Literature DB >> 25111893

Introducing diagnosis-related groups: is the information system ready?

Weiyan Jian1, Ming Lu2, Wei Han3, Mu Hu4.   

Abstract

Diagnosis-related group (DRG) system is a classification system widely used in health managements, the foundation of which lies in the medical information system. A large effort had been made to improve the quality of discharge data before the introduction of DRGs in Beijing. We extract discharge data from 108 local hospitals spanning 4 years before and after standardization to evaluate the impact of standardization on DRG grouping performance. The data was grouped on an annual basis in accordance with Beijing's local DRG system. Proportion of ungrouped data, coefficient of variation (CV) and reduction in variance (RIV) were used to measure the performance of the DRG system. Both the descriptive and regression analysis indicate a significant reduction in terms of ungrouped data and CV for expenditure, increase of RIV for expenditure and length of stay. However, when there was no intervention, that is, between 2005 and 2006 and between 2008 and 2009, changes in these indicators were all insignificant. Therefore, the standardization of discharge data did improve data quality and consequently enhanced the performance of DRGs. Developing countries with a relatively weak information infrastructure should strengthen their medical information system before the introduction of the DRG system.
Copyright © 2014 John Wiley & Sons, Ltd.

Keywords:  China; coding standardization; diagnosis-related groups (DRGs); discharge data

Mesh:

Year:  2014        PMID: 25111893     DOI: 10.1002/hpm.2270

Source DB:  PubMed          Journal:  Int J Health Plann Manage        ISSN: 0749-6753


  2 in total

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Journal:  J Med Syst       Date:  2016-02-15       Impact factor: 4.460

2.  Research on diagnosis-related group grouping of inpatient medical expenditure in colorectal cancer patients based on a decision tree model.

Authors:  Suo-Wei Wu; Qi Pan; Tong Chen
Journal:  World J Clin Cases       Date:  2020-06-26       Impact factor: 1.337

  2 in total

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