Search

PurposeThis study constructs a structure of interaction between dimensions and criteria within the diagnosis-related groups (DRGs) system from a quantitative system and identifies key factors affecting the overall performance of medical services.MethodFrom September to December 2020, the influence relation structure diagram (IRSD) of the dimensions and corresponding criteria was developed from the practical experience of a group of domain experts, based on the DEMATEL method. Subsequently, all dimensions and criteria construct influential weights from a systems perspective. Finally, the main influential factors were identified based on the analysis results.ResultsThe IRSD results showed that, in the overall performance of medical services, “Medical service capacity (C1)” was the main influential dimension, influencing both “Medical service efficiency (C2)” and “Medical service safety (C3).” At the criteria level, “Case-mix index (CMI) (C12),” “Time efficiency index (C21),” and “Inpatient mortality of medium-to-low group (C32)” were the main influential criteria in the corresponding dimensions. The influential weight results showed that “Medical service capacity (C1)” was also a key dimension. “Case-mix index (CMI) (C12),” “Cost efficiency index (C22),” and “Inpatient mortality of medium-to-low group (C32)” were the key criteria in their respective dimensions.ConclusionPatients and managers should first focus on the capacity of medical service providers when making a choice or deciding using the results of the DRGs system. Furthermore, they should pay more attention to medical safety even if it is not as weighted as medical efficiency.

BackgroundThe global shortage and turnover of nurses is a current challenge. Past studies have shown that nurse job satisfaction may ameliorate nurse shortage. Although there are many studies on the criteria influencing nurses' job satisfaction, few have examined the causal relationships and weight of each criterion from a systematic perspective.ObjectiveIdentify the key criteria and causal relationships that affect nurses' job satisfaction, and help nurse leaders identify high-weight, high-impact dimensions and contextualize them for improvement.MethodsThe study developed a hybrid multi-criterion decision-making model, which incorporated the McCloskey/Mueller satisfaction 13-item scale (MMSS-13), and the Decision-Making Trial and Evaluation Laboratory and the Importance-Performance Analysis methods the model was used to analyze key factors of nurse satisfaction and their interrelationships based on the experience of 15 clinical nurse specialists.ResultsIn MMSS-13's dimension level, “satisfaction with work conditions and supervisor support” (C5) had the highest impact, and “satisfaction with salary and benefits” (C1) had the highest weight. In criteria level, “salary” (C11), “flexibility in scheduling time off” (C24), “maternity leave time” (C31), “opportunities for social contact after work” (C41), and “your head nurse or facility manager” (C51) had high influence under their corresponding dimensions. The “benefits package” (C13) was the top criterion with the highest impact on MMSS-13.ConclusionsThis study assessed nurses' job satisfaction from a multidimensional perspective and revealed the causal relationships between the dimensions. It refined the assessment of nurse job satisfaction to help nurse leaders better assess nurse job satisfaction and make strategic improvements. The study found that compensation and benefits had the highest weight in nurses' job satisfaction. Meanwhile, support for family responsibilities and working conditions, and support from supervisors were the cause dimensions of job satisfaction. Among the more detailed criteria, salary, benefits package, maternity leave time, and leadership had a greater impact on nurses' job satisfaction. Nurse leaders should start with these dimensions to achieve efficient improvement of nurses' job satisfaction.

Purpose: To analyze the key factors and decision-making behaviors affecting overall satisfaction based on perceptual data of outpatients.Methods: The official satisfaction questionnaire developed by the National Health Commission of the People's Republic of China was used. Rough set theory was used to identify the perception patterns between condition attributes (i.e., service factors) and a decision attribute (i.e., overall service level) and to express them in rule form (i.e., if-then).Results: The four minimal-coverage rules, with strength exceeding 10% in the good class, and six crucial condition attributes were obtained: “Ease of registration (C1),” “Respected by registered staff (C2),” “Registered staff's listening (C3),” “Respected by doctor (C9),” “Signpost (C12),” and “Privacy (C16).” In addition, the average hit rate for 5-fold cross-validation was 90.86%.Conclusions: A series of decision rules could help decision-makers easily understand outpatients' situations and propose more suitable programs for improving hospital service quality because these decision rules are based on actual outpatient experiences.