitals based on size and location. Smaller hospitals, particularly rural ones, may encounter more significant obstacles as the industry transitions from fee-based to value-based care. Independent hospitals face wage inflation, staffing shortages and drug supply costs. Although state and federal policies aim to support small rural hospitals in addressing hospital deserts, uncertainties linger over federal Medicare funding and Medicaid reimbursements, which account for nearly half of hospital care spending. Even so, increasing per capita disposable income and increasing the number of individuals with private insurance will boost revenues from private insurers and out-of-pocket payments for all hospitals, big and small. Hospitals will continue incorporating technological advancements in AI, telemedicine and wearables to enhance their services and reduce cost. These technologies aid hospital systems in strategically expanding outpatient services, mitigating the increasing competitive pressures from Ambulatory Surgery Centers (ASCs) and capitalizing on the increased needs of an aging adult population and shifts in healthcare delivery preferences. As the consolidation trend advances and technology adoption further leverages economies of scale, industry revenue is expected to strengthen at a CAGR of 2.4%, reaching $1.7 trillion by 2030, with steady profit over the period.

This statistic shows the size of the ** best hospitals in the United States in 2011, sorted by the number of beds per hospital. In 2011, * out of the top 50 U.S. hospitals had between 100 and *** patient beds.

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).

US Healthcare Readmissions and Mortality

Evaluating Hospital Performance

By Health [source]

About this dataset

This dataset contains detailed information about 30-day readmission and mortality rates of U.S. hospitals. It is an essential tool for stakeholders aiming to identify opportunities for improving healthcare quality and performance across the country. Providers benefit by having access to comprehensive data regarding readmission, mortality rate, score, measure start/end dates, compared average to national as well as other pertinent metrics like zip codes, phone numbers and county names. Use this data set to conduct evaluations of how hospitals are meeting industry standards from a quality and outcomes perspective in order to make more informed decisions when designing patient care strategies and policies

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This dataset provides data on 30-day readmission and mortality rates of U.S. hospitals, useful in understanding the quality of healthcare being provided. This data can provide insight into the effectiveness of treatments, patient care, and staff performance at different healthcare facilities throughout the country.

In order to use this dataset effectively, it is important to understand each column and how best to interpret them. The ‘Hospital Name’ column displays the name of the facility; ‘Address’ lists a street address for the hospital; ‘City’ indicates its geographic location; ‘State’ specifies a two-letter abbreviation for that state; ‘ZIP Code’ provides each facility's 5 digit zip code address; 'County Name' specifies what county that particular hospital resides in; 'Phone number' lists a phone contact for any given facility ;'Measure Name' identifies which measure is being recorded (for instance: Elective Delivery Before 39 Weeks); 'Score' value reflects an average score based on patient feedback surveys taken over time frame listed under ' Measure Start Date.' Then there are also columns tracking both lower estimates ('Lower Estimate') as well as higher estimates ('Higher Estimate'); these create variability that can be tracked by researchers seeking further answers or formulating future studies on this topic or field.; Lastly there is one more measure oissociated with this set: ' Footnote,' which may highlight any addional important details pertinent to analysis such as numbers outlying National averages etc..

This data set can be used by hospitals, research facilities and other interested parties in providing inciteful information when making decisions about patient care standards throughout America . It can help find patterns about readmitis/mortality along county lines or answer questions about preformance fluctuations between different hospital locations over an extended amount of time. So if you are ever curious about 30 days readmitted within US Hospitals don't hesitate to dive into this insightful dataset!

Research Ideas

• Comparing hospitals on a regional or national basis to measure the quality of care provided for readmission and mortality rates.
• Analyzing the effects of technological advancements such as telemedicine, virtual visits, and AI on readmission and mortality rates at different hospitals.
• Using measures such as Lower Estimate Higher Estimate scores to identify systematic problems in readmissions or mortality rate management at hospitals and informing public health care policy

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: Readmissions_and_Deaths_-_Hospital.csv | Column name | Description | |:-------------------------|:---------------------------------------------------------------------------------------------------| | Hospital Name ...

