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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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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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 ...

According to a ranking by Statista and Newsweek, the best hospital in Sweden is the Karolinska Universitetssjukhuset in Stockholm. Moreover, Karolinska Universitetssjukhuset was also ranked as the seventh-best hospital in the world, among over ****** hospitals in ** countries. Sahlgrenska Universitetssjukhuset in Göteborg and Akademiska Sjukhuset in Uppsala were ranked as second and third best respectively in the Sweden, while they were **** and **** best respectively in the World.

According to a ranking by Statista and Newsweek, the best hospital in Norway is Oslo Universitetssykehus in Oslo. Moreover, Oslo Universitetssykehus was also ranked as the **** best hospital in the world, among over ****** hospitals in ** countries. St. Olavs Hospital in Trondheim and Haukeland Universitetssykehus in Bergen were ranked as second and third best respectively in the Norway, while they were ***** and ***** best respectively in the World.

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

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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...

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
   ...

Medicare Spending per Beneficiary

Detailed Hospital Expense Breakdown

By Health [source]

About this dataset

This file allows healthcare executives and analysts to make informed decisions regarding how well continued improvements are being made over time so that they can understand how efficient they are fulfilling treatments while staying within budgetary constraints. Additionally, it’ll also help them map out trends amongst different hospitals and spot anomalies that could indicate areas where decisions should be reassessed as needed

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How to use the dataset

This dataset can provide valuable insights into how Medicare is spending per patient at specific hospitals in the United States. It can be used to gain a better understanding of the types of services covered under Medicare, and to what extent those services are being used. By comparing the average Medicare spending across different hospitals, users can also gain insight into potential disparities in care delivery or availability.

To use this dataset, first identify which hospital you are interested in analyzing. Then locate the row for that hospital in the dataset and review its associated values: value, footnote (optional), and start/end dates (optional). The Value column refers to how much Medicare spends on each particular patient; this is a numerical value represented as a decimal number up to 6 decimal places. The Footnote (optional) provides more information about any special circumstances that may need attention when interpreting the value data points. Finally, if Start Date and End Date fields are present they will specify over what timeframe these values were aggregated over.

Once all relevant data elements have been reviewed successively for all hospitals of interest then comparison analysis among them can be conducted based on Value, Footnote or Start/End dates as necessary to answer specific research questions or formulate conclusions about how Medicare is spending per patient at various hospitals nationwide

Research Ideas

• Developing a cost comparison tool for hospitals that allows patients to compare how much Medicare spends per patient across different hospitals.
• Creating an algorithm to help predict Medicare spending at different facilities over time and build strategies on how best to manage those costs.
• Identifying areas in which a hospital can save money by reducing unnecessary spending in order to reduce overall Medicare expenses

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: Medicare_hospital_spending_per_patient_Medicare_Spending_per_Beneficiary_Additional_Decimal_Places.csv | Column name | Description | |:---------------|:--------------------------------------------------------------------------------------| | Value | The amount of Medicare spending per patient for a given hospital or region. (Numeric) | | Footnote | Any additional notes or information related to the value. (Text) | | Start_Date | The start date of the period for which the value applies. (Date) | | End_Date | The end date of the period for which the value applies. (Date) |

Acknowledgements

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

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

The global IV Flush Syringe market is booming, projected to reach $XX million by 2033 with a CAGR of 6%. Driven by rising healthcare spending and technological advancements, key players like BD and B. Braun dominate a market segmented by product type (saline, heparin) and end-user (hospitals, ambulatory centers). Learn more about market trends, growth drivers, and key players. Recent developments include: In July 2021, American weekly magazine newsweek recognized Medline Industries, LP by mentioning its two product names in its list 'Best infection prevention products 2021. These two products are 'Prefilled SwabFlush syringe with SwabCap and 'ReadyPrep CHG'., In April 2021, Nipro Corporation introduced CURACASE, a Hypodermic needle used for Hard-Plastic Unit Packaging. The needles could strengthen the supply and manufacturing of injection systems.. Key drivers for this market are: Rise In Awareness And Favorable Guidelines For Single Use Syringes, Rising Burden Of Chronic Diseases And Infectious Diseases. Potential restraints include: Rise In Awareness And Favorable Guidelines For Single Use Syringes, Rising Burden Of Chronic Diseases And Infectious Diseases. Notable trends are: Hospitals & clinics Segment is Expected to Witness Growth Over the Forecast Period.

