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.

Success.ai’s Healthcare Professionals Data for Healthcare & Hospital Executives in Europe provides a reliable and comprehensive dataset tailored for businesses aiming to connect with decision-makers in the European healthcare and hospital sectors. Covering healthcare executives, hospital administrators, and medical directors, this dataset offers verified contact details, professional insights, and leadership profiles.

With access to over 700 million verified global profiles and data from 70 million businesses, Success.ai ensures your outreach, market research, and partnership strategies are powered by accurate, continuously updated, and GDPR-compliant data. Backed by our Best Price Guarantee, this solution is indispensable for navigating and thriving in Europe’s healthcare industry.

Why Choose Success.ai’s Healthcare Professionals Data?

1. Verified Contact Data for Targeted Engagement

   • Access verified work emails, phone numbers, and LinkedIn profiles of healthcare executives, hospital administrators, and medical directors.
   • AI-driven validation ensures 99% accuracy, reducing data gaps and improving communication effectiveness.

2. Comprehensive Coverage of European Healthcare Professionals

   • Includes profiles of professionals from top hospitals, healthcare organizations, and medical institutions across Europe.
   • Gain insights into regional healthcare trends, operational challenges, and emerging technologies.

3. Continuously Updated Datasets

   • Real-time updates capture changes in leadership roles, organizational structures, and market dynamics.
   • Stay aligned with the fast-evolving healthcare landscape to identify emerging opportunities.

4. Ethical and Compliant

   • Fully adheres to GDPR, CCPA, and other global data privacy regulations, ensuring responsible and lawful data usage.

Data Highlights:

• 700M+ Verified Global Profiles: Connect with healthcare professionals and decision-makers in Europe’s hospital and healthcare sectors.
• 70M+ Business Profiles: Access detailed firmographic data, including hospital sizes, revenue ranges, and geographic footprints.
• Leadership Insights: Engage with CEOs, medical directors, and administrative leaders shaping healthcare strategies.
• Regional Healthcare Trends: Understand trends in digital healthcare adoption, operational efficiency, and patient care management.

Key Features of the Dataset:

1. Comprehensive Professional Profiles

   • Identify and connect with key players, including hospital executives, medical directors, and department heads in the healthcare industry.
   • Access data on professional histories, certifications, and areas of expertise for precise targeting.

2. Advanced Filters for Precision Campaigns

   • Filter professionals by hospital size, geographic location, or job function (administrative, medical, or operational).
   • Tailor campaigns to align with specific needs such as digital transformation, patient care solutions, or regulatory compliance.

3. Healthcare Industry Insights

   • Leverage data on operational trends, hospital management practices, and regional healthcare needs.
   • Refine product offerings and outreach strategies to address pressing challenges in the European healthcare market.

4. AI-Driven Enrichment

   • Profiles enriched with actionable data allow for personalized messaging, highlight unique value propositions, and improve engagement outcomes with healthcare professionals.

Strategic Use Cases:

1. Marketing and Outreach to Healthcare Executives

   • Promote healthcare IT solutions, medical devices, or operational efficiency tools to executives managing hospitals and clinics.
   • Use verified contact data for multi-channel outreach, including email, phone, and digital marketing.

2. Partnership Development and Collaboration

   • Build relationships with hospitals, healthcare providers, and medical institutions exploring strategic partnerships or new technology adoption.
   • Foster alliances that drive patient care improvements, cost savings, or operational efficiency.

3. Market Research and Competitive Analysis

   • Analyze trends in European healthcare to refine product development, marketing strategies, and engagement plans.
   • Benchmark against competitors to identify growth opportunities, underserved segments, and innovative solutions.

4. Recruitment and Workforce Solutions

   • Target HR professionals and hiring managers in healthcare institutions recruiting for administrative, medical, or operational roles.
   • Provide workforce optimization platforms, training solutions, or staffing services tailored to the healthcare sector.

Why Choose Success.ai?

1. Best Price Guarantee

   • Access premium-quality healthcare professional data at competitive prices, ensuring strong ROI for your marketing, sales, and strategic initiatives.

