This dataset shows the the world's best hospital in 2023 issued by the Newsweek and Statista.

HCAHPS Hospital Ratings Survey

Patient Experience Ratings 2018-2020

By Health [source]

About this dataset

This dataset contains ratings of hospitals, based on the Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS). This survey collects data from hospital patients on their experiences during an inpatient stay. The list includes several indicators to help gauge a hospital's quality, such as star ratings based on patient opinions and percentage of positive answers to HCAHPS questions. Additionally, there are measures such as the number of completed surveys, survey response rate percent and linear mean value which assist in evaluating patient experience at each medical institution. With this comprehensive dataset you can easily draw comparisons between hospitals and make informed decisions about healthcare services provided in your area

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This dataset provides useful information on the quality of care that hospitals provide. This dataset provides ratings and reviews of several hospitals, making it easy to compare hospitals in order to find out which hospital may best meet your needs.

The following guide will walk you through how to use this dataset effectively:

• Navigate the different columns available in this dataset by scrolling through the table. These include Hospital Name, Address, City, State, ZIP Code, County Name, Phone Number and HCAHPS Question among others.
• Examine important information such as the patient survey star rating and HCAHPS linear mean value for each hospital included in the dataset in order to evaluate it's performance against other hospitals based on standards set out by HCAHPS .
• Read any footnotes associated with each column carefully in order to fully understand what exactly is being measured. These may directly affect your evaluation of a particular hospital’s performance compared to others included in this dataset or even more so when compared against external sources of data outside this dataset such as other surveys or studies related to health care quality measurement metrics within that state or region where applicable & relevant (i..e Measure Start Date and Measure End Date).

• Pay careful attention also when evaluating factors related to survey response rates (e..g Survey Response Rate Percent Footnote) & what percentages are being reported here within each category; these figures may selectively bias results so ensure full transparency is achieved by reviewing all potential influencing factors/variables prior commencing investigations/data analysis/interpretation based upon this data-set alone(or any subset thereof).

  By following these steps you should be able set up your own criteria for measuring various aspects of health care quality across different states & cities - ensuring optimal access & safety measures for both patients & healthcare providers alike over time - thus ultimately aiding decision making processes towards improved patient outcomes worldwide!

Research Ideas

• Tracking patient experience trends over time: This dataset can be used to analyze trends in patient experience over time by identifying changes in survey responses, star ratings, and response rates across hospitals.
• Establishing a benchmark for high-quality hospital care: By studying the scores of the top-performing hospitals within each category, healthcare administrators can set standards and benchmarks for quality of care in their own hospitals.
• Comparing hospital ratings to inform decision making: Patients and family members looking to book an appointment at a hospital or doctors office can use this dataset to compare different facilities’ HCAHPS scores and make an informed decision about where they would like to go for their medical treatment

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 int...

This dataset provides values for HOSPITAL BEDS reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

Children's Hospitals Pricing Data

Hospital pricing information for Children's Hospitals and Clinics of Minnesota

By Amber Thomas [source]

About this dataset

This dataset provides machine-readable hospital pricing information from Children's Hospitals and Clinics of Minnesota. It includes three files: 2022-top-25-hospital-based-clinics-list.csv, which contains the top 25 primary care procedure prices for hospital-based clinics at Children's Hospitals; 2022-standard-list-of-charges-hospital-op.csv, which comprises the standard charges for outpatient procedures in 2022, including procedure codes, fees, and insurance coverage; and 2022-msdrg.csv, containing machine-readable hospital pricing information specifically related to the 2022 Medicare Severity Diagnosis Related Groups (MS-DRG) codes. These datasets were obtained directly from Children's Hospitals' website as part of their compliance with the Centers for Medicare and Medicaid Services IPPS Final Rule. The data was collected programmatically using a custom script written in Node.js and Microsoft Playwright, then mirrored on the data.world platform. If you come across any errors or discrepancies in this data, please report them in the Discussion tab or contact supportdata.world

How to use the dataset

• Understanding the Files:

