DeepDRG: Performance of Artificial Intelligence Model for Real-Time Prediction of Diagnosis-Related Groups

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. With the limited resources and increased complexity, policymakers are facing immense challenges of reducing health care costs while improving financial protections, high-quality care, and lowering out-of-pocket (OOP) costs for people. The Fee-for-service (FFS) is a basic payment system for both national and private hospitals in which all care providers are reimbursed for each service provided. To rein in excessive healthcare costs and maintain sustainable procedures for inpatients, prospective payment policies are often implemented to foster risk-sharing between insurers and providers. Nowadays, 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 now been widely adopted and become the principal means of reimbursement for inpatient services globally. Previous studies have shown that the implementation of DRGs helped to shorten the length of hospital stays and lower OOP by efficiently allocating hospital resources. DRGs, a prospective payment system, are used to effectively classify and combine diseases with similar characteristics into different diagnostics and treatment groups. Indeed, DRGs are set to achieve greater equality of financing based on the homogeneity of the clinical process and the similarity of resource consumption. The diagnostics codes should be accurately matched with DRGs codes to claim actual reimbursement, but this is a time‐consuming process and requires expert knowledge to manually retrieve information from patients’ clinical records. Selection of appropriate DRGs codes depends on several factors, such as patients’ comorbidities, complications, treatments, age, discharge status, and the principal diagnoses provided by physicians. Therefore, the quality of DRGs coding is a key factor that influences a hospital’s ability to receive reasonable reimbursement and its overall profits.

DRGs coding errors can influence hospitals’ income, hamper proper planning, and often lead to unfair distributions of resources. Ayub et al. evaluated coding accuracy and its impact on hospital costs, reporting 9.6% miscoding with a total lost billing opportunity of $587,799. Cheng et al. reviewed the causes and consequences of miscoding in a Melbourne tertiary hospital, finding that 16% of the 752 cases audited reflected a DRG change and caused a loss of hospital revenue of nearly AUD 575,300.

Furthermore, the incorrect selection of the principal diagnosis accounted for an additional13% of the DRG changes, which is due to the poor quality of documentation. A previous study demonstrated that the overall rate of incorrect DRGs coding was up to 52%, which may be due to a lack of professional experience, workload, and lack of automation.

The widespread application of electronic health records (EHRs) has generated large amounts of patient data and created immense opportunities to predict the primary diagnosis using deep learning (DL). In this study, we developed and validated DL models to predict the primary diagnosis for appropriate reimbursement and improve the quality of care.

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.

Islam MM, Li G-H, Poly TN, Li Y-C. DeepDRG: Performance of Artificial Intelligence Model for Real-Time Prediction of Diagnosis-Related Groups. Healthcare. 2021; 9(12):1632.