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.

Means, standard deviations (SD) of hospital costs (in CHF), and results of independent t-tests across different hospital types.

Overview

New York, NY – June 10, 2025 – The Switzerland Coil Transcatheter Embolization and Occlusion Devices Market size is expected to be worth around US$ 81.6 Million by 2034 from US$ 37.0 Million in 2024, growing at a CAGR of 8.1% during the forecast period from 2025 to 2034.

The Switzerland market for coil-based transcatheter embolization and occlusion (TEO) devices is experiencing measured growth, driven by advancements in interventional radiology, an aging population, and rising incidence of aneurysms and arteriovenous malformations. Hospitals across Zurich, Geneva, and Basel are increasingly adopting coil embolization procedures for the treatment of cerebral aneurysms, gastrointestinal bleeding, and vascular malformations.

The Swiss Federal Office of Public Health (FOPH) notes a steady annual increase in neurovascular interventions, particularly among adults aged 55 and above. Coil embolization is preferred due to its minimally invasive nature, precise occlusion control, and reduced postoperative complications. Both bare platinum and detachable coils are widely used, with demand increasing for bioactive and hydrogel-coated variants that improve clotting efficiency.

Local procurement policies, combined with Switzerland’s high healthcare expenditure per capita, have ensured broad availability of advanced coil systems. Leading hospitals such as University Hospital Zurich and Lausanne University Hospital have integrated these devices into routine endovascular care. In addition, continuous physician training in interventional radiology is enhancing clinical adoption.

Regulatory support from Swissmedic and cross-border collaboration with EU medical device standards continue to streamline market entry for new technologies. As chronic disease rates increase and healthcare infrastructure evolves, the Switzerland TEO devices market is expected to maintain positive growth momentum through 2030.

https://sp-ao.shortpixel.ai/client/to_auto,q_lossy,ret_img,w_1216,h_722/https://market.us/wp-content/uploads/2025/03/Switzerland-Coil-Transcatheter-Embolization-and-Occlusion-Devices-Market-Size.jpg" alt="Switzerland Coil Transcatheter Embolization and Occlusion Devices Market Size" class="wp-image-144115">

Means, standard deviations (SD) and ranges of hospital costs (in CHF).

Abstract

Collecting patients’ experiences with care provision is essential to evaluate the quality of care in general, and responsiveness of care in particular, one of the core dimensions of high-quality care. After a first study conducted in 2018, we conducted a second study to collect patient experience data. Our main study objective was to explore experiences of care of people treated for any type of cancer in eight hospitals in the French- and German-speaking regions of Switzerland, and to explore whether these experiences differed by linguistic region, hospital, and cancer type.

    The Swiss Cancer Patient Experience-2 (SCAPE) was a cross-sectional multicenter survey, conducted between September 2021 and February 2022, among cancer patients diagnosed with any type of cancer from four hospitals in the French-speaking region and from four hospitals in the German-speaking region. Data were collected with a self-administered questionnaire, including questions on experiences of care and the impact of COVID-19 on cancer care and patients as well as socio-demographic and clinical characteristics. Of the 6873 adult patients invited to complete the questionnaire, 3220 patients returned it (47% response rate) and were included in the analyses. 

    Patients rated their overall care at 8.9 on average on a 0-10 scale. Overall, experiences of care with diagnostic tests were positive, particularly the waiting time between the prescription of an examination and its completion, the usefulness of the tests performed, the trust in hospital staff and the fact that care was provided with respect and dignity. The experience is less positive with respect to information received at diagnosis, support for short- and long-term side effects of treatment and cancer, information about the impact of cancer on daily activities, difficulty finding a staff member to talk about concerns and fears, financial aspects of the disease, and loved ones’ involvement.

Geographic coverage

French- and German-speaking regions of Switzerland

Analysis unit

Individual. N=3220

Universe

Adult patients diagnosed with any type of cancer recruited from four hospitals in the French-speaking region – Lausanne University Hospital (CHUV), Hôpital Fribourgeois (HFR), Geneva University Hospitals (HUG), Hôpital du Valais (HVS) and from four hospitals in the German-speaking region - Cantonal Hospital of Grisons (KSGR), Luzern Cantonal Hospital (LUKS), University Hospital Zurich (USZ), Zug Cantonal Hospital (ZGKS).

Kind of data

Self-reported data collected from paper and online questionnaire

Sampling procedure

All patients meeting inclusion criteria.

Mode of data collection

Paper and online questionnaire, self-administered at home.

Research instrument

SCAPE-2 Questionnaire including 128 closed questions (79 questions on experiences of care; 23 questions on the impact of COVID-19; 12 questions on health status; 14 questions on socio-demographic characteristics) and 3 free-text sections.

