DRGC oder: Explainable Clinical Coding for the Early Prediction of Diagnostic-Related Groups (2023)

Abstract: Medical claim coding is the process of transforming medical records, usually presented as free texts written by clinicians, or discharge summaries, into structured codes in a classification system such as ICD-10 (International Classification of Diseases, Tenth Revision) or DRG (Diagnosis-Related Group) codes. This process is essential for medical billing and transitional care; however, manual coding is time-consuming, error-prone, and expensive. To solve these issues, we propose DRGCoder, an explainability-enhanced clinical claim coding system for the early prediction of medical severity DRGs (MS-DRGs), a classification system that categorizes patients’ hospital stays into various DRG groups based on the severity of illness and mortality risk. The DRGCoder framework introduces a novel multi-task Transformer model for MS-DRG prediction, modeling both the DRG labels of the discharge summaries and the important, or salient words within he discharge summaries. We allow users to inspect DRGCoder’s reasoning by visualizing the weights for each word of the input. Additionally, DRGCoder allows users to identify diseases within discharge summaries and compare across multiple discharge summaries. Our demo is available at https://huggingface.co/spaces/danielhajialigol/DRGCoder. A video demonstrating the demo can be found at https://www.youtube.com/watch?v=pcdiG6VwqlA

## introduction inpatient care, defined as care for hospital patients who stay overnight, are one of the biggest components in healthcare costs, accounting for approximately 31% of total medical expenses @xcite . appropriate determination of costs associated with inpatient care are based on assigning one (of potentially many) icd or drg codes to a given patient. this is a crucial process for medical insurance billing and healthcare improvement, but it can be very time-consuming, prone to errors, 1 our demo is available at https://huggingface.co/ spaces/danielhajialigol/drgcoder 2 a video demonstrating the demo can be found at https: //www.youtube.com/watch?@xmath0 figure 1: overview of drgcoder. given a discharge summary, drgcoder (1) identifies diseases, (2) predicts the corresponding drg, and (3) highlights the importance of each word in the discharge summary contributing to the drg prediction. and expensive when done manually. on average, it takes a medical coder about 20 minutes to code a single inpatient stay, and with 35 million inpatient stays in the united states each year, manual coding can be a very laborious and expensive process. the current process for coding inpatient records most often consists of a certified inpatient coder (cic) manually reviewing a medical record and identifying every codable entity within that text. often times, this process is completed across two completely separate software applications: the medical record is either accessed via the electronic health record (ehr) system or else "printed", usually in a digital (pdf) format, for the coder to review, while the icd-10 codes are entered into a separate computer-assisted coding (cac) application. these collection of icd codes are then used to determine the correct drg code. alternatively, early prediction of drg codes attempt to identify drg codes directly from the discharge summary, bypassing icd code prediction. there have been many attempts to automate clinical coding while incorporating explainability. al-though there are many methods that compute attention for each word from the input discharge summary @xcite @xcite , most of them don't utilize the powerful contextual embeddings backed by transformer @xcite models. only recently however has there been transformer-based methodologies for automating clinical coding @xcite . an example of this is medical concept driven attention @xcite , where they align both clinical notes and wikipedia documents into topic space via topic modeling. while their architecture can handle any encoder, they claim that using a transformer as their encoder produces inferior results, as documents are too long. our work is most similar to trigueros et al., where they use a multi-task transformer-based approach, except they perform entity-linking on medical concepts related to icd codes. despite advancements in both @xcite , they don't take into account salient words: words that are explicitly important for clinical coding. to address these issues, we propose drgcoder (figure 1 ), an explainable clinical coding system that provides both a predicted ms-drg code and highlighted areas of interest within the discharge summary text to support medical coders. drgcoder first predicts the appropriate drg based on a novel multi-task transformer architecture that incorporates both discharge summaries and salient words within the summaries. while there are many types of drg codes, we choose to focus on identifying ms-drg codes, as the ms-drg system is the most widely used, standardized drg system, and it is maintained by a single entity (the centers for medicare & medicaid services). drgcoder allows users to gain better insight into ms-drg classification by highlighting the areas of the discharge summary that explain the prediction. furthermore, drgcoder identifies diseases within discharge summaries and functionality to compare drgcoder results across multiple discharge summaries. ## system description drgcoder can be broken down into two categories: automated clinical coding and explanation visualization. the former component is home to our ms-drg classification framework while the latter contains our related summary extraction and disease named-entity recognition modules. ## evaluation we illustrate the effectiveness of drgcoder by comparing the performance of clinical-bert and caml @xcite on the ms-drg portion of the mimic-iii dataset. we evaluate our performance using f1 and auc (area under curve) performance metrics. ## conclusion we offer drgcoder, an explainability-enhanced inpatient claim coding system for ms-drg code prediction. drgcoder allows users to visualize words deemed important by our framework, as well as identify diseases and icd concepts. furthermore, we offer functionality for similar summary retrieval and comparison across summaries.