Correlation between Diagnosis-related Group Weights and Nursing Time in the Cardiology Department: A Cross-sectional Study.

Background: Diagnosis-related group (DRG) payment has become the main way of medical expenses settlement, and its application is more and more extensive.

Objective: This study aimed to explore the correlation between DRG weights and nursing time and to develop a predictive model for nursing time in the cardiology department based on DRG weights and other factors.

Methods: The convenience sampling method was used to select patients who were hospitalised in the cardiology ward of our hospital between April 2023 and April 2024 as the study participants. Nursing time was measured by direct and indirect nursing time. For the distribution of nursing time with different demographic characteristics, Pearson correlation was used to analyse the relationship between DRG weights and nursing time and multiple linear regression was used to analyse the influencing factors of total nursing time.

Results: A total of 103 subjects were included in this study. The DRG weights were positively correlated with ln(direct nursing time), ln(indirect nursing time) and ln(total nursing time) (r = 0.480, r = 0.394, r = 0.448, all P < .001). Moreover, age was positively correlated with the three nursing times (r = 0.235, r = 0.192, r = 0.235, all P < .001); activities of daily living (ADL) on admission was negatively correlated with the three nursing times (r = -0.316, r = -0.252, r = -0.301, all P < .001); and nursing level on the first day of admission was positively correlated with the three nursing times (r = 0.333, r = 0.332, r = 0.352, all P < .001). Furthermore, the multivariate analysis found that nursing levels on the first day of admission, complications or comorbidities, DRG weights and ADL on admission were the influencing factors of the nursing time of patients (R2 = 0.328, F = 69.58, P < .001).

Conclusions: Diagnosis-related group weights showed a strong correlation with nursing time and can be used to predict nursing time, which may assist in nursing resource allocation in cardiology departments.