2. Seamless Integration
   ...

VHA hospitals Timely Care Data

Performance on Clinical Measures and Processes of Care

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

About this dataset

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

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For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

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

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

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

Research Ideas

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

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

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

US Healthcare Readmissions and Mortality

Evaluating Hospital Performance

By Health [source]

About this dataset

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

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Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

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

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

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

Research Ideas

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

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

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

Children's Hospitals Pricing Information

Children's Hospitals Pricing Information

By Amber Thomas [source]

About this dataset

This dataset contains machine-readable hospital pricing information for Children's Hospitals and Clinics of Minnesota. It includes three separate files:

1. 2022-top-25-hospital-based-clinics-list.csv: This file provides the top 25 primary care procedure prices, including procedure codes, fees, and insurance coverage details.
2. 2022-standard-list-of-charges-hospital-op.csv: This file includes machine-readable hospital pricing information, including procedure codes, fees, and insurance coverage details.
3. 2022-msdrg.csv: This file also contains machine-readable hospital pricing information, including procedure codes, fees, and insurance coverage details.

The data was collected programmatically using a custom script written in Node.js and Microsoft Playwright. These files were then mirrored on the data.world platform using the Import from URL option.

If you find any errors in the dataset or have any questions or concerns, please leave a note in the Discussion tab of this dataset or contact supportdata.world for assistance

How to use the dataset

• Dataset Overview:

  • The dataset contains three files: a) 2022-top-25-hospital-based-clinics-list.csv: This file includes the top 25 primary care procedure prices for Children's Hospitals and Clinics of Minnesota, including procedure codes, fees, and insurance coverages. b) 2022-standard-list-of-charges-hospital-op.csv: This file includes machine-readable hospital pricing information for Children's Hospitals and Clinics of Minnesota, including procedure codes, fees, and insurance coverages. c) 2022-msdrg.csv: This file includes machine-readable hospital pricing information for Children's Hospitals and Clinics of Minnesota, including MSDRG (Medicare Severity Diagnosis Related Groups) codes, fees, and insurance coverages.

• Data Collection:

  • The data was collected programmatically using a custom script written in Node.js with the assistance of Microsoft Playwright.
  • These datasets were programmatically mirrored on the data.world platform using the Import from URL option.

• Usage Guidelines:

  • Explore Procedure Prices: You can analyze the top 25 primary care procedure prices by referring to the '2022-top-25-hospital-based-clinics-list.csv' file. It provides information on procedure codes (identifiers), associated fees (costs), and insurance coverage details.

  • Analyze Hospital Price Information: The '2022-standard-list-of-charges-hospital-op.csv' contains comprehensive machine-readable hospital pricing information. You can examine various procedures by their respective codes along with associated fees as well as corresponding insurance coverage details.

  • Understand MSDRG Codes & Fees: The '2022-msdrg.csv' file includes machine-readable hospital pricing information based on MSDRG (Medicare Severity Diagnosis Related Groups) codes. You can explore the relationship between diagnosis groups and associated fees, along with insurance coverage details.

• Reporting Errors:

  • If you identify any errors or discrepancies in the dataset, please leave a note in the Discussion tab of this dataset to notify others who may be interested.
  • Alternatively, you can reach out to the data.world team at supportdata.world for further assistance.

Research Ideas

• Comparative Analysis: Researchers and healthcare professionals can use this dataset to compare the pricing of primary care procedures at Children's Hospitals and Clinics of Minnesota with other hospitals. This can help identify any variations or discrepancies in pricing, enabling better cost management and transparency.
• Insurance Coverage Analysis: The insurance coverage information provided in this dataset can be used to analyze which procedures are covered by different insurance providers. This analysis can help patients understand their out-of-pocket expenses for specific procedures and choose the best insurance plan accordingly.
• Cost Estimation: Patients can utilize this dataset to estimate the cost of primary care procedures at Children's Hospitals and Clinics of Minnesota before seeking medical treatment. By comparing procedure fees across different hospitals, patients can make informed decisions about where to receive their healthcare services based on affordability and quality

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

**Unknown License - Please chec...

The average number of hospital beds available per 1,000 people in the United States was forecast to continuously decrease between 2024 and 2029 by in total 0.1 beds (-3.7 percent). After the eighth consecutive decreasing year, the number of available beds per 1,000 people is estimated to reach 2.63 beds and therefore a new minimum in 2029. Depicted is the number of hospital beds per capita in the country or region at hand. As defined by World Bank this includes inpatient beds in general, specialized, public and private hospitals as well as rehabilitation centers.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the average number of hospital beds available per 1,000 people in countries like Canada and Mexico.

