Project Overview

Developed a sophisticated crowd behaviour simulation system to predict and model emergency evacuation scenarios in real-world urban environments. This project addressed critical public safety planning needs by creating accurate behavioural models for crowd dynamics during crisis situations.

Problem Statement

The challenge was to determine how crowds would realistically react in emergency situations given specific locations and crowd densities. The project required incorporating actual geographic data, including real streets, buildings, and urban layouts, to create authentic simulation scenarios that could inform emergency response planning.

Considerations

• Human Behaviour Analysis: Understanding the psychological responses of individuals in crisis situations, specifically examining fight, flight, or freeze reactions and how these manifest in crowd dynamics.
• Demographic Considerations: Investigating how different population segments respond to emergencies, with particular attention to age groups and gender differences in evacuation behaviour and decision-making patterns.
• Environmental Impact Assessment: Analysing how physical infrastructure—buildings, obstacles, street layouts, and architectural features—influences crowd movement patterns and directional choices during evacuations.

Technical Approach

Working collaboratively within a small, specialized engineering team, I contributed to the development of a comprehensive crowd density modelling system built using Python. The solution incorporated multiple sophisticated features designed to simulate realistic human behaviour patterns in emergency scenarios.

Technical Architecture

• Agent-Based Modelling Framework: Implemented a multi-agent simulation system where each individual is represented as an autonomous agent with unique behavioural parameters, decision-making algorithms, and movement capabilities.
• Spatial Data Integration: Utilized Geographic Information System (GIS) data and OpenStreetMap APIs to import real-world building footprints, street networks, and obstacle layouts into the simulation environment.
• Behavioural Algorithm Development: Developed probabilistic decision trees and state machines to model individual responses to emergency stimuli, incorporating psychological research on panic behaviour and crowd dynamics.
• Pathfinding Algorithms: Integrated pathfinding with dynamic obstacle avoidance, modified to account for crowd pressure, visibility constraints, and emergency exit prioritisation.
• Physics Simulation: Applied social force models and collision detection systems to simulate realistic crowd movement, including pushing behaviour, flow restrictions, and stampede prevention mechanisms.
• Demographic Modelling: Created demographic-specific behavioural parameters based on mobility rates, decision-making speeds, and risk tolerance levels across different age groups and populations.

Project Overview

Developed a comprehensive healthcare claims analytics platform to optimize insurance claim success rates through systematic analysis of Electronic Data Interchange (EDI) transaction data. The solution transformed complex healthcare billing data into actionable insights for care team administrators, enabling evidence-based improvements in claim submission processes and reimbursement outcomes.

Industry Context

Operating within the complex U.S. healthcare reimbursement ecosystem, where EDI standards (X12 837, 835, 277) facilitate billions of dollars in insurance transactions annually between healthcare providers, insurance payers, and patients. Claim denial rates averaging 5-10% across the industry represent significant revenue impact for healthcare organizations.

Problem Statement

Healthcare organizations faced substantial revenue losses due to insurance claim denials and processing inefficiencies within the EDI system. The challenge was to leverage historical claims data to identify patterns, root causes of denials, and optimization opportunities that could systematically improve claim approval rates and reduce administrative overhead for care teams.

Strategic Business Objectives

The analytics platform addressed critical healthcare revenue cycle management requirements:

• Claims Performance Optimization: Analysing historical claim submission patterns, denial reasons, appeal success rates, and identifying systematic improvements to increase first-pass claim approval rates and reduce revenue cycle length.
• Care Team Decision Support: Providing healthcare administrators with digestible, actionable insights that could be immediately implemented to improve coding accuracy, documentation requirements, and submission timing strategies.
• Operational Intelligence: Establishing comprehensive analytics capabilities to support ad-hoc investigations, regulatory compliance reporting, and strategic planning for revenue cycle optimization initiatives.

Technical Architecture

• EDI Transaction Processing: Architected a specialized data processing system capable of parsing and analysing complex EDI transaction sets including 837 (Claims), 835 (Payment/Remittance), 277 (Claim Status), and 999 (Acknowledgment) formats with healthcare-specific business logic.
• Healthcare Data Standards Compliance: Implemented data handling processes compliant with HIPAA regulations, ensuring patient data privacy, audit trail maintenance, and secure data transmission protocols throughout the analytics pipeline.

