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 by Statista and Newsweek, the world's best hospital is the *********** in Rochester, Minnesota. A total of **** U.S. hospitals made it to the top ten list, while one hospital in each of the following countries was also ranked among the top ten best hospitals in the world: Canada, Sweden, Germany, Israel, Singapore, and Switzerland.

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 by Statista and Newsweek, the best hospital in Denmark is the Rigshospitalet - København in Copenhagen. Moreover, the Rigshospitalet - København was also ranked as the **** best hospital in the world, among over ****** hospitals in ** countries. Aarhus Universitetshospital in Aarhus and Odense Universitetshospital in Odense were ranked as second and third best respectively in the Denmark, while they were **** and **** 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 **** best hospital in the world, among over ****** hospitals in ** 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 ***** and ***** 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 **** best hospital in the world, among over ****** hospitals in ** 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 ***** and ***** 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 Sweden is the Karolinska Universitetssjukhuset in Stockholm. Moreover, Karolinska Universitetssjukhuset was also ranked as the seventh-best hospital in the world, among over ****** hospitals in ** countries. Sahlgrenska Universitetssjukhuset in Göteborg and Akademiska Sjukhuset in Uppsala were ranked as second and third best respectively in the Sweden, while they were **** and **** best respectively in the World.

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.

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.

IntroductionInjury accounts for more than 5.8 million deaths globally with an increasing burden in the developing world. In Kenya, trauma is one of the top 10 leading causes of death. However, no formal continuous injury surveillance systems are in place to inform injury prevention, pre-hospital care or emergency department management. The aim of this study was to implement a hospital-based trauma registry to characterize high acuity injuries presenting to a private tertiary, teaching and referral hospital in Kenya.MethodsFrom January to December 2015, data was prospectively collected at a private tertiary, teaching and referral hospital in Nairobi, Kenya. Patients presenting with a traumatic injury for the first time who were admitted to the hospital for at least 48 hours were included in the study. Basic information pertaining to demographics, details of the injury, pre-hospital care and transport, hospital-based management, length of stay and disposition were collected. An injury severity score (ISS) was calculated on each patient and stratified by the mechanism of injury. Descriptive statistics and multivariate logistic regression were used to analyze data and assess risk factors associated with injury severity.ResultsThere were 101 patients included in the study, the majority of whom were 30 to 39 years of age and male (63%). Seventy-one per cent of patients had a preexisting medical condition with hypertension (26%) and diabetes (13%) being the most common. The most common mechanism of injury was fall (46%) followed by road traffic incidents (RTI) (32%). Most injuries took place at home (43%). Most RTI were caused by cars (63%), with the driver being the most frequently injured (38%). The most common mode of arrival to the emergency department was by private car (72%). The median time between the accident and arrival at the emergency department was 1hr 10 minutes. The majority of the patients had injuries to one area (83%) with the extremities/bony pelvis (72%) being the most common. The median Injury Severity Score was 5 (range 1–34) with the majority (90%) classified as minor injuries (ISS

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

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

Current scenario of the Medical disposables market

Key drivers of the Medical Disposables market

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

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

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

Rising Diabetes Rates Will Drive Up market demand

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

Constraints for the global Medical Disposables market

The increase in waste production limits market expansion.

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

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.

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.

Introduction

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

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

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

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

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

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.

Multiple counts were allowed.

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

Hospital admissions for serious accidental injury, with a length of stay exceeding 3 days (ICD-10 primary diagnosis in the range S00 through T98X and external cause code in the following ranges: V01-V99, W00-X59, Y40-Y84), standardised for the age and sex characteristics of the population and expressed as a rate per 100,000 population. The primary diagnosis field in Hospital Episode Statistics (HES) records information about the patient’s disease or condition and the codes are defined in the International Statistical Classification of Diseases, Injuries and Causes of Death (ICD-10). Where applicable, the external cause field in HES records the environmental events and circumstances as the cause of injury, poisoning and other adverse effects. Comparison of crude episode rates between areas which may have different age structures would be inappropriate, because the age structure of the population can affect the number of episodes and thereby the crude episode rate. To overcome this problem, the common approach is to adjust or standardise the episode rates to take account of differences between the age structure of the populations. The directly age standardised episode rate is the rate of episodes that would occur in a standard population (in this case the European Standard Population) if that population were to experience the age-specific rates of the subject population (in this case individual local authority populations). The same standard population is used for males, females and persons. This means that rates can be compared across genders but also that rates for persons are standardised for age only and not for sex. This indicator relates to the Our Healthier Nation strategy target to reduce serious accidental injury. The target is monitored by the directly age-standardised episode rate for accidents for persons of all ages. The target is a 10% reduction by the year 2010 from the baseline rate in 1995/96. The strategy particularly identified that accidents are the greatest single threat to life for children and young people, and children up to the age of 15 years from unskilled families are five times more likely to die from accidental injury than those from professional families and falls are a major cause of death and disability for older people (3,000 people aged 65 and over die each year). Accidental injury is a leading cause of death and disability – the World Health Organization suggests that by 2020 injury will account for the largest single reason for loss of healthy human life-years. In the UK non-fatal injury results in 720,000 people being admitted to hospital a year and more than six million visits to accident and emergency departments. It is estimated that in the UK disability from injury is responsible for a considerably greater burden of potential healthy life-years lost than from cancer, or heart disease and stroke. This indicator has been discontinued and so there will be no further updates. Legacy unique identifier: P01059

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).