Romania RO: Hospital Beds: per 1000 People data was reported at 6.100 Number in 2011. This records a decrease from the previous number of 6.290 Number for 2010. Romania RO: Hospital Beds: per 1000 People data is updated yearly, averaging 7.667 Number from Dec 1960 (Median) to 2011, with 28 observations. The data reached an all-time high of 8.950 Number in 1987 and a record low of 6.100 Number in 2011. Romania RO: Hospital Beds: per 1000 People data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Romania – Table RO.World Bank: Health Statistics. Hospital beds include inpatient beds available in public, private, general, and specialized hospitals and rehabilitation centers. In most cases beds for both acute and chronic care are included.; ; Data are from the World Health Organization, supplemented by country data.; Weighted average;

📌 Context of the Dataset

The Healthcare Ransomware Dataset was created to simulate real-world cyberattacks in the healthcare industry. Hospitals, clinics, and research labs have become prime targets for ransomware due to their reliance on real-time patient data and legacy IT infrastructure. This dataset provides insight into attack patterns, recovery times, and cybersecurity practices across different healthcare organizations.

Why is this important?

Ransomware attacks on healthcare organizations can shut down entire hospitals, delay treatments, and put lives at risk. Understanding how different healthcare organizations respond to attacks can help develop better security strategies. The dataset allows cybersecurity analysts, data scientists, and researchers to study patterns in ransomware incidents and explore predictive modeling for risk mitigation.

📌 Sources and Research Inspiration This simulated dataset was inspired by real-world cybersecurity reports and built using insights from official sources, including:

1️⃣ IBM Cost of a Data Breach Report (2024)

The healthcare sector had the highest average cost of data breaches ($10.93 million per incident). On average, organizations recovered only 64.8% of their data after paying ransom. Healthcare breaches took 277 days on average to detect and contain.

2️⃣ Sophos State of Ransomware in Healthcare (2024)

67% of healthcare organizations were hit by ransomware in 2024, an increase from 60% in 2023. 66% of backup compromise attempts succeeded, making data recovery significantly more difficult. The most common attack vectors included exploited vulnerabilities (34%) and compromised credentials (34%).

3️⃣ Health & Human Services (HHS) Cybersecurity Reports

Ransomware incidents in healthcare have doubled since 2016. Organizations that fail to monitor threats frequently experience higher infection rates.

4️⃣ Cybersecurity & Infrastructure Security Agency (CISA) Alerts

Identified phishing, unpatched software, and exposed RDP ports as top ransomware entry points. Only 13% of healthcare organizations monitor cyber threats more than once per day, increasing the risk of undetected attacks.

5️⃣ Emsisoft 2020 Report on Ransomware in Healthcare

The number of ransomware attacks in healthcare increased by 278% between 2018 and 2023. 560 healthcare facilities were affected in a single year, disrupting patient care and emergency services.

📌 Why is This a Simulated Dataset?

This dataset does not contain real patient data or actual ransomware cases. Instead, it was built using probabilistic modeling and structured randomness based on industry benchmarks and cybersecurity reports.

How It Was Created:

1️⃣ Defining the Dataset Structure

The dataset was designed to simulate realistic attack patterns in healthcare, using actual ransomware case studies as inspiration.

Columns were selected based on what real-world cybersecurity teams track, such as: Attack methods (phishing, RDP exploits, credential theft). Infection rates, recovery time, and backup compromise rates. Organization type (hospitals, clinics, research labs) and monitoring frequency.

2️⃣ Generating Realistic Data Using ChatGPT & Python

ChatGPT assisted in defining relationships between attack factors, ensuring that key cybersecurity concepts were accurately reflected. Python’s NumPy and Pandas libraries were used to introduce randomized attack simulations based on real-world statistics. Data was validated against industry research to ensure it aligns with actual ransomware attack trends.

3️⃣ Ensuring Logical Relationships Between Data Points

Hospitals take longer to recover due to larger infrastructure and compliance requirements. Organizations that track more cyber threats recover faster because they detect attacks earlier. Backup security significantly impacts recovery time, reflecting the real-world risk of backup encryption attacks.

From the selected regions, the ranking by number of hospitals is led by China with 37,627 hospitals and is followed by the Nigeria (23,640 hospitals). In contrast, the ranking is trailed by Seychelles with one hospitals, recording a difference of 37,626 hospitals to China. Depicted is the number of hospitals in the country or region at hand. As the OECD states, the rules according to which an institution can be registered as a hospital vary across countries.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).

Context:

This synthetic healthcare dataset has been created to serve as a valuable resource for data science, machine learning, and data analysis enthusiasts. It is designed to mimic real-world healthcare data, enabling users to practice, develop, and showcase their data manipulation and analysis skills in the context of the healthcare industry.

