Undetected Clinical Deterioration
Patients with gradual long-term increases in HbA1c remained unflagged until acute complications occurred, increasing inpatient utilization risk.
Chronic Disease (Diabetes) Longitudinal Monitoring & Population Health Analytics Suite
Designed an end-to-end population health analytics platform that transforms fragmented EHR, laboratory, medication, and encounter data into continuous, time-aware intelligence for diabetes risk stratification, care-gap detection, provider-panel monitoring, and patient-level longitudinal analysis.
Diabetes Population Health
Longitudinal Monitoring
Risk Stratification
Care-Gap Detection
Provider Panel Intelligence
1.
Business Challenge
Visit-based and fragmented healthcare data architectures prevented care teams from seeing progressive deterioration across the full patient journey.
Critical Gaps in Care
Care teams lacked an automated mechanism to identify disengaged patients who exceeded the critical 9-month HbA1c monitoring threshold.
Unbalanced Provider Panel Allocation
Medical leadership could not quantify the true chronic risk burden across provider panels, limiting data-driven quality improvement.
2.
Solution
A longitudinal analytics solution was built to preserve years of clinical history and convert raw transactions into patient-level, provider-level, and population-level insight.
Enterprise Ingestion & Watermark Tracking
Azure Data Factory orchestrated full and incremental loads across longitudinal patient timelines while preserving growing event history.
Source Validation Controls
Strict row-count and null checks isolated failing inputs before they could affect downstream reporting.
Silver-to-Gold Data Architecture
A multi-tier Azure SQL design optimized high-performance time-series analysis.
Time-Aware Star Schema
Separate fact tables for labs, encounters, and medications used role-playing temporal dimensions for clean DAX calculations.
Longitudinal Data Validation
Pandas scripts audited clinical validity boundaries, irregular follow-up intervals, and misaligned categorical lab results.
Population Health Feature Engineering
A patient-grain rule engine calculated latest markers, variation coefficients, Improving/Stable/Worsening trajectories, and tracking drop-offs.
Four-Page Power BI Suite
Delivered executive commands, high-risk watchlists, provider-panel summaries, and patient longitudinal timelines.
3.
Business Impact
High-Risk Patients Identified
The active chronic cohort was stratified into actionable risk tiers.
Medium-Risk Patients Identified
The platform quantified the largest patient segment requiring continued monitoring.
Low-Risk Patients Identified
Stable patients were separated from higher-risk populations for more focused resource use.
Deteriorating Patients Exposed
Longitudinal analysis revealed that one quarter of diabetic patients were worsening despite stable population averages.
Care-Gap Population Isolated
Patients more than 9 months overdue for HbA1c monitoring were identified for immediate nurse follow-up.
Provider Resource Allocation Improved
Provider-panel summaries quantified patient complexity and supported proactive chronic-care workflows under value-based care contracts.
4.
Solution Architecture
Longitudinal Population Health Architecture
Patient DemographicsChronic Cohort
Lab ResultsHbA1c History
Medications & EncountersTreatment and Visits
↓↓↓
ADF Watermark LoadsFull + Incremental History
→
Silver / Gold Azure SQLValidated Longitudinal Model
→
Risk Feature EngineTrend and Care-Gap Logic
↓
Power BI Population Health & Patient Timeline Suite
A time-aware architecture preserves longitudinal history and converts clinical events into patient-risk and care-gap intelligence.
5.
Analytics Delivery Flow
Ingest
Labs, encounters, medications, demographics
Validate
Row counts, nulls, and clinical bounds
Model Time
Role-playing dates and longitudinal facts
Engineer Risk
Trend, tier, and care-gap features
Coordinate Care
Watchlists, provider panels, and outreach
The workflow turns years of clinical events into actionable population-health intelligence.
6.
Technology Stack
Azure Data FactoryAzure SQL Data WarehousePythonJupyter NotebooksPandasNumPyPower BIDAXPower QueryLongitudinal AnalyticsWatermark LoadingStar Schema
7.
Dashboard Gallery
8.
Project Showcase
Live Power BI Dashboard
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Video Walkthrough
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9.
GitHub Repository
Explore the Longitudinal Diabetes Analytics Project
The repository contains project documentation, pipeline details, notebooks, screenshots, and implementation context.
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