About this dataset

Looking for a dataset on hospitals in the United States? Look no further! This dataset contains information on all of the hospitals registered with Medicare in the US, including their addresses, phone numbers, hospital type, and more. With such a large amount of data, this dataset is perfect for anyone interested in studying the US healthcare system.

This dataset can also be used to study hospital ownership, emergency services

How to use the dataset

If you want to study the US healthcare system, this dataset is perfect for you. It contains information on all of the hospitals registered with Medicare, including their addresses, phone numbers, hospital type, and more. With such a large amount of data, this dataset is perfect for anyone interested in studying the US healthcare system.

This dataset can also be used to study hospital ownership, emergency services, and EHR usage. In addition, the hospital overall rating and various comparisons are included for safety of care, readmission rates

Research Ideas

1. Predicting readmission rates for different hospital conditions
2. Analyzing relationships between hospital ownership and quality of care
3. Studying the relationship between hospital type and patient experience

Acknowledgements

This dataset was originally published by Centers for Medicare and Medicaid Services and has been modified for this project

Columns

File: Hospital_General_Information.csv | Column name | Description | |:-------------------------------------------------------|:----------------------------------------------------------------------------------------------------------| | Hospital Name | The name of the hospital. (String) | | Hospital Name | The name of the hospital. (String) | | Address | The address of the hospital. (String) | | Address | The address of the hospital. (String) | | City | The city in which the hospital is located. (String) | | City | The city in which the hospital is located. (String) | | State | The state in which the hospital is located. (String) | | State | The state in which the hospital is located. (String) | | ZIP Code | The ZIP code of the hospital. (Integer) | | ZIP Code | The ZIP code of the hospital. (Integer) | | County Name | The county in which the hospital is located. (String) | | County Name | The county in which the hospital is located. (String) | | Phone Number | The phone number of the hospital. (String) | | Phone Number | The phone number of the hospital. (String) | | Hospital Type | The type of hospital. (String) | | Hospital Type | The type of hospital. (String) | | Hospital Ownership | The ownership of the hospital. (String) | | Hospital Ownership | The ownership of the hospital. (String) | | Emergency Services | Whether or not the...

This horizontal bar chart displays hospital beds (per 1,000 people) by country full name using the aggregation average, weighted by population in Mexico. The data is filtered where the date is 2021. The data is about countries per year.

VHA hospitals Timely Care Data

Performance on Clinical Measures and Processes of Care

By US Open Data Portal, data.gov [source]

About this dataset

This dataset provides an inside look at the performance of the Veterans Health Administration (VHA) hospitals on timely and effective care measures. It contains detailed information such as hospital names, addresses, census-designated cities and locations, states, ZIP codes county names, phone numbers and associated conditions. Additionally, each entry includes a score, sample size and any notes or footnotes to give further context. This data is collected through either Quality Improvement Organizations for external peer review programs as well as direct electronic medical records. By understanding these performance scores of VHA hospitals on timely care measures we can gain valuable insights into how VA healthcare services are delivering values throughout the country!

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How to use the dataset

This dataset contains information about the performance of Veterans Health Administration hospitals on timely and effective care measures. In this dataset, you can find the hospital name, address, city, state, ZIP code, county name, phone number associated with each hospital as well as data related to the timely and effective care measure such as conditions being measured and their associated scores.

To use this dataset effectively, we recommend first focusing on identifying an area of interest for analysis. For example: what condition is most impacting wait times for patients? Once that has been identified you can narrow down which fields would best fit your needs - for example if you are studying wait times then “Score” may be more valuable to filter than Footnote. Additionally consider using aggregation functions over certain fields (like average score over time) in order to get a better understanding of overall performance by factor--for instance Location.

