A large group linguistic Z-DEMATEL approach for identifying key performance indicators in hospital performance management

Identifying performance indicators to measure overall performance of telephone triage - a scoping review

OBJECTIVE

This article aims to summarize performance indicators used in telephone triage services research, and make recommendations for the selection of valid indicators to measure the performance of telephone triage. We describe what kind of frameworks, performance indicators, or variables have been used for evaluating telephone triage performance by systematically mapping the telephone triage performance measurement. The objective was to find measures for each Triple Aim dimension.

DESIGN

A scoping review method was used following Joanna Briggs Institute guidelines. Using this method, we defined indicators to measure the performance of telephone triage. We used the Triple Aim framework to identify indicators to measure the overall performance of telephone triage. The Triple Aim framework consists of improving the patient experience of care, improving the health of populations, and reducing cost per capita.

SETTING

The scoping review was performed using CINAHL, Medline, EBSCOhost, and PubMed electronic databases. The eligibility criterion was research published in English between 2015 and 2023. The inclusion focused on the use and performance of telephone triage services and system-focused studies.

RESULTS

A total of 1098 papers were screened for inclusion, with 57 papers included in our review. We identified 13 performance indicators covering all Triple Aim dimensions: waiting times, access, patient satisfaction, the accuracy of triage decision, severity and urgency of the symptoms, triage response, patient compliance with the advice given, follow-up healthcare service use, and running costs of service. We didn't find any earlier framework covering all Triple Aim dimensions properly.

CONCLUSIONS

Measuring the performance of telephone triage requires an extensive and comprehensive approach. We presented performance indicators that may be included in the framework for measuring the performance of telephone triage to support overall performance measurements of telephone triage.

A study of factors affecting the length of hospital stay (LOS) of COVID-19 patients: A qualitative evidence in Iranian hospital

BACKGROUND

The present study aimed to identify the administrators' and physicians' experiences and viewpoints about the factors affecting the length of stay (LOS) of COVID-19 patients and provide valid operational evidence.

MATERIALS AND METHODS

The current study was carried out qualitatively and phenomenologically on experts, officials, and administrators of hospitals in 2021. Purposeful sampling was performed with the maximum diversity. To achieve a comprehensive view, snowball sampling was conducted. Twenty-one experts in the field of healthcare and emerging diseases participated in this study. Semi-structured interviews were used to collect the data. The study sites were universities of medical sciences and hospitals of the Ministry of Health of Iran. The interview questions included questions about the factors affecting the LOS and strategies for controlling the LOS of COVID-19 patients in infectious units. Text analysis was performed through the content analysis method in MAXQDA-10 software.

RESULTS

Based on the experts' viewpoints, several factors affected the LOS in COVID-19 patients. These factors were divided into five clinical, preclinical, economic, social, and management subcategories. The proposed solutions included policy solutions (supportive policies, development of home care services, training and culture building, and establishment of clinical guidelines) and operational solutions (drug management, promotion of equipment and facilities, telehealth or telemedicine services, and promotion of clinical and support processes).

CONCLUSION

One of the main tasks of hospital administrators is identifying the factors affecting the reduction of LOS. Among these factors, clinical and management factors in the hospital are more important and need more planning and attention by hospital officials.

Applying the Balanced Scorecard to Build Service Performance Measurements of Medical Institutions: An AHP-DEMATEL Approach

The main purpose of this study is to explore the application of the balanced scorecard (BSC) to service performance measurements of medical institutions using the analytic hierarchy process (AHP) and decision making and trial evaluation laboratory (DEMATEL). According to the concept of BSC, a total of four evaluation dimensions and twenty-two indicators of medical service performance measurements were developed. To collect data, this study delivered expert questionnaires to medical-related professional supervisors, deans, and heads of medical institutions in Taiwan. By combining the AHP and DEMATEL, the priority and causality of service performance standards in medical institutions were obtained. The results of this study show that the customer dimension is the most important service performance measurement dimension for medical institutions. The seven key service performance measurement indicators that are most important for medical institutions, in order, are "complete and comfortable equipment", "competitiveness of the medical profession", "continuity of patient-to-hospital treatment", "classification of medical profession according to customers (VIP system)", "complete medical service", "complete salary, remuneration, and policy", and "medical incomes of institutions". In terms of causality, provided the complete services of medical institutions are improved, the continuity of patient-to-hospital treatment, the competitiveness of the medical profession, and the medical incomes of institutions would be influenced.

