Impact of Diagnosis-Related Groups on Inpatient Quality of Health Care: A Systematic Review and Meta-Analysis

The reimbursement system can influence the treatment choice and favor joint replacement versus other less invasive solutions in patients affected by osteoarthritis

PURPOSE

The aim of this study was to assess how physicians perceive the role of the reimbursement system and its potential influence in affecting their treatment choice in the management of patients affected by osteoarthritis (OA).

METHODS

A survey was administered to 283 members of SIAGASCOT (Italian Society of Arthroscopy, Knee, Upper Limb, Sport, Cartilage and Orthopaedic Technologies), a National scientific orthopaedic society. The survey presented multiple choice questions on the access allowed by the current Diagnosis-Related Groups (DRG) system to all necessary options to treat patients affected by OA and on the influence toward prosthetic solutions versus other less invasive options.

RESULTS

Almost 70% of the participants consider that the current DRG system does not allow access to all necessary options to best treat patients affected by OA. More than half of the participants thought that the current DRG system favors the choice of prosthetic solutions (55%) and that it can contribute to the increase in prosthetic implantation at the expense of less invasive solutions (54%). The sub-analyses based on different age groups, professional roles, and places of work allowed to evaluate the response in each specific category, confirming the findings for all investigated aspects.

CONCLUSIONS

This survey documented that the majority of physicians consider that the reimbursement system can influence the treatment choice when managing OA patients. The current DRG system was perceived as unbalanced in favor of the choice of the prosthetic solution, which could contribute to the increase in prosthetic implantation at the expense of other less invasive options for OA management.

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.