Ultimately this dataset provides a snapshot into how Veteran's Health Administration hospitals are performing on timely and effective care measures so any research should focus around that aspect of healthcare delivery

Research Ideas

• Analyzing and predicting hospital performance on a regional level to improve the quality of healthcare for veterans across the country.
• Using this dataset to identify trends and develop strategies for hospitals that consistently score low on timely and effective care measures, with the goal of improving patient outcomes.
• Comparison analysis between different VHA hospitals to discover patterns and best practices in providing effective care so they can be shared with other hospitals in the system

Acknowledgements

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

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: csv-1.csv | Column name | Description | |:-----------------------|:-------------------------------------------------------------| | Hospital Name | Name of the VHA hospital. (String) | | Address | Street address of the VHA hospital. (String) | | City | City where the VHA hospital is located. (String) | | State | State where the VHA hospital is located. (String) | | ZIP Code | ZIP code of the VHA hospital. (Integer) | | County Name | County where the VHA hospital is located. (String) | | Phone Number | Phone number of the VHA hospital. (String) | | Condition | Condition being measured. (String) | | Measure Name | Measure used to measure the condition. (String) | | Score | Score achieved by the VHA h...

📝 Dataset Overview: This dataset provides a comprehensive view into patient care and hospital operations at Eko Hospital, Lagos. It captures both clinical and financial details — including patient demographics, diagnoses, treatment procedures, and billing data.

It is a powerful tool for health data analysts, students, and researchers to explore real-world healthcare delivery in a Nigerian context.

🔍 Dataset Features: Column Name Description Patient_ID Unique patient identifier (anonymized) Name Patient's name (consider anonymizing further before public use) Age Age of the patient Gender Gender identity Department Medical department visited (e.g., Pediatrics, Cardiology) Doctor Name of the attending physician Diagnosis Medical condition diagnosed Admission_Date Date of hospital admission Discharge_Date Date the patient was discharged Bill_Amount (₦) Total cost incurred (in Nigerian Naira) Lab_Tests_Conducted Number or type of lab tests carried out Medications_Administered Types or count of drugs administered Nurses_Assigned Number of nurses responsible during care Surgery_Cost (₦) Cost of any surgical procedures performed

🎯 Ideal Use Cases: Create interactive Power BI dashboards for patient flow or billing breakdowns

Analyze treatment cost per diagnosis

Predict length of stay or discharge patterns using machine learning

Monitor resource allocation (nurses, doctors)

Understand clinical performance across departments

🧰 Tools to Use: Python (Pandas, Scikit-learn, Seaborn)

Power BI / Tableau for dashboarding

R (Shiny, ggplot2)

Excel pivot tables and charts

📌 Important Notes: Please ensure patient names are anonymized before full public sharing.

Excellent for portfolio projects, capstone work, or public health exploration.

👤 Created By: Fatolu Peter (Emperor Analytics) Healthcare analytics specialist working on real Nigerian datasets to bridge the gap between clinical care and data intelligence. This marks Project 10 in my growing analytics journey 🚀

✅ LinkedIn Post: 🩺 New Healthcare Dataset Alert 📊 Eko Hospital Patient Care Analytics – Now Live on Kaggle 🔗 Check it out here

Looking to sharpen your healthcare analytics or build a project with real-world medical data?

This dataset features:

Admissions & discharges

Diagnosis, medications, surgeries

Billing info (₦), lab tests, and staffing

You can use it to: ✅ Build Power BI dashboards ✅ Train ML models to predict outcomes or costs ✅ Analyze treatment patterns by age, gender, or department

Let’s use data to improve healthcare outcomes. If you build anything with it, tag me — I’d love to share and learn from you.

KaggleDataset #HealthcareAnalytics #PowerBI #PublicHealth #NigerianData #DataScience #FatoluPeter #EmperorAnalytics #Project10 #RealWorldData #PatientCare #HospitalAnalytics

This horizontal bar chart displays hospital beds (per 1,000 people) by country full name using the aggregation average, weighted by population in Caribbean. The data is about countries.