  • The dataset consists of three files: 2022-top-25-hospital-based-clinics-list.csv, 2022-standard-list-of-charges-hospital-op.csv, and 2022-msdrg.csv.
  • 2022-top-25-hospital-based-clinics-list.csv contains the top 25 primary care procedure prices for hospital-based clinics at Children's Hospitals and Clinics of Minnesota.
  • 2022-standard-list-of-charges-hospital-op.csv includes the standard list of charges for outpatient procedures at Children's Hospitals and Clinics of Minnesota, including procedure codes, fees, and insurance coverage.
  • The file 2022-msdrg.csv provides machine-readable hospital pricing information specifically related to the Medicare Severity Diagnosis Related Groups (MS-DRG) codes.

• Accessing the Data:

  • The data can be accessed from their source on the Children's Hospitals and Clinics of Minnesota website.

• Data Collection Method:

  • All data in this dataset was collected programmatically using a custom script written in Node.js and utilizing Microsoft Playwright, an open-source library for browser automation.

• How to Handle Errors or Suggestions:

  • If you have found any errors or have suggestions regarding this dataset, you can leave a note on the Discussion tab of this dataset on Kaggle or reach out via email to supportdata.world.

• Dataset Use Cases:

  a) Research & Analysis: Analyze primary care procedure prices at Children's Hospitals and Clinics of Minnesota based on different procedure codes present in the top-25 list from 2022 hospital-based clinics file (2022-top-25-hospital-based-clinics-list.csv).

  b) Cost Comparison: Compare fees and charges for outpatient procedures at Children's Hospitals and Clinics of Minnesota with other healthcare providers using the 2022 standard list of charges file (2022-standard-list-of-charges-hospital-op.csv).

  c) Insurance Coverage Analysis: Investigate insurance coverage details for outpatient procedures at Children's Hospitals and Clinics of Minnesota by referring to the insurance coverage column in the 2022 standard list of charges file (2022-standard-list-of-charges-hospital-op.csv).

  d) Medicare Severity Diagnosis Related Groups (MS-DRG): Explore machine-readable hospital pricing information specifically

Research Ideas

• Price comparison: This dataset can be used to compare the prices of different primary care procedures and outpatient procedures at Children's Hospitals and Clinics of Minnesota. This information can help patients make informed decisions about their healthcare options based on affordability.
• Insurance coverage analysis: The dataset includes information about insurance coverage for each procedure, which can be analyzed to understand which procedures are covered by different insurance providers. This analysis can help patients determine if their insurance will cover a specific procedure or if they will need to pay out-of-pocket.
• Trend analysis: By comparing the pricing information from previous years' datasets, this dataset can be used to analyze trends in healthcare costs over time at Children's Hospitals and Clinics of Minnesota. This analysis can provide insights into how healthcare costs are changing and help identify areas where cost improvements may be needed

...

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!

More Datasets

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...

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

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

<iframe title="USA counties (2018) choropleth map Mapping COVID-19 cases by county" aria-describedby="" id="datawrapper-chart-nRyaf" src="https://datawrapper.dwcdn.net/nRyaf/10/" scrolling="no" frameborder="0" style="width: 0; min-width: 100% !important;" height="400"></iframe><script type="text/javascript">(function() {'use strict';window.addEventListener('message', function(event) {if (typeof event.data['datawrapper-height'] !== 'undefined') {for (var chartId in event.data['datawrapper-height']) {var iframe = document.getElementById('datawrapper-chart-' + chartId) || document.querySelector("iframe[src*='" + chartId + "']");if (!iframe) {continue;}iframe.style.height = event.data['datawrapper-height'][chartId] + 'px';}}});})();</script>

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

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

More Datasets

For more datasets, click here.

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 ...