Inspiration:

The inspiration behind this dataset is rooted in the need for practical and diverse healthcare data for educational and research purposes. Healthcare data is often sensitive and subject to privacy regulations, making it challenging to access for learning and experimentation. To address this gap, I have leveraged Python's Faker library to generate a dataset that mirrors the structure and attributes commonly found in healthcare records. By providing this synthetic data, I hope to foster innovation, learning, and knowledge sharing in the healthcare analytics domain.

Dataset Information:

Each column provides specific information about the patient, their admission, and the healthcare services provided, making this dataset suitable for various data analysis and modeling tasks in the healthcare domain. Here's a brief explanation of each column in the dataset - - Name: This column represents the name of the patient associated with the healthcare record. - Age: The age of the patient at the time of admission, expressed in years. - Gender: Indicates the gender of the patient, either "Male" or "Female." - Blood Type: The patient's blood type, which can be one of the common blood types (e.g., "A+", "O-", etc.). - Medical Condition: This column specifies the primary medical condition or diagnosis associated with the patient, such as "Diabetes," "Hypertension," "Asthma," and more. - Date of Admission: The date on which the patient was admitted to the healthcare facility. - Doctor: The name of the doctor responsible for the patient's care during their admission. - Hospital: Identifies the healthcare facility or hospital where the patient was admitted. - Insurance Provider: This column indicates the patient's insurance provider, which can be one of several options, including "Aetna," "Blue Cross," "Cigna," "UnitedHealthcare," and "Medicare." - Billing Amount: The amount of money billed for the patient's healthcare services during their admission. This is expressed as a floating-point number. - Room Number: The room number where the patient was accommodated during their admission. - Admission Type: Specifies the type of admission, which can be "Emergency," "Elective," or "Urgent," reflecting the circumstances of the admission. - Discharge Date: The date on which the patient was discharged from the healthcare facility, based on the admission date and a random number of days within a realistic range. - Medication: Identifies a medication prescribed or administered to the patient during their admission. Examples include "Aspirin," "Ibuprofen," "Penicillin," "Paracetamol," and "Lipitor." - Test Results: Describes the results of a medical test conducted during the patient's admission. Possible values include "Normal," "Abnormal," or "Inconclusive," indicating the outcome of the test.

Usage Scenarios:

This dataset can be utilized for a wide range of purposes, including: - Developing and testing healthcare predictive models. - Practicing data cleaning, transformation, and analysis techniques. - Creating data visualizations to gain insights into healthcare trends. - Learning and teaching data science and machine learning concepts in a healthcare context. - You can treat it as a Multi-Class Classification Problem and solve it for Test Results which contains 3 categories(Normal, Abnormal, and Inconclusive).

Acknowledgments:

• I acknowledge the importance of healthcare data privacy and security and emphasize that this dataset is entirely synthetic. It does not contain any real patient information or violate any privacy regulations.
• I hope that this dataset contributes to the advancement of data science and healthcare analytics and inspires new ideas. Feel free to explore, analyze, and share your findings with the Kaggle community.

Image Credit:

Image by BC Y from Pixabay

Snapshot img

Hospital Beds Market Size 2025-2029

The hospital beds market size is forecast to increase by USD 2.69 billion, at a CAGR of 9.9% between 2024 and 2029.

The market is experiencing significant growth, driven by the rise in infectious diseases and the increasing number of medical emergencies. These factors have led to a heightened demand for advanced hospital beds that cater to the specific needs of patients. For instance, bariatric hospital beds are gaining popularity due to the increasing prevalence of obesity and related health issues. Similarly, intensive care unit (ICU) beds are in high demand due to the growing number of critical patients requiring constant monitoring and care. However, the high cost of automated hospital beds poses a significant challenge for market growth. These advanced beds come with advanced features such as adjustable heights, electric mattresses, and integrated technology for patient monitoring.
While these features offer numerous benefits, they also increase the cost of production and, subsequently, the price of the beds. This challenge may limit the adoption of automated hospital beds in some healthcare facilities, particularly in developing countries and low-income regions. Another challenge is the shortage of hospital beds, especially during outbreaks of infectious diseases. For instance, during the COVID-19 pandemic, many hospitals faced a shortage of beds, leading to overcrowding and an increased risk of infection transmission. To address this challenge, some companies have started producing modular and portable hospital beds that can be easily transported and set up in temporary hospitals or quarantine facilities. The demand for home healthcare services is also driving the market, as patients prefer to receive care