A flexible group decision-making method for green supplier selection integrating MABAC and CRITIC method under the linguistic Z-numbers environment

This paper intends to treat the green supplier selection (GSS) problem as a multi-attribute group decision making (MAGDM) problem, adopt the linguistic Z-number that can more flexibly and accurately express the evaluation information, and expand the traditional multi-attribute boundary approximate area comparison (MABAC) method, combine the CRITIC method of standard importance and consider the risk vector to finally determine the optimal solution. More specifically, the linguistic Z-number is used to describe the fuzzy evaluation information of experts on alternatives under attributes, then the expanded CRITIC method is used to obtain the weight of each given attribute, and finally the MABAC method with added risk vector and expanded is used to obtain the ranking of alternatives and obtain the best solution. Finally, taking green supplier selection as an example, and comparing with other methods, the reliability and effectiveness of the constructed method are verified. The results show that this method can express the evaluation information of experts flexibly and completely, and obtain the ranking results of given schemes through fewer steps, which is reliable and effective.

Analyzing the key performance indicators of circular supply chains by hybrid fuzzy cognitive mapping and Fuzzy DEMATEL: evidence from healthcare sector

This study presents a multi-layer fuzzy-based decision-making approach to enhance the hospital Circular Supply Chain (CSC) performance by focusing on intensive care units (ICU) via key performance indicators analysis. In this regard, a Systematic Literature Review (SLR) and Institution Fuzzy Delphi (IFD) are employed to extract the relevant and prominent KPIs. After, a hybrid Fuzzy Cognitive Mapping (FCM) and Fuzzy Decision Making Trial and Evaluation Laboratory (FDEMATEL) have been applied to illustrate a conceptual framework for the CSC performance management of the healthcare sector in the emerging economy of Iran. As a result, eight critical indicators emanated from the SLR-IFD approach. Furthermore, sixteen relationships amongst the performance indicators were identified via hybrid FCM-FDEMATEL. Inventory availability, information availability, innovation, and technology were selected as the most influential indicators. Besides, changing the information technology category, including information availability and Innovation and technology, had the most impact on the performance of the entire CSC. This study attempts to evaluate hospitals' circular supply chain performance, by designing the circular evaluation framework. Hospital managers can use the results of this research to improve their internal circular supply chain performances in the intensive care units by understanding the different scenarios.

Evaluating interaction between internal hospital supply chain performance indicators: a rough-DEMATEL-based approach

Purpose

Previous studies on hospital supply chain performance have attempted to measure the performance of the hospital supply chain either by the measurement of performance indicators or the performance of specific activities. This paper attempts to measure the internal hospital supply chain's performance indicators to find their interdependencies to understand the relationship among them and identify the key performance indicators for each of those aspects of the logistics process toward improvement.

Design/methodology/approach

In this research, a systematic assessment and analysis method under vagueness is proposed to assess, analyze and measure the internal health care performance aspects (HCPA). The proposed method combines the group Decision-Making and Trial Evaluation Laboratory (DEMATEL) method and rough set theory.

Findings

The study results indicate that the most critical aspects of hospital supply chain performance are completeness of treatment, clinical care process time and no delay in treatment.

Originality/value

The causal relationship from rough-DEMATEL can advise management officials that to improve the completeness of treatment toward patient safety, clinical care process time should be addressed initially and with it, patient safety aspects such as free from error, clinical care productivity, etc. should be improved as well. Improvement of these aspects will improve the other aspects they are related to.

