Abstract
Accurate pathology billing is crucial for financial sustainability, process optimization, and regulatory compliance. Current procedural terminology (CPT) codes 88300-88309 denote increasing complexity in gross and microscopic examination of surgical specimens, and correct assignment is critical to prevent misbilling, yet reports contain interpretive information subject to ambiguous assignment. Although machine learning methods have been developed for CPT code prediction, most fail to address challenges of integrating heterogeneous, variably structured reports across institutions with differing documentation practices. This study aims to advance machine learning approaches to leverage large-scale, diverse pathology report corpora and assess their adaptability across institutions. Transformer-based encoder models were initially fine-tuned on 59,923 cases from Dartmouth-Hitchcock Medical Center and further fine-tuned on 174,045 cases from Cedars-Sinai Medical Center, where final performance in CPT code prediction was evaluated. This sequential fine-tuning strategy was compared against models trained solely on the secondary institution's data (single-stage) and against traditional bag-of-words machine learning approaches (Naive Bayes, Random Forest, XGBoost). The best-performing model, a sequentially fine-tuned SciBERT-Longformer, achieved a macro-F1 of 0.8912. Sequential fine-tuning consistently yielded improvements in F1 for rare CPT codes for which there exists limited training data. As validation, SHAP analyses were used to interpret model predictions and verify alignment between influential keywords and established CPT code terminology. Model predictions were ultimately shown to be efficient, explainable, and grounded in clinical knowledge, further supporting potential for adaptation to real-world settings. Sequential fine-tuning on varied pathology corpora improved coding performance, highlighting the potential of institution-specific transformer models to streamline billing, reduce administrative burden, and enhance reimbursement accuracy.