Patient demographics and clinical data.

Patient demographics and baseline characteristics.

This file contains raw data of all laboratory measurements presented in the paper. In addition, the file contains raw demographic data of the patients as summarized in the paper in Table 1.

Patient Demographics and Injury Characteristics.

Patient demographics and perioperative variables before and after matching.

This dataset provides granular, patient-level diagnosis information for chronic conditions, including demographics, standardized condition codes, and diagnosis statuses. It is designed for healthcare analytics, enabling prevalence studies, trend analysis, and population health management. The schema supports interoperability and detailed stratification by demographic and clinical factors.

Medicare provides access to medical and hospital services for all Australian residents and certain categories of visitors to Australia. The Medicare Benefits Schedule (MBS) lists services that are subsidised by the Australian Government under Medicare. These reports provide patient age range and gender, number of services and total benefit amount per State/ Territory on Items in the MBS Schedule. An Item is a number that references a Medicare service. Item numbers are subject to change. Data is provided in the following formats: Excel/ xlxs: the human readable data for the current year is provided in individual excel files according to the relevant quarter. Historical data (1993-2015) may be found in the excel zipped file. CSV: the machine readable data for the current year is provided in individual csv files according to the relevant quarter. Historical data (1993-2015) may be found in the csv zipped file. Additional Medicare statistics may be found on the Department of Human Services website. Disclaimer: The information and data contained in the reports and tables have been provided by Medicare Australia for general information purposes only. While Medicare Australia takes care in the compilation and provision of the information and data, it does not assume or accept liability for the accuracy, quality, suitability and currency of the information or data, or for any reliance on the information and data. Medicare Australia recommends that users exercise their own care, skill and diligence with respect to the use and interpretation of the information and data.

Patient demographics at the time of the baseline PET/CT (total number of PET/CT examinations n = 101).

Demographics, characteristics and comorbidities of patients hospitalized with a SARS-CoV-2 infection or COVID-19 diagnosis, total and stratified by rural/urban zip codes.

On an annual basis (individual hospital fiscal year), individual hospitals and hospital systems report detailed facility-level data. The complete Data Set of annual utilization data reported by hospitals contains basic licensing information including bed classifications; patient demographics including occupancy rates, the number of discharges and patient days by bed classification, and the number of live births; as well as information on the type of services provided including the number of surgical operating rooms, number of surgeries performed (both inpatient and outpatient), the number of cardiovascular procedures performed, and licensed emergency medical services provided.

Data for this publication are extracted each month as a snapshot in time from the Primary Care Registration database within the PDS (Personal Demographics Service) system. This release is an accurate snapshot as at 1 May 2025. GP Practice; Primary Care Network (PCN); Sub Integrated Care Board Locations (SICBL); Integrated Care Board (ICB) and NHS England Commissioning Region level data are released in single year of age (SYOA) and 5-year age bands, both of which finish at 95+, split by gender. In addition, organisational mapping data is available to derive PCN; SICBL; ICB and Commissioning Region associated with a GP practice and is updated each month to give relevant organisational mapping. Quarterly publications in January, April, July and October will include Lower Layer Super Output Area (LSOA) populations.

US Healthcare Readmissions and Mortality

Evaluating Hospital Performance

By Health [source]

About this dataset

This dataset contains detailed information about 30-day readmission and mortality rates of U.S. hospitals. It is an essential tool for stakeholders aiming to identify opportunities for improving healthcare quality and performance across the country. Providers benefit by having access to comprehensive data regarding readmission, mortality rate, score, measure start/end dates, compared average to national as well as other pertinent metrics like zip codes, phone numbers and county names. Use this data set to conduct evaluations of how hospitals are meeting industry standards from a quality and outcomes perspective in order to make more informed decisions when designing patient care strategies and policies

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Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This dataset provides data on 30-day readmission and mortality rates of U.S. hospitals, useful in understanding the quality of healthcare being provided. This data can provide insight into the effectiveness of treatments, patient care, and staff performance at different healthcare facilities throughout the country.

In order to use this dataset effectively, it is important to understand each column and how best to interpret them. The ‘Hospital Name’ column displays the name of the facility; ‘Address’ lists a street address for the hospital; ‘City’ indicates its geographic location; ‘State’ specifies a two-letter abbreviation for that state; ‘ZIP Code’ provides each facility's 5 digit zip code address; 'County Name' specifies what county that particular hospital resides in; 'Phone number' lists a phone contact for any given facility ;'Measure Name' identifies which measure is being recorded (for instance: Elective Delivery Before 39 Weeks); 'Score' value reflects an average score based on patient feedback surveys taken over time frame listed under ' Measure Start Date.' Then there are also columns tracking both lower estimates ('Lower Estimate') as well as higher estimates ('Higher Estimate'); these create variability that can be tracked by researchers seeking further answers or formulating future studies on this topic or field.; Lastly there is one more measure oissociated with this set: ' Footnote,' which may highlight any addional important details pertinent to analysis such as numbers outlying National averages etc..

