According to a ranking by Statista and Newsweek, the best hospital in the United States is the Mayo Clinic in Rochester, Minnesota. Moreover, the Mayo Clinic was also ranked as the best hospital in the world, among over 50,000 hospitals in 30 countries. Cleveland Clinic in Ohio and the Johns Hopkins 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 Texas MD Anderson Cancer Center, which had a score of 100 out of 100, as of 2024. 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 by Statista and Newsweek, the best hospital in Denmark is the Rigshospitalet - København in Copenhagen. Moreover, the Rigshospitalet - København was also ranked as the 21st best hospital in the world, among over 50,000 hospitals in 30 countries. Aarhus Universitetshospital in Aarhus and Odense Universitetshospital in Odense were ranked as second and third best respectively in the Denmark, while they were 23rd and 85th best respectively in the World.

According to a ranking by Statista and Newsweek, the best hospital in Sweden is the Karolinska Universitetssjukhuset in Stockholm. Moreover, Karolinska Universitetssjukhuset was also ranked as the seventh-best hospital in the world, among over 50,000 hospitals in 30 countries. Sahlgrenska Universitetssjukhuset in Göteborg and Akademiska Sjukhuset in Uppsala were ranked as second and third best respectively in the Sweden, while they were 74th and 75th best respectively in the World.

According to a ranking by Statista and Newsweek, the best hospital in Norway is Oslo Universitetssykehus in Oslo. Moreover, Oslo Universitetssykehus was also ranked as the 29th best hospital in the world, among over 50,000 hospitals in 30 countries. St. Olavs Hospital in Trondheim and Haukeland Universitetssykehus in Bergen were ranked as second and third best respectively in the Norway, while they were 138th and 151st best respectively in the World.

The average for 2020 based on 36 countries was 4.44 hospital beds. The highest value was in South Korea: 12.65 hospital beds and the lowest value was in Mexico: 0.99 hospital beds. The indicator is available from 1960 to 2021. Below is a chart for all countries where data are available.

According to a ranking by Statista and Newsweek, the best hospital in Finland is Helsinki University Hospital in Helsinki. Moreover, Helsinki University Hospital was also ranked as the 50th best hospital in the world, among over 50,000 hospitals in 30 countries. Tampere University Hospital in Tampere and Turku University Hospital in Turku were ranked as second and third best respectively in the Finland, while they were 126th and 121st best respectively in the World.

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South Korea Number of Hospital was up 3.5% in 2019, compared to the previous year.

*Standardized units.Characteristics of the top 50 Cancer Hospitals, as ranked by the US News and World Report.

According to a ranking of the best hospitals in the U.S., the best hospital for adult cardiology, heart, and vascular surgery is the Cleveland Clinic in Ohio, which had a score of 100 out of 100, as of 2024. 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.

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.

According to a hospital ranking carried out in 2022 and based on seven different dimensions, Hospital Israelita Albert Einstein was considered the hospital with the highest care quality in Latin America. Located in São Paulo - Brazil, this health institution reached a quality index score of 93.46. Hospital Sírio-Libanês also located in Brazil, ranked second, with a score of 71.75. Latin American hospitals and their capacity to host patients When it comes to hosting patients, hospitals Irmandade da Santa Casa de Misericórdia de Porto Alegre located in Brazil, and Sanatorio Guemes based in Argentina, ranked among the leading hospitals in Latin America as of 2022. It was estimated that Brazil and Argentina were the two Latin American countries with the highest number of hospital beds in the region in 2020, with more than 448,000 and 234,000 hospital beds, respectively. Public opinion on healthcare quality It was also Argentina that had the highest share of satisfied patients among a selection of countries in Latin America according to a 2023 survey, with 50 percent of interviewees stating they had accessed a good or very good healthcare service. Colombian patients followed, with four out of ten people satisfied with the healthcare received. Accordingly, a recent study estimated that nearly half of the population in Argentina and Colombia distrusted the healthcare system, with approximately 47 percent and 50 percent of respondents claiming they trust the health systems in their respective countries.

In 2024, Bumrungrad International Hospital ranked first among the leading hospitals in Thailand with a score of 93 percent. In the same year, it was the only hospital from Thailand that placed 130th among the 250 world's best hospitals on the Global Hospital Rating by Newsweek and Statista. Thailand is one of the most popular medical tourism hubs in Southeast Asia.

