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

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

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.

Summary characteristics of hospitals comprising each neighborhood, demographics of their Hospital Service Areas, and their U.S. News and World Report, Leapfrog, Consumer Reports, and Health Grades ratings.

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).

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.

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).

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

We are working to develop a comprehensive dataset of surgical tools based on specialities, with a hierarchical structure – speciality, pack, set and tool. We belive that this dataset can be useful for computer vision and deep learning research into surgical tool tracking, management and surgical training and audit. We have therefore created an initial dataset of surgical tool (instrument and implant) images, captured using under different lighting conditions and with different backgrounds. We captured RGB images of surgical tools using a DSLR camera and webcam on site in a major hospital under realistic conditions and with the surgical tools currently in use. Image backgrounds in our initial dataset were essentially flat colours, even though different colour backgrounds were used. As we further developed our dataset, we will try to include much greater occlusions, illumination changes, and the presence of blood, tissue and smoke in the images which would be more reflective of crowded, messy, real-world conditions.

Illumination sources included natural light – direct sunlight and shaded light – LED, halogen and fluorescent lighting, and this accurately reflected the illumination working conditions within the hospital. Distances of the surgical tools to the camera to the object ranged from 60 to 150 cms., and the average class size was 74 images. Images captured included individual object images as well as cluttered, clustered and occluded objects. Our initial focus was on Orthopaedics and General Surgery, two out of the 14 surgical specialities. We selected these specialities since general surgery instruments are the most commonly used tools across all surgeries and provide instrument volume, while orthopaedics provides variety and complexity given the wide range of procedures, instruments and implants used in orthopaedic surgery. We will add other specialities as we develop this dataset, to reflect the complexities inherent in each of the surgical specialities. This dataset was designed to offer a large variety of tools, arranged hierarchically to reflect how surgical tools are organised in real-world conditions.

If you do find our dataset useful, please cite our papers in your work:

Rodrigues, M., Mayo, M, and Patros, P. (2022). OctopusNet: Machine Learning for Intelligent Management of Surgical Tools. Published in “Smart Health”, Volume 23, 2022. https://doi.org/10.1016/j.smhl.2021.100244

Rodrigues, M., Mayo, M, and Patros, P. (2021). Evaluation of Deep Learning Techniques on a Novel Hierarchical Surgical Tool Dataset. Accepted paper at The 2021 Australasian Joint Conference on Artificial Intelligence. 2021. To be Published in Lecture Notes in Computer Science series.

Rodrigues, M., Mayo, M, and Patros, P. (2021). Interpretable deep learning for surgical tool management. In M. Reyes, P. Henriques Abreu, J. Cardoso, M. Hajij, G. Zamzmi, P. Rahul, and L. Thakur (Eds.), Proc 4th International Workshop on Interpretability of Machine Intelligence in Medical Image Computing (iMIMIC 2021) LNCS 12929 (pp. 3-12). Cham: Springer.

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

Background

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

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

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

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

Dataset

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

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

Task Details

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

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

Expected Submission

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

Evaluation

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

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

Questions and More Info

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

Answers to most voted questions

Missing data

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

Variables in addition to laboratory results

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

Additional laboratory variables

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

Our message to all participants

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

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

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

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

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

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

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).

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.

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...

Analysis of ‘Patient Treatment Classification’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/manishkc06/patient-treatment-classification on 28 January 2022.

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

Context

In hospitals, medical treatments and surgeries can be categorized into inpatient and outpatient procedures. For patients, it is important to understand the difference between these two types of care, because they impact the length of a patient’s stay in a medical facility and the cost of a procedure.

Inpatient Care (Incare Patient) and Outpatient Care (Outcare Patient)

The difference between an inpatient and outpatient care is how long a patient must remain in the facility where they have the procedure done.

Inpatient care requires overnight hospitalization. Patients must stay at the medical facility where their procedure was done (which is usually a hospital) for at least one night. During this time, they remain under the supervision of a nurse or doctor.

Patients receiving outpatient care do not need to spend a night in a hospital. They are free to leave the hospital once the procedure is over. In some exceptional cases, they need to wait while anesthesia wears off or to make sure there are not any complications. As long as there are not any serious complications, patients do not have to spend the night being supervised. [source of information: pbmhealth]

Content

Problem Statement In today’s world of automation, the skills and knowledge of a person could be utilized at the best places possible by automating tasks wherever possible. As a part of the hospital automation system, one can build a system that would predict and estimate whether the patient should be categorized as an incare patient or an outcare patient with the help of several data points about the patients, their conditions and lab tests.

Objective Build a machine learning model to predict if the patient should be classified as in care or out care based on the patient's laboratory test result.

Data

About the data The dataset is Electronic Health Record Predicting collected from a private Hospital in Indonesia. It contains the patient's laboratory test results used to determine next patient treatment whether in care or out care.

Attribute Information

Given is the attribute name, attribute type, the measurement unit and a brief description.

Name / Data Type / Value Sample/ Description

HAEMATOCRIT /Continuous /35.1 / Patient laboratory test result of haematocrit

HAEMOGLOBINS/Continuous/11.8 / Patient laboratory test result of haemoglobins

ERYTHROCYTE/Continuous/4.65 / Patient laboratory test result of erythrocyte

LEUCOCYTE /Continuous /6.3 / Patient laboratory test result of leucocyte

THROMBOCYTE/Continuous/310/ Patient laboratory test result of thrombocyte

MCH/Continuous /25.4/ Patient laboratory test result of MCH

MCHC/Continuous/33.6/ Patient laboratory test result of MCHC

MCV/Continuous /75.5/ Patient laboratory test result of MCV

AGE/Continuous/12/ Patient age

SEX/Nominal – Binary/F/ Patient gender

SOURCE/Nominal/ {1,0}/The class target 1.= in care patient, 0 = out care patient

Acknowledgements

This dataset was downloaded from Mendeley Data. Sadikin, Mujiono (2020), “EHR Dataset for Patient Treatment Classification”, Mendeley Data, V1, doi: 10.17632/7kv3rctx7m.1

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

Introduction

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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:

Introduction

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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 40 Hours of dual-channel call center conversations between native Australian English 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:

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