Analysing supply chain coordination mechanisms dealing with repurposing challenges during Covid-19 pandemic in an emerging economy: a multi-layer decision making approach

Following the outbreak of the Covid-19 pandemic, there was a serious need for the pharmaceutical industry to combat the disease more quickly and effectively. In this regard, numerous companies set out to repurpose current drugs. The noticed decision has major challenges in various dimensions, including the creation and management of an efficient supply chain. The present study attempts to examine the significance and relationships of the repurposing challenges and analyze the effectiveness of supply chain coordination contracts confronting them. In this regard, a combination of Decision-Making Trial and Evaluation Laboratory (DEMATEL) and Analytic Network Process (ANP) named DANP method is applied to investigate the relationships and extracting the weights of the mentioned challenges and the multi-criteria optimization and compromise solution technique called VIKOR is employed to prioritize the supply chain coordination contracts found on their impact facing with repurposing challenges. The mentioned techniques have been conducted under the condition of linguistic Z-numbers. The results demonstrated that financial support and digitalization are the most influential challenges. Moreover, collaboration and data availability have the most weight. In addition, four contracts including effort sharing, cost-sharing, credit option and buyback are the best contracts that companies in the merging economy of Iran should concentrate on them. This research proposes a novel framework of decision-making by integrating DANP and VIKOR with linguistic Z-numbers. Additionally, this study takes a new look at the use of coordination contracts from the viewpoint of repurposing challenges which is highlighted particularly during the Covid-19 pandemic.

A fuzzy hybrid decision-making framework for increasing the hospital disaster preparedness: The colombian case

The recent increase in the number of disasters over the world has once again brought to the agenda the question of preparedness of the hospitals, which are the most necessary units of healthcare pillar to resist these disasters. The COVID-19 epidemic disease, which has affected the whole world, has caused a large number of people to die in some countries simply because of the inadequate and incomplete planning and lack of readiness of hospitals. For this reason, determining the disaster preparedness level of hospitals is an important issue that needs to be studied and it is important in terms of disaster damage reduction. In this study, a fuzzy hybrid decision-making framework is proposed to assess hospital disaster preparedness. The framework covers three important decision-making methods. For the first phase, Intuitionistic Fuzzy Analytic Hierarchy Process (IF-AHP) is used to assign relative weights for several disaster preparedness criteria considering uncertainty. Secondly, Intuitionistic Fuzzy Decision Making Trial and Evaluation Laboratory (IF-DEMATEL) is applied to identify interrelations among these criteria and feedback. Finally, via the VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method, priorities of hospitals regarding disaster readiness are obtained. A case study involving the participation of 10 Colombian tertiary hospitals is carried out to show the applicability of this fuzzy hybrid approach.

A generalized TODIM-ELECTRE II method based on linguistic Z-numbers and Dempster–Shafer evidence theory with unknown weight information

How to effectively reflect the randomness and reliability of decision information under uncertain circumstances, and thereby improve the accuracy of decision-making in complex decision scenarios, has become a crucial topic in the field of uncertain decision-making. In this article, the loss –aversion behavior of decision-makers and the non-compensation between attributes are considered. Furthermore, a novel generalized TODIM-ELECTRE II method under the linguistic Z-numbers environment is proposed based on Dempster–Shafer evidence theory for multi-criteria group decision-making problems with unknown weight information. Firstly, the evaluation information and its reliability are provided simultaneously by employing linguistic Z-numbers, which have the ability to capture the arbitrariness and vagueness of natural verbal information. Then, the evaluation information is used to derive basic probability assignments in Dempster–Shafer evidence theory, and with the consideration of both inner and outer reliability, this article employed Dempster’s rule to fuse evaluations. Subsequently, a generalized TODIM-ELECTRE II method is conceived under the linguistic Z-numbers environment, which considers both compensatory effects between attributes and the bounded rationality of decision-makers. In addition, criteria weights are obtained by applying Deng entropy which has the ability to deal with uncertainty. Finally, an example of terminal wastewater solidification technology selection is offered to prove this framework’s availability and robustness. The predominance is also verified by a comparative analysis with several existing methods.

Prioritizing enablers for service quality in healthcare sector - a DEMATEL approach

PURPOSE

The Healthcare sector is one of the important sectors of the Service Industry. It is believed that in this sector, the customer server relationship is very critical, and even the slightest gap in the people quality may have a huge impact on the delivered service quality. Some of these enablers are doctors, nursing staff and support staff. Furthermore, the nonpeople quality enablers such as diagnostic services, facilities, hygiene levels and so on are also likely to impact the delivered service quality. It was also felt that the degree of impact each enabler has on the service quality could vary. Therefore there is a need for structured and deep analysis. The paper attempts to identify, analyze and prioritize the enablers that impact the delivered service quality.

