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 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.

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.

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 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.

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.

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

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.

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.

Data on the top universities for Medical and Health in 2025, including disciplines such as Medicine and Dentistry, and Other Health Subjects.

ContextResearch-oriented cancer hospitals in the United States treat and study patients with a range of diseases. Measures of disease specific research productivity, and comparison to overall productivity, are currently lacking.HypothesisDifferent institutions are specialized in research of particular diseases.ObjectiveTo report disease specific productivity of American cancer hospitals, and propose a summary measure.MethodWe conducted a retrospective observational survey of the 50 highest ranked cancer hospitals in the 2013 US News and World Report rankings. We performed an automated search of PubMed and Clinicaltrials.gov for published reports and registrations of clinical trials (respectively) addressing specific cancers between 2008 and 2013. We calculated the summed impact factor for the publications. We generated a summary measure of productivity based on the number of Phase II clinical trials registered and the impact factor of Phase II clinical trials published for each institution and disease pair. We generated rankings based on this summary measure.ResultsWe identified 6076 registered trials and 6516 published trials with a combined impact factor of 44280.4, involving 32 different diseases over the 50 institutions. Using a summary measure based on registered and published clinical trails, we ranked institutions in specific diseases. As expected, different institutions were highly ranked in disease-specific productivity for different diseases. 43 institutions appeared in the top 10 ranks for at least 1 disease (vs 10 in the overall list), while 6 different institutions were ranked number 1 in at least 1 disease (vs 1 in the overall list).ConclusionResearch productivity varies considerably among the sample. Overall cancer productivity conceals great variation between diseases. Disease specific rankings identify sites of high academic productivity, which may be of interest to physicians, patients and researchers.

Reducing unplanned readmissions is a major focus of current hospital quality efforts. In order to avoid unfair penalization, administrators and policymakers use prediction models to adjust for the performance of hospitals from healthcare claims data. Regression-based models are a commonly utilized method for such risk-standardization across hospitals; however, these models often suffer in accuracy. In this study we, compare four prediction models for unplanned patient readmission for patients hospitalized with acute myocardial infarction (AMI), congestive health failure (HF), and pneumonia (PNA) within the Nationwide Readmissions Database in 2014. We evaluated hierarchical logistic regression and compared its performance with gradient boosting and two models that utilize artificial neural networks. We show that unsupervised Global Vector for Word Representations embedding representations of administrative claims data combined with artificial neural network classification models improves prediction of 30-day readmission. Our best models increased the AUC for prediction of 30-day readmissions from 0.68 to 0.72 for AMI, 0.60 to 0.64 for HF, and 0.63 to 0.68 for PNA compared to hierarchical logistic regression. Furthermore, risk-standardized hospital readmission rates calculated from our artificial neural network model that employed embeddings led to reclassification of approximately 10% of hospitals across categories of hospital performance. This finding suggests that prediction models that incorporate new methods classify hospitals differently than traditional regression-based approaches and that their role in assessing hospital performance warrants further investigation.

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

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

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

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).

As of 2022, the largest hospital in the world was found in Henan, China. With seven thousand beds, First Affiliated Hospital of Zhengzhou University was ranked as the largest hospital globally according to its number of beds. The second largest hospital was also found in China. No U.S. hospital made the world top 20 list, although AdventHealth Orlando was close, being the largest hospital in the United States.

Knowledge about cervical cancer screening and Human papilloma virus (HPV) influence on their awareness to the cervical cancer screening program. Most previous studies found inadequate knowledge and attitude among healthy women affect the low rate of screening. This study aimed to assess knowledge of cervical cancer screening and HPV in women who had abnormal cervical cancer screening in Bangkok. Thai women, aged ≥ 18 years old, who had abnormal cervical cancer screening and scheduled to colposcopy clinics of 10 participating hospitals were invited to participate in this cross-sectional study. The participants were asked to complete a self-answer questionnaire (Thai language). The questionnaire composed of 3 parts: (I) demographic data, (II) knowledge about cervical cancer screening and (III) knowledge about HPV. Among 499 women who answered the questionnaires, 2 had missing demographic data. The mean age of the participants was 39.28 ± 11.36 years. 70% of them had experience of cervical cancer screening, with 22.7% had previous abnormal cytologic results. Out of 14 questions, the mean score of knowledge about cervical cancer screening was 10.04 ± 2.37. Only 26.9% had good knowledge about cervical cancer screening. Nearly 96% of woman did not know that screening should be done. After excluding 110 women who had never known about HPV, 25.2% had good knowledge about HPV. From multivariable analysis, only younger age (≤ 40 years) was associated with good knowledge of cervical cancer screening and HPV. In the conclusion, only 26.9% of women in this study had good knowledge regarding cervical cancer screening. Likewise, 20.1% of women who had ever heard about HPV has good knowledge about HPV. Providing information about cervical cancer screening and HPV should improve the women’s knowledge and better adherence to the screening procedure.

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