This project used a structured hospital emergency room dataset with 9,216 patient records. The dataset contained demographic details, admission status, referral department, wait times, and satisfaction scores.

📂 Dataset Fields

• 🆔 Patient ID – Unique identifier for each patient.
• 📅 Patient Admission Date – Date & time of patient admission.
• 🔠 Patient Initial & Last Name – Basic patient identity information (de-identified).
• ⚧ Patient Gender – Male/Female distribution.
• 🎂 Patient Age – Patient’s age in years.
• 🌍 Patient Race – Ethnic/racial classification.
• 🏥 Department Referral – Department patient was referred to (Orthopedics, Cardiology, Neurology, etc.).
• 🛏️ Patient Admission Flag – Whether the patient was admitted (True/False).
• ⭐ Patient Satisfaction Score – Rating of patient satisfaction (scale 1–10).
• ⏱️ Patient Wait Time – Waiting duration before being seen by a doctor (in minutes).

🏗️ Data Preparation & Transformation

1. Data Cleaning:
   • Fixed inconsistent date formats.
   • Handled missing values in Satisfaction Score.
   • Removed duplicates for Patient IDs.
2. Data Structuring:
   • Converted Admission Flag to binary (Admitted / Not Admitted).
   • Grouped patients by Age Group, Gender, Referral Department, and Admission Status.
3. Data Modeling:
   • Built relationships between fields to analyze patterns across demographics, wait times, and satisfaction.
   • Created calculated columns & measures for KPIs like Avg Wait Time, % Seen Within 30 Min, Admission % etc.

✅ With this structured dataset, I was able to build interactive dashboards in Power BI to monitor patient flow, hospital performance, and service efficiency.