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...

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

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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...

Opioid-Related Hospital Visits in NY

Rates and Visits From 2010-2019

By Health [source]

About this dataset

This dataset contains invaluable information about opioid-related hospital facility visits in New York State. It provides a summary of inpatient discharges and outpatient visit data for those with opioid-related diagnosis codes such as poisonings by opioids, opium, heroin, methadone, and other related narcotics. This dataset is an important resource for understanding the current state of opioid addiction in New York and can be used to inform policy decisions on opioid misuse. We have detailed metrics including patient county name/code and rural/urban status to better track the geographic distribution which can provide further insight into the correlations between county demographics, access to treatment programs or other factors. In addition to rural/urban status we have payer information that shows what type of payer was associated with these visits as well as average rates per 1000 people for ambulatory surgery visits, emergency room discharges, outpatients visits and other outpatient total rate per 1000 people; ultimately providing an overview on how many people are affected by opioids per area in New York State. All this said; this datasets serves as a valuable tool for healthcare providers, policy makers and public health officials should leverage it accordingly when making policy decisions about how best to fight the opioid epidemic facing us today.

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How to use the dataset

The dataset “Opioid-Related Hospital Facility Visits in New York” provides valuable insights into the prevalence of opioid related hospital visits in New York State. This dataset can be used to better understand the scope of the opioid crisis, identify areas that are more heavily affected by it, and inform preventive policies and practices.

First, you will want to explore trends in overall hospital visits related to opioids over time using patient county name, rural/urban status or year. You may also want to view figures for overall rate per 1000 people or combined rate per 1000 people for complete clarity. Drill down further into individual column metrics such as ER inpatient total rate per 1000 people, ambulatory surgery visits or outpatient rates per 1000 people if trends warrant it.

You may also take a closer look at which counties are most impacted by opioid use disorder by looking at data across multiple years with patient county code and name columns on hand. Payer information can be useful when looking for indicators of high cost burdens associated with these health issues across different communities as well.

Finally, you can use this dataset by combining different metrics from various sources to get an even clearer picture of where opioid health care is lacking resources – identifying highly populated urban centers facing higher rates of addiction diagnosis compared to rural communities will allow policy makers and public health specialists formulate appropriate actionable steps towards addressing the epidemic that affects our nation today!

Research Ideas

• Developing predictive models to understand which areas are at higher risk for opioid-related hospital visits and designing interventions to reduce their occurrence.
• Estimating the cost associated with opioid-related hospital visits using the payer information available in the dataset and identifying ways to reduce those costs through cost effective policies or programs.
• Exploring correlations between rural/urban designation, inpatient ER discharges, ambulatory surgery visits, and other related variables from other datasets with opioid-related facility visits data set in order to identify potential risk factors for opioid abuse or dependency among specific geographic areas

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 r...

Description

Automatic detection of diseases by use of computers is an important, but still unexplored field of research. Such innovations may improve medical practice and refine health care systems all over the world. However, datasets containing medical images are hardly available, making reproducibility and comparison of approaches almost impossible. Here, we present Kvasir, a dataset containing images from inside the gastrointestinal (GI) tract. The collection of images are classified into three important anatomical landmarks and three clinically significant findings. In addition, it contains two categories of images related to endoscopic polyp removal. Sorting and annotation of the dataset is performed by medical doctors (ex- perienced endoscopists). In this respect, Kvasir is important for research on both single- and multi-disease computer aided detec- tion. By providing it, we invite and enable multimedia researcher into the medical domain of detection and retrieval.