DESIGN/METHODOLOGY/APPROACH

The enablers have been identified through literature review and inputs from experts in the healthcare fraternity. The authors have explored different decision-making tools such as analytic hierarchy process (AHP), analytic network process (ANP), stepwise weight assessment ratio analysis, Hybrid Model and DEMATEL (Decision-Making Trial and Evaluation Laboratory) for the analysis of data in this paper. Based on the strengths of the approach and careful considerations by focus group discussions, DEMATEL was chosen as the best option. It is simple, unique, sparingly used in the healthcare sector, effective in prioritizing and gives meaningful insights on importance, cause and effect factors. DEMATEL approach converts the complex problem with interrelated factors into a clear structure that makes simple interrelationships among factors in the form of cause and effects digraph, and hence, the authors chose to use it. A case study in one of the hospitals has also been conducted to demonstrate the applicability of the developed index. The case study very strongly validates the developed index.

FINDINGS

This research paper has found that there are people quality enablers such as the doctor, nursing staff, support staff and nonpeople quality enablers such as facilities, diagnostic services and hygiene levels maintenance, which impact the delivered service quality. It also concludes that the delivered service quality depends not only on the quality but also on the availability of these enablers. The inputs received from the experts have been run through the DEMATEL methodology and importance computed for each. The top five priority enablers are Quality of Doctor, Availability of Doctor, Quality of Support Staff, Quality of Nursing Staff and Availability of Support Staff.

RESEARCH LIMITATIONS/IMPLICATIONS

The weights of the enablers have been obtained using the DEMATEL tool. These weights have been calculated using the inputs from 22 experts, which meets the statistical requirement (Skulmoski, 2007). However, a larger group of experts can be reached, and based on the inputs received from them, the tool can be revalidated for repeatability and reproducibility. Using Fuzzy DEMATEL can also be explored for further analysis.

PRACTICAL IMPLICATIONS

The proposed framework to assess the service quality level of a healthcare organization is based on a sound approach of DEMATEL. The service index arrived, thereafter, can be used to rate the delivered service quality by any healthcare organization. It can be used to compare the similar type of healthcare organizations across locations. This Index can facilitate improvements in the healthcare organization through internal and external benchmarking. It also helps the organization to know the gaps, understand the root cause, improve upon them and become the best in class. This Index uses the inputs from the end customers to calculate the rating, which makes it more reliable and accurate. The overall scores obtained from the Index can provide the ranking to the healthcare providing organizations and options to customers to choose from best. The service quality index can be used by an organization to continuously monitor their delivered service quality scores and improve them to become the best in class. The research paper highlights the significant role played by the people quality and its strong impact/contribution on the delivered service quality. Hence, it is believed that it will encourage the healthcare organizations to prioritize the improvement and upgrade of the people quality over the nonpeople quality aspect.

ORIGINALITY/VALUE

Putting people and nonpeople quality enablers in one single model and assigning weights to them using the DEMATEL approach is a new application in healthcare. Developing an Index to measure the delivered service quality in the healthcare sector is also different and new.

An integrated approach for service quality evaluation of online health communities based on q-rung orthopair fuzzy linguistic aggregation operators

Online health communities (OHCs) have emerged as a significant platform for people communicating health information and self-healthcare management. In recent, the researches focusing on its performance measurement and the service quality evaluation have drawn intensive attention. Although some qualitative methods have made evaluation and analyses for the OHCs performance, the studies based on fuzzy multi-attribute decision making theory are rarely developed in the service quality evaluation of OHCs. In view of the complexity and uncertainty of evaluation mission, this paper develops an integrated evaluation approach of the OHC service quality based on the q-rung orthopair fuzzy linguistic aggregation operators. Firstly, we propose the cross-entropy of q-rung orthopair fuzzy numbers, which is applied in solving the optimal weight of indicators by a linear programming model. Next, the q-rung orthopair fuzzy linguistic power average (q-ROFLPA) and q-rung orthopair fuzzy linguistic partitioned dual Maclaurin symmetric mean (q-ROFLPDMSM) operators are developed for aggregating the assessment information and ranking the OHCs. Based on the proposed aggregation operators, the evaluation indicator system and an evaluation framework are constructed to accomplish the service quality evaluation of OHCs. Finally, a practical evaluation case of OHCs is provided to demonstrate the reliability and advantages of the proposed approach.