IntroductionIn confronting the sudden COVID-19 epidemic, China and other countries have been under great pressure to block virus transmission and reduce fatalities. Converting large-scale public venues into makeshift hospitals is a popular response. This addresses the outbreak and can maintain smooth operation of a country or region's healthcare system during a pandemic. However, large makeshift hospitals, such as the Shanghai New International Expo Center (SNIEC) makeshift hospital, which was one of the largest makeshift hospitals in the world, face two major problems: Effective and precise transfer of patients and heterogeneity of the medical care teams.MethodsTo solve these problems, this study presents the medical practices of the SNIEC makeshift hospital in Shanghai, China. The experiences include constructing two groups, developing a medical management protocol, implementing a multi-dimensional management mode to screen patients, transferring them effectively, and achieving homogeneous quality of medical care. To evaluate the medical practice performance of the SNIEC makeshift hospital, 41,941 infected patients were retrospectively reviewed from March 31 to May 23, 2022. Multivariate logistic regression method and a tree-augmented naive (TAN) Bayesian network mode were used.ResultsWe identified that the three most important variables were chronic disease, age, and type of cabin, with importance values of 0.63, 0.15, and 0.11, respectively. The constructed TAN Bayesian network model had good predictive values; the overall correct rates of the model-training dataset partition and test dataset partition were 99.19 and 99.05%, respectively, and the respective values for the area under the receiver operating characteristic curve were 0.939 and 0.957.ConclusionThe medical practice in the SNIEC makeshift hospital was implemented well, had good medical care performance, and could be copied worldwide as a practical intervention to fight the epidemic in China and other developing countries.

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.

Success.ai’s Healthcare Industry Leads Data empowers businesses and organizations to connect with key decision-makers and stakeholders in the global healthcare and pharmaceutical sectors. Leveraging over 170 million verified professional profiles and 30 million company profiles, this dataset includes detailed contact information, firmographic insights, and leadership data for hospitals, clinics, biotech firms, medical device manufacturers, pharmaceuticals, and other healthcare-related enterprises. Whether your goal is to pitch a new medical technology, partner with healthcare providers, or conduct market research, Success.ai ensures that your outreach and strategic planning are guided by reliable, continuously updated, and AI-validated data.

Why Choose Success.ai’s Healthcare Industry Leads Data?

1. Comprehensive Contact Information

   • Access verified work emails, phone numbers, and LinkedIn profiles of healthcare administrators, pharmaceutical executives, R&D directors, procurement officers, and medical staff.
   • AI-driven validation ensures 99% accuracy, reducing wasted efforts and fostering efficient communication.

2. Global Reach Across Healthcare Segments

   • Includes profiles from hospitals, private clinics, pharmaceutical companies, biotech startups, research institutions, and medical supply chain partners.
   • Covers North America, Europe, Asia-Pacific, South America, and the Middle East, enabling a global perspective on healthcare systems and opportunities.

3. Continuously Updated Datasets

   • Real-time updates reflect leadership changes, organizational shifts, and emerging trends in patient care, medical innovation, and regulatory compliance.

4. Ethical and Compliant

   • Adheres to GDPR, CCPA, and other global data privacy regulations, ensuring your data usage respects legal standards and patient confidentiality norms.

Data Highlights:

• 170M+ Verified Professional Profiles: Connect with healthcare and pharmaceutical professionals, decision-makers, and influencers worldwide.
• 50M Work Emails: AI-validated for direct, accurate communication and reduced bounce rates.
• 30M Company Profiles: Gain insights into organizational structures, operational scales, and specialization areas.
• 700M Global Professional Profiles: Enriched datasets to support market analysis, product development, and strategic planning.

Key Features of the Dataset:

1. Healthcare Decision-Maker Profiles

   • Identify and engage with CEOs, CIOs, CFOs, chief medical officers, hospital administrators, clinical directors, and procurement specialists.
   • Target professionals who influence equipment purchases, medical supply chain decisions, drug trial approvals, and healthcare delivery models.

2. Detailed Business Profiles

   • Access firmographic data, including company sizes, revenue ranges, key markets, and service lines for a holistic understanding of target organizations.
   • Leverage comprehensive insights to position your products, services, or solutions as tailored fits for specific operational needs.

3. Advanced Filters for Precision Targeting

   • Filter by region, specialty (oncology, cardiology, diagnostics, etc.), hospital size, pharmaceutical focus, or research areas.
   • Align campaigns with unique healthcare demands, reimbursement models, and regulatory environments.

