The three datasets (i.e., Transfer-Encounters, Transfer-Outcomes, Transfer-Locations) provide the number of hospital transfers and Major Diagnostic Categories (MDCs). The Transfer-Encounters Dataset provides transfer counts by patient characteristics (i.e., Race/Ethnicity, Language Group, Expected Payer, Age Group and Assigned Sex at Birth) for each MDC. The Transfer-Outcomes shows the transfer outcomes (i.e., Median Length of Stay, Percent of Leaving Against Medical Advice and Percent of Inpatient Death) by MDC. The Transfer-Location dataset shows the locations of Acute Care Hospitals which received at least 25 transfers by MDC.

Analysis of ‘Hospital ratings’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/center-for-medicare-and-medicaid/hospital-ratings on 28 January 2022.

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

Context

This are the official datasets used on the Medicare.gov Hospital Compare Website provided by the Centers for Medicare & Medicaid Services. These data allow you to compare the quality of care at over 4,000 Medicare-certified hospitals across the country.

Content

Dataset fields:

• Provider ID
• Hospital Name
• Address
• City
• State
• ZIP Code
• County Name
• Phone Number
• Hospital Type
• Hospital Ownership
• Emergency Services
• Meets criteria for meaningful use of EHRs
• Hospital overall rating
• Hospital overall rating footnote
• Mortality national comparison
• Mortality national comparison footnote
• Safety of care national comparison
• Safety of care national comparison footnote
• Readmission national comparison
• Readmission national comparison footnote
• Patient experience national comparison
• Patient experience national comparison footnote
• Effectiveness of care national comparison
• Effectiveness of care national comparison footnote
• Timeliness of care national comparison
• Timeliness of care national comparison footnote
• Efficient use of medical imaging national comparison
• Efficient use of medical imaging national comparison

Acknowledgements

Dataset was downloaded from [https://data.medicare.gov/data/hospital-compare]

Inspiration

If you just broke your leg, you might need to use this dataset to find the best Hospital to get that fixed!

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

The dataset contains risk-adjusted mortality rates, and number of deaths and cases for 6 medical conditions treated (Acute Stroke, Acute Myocardial Infarction, Heart Failure, Gastrointestinal Hemorrhage, Hip Fracture and Pneumonia) and 6 procedures performed (Abdominal Aortic Aneurysm Repair, Carotid Endarterectomy, Craniotomy, Esophageal Resection, Pancreatic Resection, Percutaneous Coronary Intervention) in California hospitals. The 2014 and 2015 IMIs were generated using AHRQ Version 5.0, while the 2012 and 2013 IMIs were generated using AHRQ Version 4.5. The differences in the statistical method employed and inclusion and exclusion criteria using different versions can lead to different results. Users should not compare trends of mortality rates over time. However, many hospitals showed consistent performance over years; “better” performing hospitals may perform better and “worse” performing hospitals may perform worse consistently across years. This dataset does not include conditions treated or procedures performed in outpatient settings. Please refer to hospital table for hospital rates: https://data.chhs.ca.gov/dataset/california-hospital-inpatient-mortality-rates-and-quality-ratings

The dataset contains risk-adjusted mortality rates, quality ratings, and number of deaths and cases for 6 medical conditions treated (Acute Stroke, Acute Myocardial Infarction, Heart Failure, Gastrointestinal Hemorrhage, Hip Fracture and Pneumonia) and 3 procedures performed (Carotid Endarterectomy, Pancreatic Resection, and Percutaneous Coronary Intervention) in California hospitals. The 2023 IMIs were generated using AHRQ Version 2024, while previous years' IMIs were generated with older versions of AHRQ software (2022 IMIs by Version 2023, 2021 IMIs by Version 2022, 2020 IMIs by Version 2021, 2019 IMIs by Version 2020, 2016-2018 IMIs by Version 2019, 2014 and 2015 IMIs by Version 5.0, and 2012 and 2013 IMIs by Version 4.5). The differences in the statistical method employed and inclusion and exclusion criteria using different versions can lead to different results. Users should not compare trends of mortality rates over time. However, many hospitals showed consistent performance over years; “better” performing hospitals may perform better and “worse” performing hospitals may perform worse consistently across years. This dataset does not include conditions treated or procedures performed in outpatient settings. Please refer to statewide table for California overall rates: https://data.chhs.ca.gov/dataset/california-hospital-inpatient-mortality-rates-and-quality-ratings/resource/af88090e-b6f5-4f65-a7ea-d613e6569d96