What will be the Size of the Hospital Beds Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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The market encompasses various product offerings, including those with remote bed control, chronic care support, and smart bed technology. Chronic care patients benefit from these advanced beds, which enhance healthcare efficiency and patient comfort. Recyclable materials and corrosion resistance are essential considerations for bed manufacturers, aligning with the industry's sustainability and cost optimization efforts. Data analytics plays a crucial role in hospital bed procurement, enabling supply chain management and clinical outcomes assessment. Lateral rotation and automatic bed turning features cater to acute care and long-term care settings, ensuring patient safety and improving sleep quality. Rental services offer flexibility for healthcare facilities, allowing them to adapt to changing patient needs while minimizing capital expenditures.
Wireless connectivity integration enables patient monitoring and data sharing, enhancing the overall quality of care. Patient safety remains a top priority, with material durability and clinical outcomes being key factors in bed selection. Smart bed technology, including automatic bed turning and home healthcare integration, further improves patient care and satisfaction. In the realm of hospital bed procurement, cost optimization and quality control are essential elements. Lease agreements provide an alternative financing option, enabling healthcare providers to access advanced bed technology while managing budgets effectively. Regardless of the specific market segment, the hospital beds industry continues to evolve, integrating the latest technology and trends to meet the unique needs of healthcare facilities and patients.

How is this Hospital Beds Industry segmented?

The hospital beds industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Product

  Manual beds
  Semi-automated beds
  Automated beds


Application

  Intensive care
  Acute care
  Home care


End-user

  Hospitals
  Home healthcare
  Elderly care facilities
  Ambulatory surgical centers


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  APAC

    China
    India
    Japan
    South Korea


  South America

    Brazil


  Rest of World (ROW)

By Product Insights

The manual beds segment is estimated to witness significant growth during the forecast period. The market encompasses various product offerings, including manual and electric beds, bariatric beds, geriatric beds, ICU beds, operating room beds, and recovery room beds. Compliance with regulatory standards is a crucial factor in this market, ensuring easy cleaning, bedside rails, and fall prevention. Manual beds, the largest segment, remain po

Integrated Geodatabase: The Global Catholic Foortprint of Care for the Vulnerable and ChildrenBurhans, Molly A., Mrowczynski, Jon M., Schweigel, Tayler C., and Burhans, Debra T., Wacta, Christine. The Catholic Foortprint of Care Around the World (1). GoodLands and GHR Foundation, 2019.Catholic Statistics Numbers:Annuarium Statisticum Ecclesiae – Statistical Yearbook of the Church: 1980 – 2018. LIBRERIA EDITRICE VATICAN.Historical Country Boundary Geodatabase:Weidmann, Nils B., Doreen Kuse, and Kristian Skrede Gleditsch. The Geography of the International System: The CShapes Dataset. International Interactions 36 (1). 2010.GoodLands created a significant new data set of important Church information regarding orphanages and sisters around the world as well as healthcare, welfare, and other child care institutions. The data was extracted from the gold standard of Church data, the Annuarium Statisticum Ecclesiae, published yearly by the Vatican. It is inevitable that raw data sources will contain errors. GoodLands and its partners are not responsible for misinformation within Vatican documents. We encourage error reporting to us at data@good-lands.org or directly to the Vatican.GoodLands worked with the GHR Foundation to map Catholic Healthcare around the world using data mined from the Annuarium Statisticum Eccleasiea.The workflows and data models developed for this project can be used to map any global, historical country-scale data in a time-series map while accounting for country boundary changes. GoodLands created proprietary software that enables mining the Annuarium Statisticum Eccleasiea (see Software and Program Library at the bottom of this page for details).

Integrated Geodatabase: The Global Catholic Foortprint of Healthcare and WelfareBurhans, Molly A., Mrowczynski, Jon M., Schweigel, Tayler C., and Burhans, Debra T., Wacta, Christine. The Catholic Foortprint of Care Around the World (1). GoodLands and GHR Foundation, 2019.Catholic Statistics Numbers:Annuarium Statisticum Ecclesiae – Statistical Yearbook of the Church: 1980 – 2018. LIBRERIA EDITRICE VATICAN.Historical Country Boundary Geodatabase:Weidmann, Nils B., Doreen Kuse, and Kristian Skrede Gleditsch. The Geography of the International System: The CShapes Dataset. International Interactions 36 (1). 2010.https://www.tandfonline.com/doi/full/10.1080/03050620903554614GoodLands created a significant new data set for GHR and the UISG of important Church information regarding orphanages and sisters around the world as well as healthcare, welfare, and other child care institutions. The data were extracted from the gold standard of Church data, the Annuarium Statisticum Ecclesiae, published yearly by the Vatican. It is inevitable that raw data sources will contain errors. GoodLands and its partners are not responsible for misinformation within Vatican documents. We encourage error reporting to us at data@good-lands.org or directly to the Vatican.GoodLands worked with the GHR Foundation to map Catholic Healthcare and Welfare around the world using data mined from the Annuarium Statisticum Eccleasiea. GHR supported the data development and GoodLands independently invested in the mapping of information.The workflows and data models developed for this project can be used to map any global, historical country-scale data in a time-series map while accounting for country boundary changes. GoodLands created proprietary software that enables mining the Annuarium Statisticum Eccleasiea (see Software and Program Library at our home page for details).The GHR Foundation supported data extraction and cleaning of this information.GoodLands’ supported the development of maps, infographics, and applications for all healthcare data.