4. AI-Driven Enrichment

   • Profiles enriched with actionable data enable personalized messaging, highlight value propositions, and enhance engagement outcomes with healthcare stakeholders.

Strategic Use Cases:

1. Sales and Business Development

   • Present medical devices, pharma products, or healthcare IT solutions to hospital administrators, chief medical officers, and procurement managers.
   • Build relationships with decision-makers who oversee budgeting, supplier selection, and patient care initiatives.

2. Market Research and Product Innovation

   • Analyze trends in patient treatments, drug pipelines, and healthcare infrastructure to inform R&D and product roadmaps.
   • Identify emerging specialties, new treatment modalities, and growth markets to focus marketing, sales, and investment efforts.

3. Strategic Partnerships and Alliances

   • Connect with R&D directors, biotech executives, or hospital groups to explore collaborations, clinical trials, and joint ventures.
   • Foster partnerships that accelerate product development, enhance patient outcomes, and drive long-term competitiveness.

4. Recruitment and Talent Acquisition

   • Target HR professionals and department heads seeking qualified medical staff, researchers, pharmaceutical reps, and administrative personnel.
   • Offer staffing, training, or professional development services to healthcare institutions aiming to improve service delivery and compliance.

Why Choose Success.ai?

1. Best Price Guarantee
   • Access high-quality, verified data at...

Introduction

This Mandarin Chinese Call Center Speech Dataset for the Healthcare industry is purpose-built to accelerate the development of Mandarin speech recognition, spoken language understanding, and conversational AI systems. With 30 Hours of unscripted, real-world conversations, it delivers the linguistic and contextual depth needed to build high-performance ASR models for medical and wellness-related customer service.

Created by FutureBeeAI, this dataset empowers voice AI teams, NLP researchers, and data scientists to develop domain-specific models for hospitals, clinics, insurance providers, and telemedicine platforms.

Speech Data

The dataset features 30 Hours of dual-channel call center conversations between native Mandarin Chinese speakers. These recordings cover a variety of healthcare support topics, enabling the development of speech technologies that are contextually aware and linguistically rich.

Topic Diversity

The dataset spans inbound and outbound calls, capturing a broad range of healthcare-specific interactions and sentiment types (positive, neutral, negative).

These real-world interactions help build speech models that understand healthcare domain nuances and user intent.

Transcription

Every audio file is accompanied by high-quality, manually created transcriptions in JSON format.

Metadata

Each conversation and speaker includes detailed metadata to support fine-tuned training and analysis.

Usage and Applications

This dataset can be used across a range of healthcare and voice AI use cases:

Thailand TH: Hospital Beds: per 1000 People data was reported at 2.100 Number in 2010. This records a decrease from the previous number of 2.200 Number for 2002. Thailand TH: Hospital Beds: per 1000 People data is updated yearly, averaging 1.684 Number from Dec 1960 (Median) to 2010, with 12 observations. The data reached an all-time high of 2.200 Number in 1999 and a record low of 0.740 Number in 1960. Thailand TH: 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 Thailand – Table TH.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;

IPPS DRG Provider Summary

Average Discharges, Charges, and Medicare Payments

By Health [source]

About this dataset

This dataset is a valuable resource for gaining insight into Inpatient Prospective Payment System (IPPS) utilization, average charges and average Medicare payments across the top 100 Diagnosis-Related Groups (DRG). With column categories such as DRG Definition, Hospital Referral Region Description, Total Discharges, Average Covered Charges, Average Medicare Payments and Average Medicare Payments 2 this dataset enables researchers to discover and assess healthcare trends in areas such as provider payment comparsons by geographic location or compare service cost across hospital. Visualize the data using various methods to uncover unique information and drive further hospital research

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This dataset provides a provider level summary of Inpatient Prospective Payment System (IPPS) discharges, average charges and average Medicare payments for the Top 100 Diagnosis-Related Groups (DRG). This data can be used to analyze cost and utilization trends across hospital DRGs.

