Fragmented Financial Visibility
Finance leaders had limited insight into why claims were rejected or how much net revenue was exposed across the billing cycle.
Healthcare Claim Denial Intelligence & Pre-Submission Risk Prediction Platform
Demonstration project
Architected a governed cloud revenue-cycle analytics and machine learning platform that identifies denial root causes, quantifies value at risk, and prioritizes high-risk claims before payer submission.
Revenue Cycle Intelligence
Denial Root-Cause Analysis
Pre-Submission Risk Prediction
Leak-Proof ML Governance
$70.1M Value at Risk
1.
Scenario Context
In this scenario, MapleCare Health faced major revenue leakage and cash-flow delays because claims were partially paid or denied after encounters without a unified analytical framework.
Disconnected Revenue-Cycle Operations
Administrative, billing, coding, and clinical documentation teams operated separately, increasing submission delays and coding errors.
Reactive Appeal Strategy
Staff spent excessive effort appealing denials after submission rather than preventing high-risk claims before they reached payers.
Machine Learning Governance Barriers
Opaque or ungoverned predictive approaches risked data leakage, over-automation, legal non-compliance, and untrustworthy decisions.
2.
Solution
A governed end-to-end cloud warehouse, analytical layer, and predictive model demonstrates how retrospective denial review can inform proactive risk mitigation.
Secure Nightly ADF Orchestration
PL_Claims_Ingestion_MapleCare ingested daily ADLS Gen2 claims extracts into Azure SQL using Managed Identities.
Automated Audit Framework
dbo.ingestion_audit captured row counts, file metadata, timestamps, and pipeline failures for end-to-end observability.
Claim-Line Star Schema
fact_claim_line represented one billed service line and connected to conformed patient, provider, payer, facility, denial, CPT, ICD, and date dimensions.
Statistical & Temporal Profiling
Python EDA validated billing logic, calculated submit_lag_days, and monitored high-value outliers with the IQR method without deleting them.
Pre-Submission Feature Engineering
Discount severity, realization efficiency, financial-volume flags, and safe historical aggregates were engineered from pre-adjudication information.
Leak-Proof Predictive Modeling
Post-adjudication proxies such as denial_amount and paid_to_allowed_ratio were removed before Gradient Boosting and Logistic Regression evaluation.
Six-Page Executive Intelligence
Power BI delivered cash-flow, payer-contract, root-cause, ML risk, documentation, and remediation perspectives.
Failure Alerts & Operational Routing
Azure Logic Apps routed pipeline errors to administrative Teams channels for rapid response.
3.
Demonstration Findings
These findings show what the solution reveals within the demonstration scenario.
Claim Rows Unified
The platform converted messy transactions into a governed enterprise source of truth.
Total Charges Centralized
Enterprise claims exposure became visible in one analytical environment.
Total Paid Volume Centralized
The dashboard shows reimbursement patterns within the scenario.
Value at Risk Quantified
The platform clearly exposed denied or underpaid financial value.
Overall Denial Rate Explained
Denial patterns were linked to coding completeness, billing lag, and payer-contract variation.
Private B Denial Rate
Commercial contract behavior was benchmarked against other payers.
OHIP Denial Rate
Payer-level comparison revealed material contract variation.
High-Risk Claims Prioritized
The decision-support engine isolated the exact high-risk tier before submission.
High-Risk Exposure Identified
The high-risk claim tier represented a significant preventable revenue opportunity.
Operational Staff Alignment
The demonstration provides a prioritized pre-submission queue and illustrates SLA tracking for coding and clinical documentation teams.
4.
Solution Architecture
Healthcare Claim Denial Intelligence Architecture
Daily Claims ExtractsHeaders and Service Lines
Clinical CodingICD, CPT, Documentation
Payer ContractsAllowed, Paid, and Denied
↓↓↓
ADF + ADLS Gen2Secure Nightly Ingestion
→
Azure SQL Star SchemaClaim-Line Source of Truth
→
Governed Risk ModelPre-Submission Tiering
↓
Power BI Denial Intelligence & Remediation Suite
A governed cloud architecture connects claims ingestion, audit logging, warehouse modeling, risk prediction, and executive remediation.
5.
Analytics Delivery Flow
Ingest
Daily claims header and line files
Audit
Rows, files, timestamps, and failures
Warehouse
Claim-line star schema and conformed dimensions
Predict
Leak-proof pre-submission risk model
Act
Prioritized remediation queue and SLA targets
Pipeline failures are logged and routed through Azure Logic Apps to administrative Teams channels.
6.
Technology Stack
Azure Data FactoryADLS Gen2Azure SQL DatabaseSQL Server / SSMSManaged IdentitiesPythonJupyter NotebooksPandasNumPyScikit-LearnGradient BoostingLogistic RegressionPower BIDAXPower QueryAzure Logic Apps
7.
Dashboard Gallery
8.
Project Showcase
Live Power BI Dashboard
9.
GitHub Repository
Explore the Claim Denial Intelligence Project
The repository contains project documentation, pipeline details, notebooks, screenshots, and predictive-model implementation context.
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