This data set can be used by hospitals, research facilities and other interested parties in providing inciteful information when making decisions about patient care standards throughout America . It can help find patterns about readmitis/mortality along county lines or answer questions about preformance fluctuations between different hospital locations over an extended amount of time. So if you are ever curious about 30 days readmitted within US Hospitals don't hesitate to dive into this insightful dataset!

Research Ideas

• Comparing hospitals on a regional or national basis to measure the quality of care provided for readmission and mortality rates.
• Analyzing the effects of technological advancements such as telemedicine, virtual visits, and AI on readmission and mortality rates at different hospitals.
• Using measures such as Lower Estimate Higher Estimate scores to identify systematic problems in readmissions or mortality rate management at hospitals and informing public health care policy

Acknowledgements

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

License

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

Columns

File: Readmissions_and_Deaths_-_Hospital.csv | Column name | Description | |:-------------------------|:---------------------------------------------------------------------------------------------------| | Hospital Name ...

Demographics of the patient population.

Patient demographics and co-morbidities in the 6-month pre-index period and during selection period.

Demographic characteristics of sampled patients in each department.

Note. Groups: SA =  subacute, CH =  chronic, CG =  control group. Pt =  patient; M/F =  male/female. NIHSS: National Institutes of Health Stroke Scale. Stroke etiology: i =  ischemic, h =  hemorrhagic stroke. V&TDS: visual and tactile double stimulation. CAV screen: CAV visual field screening. CAV-ET: CAV extinction test. NET Score: for subtests 1 to 8 and for the whole test battery. Mean (M) and standard deviation (SD) given for patients and healthy controls.

📌 Project Overview This project analyzes hospital admissions, patient stays, and cost trends using Excel. The dataset contains information on patient demographics, hospital names, insurance providers, and treatment costs. Key insights were derived using PivotTables, charts, and formulas.

📊 Key Insights & Visualizations ✅ Top Hospitals by Admissions → Bar Chart ✅ Insurance Provider with Most Patients → Pie Chart ✅ Cost per Day Trends → Line Chart ✅ Average Length of Stay per Hospital → Bar Chart

🛠 Excel Analysis Techniques Used PivotTables for summarizing patient data

Conditional Formatting to highlight cost trends

Bar, Pie, and Line Charts for visualization

Statistical Analysis (Average length of stay, cost trends)

📂 Files Included 📌 hospital_analysis.xlsx – The full Excel analysis file 📌 hospital_summary.pdf – Summary of key findings

Healthcare #HospitalData #ExcelAnalysis #DataVisualization #PivotTables #DataCleaning #MedicalAnalytics #PatientTrends #CostAnalysis #AdmissionsAnalysis #InsuranceData #DataAnalysis #ExcelDashboards #HealthTech

Overview

This dataset provides detailed information on liver health indicators, intended for use in medical research, exploratory data analysis, and predictive modeling. It includes patient demographics, liver enzyme levels, and other key clinical markers. The dataset is suitable for data science projects related to health diagnostics and machine learning.

Content

• Rows: 5,830
• Columns: 11 features, plus a target variable (Result).
• Dataset Structure: Contains patient-level data with demographic and biochemical test results.

Columns Description

1. Age of the patient: The age of the patient in years.
2. Gender of the patient: Patient gender (Male/Female).
3. Total Bilirubin: Total bilirubin level in the patient’s blood (mg/dL).
4. Direct Bilirubin: Direct bilirubin level (mg/dL).
5. Alkaline Phosphatase (Alkphos): Enzyme level indicating liver function.
6. Alanine Aminotransferase (Sgpt): Enzyme level important in diagnosing liver damage.
7. Aspartate Aminotransferase (Sgot): Another enzyme level indicating liver health.
8. Total Proteins: Total protein content in the patient’s blood (g/dL).
9. Albumin: Blood albumin level (g/dL).
10. A/G Ratio (Albumin and Globulin Ratio): Ratio of albumin to globulin proteins.
11. Result: Target variable (1 for liver disease; 0 for no liver disease).

Inspiration

This dataset can be used to:
- Perform exploratory data analysis (EDA) to identify trends in liver health.
- Build predictive models for liver disease diagnosis.
- Analyze demographic influences on liver health indicators.
- Conduct hypothesis testing for biochemical markers.

Acknowledgements

License

This dataset provides detailed records of emergency department triage decisions, including patient demographics, structured symptoms, vital signs, and triage outcomes. It enables urgent care optimization, patient flow modeling, and clinical research into triage patterns and outcomes. The comprehensive structure supports both operational analytics and advanced predictive modeling.