Cleaning operations

Data from paper and online questionnaires were merged after careful verification of all coding. Data were checked for inconsistency (multiple check marks when only one allowed). We also considered hand written comments next to questions to edit the answer if necessary.

Pearson correlations (incl. sample sizes (n), means, and standard deviations (SD)) of predictor variables with hospital costs.

The U.S., followed by Switzerland, had the highest average cost per day to stay in a hospital as of 2015. At that time the hospital costs per day in the U.S. were on average 5,220 U.S. dollars. In comparison, the hospital costs per day in Spain stood at an average of 424 U.S. dollars. Even Switzerland, also a very expensive country, had significantly lower costs than the United States.

Number of U.S. hospitals

The number of U.S. hospitals has decreased in recent years with some increase in 2017. There are several types of hospitals in the U.S. with different ownerships. In general there are more hospitals with a non-profit ownership in the U.S. than there are hospitals with state/local government or for-profit ownership.

U.S. hospital costs

Health care expenditures in the U.S. are among the highest in the world. By the end of 2019, hospital care expenditures alone across the U.S. are expected to exceed 1.2 trillion U.S. dollars. Among the most expensive medical conditions treated in U.S. hospitals are septicemia, osteoarthritis and live births. There are different ways to pay for hospital costs in the United States. Among all payers of U.S. hospital costs, Medicare and private payers are paying the largest proportion of all costs.

According to a 2024 survey, respondents from Switzerland had the most trust in their health system, with ** percent of individuals trusting it to give them the best treatment. On the other hand, only **** percent of respondents in Hungary believed that they were provided with the best healthcare treatment.

ANCOVA results of the associations between hospital type and the four extracted principal component scores (C1-C4) and hospital costs.

A cross-sectional study was conducted using a self-administered online questionnaire. The instrument was derived from a questionnaire employed in a nationwide survey of general practitioners by the Swiss Academy of Medical Sciences. It was adapted for use by all hospital health care professionals. It consisted of 15 main questions with a total of 80 sub-questions, addressing experiences and practices as well as personal attitudes concerning assisted suicide. Items on demographic characteristics were also included. The questionnaire was converted to an online form using the software REDCap (Vanderbilt University) and a link was sent to targeted population in a two-step procedures: at first participating center first (University hospitals of Geneva), then at second participating center (Centre Hospitalier Universitaire Vaudois). Finally both databases were exported in STATA format and combined in a single database.

The ADONIS (Accelerating Detection of Neonatal Sepsis) cohort is a curated, multi-center dataset developed as part of a Swiss Personalized Health Network (SPHN) demonstrator project. It comprises high-resolution, semantically harmonized clinical data from neonatal intensive care units (NICUs) at three major Swiss hospitals: Lausanne University Hospital (CHUV), University Hospital Zurich (USZ), and University Children’s Hospital Zurich (KiSpi). The dataset is designed to support the development of machine learning models for early detection of neonatal sepsis and related conditions. The dataset is stored on BioMedIT, a secure Swiss IT network for the responsible processing of health-related data.

As part of the SpiGes survey, all inpatient hospital stays are recorded. The survey, which is carried out in all hospitals and clinics, includes not only administrative data and sociodemographic characteristics of patients, but also diagnoses and treatments. To collect this information, two medical classifications are used.It is the ICD-10-GM for diagnoses and the Swiss surgical classification (CHOP) for treatments. Encoding of diagnoses and treatments is subject to precise guidelines. The Department of Medical Classifications of the Federal Office of Statistics (BFS) edits, reviews and adapts these rules at best, maintains the above classifications and supports all those involved in coding. The coding manual includes all coding guidelines published until its approval. The coding manual is the basis for coding.

Objectives: To determine the proportion of patients who received a treatment for Clostridioides difficile infection (CDI) among those presenting a discordant Clostridioides difficile diagnostic assay and to identify patient characteristics associated with the decision to treat CDI.

Design: Cross-sectional study.

Setting: Monocentric study in a tertiary care hospital, Geneva, Switzerland

Participants: Among 4562 adult patients tested for C. difficile between March 2017 and March 2019, 208 patients with discordant tests’ results (positive nucleic acid amplification test [NAAT+]/negative enzyme immunoassay [EIA-]) were included.

Main outcome measures: Treatment for CDI.

Results: CDI treatment was administered in 147 (71%) cases. In multivariate analysis, an abdominal computed tomography scan with signs of colitis (OR 14.7; 95% CI 1.96-110.8) was the only factor associated with CDI treatment.

Conclusions: The proportion of NAAT+/EIA- patients who received treatment questio...

Description of predictor variables.

Analysis of ‘Heart Disease Cleveland UCI’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/cherngs/heart-disease-cleveland-uci on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context

The data is already presented in https://www.kaggle.com/ronitf/heart-disease-uci but there are some descriptions and values that are wrong as discussed in https://www.kaggle.com/ronitf/heart-disease-uci/discussion/105877. So, here is re-processed dataset that was cross-checked with the original data https://archive.ics.uci.edu/ml/datasets/Heart+Disease.

