Abstract
Nowadays, the use of diagnosis-related groups (DRGs) has been increased to claim reimbursement for inpatient care. The overall benefits of using DRGs depend upon the accuracy of clinical coding to obtain reasonable reimbursement. However, the selection of appropriate codes is always challenging and requires professional expertise. The rate of incorrect DRGs is always high due to the heavy workload, poor quality of documentation, and lack of computer assistance. We therefore developed deep learning (DL) models to predict the primary diagnosis for appropriate reimbursement and improving hospital performance. A dataset consisting of 81,486 patients with 128,105 episodes was used for model training and testing. Patients’ age, sex, drugs, diseases, laboratory tests, procedures, and operation history were used as inputs to our multiclass prediction model. Gated recurrent unit (GRU) and artificial neural network (ANN) models were developed to predict 200 primary diagnoses. The performance of the DL models was measured by the area under the receiver operating curve, precision, recall, and F1 score. Of the two DL models, the GRU method, had the best performance in predicting the primary diagnosis (AUC: 0.99, precision: 83.2%, and recall: 66.0%). However, the performance of ANN model for DRGs prediction achieved AUC of 0.99 with a precision of 0.82 and recall of 0.57. The findings of our study show that DL algorithms, especially GRU, can be used to develop DRGs prediction models for identifying primary diagnosis accurately. DeepDRGs would help to claim appropriate financial incentives, enable proper utilization of medical resources, and improve hospital performance.
Highlights
Healthcare spending has consistently been increasing globally
We developed and validated deep learning (DL) models to predict the primary diagnosis for appropriate reimbursement and improve the quality of care
This study shows the rigorous training and testing of a novel deep learning model that has been shown to achieve a high accuracy for the prediction of diagnosis-related groups (DRGs)
Summary
Healthcare spending has consistently been increasing globally. Inpatient care is one of the most expensive hospital services, accounting for approximately 31% of the total expenditure [1]. The governments of several countries have already reformed their hospital payment policy by shifting from FFS to diagnosis-related groups (DRGs). The concept of DRGs has been widely adopted and become the principal means of reimbursement for inpatient services globally [7]. The quality of DRGs coding is a key factor that influences a hospital’s ability to receive reasonable reimbursement and its overall profits. The incorrect selection of the principal diagnosis accounted for an additional 13% of the DRG changes, which is due to the poor quality of documentation [12]. We developed and validated DL models to predict the primary diagnosis for appropriate reimbursement and improve the quality of care
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