Measuring nursing time is beneficial to effectively avoid excess nursing labor costs from unreasonable time allocations. Nursing time is closely associated with the disease severity of patients [1], which is currently assessed through quantitative scoring systems, such as the Acute Physiology and Chronic Health Evaluation II scores and the Nursing Activity Scale [2]. However, these assessment methods are only suitable for assessing critically ill patients and have limitations [3]. The Patient Classification System (PCS) involves activities such as categorizing and quantifying the nursing level required by a patient at a given time, assigning work using scales, and calculating personnel requirements [4]. The PCS-based scientific management of nursing personnel is of great significance [5]. The diagnosis-related group (DRG) system is a diagnosis and treatment classification system that can effectively evaluate the severity of a disease classification and can combine cases based on the discharge diagnosis and careful consideration of complex disease-related factors, such as patient complications, different treatment methods, and other patient-specific differences [6]. The basic principles of DRGs are as follows: they are patient-centered; grouping is performed according to factors such as disease diagnosis, treatment methods and individual characteristics; prepaid systems are used to control costs; they encourage hospitals to improve service efficiency and quality; and they ensure reasonable costs through supervision. At the same time, grouping and payment standards are continuously optimized with the development of medical technologies to improve the quality of medical services and patient satisfaction.

DRG classification is globally recognized as an effective tool for hospital management and health care quality evaluation, and there has been research on the application of DRG systems in China since the 1980s. The DRG system divides patients into different diagnostic groups for management according to factors such as disease diagnosis, treatment methods, age, comorbidities, complications, disease severity, and disease progression [7]. In 1983, Medicare (a US federal health care insurance program) used DRG systems for the first time as a health care payment method amid health care reform. The DRG system’s prepaid model has highlighted certain advantages for controlling unreasonable medical expenses, standardizing diagnosis and treatment behaviors, enhancing patients’ experience with medical treatment, and incentivizing hospitals to strengthen their internal management. Subsequently, it has been rapidly popularized, applied worldwide, and has become one of the most dominant medical payment methods for hospitals internationally [8]. Additionally, some research has been conducted internationally on the effects of the DRG payment model on nursing, mainly including the quality of nursing [9], career development of nurses [9], nursing costs [10], and nursing time. In the meantime, studies in China have also shown that DRG systems can be used as an evaluation index for the quality of medical services, work efficiency, operating costs, and performance [11]. In recent years, a meta-analysis showed that the DRG payment model can significantly reduce the length of hospital stays and medical costs for patients, help to comprehensively and effectively control medical costs, improve the quality of medical services, and help incentivize hospitals to improve their operation and management. Furthermore, it effectively reduces the management and medical expenditures of the medical insurance department, which facilitates the formation of a standardized model for medical resource expenditures [12]. Studying the relationship between DRG weight and nursing time is important for optimizing the allocation of nursing resources, improving the quality of nursing services, promoting the implementation of the DRG payment system, and promoting the development of the nursing discipline. As an advanced medical classification and payment system, DRGs have been used worldwide, but nursing management still needs to progress simultaneously. Through in-depth research, we can accurately predict the demand for nursing resources for different DRGs, avoid the wasting of resources, and improve nursing efficiency. At the same time, GRCs help nursing staff to better identify patient needs, provide personalized services, ensure patient safety, reduce adverse events, and provide a strong support for DRG payment system reform and nursing discipline innovation.

In August 2011, the General Office of the Ministry of Health of China issued the Notice on Promoting the Application of Diagnosis-Related Groups for Hospital Evaluation, which began using DRGs for related evaluations, such as hospital service performance [13]. In 2016, the National Health and Family Planning Commission stated that the DRG system is one of the critical tools for medical quality management [14]. In 2017, the Opinions of the General Office of the State Council on Further Deepening the Guidance Reform for the Payment of Basic Medical Insurance pointed out that it is necessary to carry out pilot programs for DRGs, actively explore the framework of DRG-based payment, and promote the reform of medical insurance payment mode in China. Since nursing is a part of medical treatment, the application and implementation of the DRG system not only promotes the reform of medical treatments but also exhibits a great impact on the profession of nursing. Therefore, this study explores the correlation between DRG weight and nursing time and establishes a nursing time prediction model based on DRG weight and other factors. The model can make up for DRG-related gaps in nursing time evaluation and provide a reference for the application of DRG weight and related indicators in nursing performance appraisal and staffing.

By analyzing the correlation between DRG weight and nursing time, this study established a nursing time prediction model based on DRG weight and other factors. The results showed that DRG weight was positively correlated with direct nursing time, indirect nursing time, and total nursing time. The influencing factors affecting the patient’s nursing time include DRG weight, the nursing level on the first day of admission, ADL score on admission, and complications or comorbidities.

