End-to-end Excel dashboard project analyzing hospital emergency room performance with KPIs, patient analytics, and interactive charts.

SQL project analyzing hospital records with PostgreSQL to extract insights on patient care, costs, staffing, and department performance. Uses aggregation, grouping, and date functions to answer key healthcare questions and improve hospital operations. Great for showcasing SQL skills and healthcare analytics.

📊 My first Excel MIS Dashboard project analyzing Hospital ER performance. Built using Excel tools like Pivot Tables, Sparklines, Conditional Formatting, and Charts to track patient count, wait times, referrals, etc. A hands-on project during my data analytics journey.

Power BI Dashboard for Hospital Emergency Room analytics. Visualizes patient flow, triage levels, wait times, staff workload, bed availability, and admission trends. Helps monitor ER performance and optimize resources using real-time, data-driven insights. Built with Power BI and sample hospital data.

Transforming healthcare data into actionable insights through interactive visualizations and analytics using Tableau, Excel and Python. This repository provides tools to analyze medical spending, hospital performance, and claim patterns for better decision-making.

ANALYZING ROAD SAFETY & TRAFFIC DEMOGRAPHICS IN THE UK (Multi-class Classification) SUMMARY Here, I am aim to analyze the Road Safety and Traffic Demographics dataset (UK), containing accidents reported by the police between the years of 2004 - 2017. PROJECT GOALS: Identify factors responsible for most of the reported accidents. Build a machine learning model that is capable of accurately predicting the severity of an accident. Provide recommendations to the Department of Transport (UK Government), to improve road safety policies and prevent recurrences of severe accidents where possible. PACKAGES USED: Scikit-learn, numpy, pandas, imblearn (imbalanced-learn), seaborn, Matplotlib MOTIVATION World Health Organization (WHO) reported that more than 1.25 million people die each year while 50 million are injured as a result of road accidents worldwide. Road accidents are the 10th leading cause of death globally. On current trends, road traffic accidents are to become the 7th leading cause of death by 2030 making it a major public health concern. Between the years 2005 and 2016, there were roughly 2 million road accidents reported in the United Kingdom (UK) alone of which 16,000 were fatal. As a big data project, I wanted to explore the traffic demographics data in greater detail using machine learning! CONTEXT The UK government amassed traffic data from 2004 to 2017, recording over 2 million accidents in the process and making this one of the most comprehensive traffic data sets out there. It's a huge picture of a country undergoing change. Note that all the contained accident data comes from police reports, so this data does not include minor incidents. For steps undertaken to pre-process and clean the data, please view the "Data Cleansing & Descriptive Analysis_UK Traffic Demographics.ipynb" file DESCRIPTIVE ANALYTICS (EDA) Tools used include Python, Tableau, MS PowerBI Percent (%) distribution of target classes Percent dist of Accident Severity As seen above, the data is highly imbalanced. For detailed steps undertaken to deal with the imbalanced data, please view the "Modelling_Predictive Analytics_UK Traffic Demographics.ipynb" file. This article provides some great tips on utilizing the correct performance metrics when analyzing a models performance trained on an imbalanced dataset. This article describes several strategies that can help combat the case of a severly imbalanced dataset. Methods include: Resampling strategies (under - Tomek Links, Cluster Centroids, over sampling - SMOTE) Using Decision Tree based models Using Cost-Sensitive training (Penalize algorithms) Number of accidents by Year and Accident Severity Total accidents by year and severity It can be seen above that the trend seems to be increasing as the years go. In addition, the spike between 2008 - 2009 was because of a enhancement in the reporting system introduced in the UK in 2009, where all accident including minor accidents needed to be reported by the police so as to match the counts represented by hospitals, insurance claims etc. Accidents density by Location geomap Most accidents took place in major cities - Birmingham, London, leeds, Newcastle Accidents by Gender and Age Accidents by gender and age Accidents by Day of the week and Year Accidents by year and weekday Most accidents take place on a Friday Vehicle Manoever at time of accident Vehicle Manoever at time of accident Most accidents take place as a result of overtaking For more findings, please go to the "Images" folder. For steps undertaken to carry out some predictive modeling and hyper-parameter tuning, please view the "Modelling_Predictive Analytics_UK Traffic Demographics.ipynb" file. RECOMMENDATIONS TO THE DEPARTMENT OF TRANSPORT (UK) Decrease emergency response times during afternoon rush-hours (15-19) especially on Fridays. Allocate resources to investigate high density traffic points and identify new infrastructure needs to divert traffic from dual-carriage ways. Explore conditions of vehicles and casualties such as vehicle type, age of vehicles registered, pedestrian movements, etc. for policy makers. Adopt comprehensive distracted driving laws that increase penalties for drivers who commit traffic violations like aggressive overtaking. ACKNOWLEDGEMENTS The license for this dataset is the Open Givernment Licence used by all data on data.gov.uk. The raw datasets are available from the UK Department of Transport website. I had a lot of fun working on this dataset and learned a lot in the process. I plan to further my research in the area of predictive modeling using imabalanced data and how to effectively build a highly robust model for future projects. About Here, I analyze the Road Safety and Traffic Demographics dataset (UK), containing accidents reported by the police between the years of 2004 - 2017. Topics accident-rate accident-severity imbalanced-data imbalanced-learning road-accident reported-accidents road-safety uk-government transport traffic-demographics severe-accidents pca classification Resources Readme Releases No releases published Packages No packages published Languages Jupyter Notebook 100.0% © 2020 GitHub, Inc.