The dataset contains counts for the Top Five inpatient diagnosis groups based on Major Diagnostic Categories (MDCs) from the Patient Discharge Data (PDD) for each California hospital. Each MDC corresponds to a major organ system (e.g., Respiratory System, Circulatory System, Digestive System) rather than a specific disease (e.g., cancer, sepsis). The MDCs are also generally associated with a particular medical specialty. Therefore, the MDCs can be used to help identify what types of health care specialists are needed at each facility. For instance, a facility with “Circulatory System, Disease and Disorders” as one of their Top Five MDC diagnosis groups is more likely to have a greater need for cardiac specialists. The data will be updated on an annual basis.

Attrition of nurses in the US Healthcare system is at an all-time high. It is a major area of focus, especially for hospitals.

This dataset contains employee and company data useful for supervised ML, unsupervised ML, and analytics. Attrition - whether an employee left or not - is included and can be used as the target variable.

The data is synthetic and based on the IBM Watson dataset for attrition. Employee roles and departments were changed to reflect the healthcare domain. Also, known outcomes for some employees were changed to help increase the performance of ML models.

Here's an app I use as a demo based on this dataset and an ML classification model.

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The data for this assignment come from the Hospital Compare web site (http://hospitalcompare.hhs.gov)run by the U.S. Department of Health and Human Services. The purpose of the web site is to provide data and information about the quality of care at over 4,000 Medicare-certied hospitals in the U.S. This dataset essentially covers all major U.S. hospitals. This dataset is used for a variety of purposes, including determining whether hospitals should be ned for not providing high quality care to patients (see http://goo.gl/jAXFX for some background on this particular topic).

The three datasets (i.e., Transfer-Encounters, Transfer-Outcomes, Transfer-Locations) provide the number of hospital transfers and Major Diagnostic Categories (MDCs). The Transfer-Encounters Dataset provides transfer counts by patient characteristics (i.e., Race/Ethnicity, Language Group, Expected Payer, Age Group and Assigned Sex at Birth) for each MDC. The Transfer-Outcomes shows the transfer outcomes (i.e., Median Length of Stay, Percent of Leaving Against Medical Advice and Percent of Inpatient Death) by MDC. The Transfer-Location dataset shows the locations of Acute Care Hospitals which received at least 25 transfers by MDC.

This dataset provides a historical view of proposed long term care, and individual and small group major medical/hospital rate increases starting in January 2005.

This line chart compares the median cost vs. median charge for major depressive disorders & other unspecified psychoses with a moderate severity of illness by hospital. The dataset contains information submitted by New York State Article 28 Hospitals as part of the New York Statewide Planning and Research Cooperative (SPARCS) and Institutional Cost Report (ICR) data submissions. The dataset contains information on the volume of discharges, All Payer Refined Diagnosis Related Group (APR-DRG), the severity of illness level (SOI), medical or surgical classification the median charge, median cost, average charge and average cost per discharge. When interpreting New York’s data, it is important to keep in mind that variations in cost may be attributed to many factors. Some of these include overall volume, teaching hospital status, facility specific attributes, geographic region and quality of care provided.For more information, check out: http://www.health.ny.gov/statistics/sparcs/. The "About" tab contains additional details concerning this dataset.

This dataset provides the adjusted length of stay, type of care, discharges with valid charges, charges by hospital, licensure of bed, and Major Diagnostic Category (MDC).

This data package includes information regarding All Patients Refined Diagnosis Related Groups (APR DRG) weights for average length and capital ratios for acute care hospitals. It comprises of dataset about DRGs by procedures, hospital-aquired conditions and major diagnostic categories as well as Medicare severity, payments, average and operating ratios and percentile lengths. It also contains information about the wage index table by Core Based Statistical Area (CBSA) for the fiscal year 2017.

