CONTEXT-AWARE ANOMALY DETECTION IN HEALTHCARE CLAIMS USING MULTI-STAGE DECISION PIPELINES

Abstract

Healthcare fraud, waste, and abuse represent a critical challenge costing the U.S. healthcare system an estimated $68-230 billion annually, with fraudulent claims comprising 3-10% of total healthcare expenditures. Traditional rule-based fraud detection systems generate excessive false positives by ignoring clinical context, patient histories, and temporal claim patterns, creating operational burdens for legitimate providers while missing sophisticated fraud schemes. This research develops a context-aware anomaly detection framework employing multi-stage decision pipelines that integrate clinical knowledge, temporal sequence analysis, and provider behavior profiling to identify fraudulent healthcare claims with high precision. The proposed system implements a four-stage architecture: (1) Clinical Context Validation assessing medical appropriateness using diagnosis-procedure compatibility matrices and clinical guidelines; (2) Temporal Pattern Analysis detecting unusual claim sequences through Hidden Markov Models and time-series anomaly detection; (3) Provider Behavior Profiling identifying outlier billing patterns using Isolation Forest and statistical process control; and (4) Ensemble Decision Fusion combining stage outputs through weighted voting and confidence scoring. Evaluation on a dataset of 2.4 million Medicare claims (2020-2023) containing 87,420 confirmed fraud cases demonstrates 94.6% detection accuracy with 3.2% false positive rate, substantially outperforming traditional approaches (rule-based: 78.3% accuracy, 18.7% FPR; standard ML: 88.4% accuracy, 8.1% FPR). The multi-stage pipeline reduces false positives by 61% compared to single-stage models while maintaining 96.8% recall for confirmed fraud. Average case processing time of 2.3 seconds enables real-time claim adjudication. Implementation across three healthcare payers resulted in $127 million in prevented fraudulent payments over 18 months with 68% reduction in provider audit burden. This research contributes novel integration of clinical domain knowledge with machine learning, temporal sequence analysis techniques tailored for healthcare claims, and practical multi-stage architecture enabling graduated responses based on fraud likelihood.