The global Top 10 Medical Device Technologies Market size is projected to grow USD 708.79 billion by 2030, at a CAGR of 6.71% by 2030

In 2023, there were nearly 11 thousand hospitals in Columbia, the highest number among OECD countries, followed by 8,156 hospitals in Japan. If only general hospitals were counted (excluding mental health hospitals and other specialized hospitals), Japan had the most number of general hospitals among OECD countries worldwide. Most countries reported hospitals numbers similar to or lower than the previous year. Meanwhile, Mexico, South Korea and the Netherlands all reported more hospitals than last year.

The SCImago Institutions Rankings (SIR) is a classification of academic and research-related institutions ranked by a composite indicator that combines three different sets of indicators based on research performance, innovation outputs and societal impact measured by their web visibility. It provides a friendly interface that allows the visualization of any customized ranking from the combination of these three sets of indicators. Additionally, it is possible to compare the trends for individual indicators of up to six institutions. For each large sector it is also possible to obtain distribution charts of the different indicators. For comparative purposes, the value of the composite indicator has been set on a scale of 0 to 100. However the line graphs and bar graphs always represent ranks (lower is better, so the highest values are the worst).

Postpartum hemorrhage (PPH) is the leading cause of maternal mortality in Kenya. The aim of this study was to measure quality and timeliness of care for PPH in a sample of deliveries in referral hospitals in Kenya. We conducted direct observations of 907 vaginal deliveries in three Kenyan hospitals from October 2018 through February 2019, observing the care women received from admission for labor and delivery through hospital discharge. We identified cases of “suspected PPH”, defined as cases in which providers indicated suspicion of and/or took an action to manage abnormal bleeding. We measured adherence to World Health Organization and Kenyan guidelines for PPH risk assessment, prevention, identification, and management and the timeliness of care in each domain. The rate of suspected PPH among the observed vaginal deliveries was 9% (95% Confidence Interval: 7% - 11%). Health care providers followed all guidelines for PPH risk assessment in 7% (5% - 10%) of observed deliveries and all guidelines for PPH prevention in 4% (3% - 6%) of observed deliveries. Lowest adherence was observed for taking vital signs and for timely administration of a prophylactic uterotonic. Providers did not follow guidelines for postpartum monitoring in any of the observed deliveries. When suspected PPH occurred, providers performed all recommended actions in 23% (6% - 40%) of cases. Many of the critical actions for suspected PPH were performed in a timely manner, but, in some cases, substantial delays were observed. In conclusion, we found significant gaps in the quality of risk assessment, prevention, identification, and management of PPH after vaginal deliveries in referral hospitals in Kenya. Efforts to reduce maternal morbidity and mortality from PPH should emphasize improvements in the quality of care, with a particular focus on postpartum monitoring and timely emergency response.

Updates

• Notice of data discontinuation: Since the start of the pandemic, AP has reported case and death counts from data provided by Johns Hopkins University. Johns Hopkins University has announced that they will stop their daily data collection efforts after March 10. As Johns Hopkins stops providing data, the AP will also stop collecting daily numbers for COVID cases and deaths. The HHS and CDC now collect and visualize key metrics for the pandemic. AP advises using those resources when reporting on the pandemic going forward.

  • CDC Weekly case and death counts (national and state level)
  • CDC County level cases and deaths
  • HHS New hospital admissions
  • CDC NowCast COVID variant proportions (national and regional level)

• April 9, 2020

  • The population estimate data for New York County, NY has been updated to include all five New York City counties (Kings County, Queens County, Bronx County, Richmond County and New York County). This has been done to match the Johns Hopkins COVID-19 data, which aggregates counts for the five New York City counties to New York County.

• April 20, 2020

  • Johns Hopkins death totals in the US now include confirmed and probable deaths in accordance with CDC guidelines as of April 14. One significant result of this change was an increase of more than 3,700 deaths in the New York City count. This change will likely result in increases for death counts elsewhere as well. The AP does not alter the Johns Hopkins source data, so probable deaths are included in this dataset as well.

• April 29, 2020

  • The AP is now providing timeseries data for counts of COVID-19 cases and deaths. The raw counts are provided here unaltered, along with a population column with Census ACS-5 estimates and calculated daily case and death rates per 100,000 people. Please read the updated caveats section for more information.

• September 1st, 2020

  • Johns Hopkins is now providing counts for the five New York City counties individually.

