"AI-Powered Healthcare Insights Dashboard"

📌 About the Project Healthcare systems often face challenges in tracking patient admissions, predicting risks, and balancing limited resources. Inspired by the recent need for efficient hospital management during pandemics and high patient loads, we built an AI-Powered Healthcare Dashboard. This system integrates admissions, disease trends, health risk analysis, and doctor workload into one clear and interactive view. By doing so, it empowers hospitals to make faster, data-driven decisions that directly improve patient care and efficiency.

🌟 What Inspired Us During COVID-19, hospitals struggled with overcrowding, limited resources, and delayed decisions. This inspired us to create a system that provides real-time insights so administrators and doctors can act quickly. We wanted to answer simple but crucial questions: • How many patients are admitted right now? • Which diseases are most common? • Who is at high risk of readmission? • Are doctors and resources being overloaded?

🛠️ How We Built It

Dataset Preparation – Created a structured healthcare dataset containing: Patient_ID, Age, Gender, Disease, Blood Pressure, Sugar, Admission Date, Readmission, Bed Occupancy, Doctor_Assigned.
Tableau Next (Salesforce) – Used the platform to build 5 visualizations: o KPI Cards → Total Patients, Readmission %, Bed Occupancy % o Line Chart → Admissions Trend o Bar Chart → Disease Distribution o Circle Plot → Patient Health Risk (BP vs Sugar) o Donut Chart → Resource Utilization per Doctor
Dashboard Integration – Combined all visuals into a single interactive dashboard with a clear layout and real-time insights. ________________________________________ 📚 What We Learned • Hands-on experience with Tableau Next and Salesforce ecosystem. • How to design effective data visualizations for healthcare analytics. • The importance of storytelling with data in hackathons. • Learned to simplify complex medical data into actionable insights. ________________________________________ 🚧 Challenges We Faced • Forecasting setup: Making date fields continuous for trend analysis. • Choosing the right chart types: Selecting circle plots and donut charts that best convey healthcare risks and utilization. • Time management: Building, testing, and polishing the dashboard within hackathon deadlines. • Data design: Creating a synthetic but realistic dataset that demonstrates healthcare challenges clearly. ________________________________________ ✅ Outcome & Impact The final dashboard acts as a decision-support tool for hospitals, enabling: • Early detection of high-risk patients. • Better allocation of doctors and beds. • Clear understanding of disease patterns. • Real-time operational efficiency. ________________________________________ 🔮 Future Scope • Integrate live hospital data feeds (IoT devices, patient management systems). • Add predictive AI models for disease outbreak forecasting. • Connect with blockchain for secure patient data storage. • Extend dashboard to mobile platforms for real-time use by doctors.