Behaviours and drivers of diagnosis-related group upcoding in China: A mixed-methods study.

As a highly destructive gaming behaviour in Diagnosis-Related Group (DRG), upcoding has garnered increasing scholarly attention. This study considers the prevalence, types and risk characteristics of upcoding during the pilot implementation of DRG payments in China, and it also explores the drivers of upcoding and provides corresponding policy recommendations for improving the system. Quantitative research data were sourced from the DRG payment audit database in City Z between the dates of June 1, 2019 and May 31, 2020, encompassing audit results comprising 200 medical records randomly selected from 28 hospitals. Qualitative research methods were used, including semi-structured interviews conducted with 10 stakeholders with interests in the DRG payment system, and thematic framework of the consequent data. 5,157 (92.01%) valid records were re-abstracted. 666 (12.91%) evaluated records were found to be upcoded, resulting in an additional payment at a rate of 45.27%. Several factors emerged as shedding light on the probability of upcoding, including cases with comorbidities, those undergoing non-operating room procedures and internal medical treatments, cases in for-profit hospitals and cases in tertiary hospitals. The main drivers of upcoding were found to be financial and administrative pressures, dysfunctional attitudes towards upcoding, technical facilitation and lack of supervision. This paper provides a comprehensive analysis of the behaviours and drivers of DRG upcoding in China, considering the unique hospital management system and incentive mechanisms in place. The results demonstrate that, following the initiation of the DRG payment system, providers have begun to engage in upcoding behaviour under various drivers, leading to additional health care expenditures and undermining the effectiveness of the scheme. In terms of mounting a response to this behaviour, understanding it and what drives it can aid in its prevention. This study suggests implementing intelligent audits to strengthen supervision and supporting hospitals in cost management.