To make the most use of this dataset, here are some steps to consider:

• Understand what each column means in the table: Each column provides different information from the DRG Definition to Hospital Referral Region Description and Average Medicare Payments.
• Analyze the data by looking for patterns amongst the relevant columns: Compare different aspects such as total discharges or average Medicare payments by hospital referral region or DRG Definition. This can help identify any potential trends amongst different categories within your analysis.
• Generate visualizations: Create charts, graphs, or maps that display your data in an easy-to-understand format using tools such as Microsoft Excel or Tableau. Such visuals may reveal more insights into patterns within your data than simply reading numerical values on a spreadsheet could provide alone.

Research Ideas

• Identifying potential areas of cost savings by drilling down to particular DRGs and hospital regions with the highest average covered charges compared to average Medicare payments.
• Establishing benchmarks for typical charges and payments across different DRGs and hospital regions to help providers set market-appropriate prices.
• Analyzing trends in total discharges, charges and Medicare payments over time, allowing healthcare organizations to measure their performance against regional peers

Acknowledgements

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

License

License: Open Database License (ODbL) v1.0 - You are free to: - Share - copy and redistribute the material in any medium or format. - 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. - No Derivatives - If you remix, transform, or build upon the material, you may not distribute the modified material. - No additional restrictions - You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.

Columns

File: 97k6-zzx3.csv | Column name | Description | |:-----------------------------------------|:------------------------------------------------------| | drg_definition | Diagnosis-Related Group (DRG) definition. (String) | | average_medicare_payments | Average Medicare payments for each DRG. (Numeric) | | hospital_referral_region_description | Description of the hospital referral region. (String) | | total_discharges | Total number of discharges for each DRG. (Numeric) | | average_covered_charges | Average covered charges for each DRG. (Numeric) | | average_medicare_payments_2 | Average Medicare payments for each DRG. (Numeric) |

**File: Inpatient_Prospective_Payment_System_IPPS_Provider_Summary_for_the_Top_100_Diagnosis-Related_Groups_DRG...

AimTo describe self-reported practices and assess knowledge and attitudes regarding hand hygiene among healthcare workers in a rural Indian teaching hospital.SettingA rural teaching hospital and its associated medical and nursing colleges in the district of Ujjain, India.MethodThe study population consisted of physicians, nurses, teaching staff, clinical instructors and nursing students. Self-administered questionnaires based on the World Health Organization Guidelines on Hand Hygiene in Healthcare were used.ResultsOut of 489 healthcare workers, 259 participated in the study (response rate = 53%). The proportion of healthcare workers that reported to ‘always’ practice hand hygiene in the selected situations varied from 40–96% amongst categories. Reported barriers to maintaining good hand hygiene were mainly related to high workload, scarcity of resources, lack of scientific information and the perception that priority is not given to hand hygiene, either on an individual or institutional level. Previous training on the topic had a statistically significant association with self-reported practice (p = 0.001). Ninety three per cent of the respondents were willing to attend training on hand hygiene in the near future.ConclusionSelf-reported knowledge and adherence varied between situations, but hand hygiene practices have the potential to improve if the identified constraints could be reduced. Future training should focus on enhancing healthcare workers’ knowledge and understanding regarding the importance of persistent practice in all situations.

Introduction

This Tamil Call Center Speech Dataset for the Healthcare industry is purpose-built to accelerate the development of Tamil speech recognition, spoken language understanding, and conversational AI systems. With 30 Hours of unscripted, real-world conversations, it delivers the linguistic and contextual depth needed to build high-performance ASR models for medical and wellness-related customer service.

Created by FutureBeeAI, this dataset empowers voice AI teams, NLP researchers, and data scientists to develop domain-specific models for hospitals, clinics, insurance providers, and telemedicine platforms.

Speech Data

The dataset features 30 Hours of dual-channel call center conversations between native Tamil speakers. These recordings cover a variety of healthcare support topics, enabling the development of speech technologies that are contextually aware and linguistically rich.

Topic Diversity

The dataset spans inbound and outbound calls, capturing a broad range of healthcare-specific interactions and sentiment types (positive, neutral, negative).