Scholars in China have learned from more advanced nursing time measurement tools that are used globally, such as the Severity of Illness Index for Pediatric Patients, Comprehensive Nursing Intervention Score, and Patient Illness Severity Nursing Index, and they have conducted weighted evaluations of factors such as project difficulty and risk, without considering factors such as the complexity of disease and treatment methods for patients. Currently, the most commonly used metric for nursing time in China is the measurement of work hours [20]; however, this is time-consuming and labor-intensive with many influencing factors, making it difficult to implement in clinical practice. Kang et al [21] performed nursing time statistics based on the hospital information platform, and despite the slightly improved efficiency, the statistical workload was still a lot due to the extraction of data from multiple systems. Meanwhile, Zhang et al [22] calculated nursing time using the load weighting method. The assignment of load weights mainly depended on the subjective assessment of experts, with a large influence from human factors. In contrast, DRG weights comprehensively considered factors such as the severity and complexity of the disease, different treatment methods, individual patient differences, and discharge outcomes [23]. These included many patient factors related to nursing time, such as the disease itself and individual characteristics, which could objectively reflect the nursing time, and the acquisition of parameters was relatively simple and easy to implement.

This study found that the average daily nursing time per patient during hospitalization in the cardiology department was 134.58 minutes. This was close to the average daily nursing time per patient (113.59 minutes) in a study by Yang et al [24] but less than the average daily total nursing time (171.02 minutes) calculated by Cai et al [25] for neurosurgery patients. This discrepancy was mainly due to the different types of diseases treated in the cardiology and neurosurgery departments, where the nursing times for neurosurgical operations were greater than that for internal medicine operations. In this study, the average daily direct nursing time per patient was 70.34 minutes, which was less than the average daily direct nursing time per patient (75 minutes) measured by Wang et al [26] for neurology patients. This was mainly due to the overall self-care ability of the cardiology patients included in this study, with 35.9% (37/103) having an ADL score of 100 on admission and 49.5% (51/103) having a score between 40‐99, while neurology patients experienced more severe functional impairments and greater dependence on care, thus increasing the burden on nursing staff. Moreover, the average daily indirect nursing time per cardiology patient in this study was 64.24 minutes, which was longer than the average daily indirect nursing time per patient (45.34 minutes) measured by Zhang et al [27]. This was directly due to the different clinical support systems of the hospitals [28], in which a well-developed clinical support system helped to reduce indirect nursing time while increasing direct nursing time.

Specifically, DRG weights regard the treatment cost of the disease as the main factor while comprehensively considering factors such as primary nursing care, average length of the hospital stay, and mortality. This was followed by obtaining the DRG weight value through data modelling, with the corresponding DRG weight value for a disease type calculated through the DRG Grouper tool. In the univariate analysis, despite the correlation between complications or comorbidities, surgery, nursing level on the first day of admission, and age, the correlation between DRG weight and nursing time was more prominent and evident. This is because DRG weights have incorporated factors related to nursing labor costs, thereby making them more valuable for implementation in the management of nursing personnel. Furthermore, the results showed that DRG weights had the greatest impact on nursing time, and although evaluating nursing time with nursing levels alone is not desirable [29], nursing levels, to some extent, reflect the severity of the disease. Moreover, the time required for some direct nursing operations was different, such as bed-making and admission assessment, which corresponded to different ADL scores. The higher the ADL score was, the less the time was required for these items, indicating a negative correlation, thereby affecting the total nursing time [29]. Furthermore, the comorbidities of cardiovascular diseases were mainly hypertension and diabetes, with 45.6% (47/103) of the patients experiencing hypertension and 9.7% (10/103) experiencing diabetes, requiring an increased monitoring of blood pressure and blood glucose, respectively, which increased nursing time. Therefore, the above factors were all related to the nursing time in the multivariate regression analysis. At the same time, the prediction of workload using a multivariate regression equation with DRG weight as the main factor can be helpful for nursing managers to measure and adjust future staffing needs based on the current staffing situation in combination with other departmental needs.

DRGs have many contributions to nursing management. They promote the standardization of the nursing process, optimize the allocation of resources, improve the efficiency of nursing work, and make nursing services more efficient and stable [24]. At the same time, DRGs promote the transformation of hospital management from extensive to refined, provide data support for nursing management departments, and enable them to make more scientific, data-based decisions [21]. In addition, DRGs help to reduce nursing risks, improve patient satisfaction, and provide more personalized nursing services for patients. Through DRG systems, hospitals can more accurately meet the nursing needs of patients and ensure patient safety. Finally, the application of DRGs puts forward higher requirements for the professional skills of nursing staff, promotes the professional development of nursing staff, and promotes the innovation of nursing research. In short, the application of DRGs has brought many positive effects to nursing management, which helps improve the quality of nursing services and promotes the modernization and scientific process of the nursing speciality.

However, this study also has some limitations. First, due to the limitation of data accessibility, this study only used the data of one hospital, and the number of cases was small. This could lead to a deviation in data selection, and there may have been some bias in the extrapolation of results. Therefore, in future studies, multicenter longitudinal studies with a larger sample size can be used for further verification. In addition, due to an untimely update of hospital disease diagnosis and surgical coding, as well as operational errors by medical staff, the data quality of the first page of medical records was low, which affected the case registration rate. The training and practical operation of medical staff on DRG-related knowledge should be strengthened before future research.

In summary, DRG weight has a strong correlation with nursing time, which can be used to predict nursing time and contribute to the allocation of nursing resources in cardiology departments. This study can improve the satisfaction of patients and their families with hospital nursing work and reduce the pain of patients. In addition, this study can also make up for the DRG-related gap in the evaluation of nursing time in cardiology departments and provide a reference for the application of DRG weight and related indicators in nursing performance evaluations and staffing.