After several turbulent years, hospital construction activity is bouncing back, with contractors experiencing a much-needed rebound after the disruption brought on by the pandemic. The industry hit a low point between 2020 and 2023, when project activity stalled because of strict capital budgets, labor and material shortages and uncertainty about hospital demand. Contractors who saw a dwindling backlog during this time have enjoyed a resurgence in bidding and a ramping up of both new construction and remodeling jobs, spurred in part by rising hospital occupancy and the lure of federal tax incentives for energy-efficient upgrades. Climbing occupancy at hospitals has also boosted remodeling work, benefiting contractors. Overall, industry revenue has been increasing at a CAGR of 2.1% to total an estimated $34.6 billion in 2025, including an estimated 3.4% increase in 2025. Hospital construction contractors had to navigate persistent cost pressures and tough competition, all while handling shifts in hospital funding. Profitability took a hit as material prices and wages soared through 2022, with heightened material costs and labor shortages complicating job pricing and scheduling. Contractors were forced to accept slimmer profit just to keep projects moving when private hospitals delayed or downsized capital investments and nonprofit community hospitals struggled under tighter Medicaid reimbursements and operational losses. Only as pandemic-era constraints eased did capital flows begin to strengthen, allowing contractors to rebuild lost ground and pass on more costs to end customers from 2023 to 2025. Still, tariffs have led to climbing construction material costs, putting additional pressure on profit. Looking ahead, the outlook is a mix of opportunity and risk. Federal policy changes, including the One Big Beautiful Bill Act and the expiration of 179D tax credits, will shake up funding streams, pushing rural hospitals in particular to reshape their construction plans as they work through shrinking Medicaid reimbursements and temporary relief programs. On the other hand, rising occupancy rates and looming seismic retrofit mandates in states like California are expected to drive a wave of new projects, especially modernization and expansion work. Industry revenue is forecast to increase at a CAGR of 3.5% to total an estimated $41.1 billion through the end of 2030.

Graph and download economic data for Total Revenue for Hospitals, All Establishments (REV622ALLEST144QSA) from Q4 2004 to Q2 2025 about hospitals, revenue, establishments, and USA.

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.

Backed by AI-driven validation technology for unmatched accuracy and reliability, this contact data empowers your marketing, sales, and recruitment strategies. Designed for industry professionals, our continuously updated profiles provide the actionable insights you need to grow your business in the competitive healthcare sector.

Key Features of Success.ai’s US Healthcare Contact Data:

• Comprehensive Healthcare Sector Coverage Access detailed contact information for professionals across the healthcare spectrum:

Hospital Executives: CEOs, CFOs, and COOs managing top-tier facilities. Healthcare Administrators: Decision-makers driving operational excellence. Medical Professionals: Physicians, specialists, and nurse practitioners. Clinic Managers: Leaders in small and mid-sized healthcare organizations.

• AI-Validated Accuracy and Updates

  99% Verified Accuracy: Our advanced AI technology ensures data reliability for optimal engagement. Real-Time Updates: Profiles are continuously refreshed to maintain relevance and accuracy. Minimized Bounce Rates: Save time and resources by reaching verified contacts.

• Customizable Delivery Options Choose how you access the data to match your business requirements:

API Integration: Connect our data directly to your CRM or sales platform. Flat File Delivery: Receive customized datasets in formats suited to your needs.

Why Choose Success.ai for Healthcare Data?

• Best Price Guarantee We ensure competitive pricing for our verified contact data, offering the most comprehensive and cost-effective solution in the market.

• Compliance-Driven and Ethical Data Our data collection adheres to strict global standards, including HIPAA, GDPR, and CCPA compliance, ensuring secure and ethical usage.

