Detecting hospital behaviors of up-coding on DRGs using Rasch model of continuous variables and online cloud computing in Taiwan

• Published in: BMC health services research Vol. 19; no. 1; p. 630
• Main Authors: Chien, Tsair-Wei, Lee, Yi-Lien, Wang, Hsien-Yi
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 04.09.2019; BioMed Central; BMC
• Subjects: Algorithms; Analysis; Application programming interface; Chi-square test; Cloud Computing; Control; Dashboard; Diagnosis related groups; Diagnosis-Related Groups - economics; Expenditures; Fees; Fees & charges; Fees, Medical; Health care costs; Health insurance; Health services; Hospital admission and discharge; Hospitals; Humans; Insurance, Health, Reimbursement - statistics & numerical data; Item response theory; Medical center; Medical centers; Missing data; National health insurance; National Health Programs - economics; National Health Programs - statistics & numerical data; Patient Discharge - statistics & numerical data; Physicians; Rasch analysis; Rasch model; Reimbursement; Standard deviation; Standard scores; Standardized residual analysis; Taiwan; Transaction processing services; Variables; Taiwan; Rasch analysis; Dashboard; Medical center; Standardized residual analysis
• ISSN: 1472-6963; 1472-6963
• DOI: 10.1186/s12913-019-4417-2

This work aims to apply data-detection algorithms to predict the possible deductions of reimbursement from Taiwan's Bureau of National Health Insurance (BNHI), and to design an online dashboard to send alerts and reminders to physicians after completing their patient discharge summaries. Reimbursement data for discharged patients were extracted from a Taiwan medical center in 2016. Using the Rasch model of continuous variables, we applied standardized residual analyses to 20 sets of norm-referenced diagnosis-related group (DRGs), each with 300 cases, and compared these to 194 cases with deducted records from the BNHI. We then examine whether the results of prediction using the Rasch model have a high probability associated with the deducted cases. Furthermore, an online dashboard was designed for use in the online monitoring of possible deductions on fee items in medical settings. The results show that 1) the effects deducted by the NHRI can be predicted with an accuracy rate of 0.82 using the standardized residual approach of the Rasch model; 2) the accuracies for drug, medical material and examination fees are not associated among different years, and all of those areas under the ROC curve (AUC) are significantly greater than the randomized probability of 0.50; and 3) the online dashboard showing the possible deductions on fee items can be used by hospitals in the future. The DRG-based comparisons in the possible deductions on medical fees, along with the algorithm based on Rasch modeling, can be a complementary tool in upgrading the efficiency and accuracy in processing medical fee applications in the discernable future.