Content

There are 13 attributes 1. age: age in years 2. sex: sex (1 = male; 0 = female) 3. cp: chest pain type -- Value 0: typical angina -- Value 1: atypical angina -- Value 2: non-anginal pain -- Value 3: asymptomatic 4. trestbps: resting blood pressure (in mm Hg on admission to the hospital) 5. chol: serum cholestoral in mg/dl 6. fbs: (fasting blood sugar > 120 mg/dl) (1 = true; 0 = false) 7. restecg: resting electrocardiographic results -- Value 0: normal -- Value 1: having ST-T wave abnormality (T wave inversions and/or ST elevation or depression of > 0.05 mV) -- Value 2: showing probable or definite left ventricular hypertrophy by Estes' criteria 8. thalach: maximum heart rate achieved 9. exang: exercise induced angina (1 = yes; 0 = no) 10. oldpeak = ST depression induced by exercise relative to rest 11. slope: the slope of the peak exercise ST segment -- Value 0: upsloping -- Value 1: flat -- Value 2: downsloping 12. ca: number of major vessels (0-3) colored by flourosopy 13. thal: 0 = normal; 1 = fixed defect; 2 = reversable defect and the label 14. condition: 0 = no disease, 1 = disease

Acknowledgements

Data posted on Kaggle: https://www.kaggle.com/ronitf/heart-disease-uci Description of the data above: https://www.kaggle.com/ronitf/heart-disease-uci/discussion/105877 Original data https://archive.ics.uci.edu/ml/datasets/Heart+Disease

Creators: Hungarian Institute of Cardiology. Budapest: Andras Janosi, M.D. University Hospital, Zurich, Switzerland: William Steinbr Creators: Hungarian Institute of Cardiology. Budapest: Andras Janosi, M.D. University Hospital, Zurich, Switzerland: William Steinbrunn, M.D. University Hospital, Basel, Switzerland: Matthias Pfisterer, M.D. V.A. Medical Center, Long Beach and Cleveland Clinic Foundation: Robert Detrano, M.D., Ph.D. Donor: David W. Aha (aha '@' ics.uci.edu) (714) 856-8779

Inspiration

With the attributes described above, can you predict if a patient has heart disease?

--- Original source retains full ownership of the source dataset ---

Excel file: contains the notebook (variable description) and the data used to compute results for the publication https://doi.org/10.4236/psych.2024.152015

Data: The data concern smoking prevalence for staff and inpatients of a public psychiatric hospital (HUG - University Hospitals of Geneva - Department of Psychiatry - Switzerland) in 2001 (110 staff and 91 patients); 2005 (104 staff and 183 patients); 2009 (155 staff and 175 patients); 2020 (106 staff and 179 patients).

Abstract of the paper:
Smoking, as a major risk factor for non-communicable diseases (NCD), led the World Health Organization (WHO) to recommend measures to decrease tobacco consumption. Declines were observed for the general population in western countries. The present work is a naturalistic observational study which assessed tobacco use on 4 independent occasions for patients and staff in a Swiss public psychiatric hospital between 2001 and 2020. High smoking prevalence was observed, varying between 31% and 39% for staff and 66% and 74% for patients. Despite the implementation of a partial and later a total indoor smoking ban, data showed no decline of cigarette consumption between 2005 and 2020 among patients. These observations are in line with literature showing high smoking rates and no trend of a decline for people presenting with mental health disorders. This study controlled for substance use disorder (SUD), known to be related to higher nicotine dependence, and showed that smoking was not associated with psychiatric diagnosis (mood or psychotic disorders). These elements lead to recommend a global approach using smoking cessation strategies designed for all patients receiving mental health care. Although the alarming state of the tobacco epidemic for these persons is known and evidence-based strategies for smoking cessation exist, implementation of interventions to reduce smoking within mental health settings remains sorely lacking. This paper summarizes smoking cessation interventions that should be used in psychiatry and puts forward the necessity to develop strategies for the large group of not (yet) motivated to quit smokers. Tobacco consumption is a modifiable behavior and changes in mental healthcare routines should allow important health related benefits for smokers presenting with psychic disorders.

The Epinettes database is the collection of clinical and neuropsychological data, acquired during routine care of patients consulting at the Memory Centre of the University Hospital of Geneva in Switzerland. The database comprises different diagnosis groups, hence allows observational study. It is a transversal data set, as it aims to compare the groups, as well as a longitudinal, as it aims to analyse the progression of each type of dementia.