A survey of group decision making methods in Healthcare Industry 4.0: bibliometrics, applications, and directions

Healthcare Industry 4.0 refers to intelligent operation processes in the medical industry. With the development of information technology, large-scale group decision making (GDM), which allows a larger number of decision makers (DMs) from different places or sectors to participate in decision making, has been rapidly developed and applied in Healthcare Industry 4.0 to help to make decisions efficiently and smartly. To make full use of GDM methods to promote the developments of the medical industry, it is necessary to review the existing relevant achievements. Therefore, this paper conducts an overview to generate a comprehensive understanding of GDM in Healthcare Industry 4.0 and to identify future development directions. Bibliometric analyses are conducted in order to learn the development trends from published papers. The implementations of GDM methods in Healthcare Industry 4.0 are reviewed in accordance with the paradigm of the general GDM process, which includes information representation, dimension reduction, consensus reaching, and result elicitation. We also provide current research challenges and future directions regarding medical GDM. It is hoped that our study will be helpful for researchers in the field of GDM in Healthcare Industry 4.0.

Evaluating Factors for Successful Technological Implementation in Indian Banking Industry using DEMATEL

The banking system is a crucial constituent of the financial ecosystem. Over the last two decades, there is a continuous movement in the banking industry from physical banking towards digital banking. The banks have started recognising that adopting new technologies is essential to remain competitive. Yet, the process of digital transformation is slow. The paper aims to identify and analyse the factors for successful technological implementation in the Indian Banking Industry. Further, this research seeks to establish a cause-effect relationship among them using the DEMATEL approach. For the purpose of the study, twelve factors were identified that are contributing towards successful implementation of technology using literature reviews and opinion of an expert. The study highlighted the crucial component for the successful technological implementation in the Indian Banking Sector are: regulations, government support, bank size, top management support.

The key enabling criteria of e-entrepreneurship evolving practices and implementation in Saudi Arabia

Businesses that are entirely conducted online have achieved tremendous success. The system has improved to provide a path for the development of online entrepreneurship where individuals can become founders of online businesses or manage internet-based businesses. There are many research studies from different contexts to understand the importance of E-Entrepreneurship. Unfortunately, not until recently, E-Entrepreneurship business impact and opportunities were not being seriously discussed. Furthermore, the complexity and dynamic flow-process towards adopting E-Entrepreneurship were also ignored. As a result, this study is aimed at examining the adoption of E-Entrepreneurship among Saudi Arabians. Four main variables were conceptualized, "E-entrepreneurship behavioral intention (EBI)", "perceived usefulness (PU)", "social pressure (SP)", and "online transactions (OT)". Quantitative surveys were utilized to test the proposed hypothesis. Furthermore, DEMATEL evaluation analysis was used to find the most important criteria of E-Entrepreneur adoption. The samples of the study are obtained from Saudis. Analysis of the results revealed that all the hypotheses are positively significant and supported. This indicates the influences of all the variables on E-Entrepreneurship Behavioral Intention. DEMATEL results are also consistent with the survey finding. It indicates that "social pressure" in E-Entrepreneurship adoption is the key criteria. The study reveals the important factors that affect E-Entrepreneurship acceptance in Saudi Arabia. The finding of the study significantly implicates E-Entrepreneurship service providers and e-marketers to develop a more complete understanding of the factors that affect the Individual intention to accept E-Entrepreneurship in Saudi Arabia.

Supplementary Information

The online version contains supplementary material available at 10.1007/s43546-021-00110-4.