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.

Mapping hospitals allows users to visualize its distribution across Somerset County. It provides contact and services information, in which a user can directly reach out to the hospitals they are interested in for inquiries about the specific services offered, (such as medical specialties, emergency care, surgical procedures, diagnostic facilities, and other healthcare services).

This line chart compares the median costs vs. median charges for renal failure with a major severity of illness by hospital. The dataset contains information submitted by New York State Article 28 Hospitals as part of the New York Statewide Planning and Research Cooperative (SPARCS) and Institutional Cost Report (ICR) data submissions. The dataset contains information on the volume of discharges, All Payer Refined Diagnosis Related Group (APR-DRG), the severity of illness level (SOI), medical or surgical classification the median charge, median cost, average charge and average cost per discharge. When interpreting New York’s data, it is important to keep in mind that variations in cost may be attributed to many factors. Some of these include overall volume, teaching hospital status, facility specific attributes, geographic region and quality of care provided.For more information, check out: http://www.health.ny.gov/statistics/sparcs/. The "About" tab contains additional details concerning this dataset.

This study is to investigate the effect of public reporting of acute myocardial infarction (AMI) care on the people’s choice of hospitals. A cross-sectional study was conducted using an online questionnaire. The survey questions include the awareness and usage of public reporting, and the impact of the public reporting on the choice of hospitals. The difference in responses before and after acquiring information about public reporting was compared using multinomial logistic regression. Following a thorough validity check, 740 respondents are included in the final survey data set. The average age of respondents was 38.7 years (SD: 11.8), with 75.3% being female. Age distribution was as follows: 26.3% in their 20s, 23.5% in their 30s, 30.0% in their 40s, and 20.2% in their 50s. Most participants (73.7%) lived in metropolitan areas, and 75.1% had a university degree or higher. Before providing information about public reporting of AMI care, 62.8% of respondents selected ‘nearby hospitals’ as the best option for AMI patients, followed by ‘famous hospitals’, ‘usual hospital’, and ‘hospitals with good rates’. Non-health-related occupation shows significantly changed results of hospital choice between before and after obtaining public reporting information (p 

This Dataset is Copied From the Orignal Dataset. This Dataset is Preprocess with Advance Method. This Dataset is Cleaned From Missing Values.

Context

This is a multivariate type of dataset which means providing or involving a variety of separate mathematical or statistical variables, multivariate numerical data analysis. It is composed of 14 attributes which are age, sex, chest pain type, resting blood pressure, serum cholesterol, fasting blood sugar, resting electrocardiographic results, maximum heart rate achieved, exercise-induced angina, oldpeak — ST depression induced by exercise relative to rest, the slope of the peak exercise ST segment, number of major vessels and Thalassemia. This database includes 76 attributes, but all published studies relate to the use of a subset of 14 of them. The Cleveland database is the only one used by ML researchers to date. One of the major tasks on this dataset is to predict based on the given attributes of a patient that whether that particular person has heart disease or not and other is the experimental task to diagnose and find out various insights from this dataset which could help in understanding the problem more.

Content

Column Descriptions: - id (Unique id for each patient) - age (Age of the patient in years) - origin (place of study) - sex (Male/Female) - cp chest pain type ([typical angina, atypical angina, non-anginal, asymptomatic]) - trestbps resting blood pressure (resting blood pressure (in mm Hg on admission to the hospital)) - chol (serum cholesterol in mg/dl) - fbs (if fasting blood sugar > 120 mg/dl) - restecg (resting electrocardiographic results) -- Values: [normal, stt abnormality, lv hypertrophy] - thalach: maximum heart rate achieved - exang: exercise-induced angina (True/ False) - oldpeak: ST depression induced by exercise relative to rest - lope: the slope of the peak exercise ST segment - ca: number of major vessels (0-3) colored by fluoroscopy - thal: [normal; fixed defect; reversible defect] - num: the predicted attribute

Acknowledgements

Creators:

1. Hungarian Institute of Cardiology. Budapest: Andras Janosi, M.D.
2. University Hospital, Zurich, Switzerland: William Steinbrunn, M.D.
3. University Hospital, Basel, Switzerland: Matthias Pfisterer, M.D.
4. V.A. Medical Center, Long Beach and Cleveland Clinic Foundation: Robert Detrano, M.D., Ph.D.