In 2023, the United States had the highest per capita health expenditure among OECD countries. At that time, per capita health expenditure in the U.S. amounted over ****** U.S. dollars, significantly higher than in Switzerland, the country with the second-highest per capita health expenditure. Norway, Germany and Austria are also within the top five countries with the highest per capita health expenditure. The United States also spent the highest share of it’s gross domestic product on health care, with **** percent of its GDP spent on health care services. Health Expenditure in the U.S. The United States is the highest spending country worldwide when it comes to health care. In 2022, total health expenditure in the U.S. exceeded **** trillion dollars. Expenditure as a percentage of GDP is projected to increase to approximately ** percent by the year 2031. Distribution of Health Expenditure in the U.S. Health expenditure in the United States is spread out across multiple categories such as nursing home facilities, home health care, and prescription drugs. As of 2022, the majority of health expenditure in the United States was spent on hospital care, accounting for a bit less than *** third of all health spending. Hospital care was followed by spending on physician and clinical services which accounted for ** percent of overall health expenditure.

Introduction: Readmissions after an acute care hospitalization are relatively common, costly to the health care system, and are associated with significant burden for patients. As one way to reduce costs and simultaneously improve quality of care, hospital readmissions receive increasing interest from policy makers. It is only relatively recently that strategies were developed with the specific aim of reducing unplanned readmissions using prediction models to identify patients at risk. EPIC's Risk of Unplanned Readmission model promises superior performance. However, it has only been validated for the US setting. Therefore, the main objective of this study is to externally validate the EPIC's Risk of Unplanned Readmission model and to compare it to the internationally, widely used LACE+ index, and the SQLAPE® tool, a Swiss national quality of care indicator.

Methods: A monocentric, retrospective, diagnostic cohort study was conducted. The study included inpatients, who were discharged between the 1^st of January 2018 and the 31^st of December 2019 from the Lucerne Cantonal Hospital, a tertiary-care provider in Central Switzerland. The study endpoint was an unplanned 30-day readmission. Models were replicated using the original intercept and beta coefficients as reported. Otherwise, score generator provided by the developers were used. For external validation, discrimination of the scores under investigation were assessed by calculating the area under the receiver operating characteristics curves (AUC). Calibration was assessed with the Hosmer-Lemeshow X² goodness-of-fit test This report adheres to the TRIPOD statement for reporting of prediction models.

Results: At least 23,116 records were included. For discrimination, the EPIC´s prediction model, the LACE+ index and the SQLape® had AUCs of 0.692 (95% CI 0.676-0.708), 0.703 (95% CI 0.687-0.719) and 0.705 (95% CI 0.690-0.720). The Hosmer-Lemeshow X² tests had values of p<0.001.

Conclusion: In summary, the EPIC´s model showed less favorable performance than its comparators. It may be assumed with caution that the EPIC´s model complexity has hampered its wide generalizability - model updating is warranted.

Analysis of ‘Heart Disease UCI’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/ronitf/heart-disease-uci on 12 November 2021.

--- Dataset description provided by original source is as follows ---

Context

This database contains 76 attributes, but all published experiments refer to using a subset of 14 of them. In particular, the Cleveland database is the only one that has been used by ML researchers to this date. The "goal" field refers to the presence of heart disease in the patient. It is integer valued from 0 (no presence) to 4.

Content

Attribute Information:

1. age
   
2. sex
   
3. chest pain type (4 values)
   
4. resting blood pressure
   
5. serum cholestoral in mg/dl
   
6. fasting blood sugar > 120 mg/dl
   
7. resting electrocardiographic results (values 0,1,2)
   
8. maximum heart rate achieved
   
9. exercise induced angina
   
10. oldpeak = ST depression induced by exercise relative to rest
    
11. the slope of the peak exercise ST segment
    
12. number of major vessels (0-3) colored by flourosopy
    
13. thal: 3 = normal; 6 = fixed defect; 7 = reversable defect
    

The names and social security numbers of the patients were recently removed from the database, replaced with dummy values. One file has been "processed", that one containing the Cleveland database. All four unprocessed files also exist in this directory.

To see Test Costs (donated by Peter Turney), please see the folder "Costs"

Acknowledgements

Creators:
1. Hungarian Institute of Cardiology. Budapest: Andras Janosi, M.D.
2. University Hospital, Zurich, Switzerland: William Steinbrunn, M.D.
3. University Hospital, Basel, Switzerland: Matthias Pfisterer, M.D.
4. V.A. Medical Center, Long Beach and Cleveland Clinic Foundation: Robert Detrano, M.D., Ph.D.

Donor: David W. Aha (aha '@' ics.uci.edu) (714) 856-8779

Inspiration

Experiments with the Cleveland database have concentrated on simply attempting to distinguish presence (values 1,2,3,4) from absence (value 0).

See if you can find any other trends in heart data to predict certain cardiovascular events or find any clear indications of heart health.

--- Original source retains full ownership of the source dataset ---