• Strategic Benefits for Your Business Success.ai’s US healthcare professional data unlocks numerous business opportunities:

Targeted Marketing: Develop tailored campaigns aimed at healthcare executives and decision-makers. Efficient Sales Outreach: Engage with key contacts to accelerate your sales process. Recruitment Optimization: Access verified profiles to identify and recruit top talent in the healthcare industry. Market Intelligence: Use detailed firmographic and demographic insights to guide strategic decisions. Partnership Development: Build valuable relationships within the healthcare ecosystem.

• Data Highlights 170M+ Verified Profiles 50M Direct Phone Numbers 700M Global Professional Profiles 70M Verified Company Profiles

Key APIs for Advanced Functionality

• Enrichment API Enhance your existing contact data with real-time updates, ensuring accuracy and relevance for your outreach initiatives.

• Lead Generation API Drive high-quality lead generation efforts by utilizing verified contact information, including work emails and direct phone numbers, for up to 860,000 API calls per day.

• Use Cases

1. Healthcare Marketing Campaigns Target verified executives and administrators to deliver personalized and impactful marketing campaigns.

2. Sales Enablement Connect with key decision-makers in healthcare organizations, ensuring higher conversion rates and shorter sales cycles.

3. Talent Acquisition Source and engage healthcare professionals and administrators with accurate, up-to-date contact information.

4. Strategic Partnerships Foster collaborations with healthcare institutions and professionals to expand your business network.

5. Industry Analysis Leverage enriched contact data to gain insights into the US healthcare market, helping you refine your strategies.

• What Sets Success.ai Apart?

Verified Accuracy: AI-driven technology ensures 99% reliability for all contact details. Comprehensive Reach: Covering healthcare professionals from large hospital systems to smaller clinics nationwide. Flexible Access: Customizable data delivery methods tailored to your business needs. Ethical Standards: Fully compliant with healthcare and data protection regulations.

Success.ai’s B2B Contact Data for US Healthcare Professionals is the ultimate solution for connecting with industry leaders, driving impactful marketing campaigns, and optimizing your recruitment strategies. Our commitment to quality, accuracy, and affordability ensures you achieve exceptional results while adhering to ethical and legal standards.

No one beats us on price. Period.

VHA hospitals Timely Care Data

Performance on Clinical Measures and Processes of Care

By US Open Data Portal, data.gov [source]

About this dataset

This dataset provides an inside look at the performance of the Veterans Health Administration (VHA) hospitals on timely and effective care measures. It contains detailed information such as hospital names, addresses, census-designated cities and locations, states, ZIP codes county names, phone numbers and associated conditions. Additionally, each entry includes a score, sample size and any notes or footnotes to give further context. This data is collected through either Quality Improvement Organizations for external peer review programs as well as direct electronic medical records. By understanding these performance scores of VHA hospitals on timely care measures we can gain valuable insights into how VA healthcare services are delivering values throughout the country!

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This dataset contains information about the performance of Veterans Health Administration hospitals on timely and effective care measures. In this dataset, you can find the hospital name, address, city, state, ZIP code, county name, phone number associated with each hospital as well as data related to the timely and effective care measure such as conditions being measured and their associated scores.

To use this dataset effectively, we recommend first focusing on identifying an area of interest for analysis. For example: what condition is most impacting wait times for patients? Once that has been identified you can narrow down which fields would best fit your needs - for example if you are studying wait times then “Score” may be more valuable to filter than Footnote. Additionally consider using aggregation functions over certain fields (like average score over time) in order to get a better understanding of overall performance by factor--for instance Location.