Analysis of ‘Daily Covid19 Hospitalisation Data’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/shivamb/daily-covid19-hospitalisation-data on 28 January 2022.

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

Daily Covid19 Hospitalization Data by Region

Fields

Indicators

Ackwnowledgements

All visualizations, data, and code produced by Our World in Data are completely open access under the Creative Commons BY license.

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

San Marino Hospital Beds: per 1000 People data was reported at 3.800 Number in 2012. This records a decrease from the previous number of 3.850 Number for 2011. San Marino Hospital Beds: per 1000 People data is updated yearly, averaging 6.734 Number from Dec 1980 (Median) to 2012, with 10 observations. The data reached an all-time high of 7.657 Number in 1988 and a record low of 3.800 Number in 2012. San Marino Hospital Beds: per 1000 People data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s San Marino – Table SM.World Bank: Health Statistics. Hospital beds include inpatient beds available in public, private, general, and specialized hospitals and rehabilitation centers. In most cases beds for both acute and chronic care are included.; ; Data are from the World Health Organization, supplemented by country data.; Weighted average;

Overview: This is a large-scale real-world dataset with videos recording medical staff washing their hands as part of their normal job duties in the Jurmala Hospital located in Jurmala, Latvia. There are 2427 hand washing episodes in total, almost all of which are annotated by two persons. The annotations classify the washing movements according to the World Health Organization's (WHO) guidelines by marking each frame in each video with a certain movement code. This dataset is part on three dataset series all following the same format: https://zenodo.org/record/4537209 - data collected in Pauls Stradins Clinical University Hospital https://zenodo.org/record/5808764 - data collected in Jurmala Hospital https://zenodo.org/record/5808789 - data collected in the Medical Education Technology Center (METC) of Riga Stradins University Applications: The intention of this dataset is twofold: to serve as a basis for training machine learning classifiers for automated hand washing movement recognition and quality control, and to allow to investigate the real-world quality of washing performed by working medical staff. Statistics: Frame rate: 30 FPS Resolution: 320x240 and 640x480 Number of videos: 2427 Number of annotation files: 4818 Movement codes (both in CSV and JSON files): 1: Hand washing movement ��� Palm to palm 2: Hand washing movement ��� Palm over dorsum, fingers interlaced 3: Hand washing movement ��� Palm to palm, fingers interlaced 4: Hand washing movement ��� Backs of fingers to opposing palm, fingers interlocked 5: Hand washing movement ��� Rotational rubbing of the thumb 6: Hand washing movement ��� Fingertips to palm 7: Turning off the faucet with a paper towel 0: Other hand washing movement Acknowledgments: The dataset collection was funded by the Latvian Council of Science project: "Automated hand washing quality control and quality evaluation system with real-time feedback", No: lzp - Nr. 2020/2-0309. References: For more detailed information, see this article, describing a similar dataset collected in a different project: M. Lulla, A. Rutkovskis, A. Slavinska, A. Vilde, A. Gromova, M. Ivanovs, A. Skadins, R. Kadikis, A. Elsts. Hand-Washing Video Dataset Annotated According to the World Health Organization���s Hand-Washing Guidelines. Data. 2021; 6(4):38. https://doi.org/10.3390/data6040038 Contact information: atis.elsts@edi.lv

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Background

The World Health Organization (WHO) characterized the COVID-19, caused by the SARS-CoV-2, as a pandemic on March 11, while the exponential increase in the number of cases was risking to overwhelm health systems around the world with a demand for ICU beds far above the existing capacity, with regions of Italy being prominent examples.

Brazil recorded the first case of SARS-CoV-2 on February 26, and the virus transmission evolved from imported cases only, to local and finally community transmission very rapidly, with the federal government declaring nationwide community transmission on March 20.