These real-world interactions help build speech models that understand healthcare domain nuances and user intent.

Transcription

Every audio file is accompanied by high-quality, manually created transcriptions in JSON format.

Metadata

Each conversation and speaker includes detailed metadata to support fine-tuned training and analysis.

Usage and Applications

This dataset can be used across a range of healthcare and voice AI use cases:

IntroductionItalian administrative healthcare databases are frequently used for studies on real-world drug utilization. However, there is currently a lack of evidence on the accuracy of administrative data in describing the use of infusive antineoplastics. In this study, we used rituximab as a case study to investigate the validity of the regional administrative healthcare database of Tuscany (RAD) in describing the utilization of infusive antineoplastics.MethodsWe identified patients aged 18 years or older who had received ≥1 rituximab administration between 2011 and 2014 in the onco-haematology ward of the University Hospital of Siena. We retrieved this information from the Hospital Pharmacy Database (HPD-UHS) and linked the person-level information to RAD. Patients who had received ≥1dispensing of rituximab, single administration episodes, and patients treated for non-Hodgkin Lymphoma (nHL) or Chronic Lymphocytic Leukemia (CLL) were identified in RAD and validated using HPD-UHS as the reference standard. We identified the indications of use using algorithms based on diagnostic codes (ICD9CM codes, nHL=200*, 202*; CLL=204.1). We tested 22 algorithms of different complexity for each indication of use and calculated sensitivity and positive predictive value (PPV), with 95% confidence intervals (95%CI), as measures of validity.ResultsAccording to HPD-UHS, 307 patients received rituximab for nHL (N=174), CLL (N=21), or other unspecified indications (N=112) in the onco-haematology ward of the University Hospital of Siena. We identified 295 rituximab users in RAD (sensitivity=96.1%), but PPV could not be assessed due to missing information in RAD on dispensing hospital wards. We identified individual rituximab administration episodes with sensitivity=78.6% [95%CI: 76.4-80.6] and PPV=87.6% [95%CI: 86.1-89.2]. Sensitivity of algorithms tested for identifying nHL and CLL ranged from 87.7% to 91.9% for nHL and from 52.4% to 82.7% for CLL. PPV ranged from 64.7% to 66.1% for nHL and from 32.4% to 37.5% for CLL.DiscussionOur findings suggest that RAD is a very sensitive source of information for identifying patients who received rituximab for onco-haematological indications. Single administration episodes were identified with good-to-high accuracy. Patients receiving rituximab for nHL were identified with high sensitivity and acceptable PPV, while the validity for CLL was suboptimal.

DISCLAIMER

Dataset consists of historical data of pre-pandemic period and doesn’t represent the current reality which may have changed due to the spikes in demand. This dataset has been generated in collaboration of efforts within CoronaWhy community.

Context

Last updated: April 26th 2020 Updates: April 14th 2020 - Added missing population data April 15th 2020 - Added Brazil statewise ICU hospital beds dataset April 21th 2020 - Added Italy, Spain statewise ICU hospital beds dataset, India statewise TOTAL hospital beds dataset April 26th 2020 - Added Sweden ICU(2019) and TOTAL(2018) beds datasets

Purpose of the dataset

I am trying to produce a dataset that will provide a foundation for policymakers to understand the realistic capacity of healthcare providers being able to deal with the spikes in demand for intensive care. As a way to help, I’ve prepared a dataset of beds across countries and states. Work in progress dataset that should and will be updated as more data becomes available and public on weekly basis.

Importance

This dataset is intended to be used as a baseline for understanding the typical bed capacity and coverage globally. This information is critical for understanding the impact of a high utilization event, like COVID-19.

Current challenges

Datasets are scattered across the web and are very hard to normalize, I did my best but help would be much appreciated.

