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.

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1. Verified Contact Data for Targeted Engagement

   • Access verified work emails, phone numbers, and LinkedIn profiles of healthcare executives, hospital administrators, and medical directors.
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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

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   • 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
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In 2023, there were nearly 11 thousand hospitals in Columbia, the highest number among OECD countries, followed by 8,156 hospitals in Japan. If only general hospitals were counted (excluding mental health hospitals and other specialized hospitals), Japan had the most number of general hospitals among OECD countries worldwide. Most countries reported hospitals numbers similar to or lower than the previous year. Meanwhile, Mexico, South Korea and the Netherlands all reported more hospitals than last year.

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

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

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

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

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

Restoration of elective activity is one of the highest priorities for NHS England and NHS Improvement following the impact of the Covid-19 pandemic. Understanding the composition of the waiting list is critical to managing restoration within North West London.

Data will be collected via data submissions made by each individual provider of NHS Acute healthcare services in North West London. This dataset includes data from Imperial College Healthcare NHS Trust, Chelsea and Westminster NHS Foundation Trust, London North West Healthcare NHS Trust and The Hillingdon Hospital NHS Trust. Data will be processed under an Information Sharing Agreement between North West London CCG and each organisation. Data submissions will be processed and used for the following purposes:

1. Developing a visual display of the waiting list composition (Elective Waiting List Data Dashboard).
2. Developing a data quality improvement programme with providers.

All RTT pathways with a clock start date after 23:59 on Sunday 4th April 2021 and before 23:59 on the Sunday of the reporting period and not recorded to date (in a previous submission).

Access one of the most robust, up-to-date databases in the industry with McGRAW's Global Healthcare Professionals Masterfile. Our database includes 16 million verified healthcare professionals from around the world, offering an unparalleled resource for B2B marketing, lead generation, and data enhancement. McGRAW's proprietary sources and extensive validation processes ensure the highest accuracy in our records, making it a trusted choice for connecting with healthcare experts.

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Each list is updated with USPS’s 48-month NCOA (National Change of Address) data before shipment, ensuring address accuracy. All records are 100% DPV (Delivery Point Validation) coded, and phone numbers are appended upon request, with DNC (Do Not Call) scrubbing performed within the last 30 days to guarantee top-tier data hygiene and compliance.

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Comparing the 148 selected regions regarding the average number of hospital beds available per 1,000 people , South Korea is leading the ranking (12.98 beds) and is followed by Japan with 12.5 beds. At the other end of the spectrum is Burkina Faso with 0.18 beds, indicating a difference of 12.8 beds to South Korea. Depicted is the number of hospital beds per capita in the country or region at hand. As defined by World Bank this includes inpatient beds in general, specialized, public and private hospitals as well as rehabilitation centers.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).

BackgroundIn a global effort to design better hospital buildings for people and organizations, some design principles are still surrounded by great mystery. The aim of this online study was to compare anxiety in an existing single-bed inpatient hospital room with three redesigns of this room in accordance with the principles of Golden Ratio, Feng Shui, and Evidence-Based Design.MethodsIn this online multi-arm parallel-group randomized trial participants were randomly assigned (1:1:1:1) to one of four conditions, namely Golden Ratio condition, Feng Shui condition, Evidence-Based Design condition, or the control condition. The primary outcomes were anxiety, sense of control, social support, positive distraction, and pleasantness of the room.FindingsBetween June 24, 2022, and August 22, 2022, 558 individuals were randomly assigned to one of the four conditions, 137 participants to the control condition, 138 participants to the Golden Ratio condition, 140 participants to the Feng Shui condition, and 143 participants to the Evidence-Based Design condition. Compared with baseline, participants assigned to the Evidence-Based Design condition experienced less anxiety (mean difference -1.35, 95% CI -2.15 to -0.55, Cohen’s d = 0.40, p < 0.001). Results also showed a significant indirect effect of the Feng Shui condition on anxiety through the pleasantness of the room (B = -0.85, CI = -1.29 to -0.45) and social support (B = -0.33, CI = -0.56 to -0.13). Pleasantness of the room and social support were mediators of change in anxiety in the Evidence-Based Design and Feng Shui conditions. In contrast, application of the design principle Golden Ratio showed no effect on anxiety and remains a myth.InterpretationTo our knowledge, this is the first randomized controlled trial linking design principles directly to anxiety in hospital rooms. The findings of our study suggest that Feng Shui and Evidence-Based Design hospital rooms can mitigate anxiety by creating a pleasant looking hospital room that fosters access to social support.Clinical trial registrationThe trial is registered with ISRCTN, ISRCTN10480033.