Until March 27, the state of São Paulo had recorded 1,223 confirmed cases of COVID-19, with 68 related deaths, while the county of São Paulo, with a population of approximately 12 million people and where Hospital Israelita Albert Einstein is located, had 477 confirmed cases and 30 associated death, as of March 23. Both the state and the county of São Paulo decided to establish quarantine and social distancing measures, that will be enforced at least until early April, in an effort to slow the virus spread.

One of the motivations for this challenge is the fact that in the context of an overwhelmed health system with the possible limitation to perform tests for the detection of SARS-CoV-2, testing every case would be impractical and tests results could be delayed even if only a target subpopulation would be tested.

Dataset

This dataset contains anonymized data from patients seen at the Hospital Israelita Albert Einstein, at São Paulo, Brazil, and who had samples collected to perform the SARS-CoV-2 RT-PCR and additional laboratory tests during a visit to the hospital.

All data were anonymized following the best international practices and recommendations. All clinical data were standardized to have a mean of zero and a unit standard deviation.

Task Details

TASK 1 • Predict confirmed COVID-19 cases among suspected cases. Based on the results of laboratory tests commonly collected for a suspected COVID-19 case during a visit to the emergency room, would it be possible to predict the test result for SARS-Cov-2 (positive/negative)?

TASK 2 • Predict admission to general ward, semi-intensive unit or intensive care unit among confirmed COVID-19 cases. Based on the results of laboratory tests commonly collected among confirmed COVID-19 cases during a visit to the emergency room, would it be possible to predict which patients will need to be admitted to a general ward, semi-intensive unit or intensive care unit?

Expected Submission

Submit a notebook that implements the full lifecycle of data preparation, model creation and evaluation. Feel free to use this dataset plus any other data you have available. Since this is not a formal competition, you're not submitting a single submission file, but rather your whole approach to building a model.

Evaluation

This is not a formal competition, so we won't measure the results strictly against a given validation set using a strict metric. Rather, what we'd like to see is a well-defined process to build a model that can deliver decent results (evaluated by yourself).

Our team will be looking at: 1. Model Performance - How well does the model perform on the real data? Can it be generalized over time? Can it be applied to other scenarios? Was it overfit? 2. Data Preparation - How well was the data analysed prior to feeding it into the model? Are there any useful visualisations? Does the reader learn any new techniques through this submission? A great entry will be informative, thought provoking, and fresh all at the same time. 3. Documentation - Are your code, and notebook, and additional data sources well documented so a reader can understand what you did? Are your sources clearly cited? A high quality analysis should be concise and clear at each step so the rationale is easy to follow and the process is reproducible.

Questions and More Info

Additional questions and clarifications can be obtained at data4u@einstein.br

Answers to most voted questions

Missing data

Decision making by health care professionals is a complex process, when physicians see a patient for the first time with an acute complaint (e.g., recent onset of fever and respiratory symptoms) they will take a medical history, perform a physical examination, and will base their decisions on this information. To order or not laboratory tests, and which ones to order, is among these decisions, and there is no standard set of tests that are ordered to every individual or to a specific condition. This will depend on the complaints, the findings on the physical examination, personal medical history (e.g., current and prior diagnosed diseases, medications under use, prior surgeries, vaccination), lifestyle habits (e.g., smoking, alcohol use, exercising), family medical history, and prior exposures (e.g., traveling, occupation). The dataset reflects the complexity of decision making during routine clinical care, as opposed to what happens on a more controlled research setting, and data sparsity is, therefore, expected.

Variables in addition to laboratory results

We understand that clinical and exposure data, in addition to the laboratory results, are invaluable information to be added to the models, but at this moment they are not available.

Additional laboratory variables

A main objective of this challenge is to develop a generalizable model that could be useful during routine clinical care, and although which laboratory exams are ordered can vary for different individuals, even with the same condition, we aimed at including laboratory tests more commonly order during a visit to the emergency room. So, if you found some additional laboratory test that was not included, it is because it was not considered as commonly order in this situation.

Our message to all participants

Hospital Israelita Albert Einstein would like to thank you for all the effort and time dedicated to this challenge, the community interest and the number of contributions have surpassed our expectations, and we are extremely satisfied with the results.

These have been challenging times, and we believe that promoting information sharing and collaboration will be crucial to gain insights, as fast as possible, that could help to implement measures to diminish the burden of COVID-19.

The multitude of solutions presented focusing on different aspects of the problem could represent a valuable resource in the evaluation of different strategies to implement predictive models for COVID-19. Besides the data visualization methods employed could make it easier for multidisciplinary teams to collaborate around COVID-19 real-world data.

Although this was not a competition, we would like to highlight some solutions, based on the community and our review of results.

