Fine-Tuning for Accuracy: Evaluation of GPT for Automatic Assignment of ICD Codes to Clinical Documentation

Abstract Background: Assignment of International Classification of Disease (ICD) codes to clinical documentation is a tedious but important task that is mostly done manually. This study evaluated the widely popular OpenAI’s Generative Pretrained Model (GPT) 3.5 Turbo in facilitating the automation of assigning ICD codes to clinical notes. Methods: We identified the 10 most prevalent ICD-10 codes in the Medical Information Mart for Intensive Care (MIMIC-IV) dataset. We selected 200 notes for each code, and then split them equally into two groups of 100 each (randomly selected) for training and testing. We then passed each note to GPT 3.5 Turbo via OpenAI’s API, prompting the model to assign ICD-10 codes to each note. We evaluated the model’s response for the presence of the target ICD-10 code. After fine-tuning the GPT model on the training data, we repeated the process with the test data, comparing the fine-tuned model’s performance against the default model. Results: Initially the target ICD-10 code was present in the assigned codes by the default GPT 3.5 Turbo model in 29.7% of the cases. After fine-tuning with 100 notes for each top code, the accuracy improved to 62.6%. Conclusions: Historically, GPT’s performance for healthcare related tasks is sub-optimal. Fine-tuning as in this study provides great potential for improved performance, highlighting a path forward for integration of Artificial Intelligence (AI) in healthcare for improved efficiency and accuracy of this administrative task. Future research should focus on expanding the training datasets with specialized data and exploring the potential integration of these models into existing healthcare systems to maximize their utility and reliability.