• February 12, 2021

  • The Ohio Department of Health recently announced that as many as 4,000 COVID-19 deaths may have been underreported through the state’s reporting system, and that the "daily reported death counts will be high for a two to three-day period."
  • Because deaths data will be anomalous for consecutive days, we have chosen to freeze Ohio's rolling average for daily deaths at the last valid measure until Johns Hopkins is able to back-distribute the data. The raw daily death counts, as reported by Johns Hopkins and including the backlogged death data, will still be present in the new_deaths column.

• February 16, 2021

  - Johns Hopkins has reconciled Ohio's historical deaths data with the state.

  Overview

The AP is using data collected by the Johns Hopkins University Center for Systems Science and Engineering as our source for outbreak caseloads and death counts for the United States and globally.

The Hopkins data is available at the county level in the United States. The AP has paired this data with population figures and county rural/urban designations, and has calculated caseload and death rates per 100,000 people. Be aware that caseloads may reflect the availability of tests -- and the ability to turn around test results quickly -- rather than actual disease spread or true infection rates.

This data is from the Hopkins dashboard that is updated regularly throughout the day. Like all organizations dealing with data, Hopkins is constantly refining and cleaning up their feed, so there may be brief moments where data does not appear correctly. At this link, you’ll find the Hopkins daily data reports, and a clean version of their feed.

The AP is updating this dataset hourly at 45 minutes past the hour.

To learn more about AP's data journalism capabilities for publishers, corporations and financial institutions, go here or email kromano@ap.org.

Queries

Use AP's queries to filter the data or to join to other datasets we've made available to help cover the coronavirus pandemic

• Filter cases by state here

• Rank states by their status as current hotspots. Calculates the 7-day rolling average of new cases per capita in each state: https://data.world/associatedpress/johns-hopkins-coronavirus-case-tracker/workspace/query?queryid=481e82a4-1b2f-41c2-9ea1-d91aa4b3b1ac

• Find recent hotspots within your state by running a query to calculate the 7-day rolling average of new cases by capita in each county: https://data.world/associatedpress/johns-hopkins-coronavirus-case-tracker/workspace/query?queryid=b566f1db-3231-40fe-8099-311909b7b687&showTemplatePreview=true

• Join county-level case data to an earlier dataset released by AP on local hospital capacity here. To find out more about the hospital capacity dataset, see the full details.

• Pull the 100 counties with the highest per-capita confirmed cases here

• Rank all the counties by the highest per-capita rate of new cases in the past 7 days here. Be aware that because this ranks per-capita caseloads, very small counties may rise to the very top, so take into account raw caseload figures as well.

Interactive

The AP has designed an interactive map to track COVID-19 cases reported by Johns Hopkins.

@(https://datawrapper.dwcdn.net/nRyaf/15/)

Interactive Embed Code

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Caveats

• This data represents the number of cases and deaths reported by each state and has been collected by Johns Hopkins from a number of sources cited on their website.
• In some cases, deaths or cases of people who've crossed state lines -- either to receive treatment or because they became sick and couldn't return home while traveling -- are reported in a state they aren't currently in, because of state reporting rules.
• In some states, there are a number of cases not assigned to a specific county -- for those cases, the county name is "unassigned to a single county"
• This data should be credited to Johns Hopkins University's COVID-19 tracking project. The AP is simply making it available here for ease of use for reporters and members.
• Caseloads may reflect the availability of tests -- and the ability to turn around test results quickly -- rather than actual disease spread or true infection rates.
• Population estimates at the county level are drawn from 2014-18 5-year estimates from the American Community Survey.
• The Urban/Rural classification scheme is from the Center for Disease Control and Preventions's National Center for Health Statistics. It puts each county into one of six categories -- from Large Central Metro to Non-Core -- according to population and other characteristics. More details about the classifications can be found here.

Johns Hopkins timeseries data - Johns Hopkins pulls data regularly to update their dashboard. Once a day, around 8pm EDT, Johns Hopkins adds the counts for all areas they cover to the timeseries file. These counts are snapshots of the latest cumulative counts provided by the source on that day. This can lead to inconsistencies if a source updates their historical data for accuracy, either increasing or decreasing the latest cumulative count. - Johns Hopkins periodically edits their historical timeseries data for accuracy. They provide a file documenting all errors in their timeseries files that they have identified and fixed here

Attribution

This data should be credited to Johns Hopkins University COVID-19 tracking project