Re-evaluation of the healthcare service quality criteria for the Covid-19 pandemic: Z-number fuzzy cognitive map

Hospitals as healthcare centers have faced many challenges with the Covid-19 spread, which results in a decline in the quality of health care. Because the number of patients referred to hospitals increases dramatically during the pandemic, providing high-quality services and satisfying them is more important than ever to maintain community health and create loyal customers in the future. However, health care quality standards are generally designed for normal circumstances. The SERVPERF standard, which measures customer perceptions of service quality, has also been adjusted for hospital service quality measurement. In this study, the SERVPERF standard criteria for health services are evaluated in the Covid-19 pandemic. For this purpose, by considering the causal relationships between the criteria and using Z-Number theory and Fuzzy Cognitive Maps (FCMs), the importance of these criteria in the prevalence of infectious diseases was analyzed. According to the results, hospital reliability, hospital hygiene, and completeness of the hospital with ratios 0.9559, 0.9305, and 0.9268 are respectively the most influential criteria in improving the quality of health services in the spread of infectious diseases circumstances such as the Covid-19 pandemic. A review of the literature shows that in previous studies, comprehensive research has not been done on prioritizing the criteria for measuring the quality of health services in the context of the spread of infectious diseases.

Evaluating the Intertwined Relationships of the Drivers for University Technology Transfer

Universities are the best places to gather knowledge and innovation, but the basis of technological development lies in talented R&D personnel who provide services to enterprise R&D activities, research institutions, and universities. Universities also gather many elites who cultivate talent for the country. However, universities do not have adequate technology transfer experience and do not network in industrial circles, which results in a huge gap between the concept and the practical result. This research determined the critical success factors and explored the causal relationships influencing the technology transfer from universities. We found that the main impact of the key factors was the technology market dimension. University technology transfer activities had to be classified as top priority. Technology industrial applicability was the main key evaluation factor, along with inventor attitude. In the organizational culture dimension, the attitude and ability of top management were the main evaluation factors. We attempted to draw upon our analysis results to make recommendations related to technology transfer to assist universities in successfully transferring their research results.

Designing a Clinical Education Tracking System: An Innovative Approach

In clinical education, there is a need for constant evaluation, assessment, and at times immediate feedback for students to recognize areas of success, learning, and areas for improvement. The clinical educator is expected to manage the number of student evaluations and assess their performance, regularly, as needed, and over time. This allows appropriate and timely formative, as well as summative feedback to students and the program. Clinical programs face challenges in finding innovative and efficient methods to track and manage this large volume of seemingly disparate needed and required data. Traditional solutions entail paper-based systems or a paid proprietary clinical tracking system. However, both of these options have their own unique challenges. This perspective article proposes an innovative approach of collecting data electronically via online forms, processing and storing data in cloud-based databases and providing appropriate visualizations for the end users (i.e., students and educators) to analyze and assess the information. Migrating data to be collected in this manner allows for the integration of business intelligence (BI) techniques that provide data mining and machine learning in various user needed methods. This article presents a perspective and an innovative guide for clinical educators on how to develop and design a system that leads to positive sentiments and immediate student feedback regarding their progress to improve student outcomes.

The Managerial Implications of the Key Performance Indicators in Healthcare Sector: A Cluster Analysis

The aim of the paper is to identify a set of the key performance indicators (KPIs) in order to provide managers and employees from the healthcare system with recommendations for evaluating, monitoring, and controlling the critical factors that influence the performance of the healthcare sector in Algeria during a pandemic crisis. During February–August 2020, a cross-sectional survey design was administrated to medical employees from hospitals situated in the northeastern part of Algeria. Our findings proved that the four groups of KPIs correlate to each other, and during this period, the triple relationship among human factor-technology-medication plays a decisive role in reducing the pressure on the medical system and overcoming the crisis. In order to increase the efficiency of the decision-making process, a hierarchy of KPIs is recommended in terms of their impact on the performance of medical staff. The practical importance of our research consists in ranking KPIs on four clusters that support managers to focus on both the human factor (clinical errors, infection rate, and medication errors) and the technical elements of maximum importance (laboratory test time, location of the facility, and sufficient air).

A healthcare evaluation system based on automated weighted indicators with cross-indicators based learning approach in terms of energy management and cybersecurity

OBJECTIVE

Hospital performance evaluation is vital in terms of managing hospitals and informing patients about hospital possibilities. Also, it plays a key role in planning essential issues such as electrical energy management and cybersecurity in hospitals. In addition to being able to make this measurement objectively with the help of various indicators, it can become very complicated with the participation of subjective expert thoughts in the process.