Data Collection by original author

The data is collected using endoscopic equipment at Vestre Viken Health Trust (VV) in Norway. The VV consists of 4 hospitals and provides health care to 470.000 people. One of these hospitals (the Bærum Hospital) has a large gastroenterology department from where training data have been collected and will be provided, making the dataset larger in the future. Furthermore, the images are carefully annotated by one or more medical experts from VV and the Cancer Registry of Norway (CRN). The CRN provides new knowledge about cancer through research on cancer. It is part of South-Eastern Norway Regional Health Authority and is organized as an independent institution under Oslo University Hospital Trust. CRN is responsible for the national cancer screening programmes with the goal to prevent cancer death by discovering cancers or pre-cancerous lesions as early as possible.

Applications of the Dataset

Our vision is that the available data may eventually help researchers to develop systems that improve the health-care system in the context of disease detection in videos of the GI tract. Such a system may automate video analysis and endoscopic findings detection in the esophagus, stomach, bowel and rectum. Important results will include higher detection accuracies, reduced manual labor for medical personnel, reduced average cost, less patient discomfort and possibly increased willingness to undertake the examination. In the end, the improved screening will probably significantly reduce mortality and number of luminal GI disease incidents. With respect to direct use in the multimedia research areas, the main application area of Kvasir is automatic detection, classification and localization of endoscopic pathological findings in an image captured in the GI tract. Thus, the provided dataset can be used in several scenarios where the aim is to develop and evaluate algoritmic analysis of images. Using the same collection of data, researchers can easier compare approaches and experimental results, and results can easier be reproduced. In particular, in the area of image retrieval and object detection, Kvasir will play an important initial role where the image collection can be divided into training and test sets for developments of and experiments for various image retrieval and object localization methods including search-based systems, neural-networks, video analysis, information retrieval, machine learning, object detection, deep learning, computer vision, data fusion and big data processing.

Provenance

The Kvasir dataset created within the Norwegian FRINATEK project "EONS" (#231687) at Simula Research Laboratory, Norway.

Data Documentation

The data and the detailed description and usage instructions are published online at the dataset web-page http://datasets.simula.no/kvasir/

Work Flows:

The images provided can be used for developing, testing and comparison of different image recognition and classification approaches regarding to their specific procedures.

Suggested Metrics

Looking at the list of related work in this area, there are a lot of different metrics used, with potentially different names when used in the medical area and the computer science (information retrieval) area. Here, we provide a small list of the most important metrics. For future research, in addition to describing the dataset with respect to total number of images, total number of images in each class and total number of positives, it might be good to provide as many of the metrics below as possible in order to enable an indirect comparison with older work:

• True positive (TP) The number of correctly identified samples. The number of frames with an endoscopic finding which correctly is identified as a frame with an endoscopic finding.
• True negative (TN) The number of correctly identified negative samples, i.e., frames wi...

During the financial year 2024/25, the busiest hospital provider in England was the ************************************************ with over *** thousand admissions. This trust encompasses four hospitals in the Birmingham area, one of the largest urban areas in England. The second-busiest trust this year was the ******************************************, with approximately *** thousand admissions. Accident and emergency admissionsIn the second quarter of 2024/25, there were around *** million accident and emergency (A&E) attendees in England (including at A&E departments not in hospitals). After the drop in A&E attendances during the COVID-pandemic, numbers have risen again to previous levels, with a trend towards an increasing number of individuals seeking emergency care. Around ****percent of A&E attendees in England in 2024/5 were first diagnosed with a lower respiratory infection. Furthermore, over**** percent were found to have ‘no abnormality detected’ which could be detrimental to a service that is already stretched. Waiting too longOver the last few years in the A&E department, the NHS has been falling behind the target that ** percent of patients should be seen within **** hours of arrival. The last time this target was reached was back in July 2015. Not just the A&E department, but other services also require lengthy waits. It is no wonder that the levels of satisfaction with the way the NHS runs is at an all-time low.

