Diagnosis-Related Group Weight and Derived Case Mix Index to Assess the Complexity among Twins

Construction and application of a nursing human resource allocation model based on the case mix index

BACKGROUND

The case mix index (CMI) may reflect the severity of disease and the difficulty of care objectively, and is expected to be an ideal indicator for assessing the nursing workload. The purpose of this study was to explore the quantitative relationship between daily nursing worktime (DNW) and CMI to provide a method for the rational allocation of nursing human resources.

METHODS

Two hundred and seventy-one inpatients and 36 nurses of the department of hepatobiliary surgery were prospectively included consecutively from August to September 2022. The DNW of each patient were accurately measured, and the CMI data of each patient were extracted. Among 10 curve estimations, the optimal quantitative model was selected for constructing the nursing human resource allocation model. Finally, the applicability of the allocation model was preliminarily assessed by analyzing the relationship between the relative gap in nursing human resources and patient satisfaction, as well as the incidence of adverse events in 17 clinical departments.

RESULTS

The median (P25, P75) CMI of the 271 inpatients was 2.62 (0.92, 4.07), which varied by disease type (F = 3028.456, P < 0.001), but not by patient gender (F = 0.481, P = 0.488), age (F = 2.922, P = 0.089), or level of care (F = 0.096, P = 0.757). The median (P25, P75) direct and indirect DNW were 76.07 (57.98, 98.85) min and 43.42 (39.42, 46.72) min, respectively. Among the 10 bivariate models, the quadratic model established the optimal quantitative relationship between CMI and DNW; DNW = 92.3 + 4.8*CMI + 2.4*CMI2 (R2 = 0.627, F = 225.1, p < 0.001). The relative gap between theoretical and actual nurse staffing in the 17 clinical departments were linearly associated with both patient satisfaction (r = 0.653, P = 0.006) and incidence of adverse events (r = - 0.567, P = 0.021). However, after adjusting for other factors, it was partially correlated only with patient satisfaction (rpartial = 0.636, P = 0.026).

CONCLUSION

The DNW derived from CMI can be used to allocate nursing human resources in a rational and convenient way, improving patient satisfaction while ensuring quality and safety.

A Bibliometric Analysis of Diagnosis Related Groups from 2013 to 2022

Purpose

As an important management method of the global healthcare system, diagnosis related groups (DRGs) classify patients into different cost groups and pay more attention to the equitable distribution of medical resources and the quality of medical services. At present, most countries have used DRGs to help medical institutions and doctors to treat patients more accurately, avoid the waste of medical resources, and improve treatment efficiency.

Methods

The Web of Science database was searched to collect all relevant literature on DRGs from 2013 to 2022. The literature information was imported into CiteSpace, Vosviewer, and Histcite for data analysis and visualization of the results. Analyze the cooperative relationship among the countries, institutions, journals, and authors. The usage trend of keywords; Highlight the content of the cited articles.

Results

The number of articles published in this decade was stable, and the number of citations in 2014 was the highest. The United States and Germany, as the first countries to use the DRGs system, are ahead of other countries in terms of the number and quality of articles. We have carried out content research on the articles with high citations, and summarized the application range of DRGs; classification method; advantages and disadvantages of the application. In general, the development trend of DRGs in foreign countries is to continuously optimize the classification method, expand the scope of application, and improve the application effect. These provide support and reference for the improvement of medical services and the perfection of the medical insurance system.

Conclusion

The application of DRGs can improve the quality and efficiency of medical services, and reduce the waste of medical expenses. It can also promote the rational allocation of medical resources and the equity of medical services. In the future, DRGs will pay more attention to the personalized diagnosis and treatment and fine management of patients, and the sharing and standardization of medical data, to promote the development of medical informatization.

Impact of quality certification of multidisciplinary head and neck tumor centers

Background

Certification of multidisciplinary tumor centers is nowadays seen as the gold standard in modern oncological therapy for optimization and realization of guideline-based therapy and better outcomes. Single cases are reimbursed based on diagnosis-related groups (DRG). We aimed to review efficiency, cost analysis, and profitability following a certification.

Methods

Tumor board certification at the university hospital Aachen was implemented in 2013. We compared 1251 cases of oropharyngeal cancer treated from 2008 to 2017 before and after certification. For this purpose, several patient characteristics, surgery, and stay-related constants, as well as expenses and reimbursement heights were analyzed statistically.

Results

Following certification, the total case and patient number, surgery duration, hours of mechanical ventilation, case mix index points, DRG reimbursements as well as the costs increased significantly, whereas days of intensive care unit, amount of blood transfusions, patient clinical complexity level (PCCL) and the overall stay were significantly lowered. No changes were observed for the patient’s age and gender distribution. Also, the predetermined stay duration stayed constant.

Conclusions

Certification of head-neck tumor centers causes a concentration of more complex cases requiring higher surgical efforts, which can be processed more efficiently due to a higher level of professionalism. Despite their benefits in cancer care, without compensation, centers may be struggling to cover their expenses in a system, which continuously underestimates them.