A Clustering-Based Optimization Approach for Hospital Miscoding Correction

Abstract

This paper addresses the problem of correcting medical coding errors with respect to some coding recommendations. The problem consists in clustering medical codings and determining for each cluster the set of features to correct in order to maximize the financial benefits subject to coding correction effort constraints. For this purpose, we model the coding recommendation as a disjunction of hypercubes and introduce the concept of correction sets. A mixed integer linear programming model is then proposed to assign medical codes to correction sets in order to maximize the financial benefits. The miscoding is then explained by characterizing optimal clusters with association rules and coding error distribution. A case study on patient stays associated with malnutrition-related ICD codes is presented, and the performance of the proposed methodology is assessed in regard to the current coding staff practice. A significant increase in health services reimbursement is achieved with a limited number of subjects’ features reviewed. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Medical miscoding has a significant negative impact on hospitals with a financial loss for under coding and a penalty for over coding. Whether a medical review is necessary for all descriptive features of a miscoded subject? Is it possible to reduce unnecessary medical reviews without compromising the goal of increasing hospital financial benefits? This article attempts to answer these questions with a data-driven optimization approach to determine a limited number of miscoding clusters and the set of features to review for each in order to best balance the financial benefits and the medical review workload. The application to a real-life case study leads to a significant increase in hospital fiscal revenue of nearly 6,992,489.69, while reviewing only a small number of descriptive features (5293 out of 22056 features, or 24% of features). Causes are also provided for each discovered coding error subtype to ameliorate medical coders’ coding practices. Furthermore, the proposed approach allows the decision-maker to balance the cost-benefit and the requirement of public health institutions (i.e., miscoding rate).