Research on the value of DRG on nursing workload prediction in respiratory medicine

WANG Mengmeng; SONG Yulei; BAI Yamei; ZHU Yujie; TANG Huangmei; ZHANG Xueqing; GAO Jiaojiao; XU Guihua

doi:10.3969/j.issn.1671-315x.2023.10.001

Abstract：Objective To investigate the influence of diagnosis related groups(DRG)of core diseases in respiratory medicine on nurs-ing workload and construct a prediction model of nursing workload that based on DRG.Methods A total of 1 121 inpatients in the respiratory department of a hospital from January to December 2021 were selected as the research objects.Multiple linear regression was used to analyze the influencing factors of nursing hours,screening predictive indicators,and construct a nursing workload prediction model based on disease diagnosis groups by random forest.Results Seven disease diagnosis groups were included,among them that respiratory infection/inflamma-tion,pulmonary edema and respiratory failure,chronic airway obstructive pulmonary disease were the most common three groups,pulmonary e-dema and respiratory failure group had the most nursing hours and the highest diagnosis weight.There were 7 factors affecting the nursing hours,including age,the way of admission,the numbers of admission,the use of ventilator and antibiotics,the weight of the groups related to disease diagnosis and the degree of complications and comorbidities.The results of random forest prediction model showed that age,the degree of complications and comorbidities and the weight of disease diagnosis had greater predictive value on nursing workload.Conclusion The di-agnostic grouping of core diseases in the respiratory department can be an important indicator for the prediction of nursing workload.The pre-diction model of respiratory nursing hours based on the diagnostic grouping of diseases is scientific and reasonable,it can provide a reference for human resource management of clinical nursing.