Sepsis is the leading cause of global death with the highest burden found in sub-Saharan Africa (sSA). The Universal Vital Assessment (UVA) score is a validated resource-appropriate clinical tool to identify hospitalized patients in sSA who are at risk of in-hospital mortality. Whether a decrease in the UVA score over 6 hours of resuscitation from sepsis is associated with improved outcomes is unknown. We aimed to determine (1) the association between 6-hour UVA score and in-hospital mortality, and (2) if a decrease in UVA score from admission to 6 hours was associated with improved in-hospital mortality. We analyzed data from participants with severe sepsis aged ≥14 years enrolled at the Mbarara Regional Referral Hospital in Uganda from October 2014 through May 2015. Among 197 participants, the median (interquartile range) age was 34 (27–47) years, 99 (50%) were female and 116 (59%) were living with HIV. At 6 hours, of the 65 participants in the high-risk group, 28 (43%) died compared to 28 (30%) of 94 in the medium-risk group (odds ratio [OR] 0.56, 95% confidence interval [CI] 0.29,1.08, p = 0.086) and 3 (9%) of 33 in the low-risk group (OR 0.13, 95% CI 0.03, 0.42, p = 0.002). In a univariate analysis of the 85 participants who improved their UVA risk group at 6 hours, 20 (23%) died compared to 39 (36%) of 107 participants who did not improve (OR 0.54, 95% CI 0.27–1.06, p = 0.055). In the multivariable analysis, the UVA score at 6 hours (adjusted OR [aOR] 1.26, 95%CI 1.10–1.45, p

Government Hospital: Maharashtra: Number of Hospital Beds: Urban data was reported at 16,417.000 Unit in 2022. This records a decrease from the previous number of 22,078.000 Unit for 2020. Government Hospital: Maharashtra: Number of Hospital Beds: Urban data is updated yearly, averaging 36,730.000 Unit from Dec 2006 (Median) to 2022, with 10 observations. The data reached an all-time high of 151,445.000 Unit in 2013 and a record low of 16,417.000 Unit in 2022. Government Hospital: Maharashtra: Number of Hospital Beds: Urban data remains active status in CEIC and is reported by Central Bureau of Health Intelligence. The data is categorized under India Premium Database’s Health Sector – Table IN.HLA002: Health Infrastructure: Government Hospital Beds.

BackgroundMaternal mortality is of global concern, almost 800 women die every day due to maternal complications. The maternal death surveillance and response (MDSR) system is one strategy designed to reduce maternal mortality. In 2021 Makonde District reported a maternal mortality ratio of 275 per 100 000 and only sixty-two percent of deaths recorded were audited. We evaluated the MDSR system in Makonde to assess its performance.MethodsA descriptive cross-sectional study was conducted using the CDC guidelines for evaluating public health surveillance systems. An Interviewer-administered questionnaire was used to collect data from 79 health workers involved in MDSR and healthcare facilities. All maternal death notification forms, weekly disease surveillance forms, and facility monthly summary forms were reviewed. We assessed health workers’ knowledge, usefulness and system attributes.ResultsWe interviewed 79 health workers out of 211 workers involved in MDSR and 71 (89.9%) were nurses. The median years in service was 8 (IQR: 4–12). Overall health worker knowledge (77.2%) was good. Ninety-three percent of the deaths audited were of avoidable causes. Twelve out of the thirty-eight (31.6%) facilities were using electronic health records system. Feedback and documented shared information were evident at four facilities (21%) including the referral hospital. Nineteen (67.9%) out of 28 maternal death notification forms were completed within seven days and none were submitted to the PMD on time.ConclusionThe MDSR system was acceptable and simple but not timely, stable and complete. Underutilization of the electronic health system, work load, poor documentation and data management impeded performance of the system. We recommended appointment of an MDSR focal person, sharing audit minutes and improved data management.

Description of model state variables.

The average number of hospital beds available per 1,000 people in Argentina was forecast to continuously decrease between 2024 and 2029 by in total 0.2 beds (-6.56 percent). After the eighth consecutive decreasing year, the number of available beds per 1,000 people is estimated to reach 2.85 beds and therefore a new minimum in 2029. Depicted is the number of hospital beds per capita in the country or region at hand. As defined by World Bank this includes inpatient beds in general, specialized, public and private hospitals as well as rehabilitation centers.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the average number of hospital beds available per 1,000 people in countries like Paraguay and Uruguay.

Number and percentage of live births, by month of birth, 1991 to most recent year.

The number of hospital beds in Spain was forecast to continuously decrease between 2024 and 2029 by in total 2.6 thousand beds (-1.95 percent). After the tenth consecutive decreasing year, the number of hospital beds is estimated to reach 130.51 thousand beds and therefore a new minimum in 2029. Depicted is the estimated total number of hospital beds in the country or region at hand.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).

The number of hospitals in Spain was forecast to remain on a similar level in 2029 as compared to 2024 with 775 hospitals. According to this forecast, the number of hospitals will stay nearly the same over the forecast period. Depicted is the number of hospitals in the country or region at hand. As the OECD states, the rules according to which an institution can be registered as a hospital vary across countries.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).

Distribution of resistance patterns according to hospital source, gender and type of clinical specimen.