SER STORY Questions: 1. If you had more time, what would you change or focus more time on? If I had more time and also access to the appropriate systems I would have completely finished the application to demonstrate rather than only joining the data sources. Furthermore, as my application details, I am more of data manager and research analyst than programmer, and in particular I am very aware of the clinical issues from my biomedical science background. Furthermore my answers to the application questionnaires indicate that I have not used particular software developmental methods since starting my recent Doctoral thesis work in 2015. So while I am able to manage and understand the medical information projects I would need more time for developing applications with particular programming languages that I have not used. 2. Which part of the solution consumed the most amount of time? The parts of the solution that consumed a lot of time included understanding the details of what was required. When reading the advertisement I understood that the position was for the lead developer for both data research and operational data. So rather than an IT person I had expected the role to focus on the medical and analytical issues (including research analysis) and so a biomedical science and hospital performance knowledge would be relevant. The lead person could then discuss with the IT staff the appropriate systems, which I am very adaptable to as my work history demonstrates. On the other hand this exam / test appears to be more about programming rather than examining the clinical issues etc. 3. What would you suggest to the customer that they may not have thought of in regards to their request? There are a few things in the design of this project that may have some issues. One is the readmissions. So as an example if we just rely on name and birth date to ID the episode on patient episode may be connected with others with regards tot he ICD coding or pathology testing. Even the imaging may be outdated and need redoing, for example. So the linking should include the dates of the activities or the admission number to link the patient information and the DRG / ICD codes for example. Other things of interest could include admission type (emergency etc) and discharge type (if the patient has been discharged) as well as readmissions (particularly emergency readmission within a set time period for the same diagnosis). As well the outpatient data, including allied health follow up, would be significant in some cases, such as diabetes etc.

This project is a Healthcare Analytics Dashboard built in Power BI to provide actionable insights into patient care, hospital performance, and resource utilization.

Caduceus-One-Hospital-Dashboard is a comprehensive hospital management dashboard system that provides real-time analytics and insights for healthcare facilities. This project integrates multiple data sources to deliver interactive visualizations for patient care, doctor performance, financial management, and hospital operations.

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End-to-end Excel data analytics dashboard analyzing hospital emergency room performance

Hospital Performance Analytics: Billing Drivers, Patient Outcomes & Operational Efficiency across 55,000 admissions

The Hospital Emergency Room Dashboard is an interactive Excel-based analytics solution designed to monitor and evaluate monthly performance across key hospital metrics.

A Healthcare Analytics Project using SQL to analyze patient records, hospital performance, disease trends, and treatment costs.

Interactive Hospital Emergency Room Dashboard — Tracking patient flow, wait times, satisfaction scores, and departmental performance using data-driven analytics.

SQL data analysis project using PostgreSQL on a hospital dataset. Includes analytical queries for patient statistics, hospital expenses, department performance, and patient stay analysis.

A data analytics project showcasing patient management, doctor performance, hospital operations, medicine stock, and financial insights. Includes multiple interactive pages (Home, Overview, Patient, Doctor, Hospital, Finance) with clean UI and storytelling for healthcare data visualization.

This project demonstrates an end-to-end data pipeline and analytics workflow built using dbt, Snowflake, and Power BI — designed to analyze hospital performance, patient outcomes, and operational efficiency.

An interactive Healthcare Analytics Dashboard designed to visualize patient data and hospital KPIs. Showcases insights into patient admissions, treatment outcomes, and performance metrics to improve healthcare efficiency and decision-making.

Hospital analytics involves the systematic use of data to improve healthcare delivery, operational efficiency, and patient outcomes. By analyzing patient records, treatment patterns, resource utilization, and financial performance, hospitals can make data-driven decisions that enhance care quality, reduce costs, and streamline operations.

A C++ simulation of an Ambulance Management System that models hospital operations, patient prioritization, and car assignments using custom-built data structures. It features real-time event simulation, rerouting, and performance analytics.

This project focuses on analyzing hospital revenue trends to identify key factors influencing financial performance. Using data analytics and visualization techniques, it provides insights into revenue streams, patient demographics, service utilization, and operational efficiency.

A modern healthcare data analytics platform designed to support HMIS (Health Management Information System) and Monitoring & Evaluation (M&E) teams in hospitals. ensure reporting, data quality checks, and evidence-based decision-making to improve health system performance.

The Healthcare Executive Dashboard is a full-stack analytics application designed to assist stakeholders in making data-driven decisions. It transitions raw hospital admission data into an interactive interface, allowing users to monitor revenue performance, patient demographics, and doctor workload without writing SQL.

An industry-level Hospital Data Analytics Dashboard project focused on analyzing patient records, operational efficiency, and healthcare performance metrics using Python, SQL, and Excel/Power BI. The workflow included storing and managing structured healthcare datasets in MySQL, executing advanced SQL queries, joins, and aggregations to derive insi

This project showcases an end-to-end Power BI solution for hospital analytics, focusing on revenue, utilization, doctor performance, patient visits, and no-show analysis. It includes interactive dashboards with DAX-driven KPIs, scenario-based insights, and role-specific views to support data-driven decision-making in healthcare operations.

This Power BI dashboard provides in-depth insights into hospital performance, patient demographics, and key healthcare metrics. Built with a focus on data-driven decision-making, it showcases my expertise in Power BI, data visualization, and analytical storytelling.

I built a Healthcare Data Analytics Dashboard in Power BI to analyze hospital performance and patient care efficiency. I used Power Query for cleaning and modeling, and DAX for KPI calculations such as readmission rate and bed occupancy. The dashboard includes dynamic titles, slicers, and bookmarks for smooth navigation and better insights.

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Data-driven analysis of hospital performance and patient trends using analytics to improve decision-making.