This hospitals GIS data represents the locations and selected attributes for hospitals included in the FY2005 edition of the American Hospital Association (AHA) Annual Survey Database and located in Vermont or within 25 miles of Vermont in Massachusetts, New Hampshire, or New York. Data fields detail hospital names, services, admissions, visits, beds, Medicare, health, society, structure, and location. Fields were added by the Vermont Dept. of Health (VDH) detailing hospital type and primary phone number. July 2021: Added webite hyperlinks and changed projection to WGS_1984_Web_Mercator_Auxiliary_Sphere for feeding into web maps.

Names of hospitals in Australia, their geographic coordinates (Longitude and Latitude) and an assigned hospital identifier between 1 and 1,011 (Hospital_ID).

The Hospital Enrollments dataset provides enrollment information of all Hospitals currently enrolled in Medicare. This data includes information on the Hospital's sub-group type, legal business name, doing business as name, organization type and address.

Names of hospitals in Canada, their addresses, geographic coordinates (Longitude and Latitude) and an assigned hospital identifier

This 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/ or go to the "About" tab.

This horizontal bar chart displays hospital beds (per 1,000 people) by country full name using the aggregation average, weighted by population in South America. The data is about countries per year.

HCAHPS Hospital Ratings Survey

Patient Experience Ratings 2018-2020

By Health [source]

About this dataset

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

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Featured Notebooks

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How to use the dataset

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

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

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

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

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

Research Ideas

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

Acknowledgements

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

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - **Keep int...

This synthetic dataset simulates the end-to-end operations of a California-based hospital for Q1 2025. It includes over 126,000 rows across 9 fully integrated tables that capture patient visits, clinical procedures, diagnoses, lab tests, medication prescriptions, provider details, billing, claims, and denials — designed for data analytics, machine learning, and healthcare research.

📦 Tables Included: patients.csv – Patient demographics, insurance, DOB, gender

encounters.csv – Admission/discharge details, visit types, departments

diagnoses.csv – ICD-10 diagnosis codes linked to encounters

procedures.csv – CPT/ICD-10-PCS procedure codes per patient

medications.csv – Drug names, dosages, prescription data

lab_tests.csv – Test names, result values, normal ranges

claims_and_billing.csv – Financial charges, insurance claims, payments

providers.csv – Doctors, specializations, provider roles

denials.csv – Reasons for claim denial, status, appeal info

This dataset was custom-built to reflect real-world healthcare challenges including:

Messy and missing data (for cleaning exercises)

Insurance claim workflows and denial patterns

Analysis of repeat admissions and chronic disease trends

Medication brand usage, cost patterns, and outcomes

🧠 Ideal For: Healthcare Data Science Projects

Revenue Cycle Management (RCM) analytics

Power BI & Tableau Dashboards

Machine Learning modeling (readmission, denial prediction, etc.)

Python/SQL Data Cleaning Practice

This dataset is completely synthetic and safe for public use. It was generated using custom rules, distributions, and logic reflective of real hospital operations.

Objectives This study examined condom acquisition by persons in a hospital setting when single versus assorted brand name condoms were provided. Methods Condom receptacles were placed in exam rooms of two clinics. During Phase 1, a single brand name was provided; for Phase 2, assorted brand names were added. Number of condoms taken was recorded for each phase. Results For one clinic there was nearly a two-fold increase in number of condoms taken (Phase 1 to Phase 2); for the second clinic there was negligible difference in number of condoms taken. Conclusions The provision of assorted brand name condoms, over a single brand name, can serve to increase condom acquisition. Locations of condoms and target population characteristics are related factors.

This dataset contains anonymized information on hospitals across India sourced from public data by NIT Jalandhar and expanded through web scraping from an online maps platform. It includes location information, ratings, and the number of reviews. Ideal for anyone interested in analyzing healthcare access and distribution.

Each entry includes the hospital name, city, state, and geographic coordinates, with cluster-preserving techniques applied to anonymize sensitive location data while retaining each hospital’s effective influence. This means the coordinates are not exact, but the clustering of hospitals even when adjusted for their prominence remains the same on a state and national level.