METHOD

As a result of budget cuts in health expenditures worldwide, the necessity of using hospital resources most efficiently emerges. The most effective way to do this is to determine the evaluation criteria effectively. Machine learning (ML) is the current method to determine these criteria, determined by consulting with experts in the past. ML methods, which can remain utterly objective concerning all indicators, offer fair and reliable results quickly and automatically. Based on this idea, this study provides an automated healthcare system evaluation framework by automatically assigning weights to specific indicators. First, the ability of hands to be used as input and output is measured.

RESULTS

As a result of this measurement, indicators are divided into only input group (group A) and both input and output group (group B). In the second step, the total effect of each input on the output is calculated by using the indicators in group B as output sequentially using the random forest of the regression tree model.

CONCLUSION

Finally, the total effect of each indicator on the healthcare system is determined. Thus, the whole system is evaluated objectively instead of a subjective evaluation based on a single output.

An Integrated Decision-Making Model for Analyzing Key Performance Indicators in University Performance Management

University performance has an important effect on the social influence of universities. With increasing emphasis placed on higher education, it is important to improve and optimize university performance management. However, the performance of university management is affected by numerous indicators in practice, and it is difficult for administrators to optimize all of them because of resource restriction. To address this concern, in this paper, we design a novel integrated model by combining linguistic hesitant fuzzy sets (LHFSs) with the decision-making trial and evaluation laboratory (DEMATEL) method to identify key performance indicators (KPIs) for improving the level of university performance management. Specifically, the LHFSs are utilized to express the hesitant and vague interrelationship assessment of performance indicators provided by experts. A modified DEMATEL is adopted to visualize the causal relationship between performance indicators and determine critical ones. Moreover, we introduce a gray relation analysis (GRA)-based method to derive experts’ weights when their weight information is unknown. Finally, a comprehensive university in Shanghai, China, is employed as an example to illustrate the practicability and availability of the proposed linguistic hesitant fuzzy DEMATEL model.

Supplier Selection in the Nuclear Power Industry with an Integrated ANP-TODIM Method under Z-Number Circumstances

Under the double pressure of global energy consumption and climate change, nuclear power has become a low-carbon alternative energy source that could transform the energy structure of the globe. In the nuclear power industry, selecting suitable suppliers plays a significant role in improving the overall performance of nuclear power projects. Along with this symmetrical impact, this paper aims to develop a multistage decision-support framework to determine the optimal nuclear power equipment supplier, which is constructed in the context of Z-number information. Concretely, the Analytic Network Process (ANP) and Tomada de Decisão Iterativa Multicritério (TODIM) are extended by Z-numbers symmetrically—namely, Z-ANP and Z-TODIM. Z-ANP is first applied to analyze the symmetrical interdependence of criteria, so as to accurately determine the criterion weights. Further, the ranking of alternatives is obtained by Z-TODIM, which sufficiently considers the risk preference and psychological states of decision-makers. Finally, a practical case of nuclear-grade cable procurement in the Karachi 2-3 international nuclear power project is performed to illustrate the practicality of the proposed method, and its robustness and superiority are proven by comparing it with current representative approaches.

Identification of Key Performance Indicators for Hospital Management Using an Extended Hesitant Linguistic DEMATEL Approach

Performance analysis is of great significance to increase the operational efficiency of healthcare organizations. Healthcare performance is influenced by numerous indicators, but it is unrealistic for administrators to improve all of them due to the restriction of resources. To solve this problem, we integrated double hierarchy hesitant fuzzy linguistic term sets (DHHFLTSs) with the decision-making trial and evaluation laboratory (DEMATEL) and proposed a DHHFL– DEMATEL method to identify key performance indicators (KPIs) in healthcare management. For the developed approach, the judgments of experts on the inter-relationships among indicators were represented by DHHFLTSs, and a novel combination weighting approach was proposed to obtain experts’ weights in line with hesitant degree and consensus degree. Then, the normal DEMATEL method was extended and used for examining the cause and effect relationships between indicators; the technique for the order of preference by similarity to the ideal solution (TOPSIS) method was utilized to generate the ranking of performance indicators. Finally, the feasibility and effectiveness of the proposed DHHFL–DEMATEL approach were illustrated by a practical example in a rehabilitation hospital.