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

doi:10.1186/s12913-019-4417-2

CNU IR > College of Sustainable Environment and Recreation > Dept. of Sports Management > Periodical Articles > Item 310902800/32638

Please use this identifier to cite or link to this item: https://ir.cnu.edu.tw/handle/310902800/32638

Title:	Detecting hospital behaviors of up-coding on DRGs using Rasch model of continuous variables and online cloud computing in Taiwan
Authors:	Chien, Tsair-Wei Lee, Yi-Lien Hsien-Yi Wang(王憲奕)
Contributors:	Chi Mei Med Ctr, Med Res Dept Chi Mei Med Ctr, Dept Med Affairs Natl Chung Cheng Univ, Inst Informat Management Chia Nan Univ Pharm & Sci, Coll Leisure & Recreat Management, Dept Sport Management Chi Mei Med Ctr, Nephrol Dept, 901 Chung Hwa Rd
Keywords:	Rasch analysis Standardized residual analysis Dashboard Medical center
Date:	2019-09
Issue Date:	2020-07-29 13:53:15 (UTC+8)
Publisher:	BMC
Abstract:	Background 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. Methods 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. Results 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. Conclusion 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.
Relation:	Bmc Health Services Research, v.19, n.1, 630
Appears in Collections:	[Dept. of Sports Management] Periodical Articles

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