Deepti Bahel
Engineer of Data. Advocate for Health. Believer in Better Systems.
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Deepti Bahel is a seasoned Senior Business Intelligence Engineer with over 12 years of experience transforming complex data into meaningful, actionable insights. With a Master’s in Business Analytics from Purdue University and a strong background in BI tools like Power BI, Tableau, and Looker, Deepti has led analytics transformations at companies like Intuit, Google, Wayfair, and Hanna Andersson.
As a kidney transplant recipient and health coach, Deepti brings a unique perspective to healthcare analytics—combining deep technical knowledge with a mission-driven approach to improving patient outcomes through data.
She specializes in building self-service BI platforms, streamlining data pipelines using tools like Superglue, Databricks, and Snowflake, and bridging the gap between technical teams and non-technical decision-makers. Her recent work focuses on embedding analytics into operational workflows through interactive apps powered by Streamlit and AI-driven assistants.
Deepti is also an active mentor, speaker, and advocate for data equity in healthcare, featured by media outlets like India Currents and KnowFix News, and regularly invited to speak at data and health tech panels.
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This session introduces a no-code, AI-enhanced Hospital Strategy Optimization Dashboard that helps a
This session introduces a no-code, AI-enhanced Hospital Strategy Optimization Dashboard that helps administrators make cost-efficient care decisions. Built with Streamlit, the app integrates linear programming, anomaly detection (Isolation Forest), and GPT-driven strategic recommendations to evaluate three patient selection strategies: Linear Programming, Greedy, and Heuristic. Users can set real-time constraints, compare cost savings, analyze patient trends, and explore actionable strategy insights. A must-see for anyone interested in operational healthcare analytics and data-driven decision support.
Chat-Based Anomaly Detection for Hospitals: An AI-Powered Streamlit App for Insightful Audits
Discover how to detect hospital billing anomalies with a no-code, AI-powered Streamlit app. This session walks through a real-world solution combining ML models, SHAP explainability, and GPT-generated insights. Learn to automate audits, visualize outliers, and communicate findings with business users—all in one interactive dashboard. Ideal for healthcare BI teams, analysts, and data scientists.
Optimizing Healthcare Efficiency with Power BI: A Live Case Study Using Real-World Hospital Data
Optimizing Healthcare Efficiency with Power BI: A Live Case Study Using Real-World Hospital Data
Healthcare systems today are drowning in fragmented data. Billing inefficiencies, reporting delays, and manual processes cost organizations both time and money. In this talk, I’ll present a real-world solution: an end-to-end Power BI dashboard that transforms raw hospital data into actionable insights.
Built on a cleaned and enriched version of the Kaggle Hospital Dataset (2019–2024), this dashboard identifies billing anomalies, visualizes cost drivers by medical condition, and segments patient stays to uncover operational inefficiencies.
🔍 Key Features Showcased:
MoM (Month-over-Month) Billing Trend Analysis – Detect unusual cost spikes in real-time
Stay Category Segmentation – Short, Medium, Long, Very Long stays for targeted care strategy
ICD Code Mapping – Linking diagnoses to billing impact for smarter planning
Multi-Hospital Comparison – KPI breakdown across six leading institutions
Self-Service Filters – Designed for non-technical users to explore the data dynamically
⚙️ Technical Stack: Power BI, DAX, Python (data cleanup), Streamlit (embedding)
🤝 Collaborative Credit: Special thanks to Andrew Malinow for the ICD-10 integration guidance
🔊 Audience Takeaways:
How to go from raw, unstructured hospital data to a business-ready reporting tool
Strategies to visualize and act on billing inefficiencies using Power BI
The impact of accessible analytics tools in operational healthcare decision-making
Practical steps to integrate anomaly detection and forecasting into healthcare BI workflows
Deepti Bahel
Engineer of Data. Advocate for Health. Believer in Better Systems.
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