Data sources / Acknowledgments

arcgis (USA) - https://services1.arcgis.com/Hp6G80Pky0om7QvQ/arcgis/rest/services/Hospitals_1/FeatureServer/0 KHN (USA) - https://khn.org/news/as-coronavirus-spreads-widely-millions-of-older-americans-live-in-counties-with-no-icu-beds/ datahub.io (World) - https://datahub.io/world-bank/sh.med.beds.zs eurostat - https://data.europa.eu/euodp/en/data/dataset/vswUL3c6yKoyahrvIRyew OECD - https://data.oecd.org/healtheqt/hospital-beds.htm WDI (World) - https://data.worldbank.org/indicator/SH.MED.BEDS.ZS NHP(India) - http://www.cbhidghs.nic.in/showfile.php?lid=1147 data.gov.sg (Singapore) - https://data.gov.sg/dataset/health-facilities?view_id=91b4feed-dcb9-4720-8cb0-ac2f04b7efd0&resource_id=dee5ccce-4dfb-467f-bcb4-dc025b56b977 dati.salute.gov.it (Italy)- http://www.dati.salute.gov.it/dati/dettaglioDataset.jsp?menu=dati&idPag=96 portal.icuregswe.org (Sweden) - https://portal.icuregswe.org/seiva/en/Rapport publications: Intensive Care Medicine Journal (Europe) - https://link.springer.com/article/10.1007/s00134-012-2627-8 Critical Care Medicine Journal (Asia) - https://www.researchgate.net/figure/Number-of-critical-care-beds-per-100-000-population_fig1_338520008 Medicina Intensiva (Spain) - https://www.medintensiva.org/en-pdf-S2173572713000878 news: https://lanuovaferrara.gelocal.it/italia-mondo/cronaca/2020/03/19/news/dietro-la-corsa-a-nuovi-posti-in-terapia-intensiva-gli-errori-del-passato-1.38611596 kaggle: germany - https://www.kaggle.com/manuelblechschmidt/icu-beds-in-germany brazil (IBGE) - https://www.kaggle.com/thiagobodruk/brazilianstates Manual population data search from wiki

Data columns

country,state,county,lat,lng,type,measure,beds,population,year,source,source_url - country - country of origin, if present - state - more granular location, if present - lat - latitude - lng - longtitude - type - [TOTAL, ICU, ACUTE(some data could include ICU beds too), PSYCHIATRIC, OTHER(merged ‘SPECIAL’, ‘CHRONIC DISEASE’, ‘CHILDREN’, ‘LONG TERM CARE’, ‘REHABILITATION’, ‘WOMEN’, ‘MILITARY’] - measure - type of measure (per 1000 inhabitants) - beds - number of beds per 1000 - population - population of location based on multiple sources and wikipedia - year - source year for beds and population data - source - source of data - source_url - URL of the original source

Files

for each of datasource: hospital_beds_per_source.csv

US only: US arcgis + khn (state/county granularity): hospital_beds_USA.csv

Global (state(region)/county granularity): hospital_beds_global_regional.csv

Global (country granularity): hospital_beds_global_v1.csv

Contributors

Igor Kiulian - extracting/normalizing/formatting/merging data Artur Kiulian - helped with Kaggle setup Augaly S. Kiedi - helped with country population data Kristoffer Jan Zieba - found Swedish data sources

Possible Improvements

Find and megre more detailed (state/county wise) or newer datasource

Tuberculosis is a communicable chronic disease and one of the top ten causes of death worldwide according to World Health Organization (WHO). With availability of clean and well encoded clinical data from tuberculosis patients, artificial intelligence and machine learning algorithms would be able to transform the management of tuberculosis patients through intelligent prediction and intervention. This dataset contains four hundred and thirty (430) clinical data from patients with tuberculosis at Tuberculosis and Leprosy Hospital, Eku, Delta State, Nigeria. The dataset was gathered through validated and structured questionnaire administered using random sampling after obtaining the patients' consent. The collated dataset was pre-processed and encoded with variables (features) for prediction which include cough, night sweat, breathing difficulty, fever, chest pain, sputum, immune suppression, loss of pleasure, chill, lack of concentration, irritation, loss of appetite, loss of energy, lymph node enlargement, systolic blood pressure and BMI. Prediction of tuberculosis based on the clinical data from patients' features would play an essential role in diagnosis, intervention and management of tuberculosis patient.