Relevant Papers:

• Detrano, R., Janosi, A., Steinbrunn, W., Pfisterer, M., Schmid, J., Sandhu, S., Guppy, K., Lee, S., & Froelicher, V. (1989). International application of a new probability algorithm for the diagnosis of coronary artery disease. American Journal of Cardiology, 64,304--310. Web Link
• David W. Aha & Dennis Kibler. "Instance-based prediction of heart-disease presence with the Cleveland database." Web Link
• Gennari, J.H., Langley, P, & Fisher, D. (1989). Models of incremental concept formation. Artificial Intelligence, 40, 11-61. Web Link

Citation Request:

The authors of the databases have requested that any publications resulting from the use of the data include the names of the principal investigator responsible for the data collection at each institution. They would be:

• Hungarian Institute of Cardiology. Budapest: Andras Janosi, M.D.
• University Hospital, Zurich, Switzerland: William Steinbrunn, M.D.
• University Hospital, Basel, Switzerland: Matthias Pfisterer, M.D.
• V.A. Medical Center, Long Beach and Cleveland Clinic Foundation:Robert Detrano, M.D., Ph.D.

The data set was created by geocoding the addresses of facilities extracted from AHCA Florida Health Facility Finder with the Hospital option selected for facility type.The data is being verified quarterly. Major Broward County health facilities, including public and privately-owned hospitals. Hospitals are classified based on existing emergency departments, number of beds available, and trauma. Only major facilities are included in this dataset, excluding smaller clinics and urgent care centers. All AHCA licensed facilities are listed. Source: AHCAEffective Date: March 2020Last Update: June 2021Update Cycle: Quarterly

This dataset tracks the updates made on the dataset "Acute Care Hospital Transfers by Major Diagnostic Category (MDC)" as a repository for previous versions of the data and metadata.

The largest all-payer ambulatory surgery database in the United States, the Healthcare Cost and Utilization Project (HCUP) Nationwide Ambulatory Surgery Sample (NASS) produces national estimates of major ambulatory surgery encounters in hospital-owned facilities. Major ambulatory surgeries are defined as selected major therapeutic procedures that require the use of an operating room, penetrate or break the skin, and involve regional anesthesia, general anesthesia, or sedation to control pain (i.e., surgeries flagged as "narrow" in the HCUP Surgery Flag Software). Unweighted, the NASS contains approximately 9.0 million ambulatory surgery encounters each year and approximately 11.8 million ambulatory surgery procedures. Weighted, it estimates approximately 11.9 million ambulatory surgery encounters and 15.7 million ambulatory surgery procedures. Sampled from the HCUP State Ambulatory Surgery and Services Databases (SASD) and State Emergency Department Databases (SEDD) in order to capture both planned and emergent major ambulatory surgeries, the NASS can be used to examine selected ambulatory surgery utilization patterns. Developed through a Federal-State-Industry partnership sponsored by the Agency for Healthcare Research and Quality, HCUP data inform decision making at the national, State, and community levels. The NASS contains clinical and resource-use information that is included in a typical hospital-owned facility record, including patient characteristics, clinical diagnostic and surgical procedure codes, disposition of patients, total charges, facility characteristics, and expected source of payment, regardless of payer, including patients covered by Medicaid, private insurance, and the uninsured. The NASS excludes data elements that could directly or indirectly identify individuals, hospitals, or states. The NASS is limited to encounters with at least one in-scope major ambulatory surgery on the record, performed at hospital-owned facilities. Procedures intended primarily for diagnostic purposes are not considered in-scope. Restricted access data files are available with a data use agreement and brief online security training.