The number of smokers in Saudi Arabia was forecast to continuously increase between 2024 and 2029 by in total 0.4 million individuals (+8.85 percent). After the fifteenth consecutive increasing year, the number of smokers is estimated to reach 4.88 million individuals and therefore a new peak in 2029. Notably, the number of smokers of was continuously increasing over the past years.Shown is the estimated share of the adult population (15 years or older) in a given region or country, that smoke. According to the WHO and World bank, smoking refers to the use of cigarettes, pipes or other types of tobacco, be it on a daily or non-daily basis.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 number of smokers in countries like Qatar and Oman.

Medicare FFS Beneficiary Utilization and Costs

2007-2014 Washington State and Counties

By Health [source]

About this dataset

This dataset contains info on the number of Medicare Fee-for-Service Beneficiaries (FFS) receiving healthcare services from hospitals, physicians, and other providers, as well as their associated charges and payments. It provides in-depth, detailed demographics like age group, gender, all kinds of race/ethinicity data and geographical regions. This information can be used to better understand existing health disparities among Medicare FFS beneficiaries across the U.S., examine trends in utilization over time to identify areas where changes are needed within the system or research a wide range of policy issues in healthcare

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How to use the dataset

This dataset provides a look into Medicare Fee-for-Service beneficiaries in health services being utilized by those enrolled in the Medicare program. The information included can help to paint a picture of how Medicare recipients are using health services, such as hospital and physician visits, laboratory tests and procedures, prescription drugs and imaging services.

In order to make the most use of this data set for research or analysis purposes, there are several key pieces of information that should be taken into account. This includes examining both utilization data (such as the numbers of recommended specific procedures) as well as cost components (such as fee schedules). Specifics within this data set include the average estimated Submitted Charges for each procedure code from nationwide claims from 2011 to 2018.

When looking at utilization portion of this dataset it is important to consider: • Total number of services provided for each condition identified by ICD-9 or ICD10 code • Average total service minutes per beneficiary / patient with national average levels listed across five years throughout the period previously mentioned • Percentage change across accessed service types over time period wherein 2011 have been viewed versus more recent statistics • Top five provider specialty types who render service • Number of facilities providing care on annual basis along with percentages utilizing Rural Health Centers grouped together categories including but not exclusive not limited to metropolitan areas; counties; Congressional Districts ; Regions; states plus other geographic entities • Age groups who have used these facilities based on gender plus new acute admissions reported same time frame

A secondary component yet equally important component regarding fees associated with different medical therapies should be considered additionally when uses dataset  which includes:

• Amounts charged by certain facilities based upon current expenses related dates whether patient purchased generic version or brand-name medication due its additional costs relates most significantly towards said medication choices National level along with regional percentage splits relating drug alternatives utilized per given month Actual recharge associated calculated mechanism/formulae , sometimes may refer UPFS methodology Those charges represent sum total averages against whom paid expense examples include: Part B drugs recipients outpatient surgeries & facility visits Note future amounts collected depend upon patients Choice whether require certain distinct E&M codes sometimes need submit ancillary components( diagnoses codes ) separate selections meant cater both facility site & practitioner’s overall needs Sometimes technology assigns relative value unit ( RVU ) defining severity factors linked coding differing specialties so their respective fields well documented Finally analyzing any detail reporting requirements varying specialties

Research Ideas

• Analyze various patterns in health services utilization by Medicare beneficiaries to provide insight into the most commonly used services and ways to improve care.
• Track the number of Medicare beneficiaries using each type of health service in order to identify potential underserved populations or areas with high usage levels that necessitate additional coverage or resources.
• Identify regional differences in provider use rates and payment amounts for specific types of health services, which can help inform efforts to improve equity and access across different geographical regions

Acknowledgements

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