Additionally, population densities for districts have been added, allowing for more granular insights.

If you're a researcher, policymaker, or healthcare analyst, you can use this to gain insights into the accessibility of healthcare services in India.

Taipei City Medical Travel Clinic Hospital Directory.

This dataset is being provided under creative commons License (Attribution-Non-Commercial-Share Alike 4.0 International (CC BY-NC-SA 4.0)) https://creativecommons.org/licenses/by-nc-sa/4.0/

Context

This data was collected from patients admitted over a period of two years (1 April 2017 to 31 March 2019) at Hero DMC Heart Institute, Unit of Dayanand Medical College and Hospital, Ludhiana, Punjab, India. This is a tertiary care medical college and hospital. During the study period, the cardiology unit had 14,845 admissions corresponding to 12,238 patients. 1921 patients who had multiple admissions.

Specifically, data were related to patients ; date of admission; date of discharge; demographics, such as age, sex, locality (rural or urban); type of admission (emergency or outpatient); patient history, including smoking, alcohol, diabetes mellitus (DM), hypertension (HTN), prior coronary artery disease (CAD), prior cardiomyopathy (CMP), and chronic kidney disease (CKD); and lab parameters corresponding to hemoglobin (HB), total lymphocyte count (TLC), platelets, glucose, urea, creatinine, brain natriuretic peptide (BNP), raised cardiac enzymes (RCE) and ejection fraction (EF). Other comorbidities and features (28 features), including heart failure, STEMI, and pulmonary embolism, were recorded and analyzed.

Shock was defined as systolic blood pressure < 90 mmHg, and when the cause for shock was any reason other than cardiac. Patients in shock due to cardiac reasons were classified into cardiogenic shock. Patients in shock due to multifactorial pathophysiology (cardiac and non-cardiac) were considered for both categories. The outcomes indicating whether the patient was discharged or expired in the hospital were also recorded.

Further details about this dataset can be found here: https://doi.org/10.3390/diagnostics12020241

If you use this dataset in academic research all publications arising out of it must cite the following paper: Bollepalli, S.C.; Sahani, A.K.; Aslam, N.; Mohan, B.; Kulkarni, K.; Goyal, A.; Singh, B.; Singh, G.; Mittal, A.; Tandon, R.; Chhabra, S.T.; Wander, G.S.; Armoundas, A.A. An Optimized Machine Learning Model Accurately Predicts In-Hospital Outcomes at Admission to a Cardiac Unit. Diagnostics 2022, 12, 241. https://doi.org/10.3390/diagnostics12020241

If you intend to use this data for commercial purpose explicit written permission is required from data providers.

Content

table_headings.csv has explanatory names of all columns.

Acknowledgements

Data was collected from Hero Dayanand Medical College Heart Institute Unit of Dayanand Medical College and Hospital, Ludhiana, Punjab, India.

Inspiration

For any questions about the data or collaborations please contact ashish.sahani@iitrpr.ac.in

Alameda County and surrounding area Hospitals with Bed Counts. Bed Count Source:* American Hospital Directory - https://www.ahd.com/states/hospital_CA.htmlDisclaimer: Bed count values are to be used only for exploratory analysis and demonstration purposes. Discrepancies may be found in actual bed count values.*Some bed counts taken from direct web browser searches where data was not available from exact match for hospital name.

Compilation of hospitals in Västra Götaland. The data set contains id, hospital name, street name, street number, postal code, postal code area, owner, hospital group, x coordinate and y coordinate. The addresses are visital addresses. The spatial reference system is SWEREF 99 TM (EPSG:3006).

This dataset presents the number of beds available in government hospitals in Qatar, recorded by individual hospital name. Each row indicates the number of beds in a specific government hospital during a particular year.The data provides insights into the capacity of government-run healthcare institutions in Qatar. It is useful for analyzing healthcare infrastructure, planning hospital resource allocation, and monitoring trends in hospital capacity over time.