Ultimately this dataset provides a snapshot into how Veteran's Health Administration hospitals are performing on timely and effective care measures so any research should focus around that aspect of healthcare delivery

Research Ideas

• Analyzing and predicting hospital performance on a regional level to improve the quality of healthcare for veterans across the country.
• Using this dataset to identify trends and develop strategies for hospitals that consistently score low on timely and effective care measures, with the goal of improving patient outcomes.
• Comparison analysis between different VHA hospitals to discover patterns and best practices in providing effective care so they can be shared with other hospitals in the system

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: csv-1.csv | Column name | Description | |:-----------------------|:-------------------------------------------------------------| | Hospital Name | Name of the VHA hospital. (String) | | Address | Street address of the VHA hospital. (String) | | City | City where the VHA hospital is located. (String) | | State | State where the VHA hospital is located. (String) | | ZIP Code | ZIP code of the VHA hospital. (Integer) | | County Name | County where the VHA hospital is located. (String) | | Phone Number | Phone number of the VHA hospital. (String) | | Condition | Condition being measured. (String) | | Measure Name | Measure used to measure the condition. (String) | | Score | Score achieved by the VHA h...

Context

This dataset contains Hospital General Information from the U.S. Department of Health & Human Services. This is the BigQuery COVID-19 public dataset. This data contains a list of all hospitals that have been registered with Medicare. This list includes addresses, phone numbers, hospital types and quality of care information. The quality of care data is provided for over 4,000 Medicare-certified hospitals, including over 130 Veterans Administration (VA) medical centers, across the country. You can use this data to find hospitals and compare the quality of their care

Querying BigQuery tables

You can use the BigQuery Python client library to query tables in this dataset in Kernels. Note that methods available in Kernels are limited to querying data. Tables are at bigquery-public-data.cms_medicare.hospital_general_info.

Sample Query

How do the hospitals in Mountain View, CA compare to the average hospital in the US? With the hospital compare data you can quickly understand how hospitals in one geographic location compare to another location. In this example query we compare Google’s home in Mountain View, California, to the average hospital in the United States. You can also modify the query to learn how the hospitals in your city compare to the US national average.

“#standardSQL SELECT MTV_AVG_HOSPITAL_RATING, US_AVG_HOSPITAL_RATING FROM ( SELECT ROUND(AVG(CAST(hospital_overall_rating AS int64)),2) AS MTV_AVG_HOSPITAL_RATING FROM bigquery-public-data.cms_medicare.hospital_general_info WHERE city = 'MOUNTAIN VIEW' AND state = 'CA' AND hospital_overall_rating <> 'Not Available') MTV JOIN ( SELECT ROUND(AVG(CAST(hospital_overall_rating AS int64)),2) AS US_AVG_HOSPITAL_RATING FROM bigquery-public-data.cms_medicare.hospital_general_info WHERE hospital_overall_rating <> 'Not Available') ON 1 = 1”

What are the most common diseases treated at hospitals that do well in the category of patient readmissions? For hospitals that achieved “Above the national average” in the category of patient readmissions, it might be interesting to review the types of diagnoses that are treated at those inpatient facilities. While this query won’t provide the granular detail that went into the readmission calculation, it gives us a quick glimpse into the top disease related groups (DRG)
, or classification of inpatient stays that are found at those hospitals. By joining the general hospital information to the inpatient charge data, also provided by CMS, you could quickly identify DRGs that may warrant additional research. You can also modify the query to review the top diagnosis related groups for hospital metrics you might be interested in. “#standardSQL SELECT drg_definition, SUM(total_discharges) total_discharge_per_drg FROM bigquery-public-data.cms_medicare.hospital_general_info gi INNER JOIN bigquery-public-data.cms_medicare.inpatient_charges_2015 ic ON gi.provider_id = ic.provider_id WHERE readmission_national_comparison = 'Above the national average' GROUP BY drg_definition ORDER BY total_discharge_per_drg DESC LIMIT 10;”

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

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.

Graph and download economic data for Rate of Preventable Hospital Admissions (5-year estimate) in Cascade County, MT (DISCONTINUED) (DMPCRATE030013) from 2008 to 2015 about Cascade County, MT; Great Falls; preventable; admissions; hospitals; MT; 5-year; rate; and USA.