Data Pipeline and Processing

• ETL Framework Development: Built robust data extraction, transformation, and loading processes specifically designed for healthcare EDI data formats
• Historical Data Integration: Implemented comprehensive data backfill processes to incorporate years of historical claims data, enabling trend analysis, seasonal pattern identification, and longitudinal claim performance evaluation.

Analytics and Reporting Infrastructure

• SQL-Based Analytics Engine: Developed comprehensive SQL query library for standard healthcare metrics including claim approval rates, denial reason analysis, provider performance benchmarking, and revenue cycle KPIs with optimized execution plans.
• Google Sheets Integration: Created automated data extraction and visualization workflows using Google Sheets API integration, enabling real-time dashboard updates and collaborative analytics capabilities for healthcare administrators.
• Ad-Hoc Query Framework: Established self-service analytics capabilities allowing care team administrators to execute custom queries for specific investigations, trend analysis, and performance monitoring without technical dependencies.

Key Technical Achievements

• Healthcare-Specific Analytics: Developed specialized analytical models for claim denial prediction, coding accuracy assessment, and provider performance benchmarking using healthcare industry best practices and regulatory requirements
• Dashboard Integration: Implemented automated data pipeline delivering daily updated analytics dashboards
• Performance Optimization: Achieved sub-second query response times for standard healthcare reports through advanced indexing strategies, query optimization, and intelligent data partitioning schemes
• Self-Service Analytics Platform: Created user-friendly interface enabling non-technical healthcare administrators to access complex claims analytics through intuitive Google Sheets dashboards and automated reporting

Project Overview

Worked on a comprehensive healthcare reporting and analytics platform to empower healthcare hub organizations with actionable insights into patient care pathways and care coordinator performance. The solution transformed existing healthcare data infrastructure into a robust business intelligence ecosystem, delivering both standardized operational reports and self-service analytics capabilities for improved patient care management and operational efficiency.

Industry Context

Operating within the complex healthcare delivery ecosystem where care coordination hubs serve as central points for managing patient journeys across multiple providers, specialists, and care settings. These organizations require sophisticated analytics to track patient outcomes, optimize care pathways, and demonstrate value-based care performance to payers and regulatory bodies.

Problem Statement

Healthcare hub organizations lacked comprehensive visibility into their operational performance, patient care outcomes, and care coordinator effectiveness. The challenge was to leverage existing healthcare data infrastructure to provide actionable insights that could improve patient care coordination, optimize resource allocation, and support evidence-based decision-making for hub administrators and clinical leadership teams.

Strategic Healthcare Objectives

The reporting platform addressed critical healthcare operations and quality improvement requirements:

• Operational Excellence & Performance Monitoring: Providing healthcare hub administrators with comprehensive operational reports to track patient flow, care coordinator productivity, appointment scheduling efficiency, and resource utilization across multiple care delivery channels.
• Patient Care Pathway Analytics: Enabling detailed analysis of patient journeys through healthcare systems, identifying bottlenecks, care gaps, and optimization opportunities to improve patient outcomes and satisfaction scores.
• Self-Service Clinical Intelligence: Delivering flexible, user-friendly dashboards allowing healthcare professionals to independently access and analyse patient data, care coordination metrics, and performance indicators without technical dependencies.

Technical Achievements

• Custom Healthcare Query Library: Developed extensive library of 200+ specialized SQL queries optimized for healthcare operations, including patient census reports, care coordinator performance metrics, treatment outcome analysis, and regulatory compliance reporting.
• Performance-Optimized Query Architecture: Implemented advanced SQL optimization techniques including query plan analysis, index utilization strategies, and execution time monitoring to ensure sub-second response times for complex healthcare analytical queries.
• Clinical Data Security Framework: Developed comprehensive SQL-based access control mechanisms ensuring HIPAA compliance, patient data privacy, and role-based access to sensitive healthcare information with comprehensive audit trail logging.
• Oracle APEX Implementation: Worked with Oracle Application Express (APEX) as primary reporting delivery platform, creating custom healthcare-focused user interfaces with responsive design, mobile accessibility, and integrated security for healthcare professionals.
• Healthcare-Specific Report Templates: Designed standardized report templates addressing common healthcare operational questions including patient flow analysis, care team performance evaluation, treatment adherence monitoring, and resource utilization optimization.
• Tableau Dashboards: Developed sophisticated Tableau visualization platform with healthcare-specific design patterns, clinical workflow integration, and interactive filtering capabilities optimized for healthcare professional user experience.