Lucas Moda (https://www.kaggle.com/lukmoda/covid-19-optimizing-recall-with-smote) utilized interesting data visualization methods for the interpretability of models. Fellipe Gomes (https://www.kaggle.com/gomes555/task2-covid-19-admission-ac-94-sens-0-92-auc-0-96) used concise descriptions of the data and model results. We saw interesting ideas for visualizing and understanding the data, like the dendrogram used by CaesarLupum (https://www.kaggle.com/caesarlupum/brazil-against-the-advance-of-covid-19). Ossamu (https://www.kaggle.com/ossamum/eda-and-feat-import-recall-0-95-roc-auc-0-61) also sought to evaluate several data resampling techniques, to verify how it can improve the performance of predictive models, which was also done by Kaike Reis (https://www.kaggle.com/kaikewreis/a-second-end-to-end-solution-for-covid-19) . Jairo Freitas & Christian Espinoza (https://www.kaggle.com/jairofreitas/covid-19-influence-of-exams-in-recall-precision) sought to understand the distribution of exams regarding the outcomes of task 2, to support the decisions to be made in the construction of predictive models.

We thank you all for the feedback on available data, helping to show its potential, and taking the challenge of dealing with real data feed. Your efforts let the feeling that it is possible to build good predictive models in real life healthcare settings.

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Hospital ERP Software Market Outlook

The global hospital ERP software market size was valued at approximately $2.5 billion in 2023 and is projected to reach $7.6 billion by 2032, growing at a compound annual growth rate (CAGR) of 12.8% during the forecast period. The growth factor driving this market is primarily the increasing need for efficient hospital management and the integration of advanced technologies in healthcare systems.

One of the fundamental growth drivers of the hospital ERP software market is the surge in healthcare data and the need for effective management solutions. With hospitals and healthcare facilities generating vast amounts of data daily, there is a pressing need for systems that can handle patient information, inventory details, financial records, and more in a streamlined manner. This necessity is pushing hospitals to adopt ERP software that not only manages these aspects but also ensures data accuracy, security, and accessibility. The implementation of such software helps in minimizing errors, reducing operational costs, and improving patient care quality, thereby propelling market growth.

Another significant factor contributing to the market's growth is the rising adoption of cloud-based deployments. Cloud-based hospital ERP solutions offer numerous advantages, including lower initial costs, scalability, and ease of updates and maintenance. These solutions allow hospitals to access critical data from anywhere at any time, facilitating better decision-making processes. Moreover, the ongoing advancements in cloud technology and the increasing reliability and security of cloud services are encouraging more healthcare institutions to transition from traditional on-premises systems to cloud-based ERP solutions.

The growing emphasis on regulatory compliance in the healthcare sector is also fueling the demand for hospital ERP software. Regulatory bodies around the globe have stringent guidelines regarding patient data management, financial reporting, and operational transparency. Hospital ERP systems are designed to help healthcare providers comply with these regulations efficiently. By automating compliance processes and maintaining accurate records, ERP software reduces the risk of non-compliance penalties and enhances the overall credibility of healthcare institutions.

Regionally, North America is expected to dominate the hospital ERP software market, owing to the region's advanced healthcare infrastructure and high adoption rate of innovative technologies. Following closely is Europe, where the demand for efficient hospital management systems is on the rise due to increasing healthcare expenditure and the need for improved patient care. The Asia Pacific region is anticipated to witness the fastest growth during the forecast period, driven by the expanding healthcare sector, government initiatives for healthcare digitization, and the increasing number of hospitals in countries like China and India.

Component Analysis

The hospital ERP software market can be segmented by components into software and services. The software segment is further divided into various modules that cater to different hospital management needs, while the services segment includes consulting, implementation, training, and support services. The software component holds a significant share of the market due to the increasing demand for comprehensive and integrated solutions that can manage multiple hospital functions.

Within the software segment, the emphasis is on developing modular solutions that can be customized according to the specific needs of different healthcare providers. These solutions often include modules for patient management, financial management, inventory management, and more, enabling hospitals to select and implement only those functionalities that are relevant to their operations. This modular approach not only makes the software more flexible but also cost-effective, as hospitals do not have to invest in unnecessary features.

The services component is also crucial for the successful deployment and utilization of hospital ERP systems. Consulting services are essential in helping healthcare providers understand their specific needs and select the appropriate ERP solution. Implementation services ensure that the software is correctly installed and integrated with existing systems, while training services help hospital staff learn how to use the new software effectively. Ongoing support services are vital for addressing any issues that may arise and ensuring that the ERP system continues to function