Using the Importance–Satisfaction Model and Service Quality Performance Matrix to Improve Long-Term Care Service Quality in Taiwan

Predicting Long-Term Care Service Demands for Cancer Patients: A Machine Learning Approach

BACKGROUND

Long-term care (LTC) service demands among cancer patients are significantly understudied, leading to gaps in healthcare resource allocation and policymaking.

OBJECTIVE

This study aimed to predict LTC service demands for cancer patients and identify the crucial factors.

METHODS

3333 cases of cancers were included. We further developed two specialized prediction models: a Unified Prediction Model (UPM) and a Category-Specific Prediction Model (CSPM). The UPM offered generalized forecasts by treating all services as identical, while the CSPM built individual predictive models for each specific service type. Sensitivity analysis was also conducted to find optimal usage cutoff points for determining the usage and non-usage cases.

RESULTS

Service usage differences in lung, liver, brain, and pancreatic cancers were significant. For the UPM, the top 20 performance model cutoff points were adopted, such as through Logistic Regression (LR), Quadratic Discriminant Analysis (QDA), and XGBoost (XGB), achieving an AUROC range of 0.707 to 0.728. The CSPM demonstrated performance with an AUROC ranging from 0.777 to 0.837 for the top five most frequently used services. The most critical predictive factors were the types of cancer, patients' age and female caregivers, and specific health needs.

CONCLUSION

The results of our study provide valuable information for healthcare decisions, resource allocation optimization, and personalized long-term care usage for cancer patients.

Research on service quality evaluation of medical caring and nursing combined institutions for the aged based on interval MTS-grey cloud clustering

The lack of a scientific and complete service quality evaluation system for Medical Caring and Nursing Combined Institutions for the Aged is a critical factor that makes it difficult to improve the quality of their services. Based on the SERVQUAL model, the service quality evaluation index system of Medical Caring and Nursing Combined Institutions for the Aged is constructed from tangibles, security, reliability, responsiveness, and empathy. Considering the ambiguity, randomness, grey characteristics, and the interaction between indicators in the service evaluation process of Medical Caring and Nursing Combined Institutions for the Aged, the interval Mahalanobis-Taguchi system (MTS) is introduced into the grey cloud clustering model, and a service quality evaluation model of the interval MTS— grey cloud clustering is proposed. The Medical Caring and Nursing Combined Institutions for the Aged in four typical cities of Jiangsu Province are taken as examples in this study. Feasibility of the proposed method is verified, and targeted measures are thus proposed to provide stronger support and reference for improving the service quality of these institutions.

Implementation and Acceptance of Information and Communication Technology Incorporated into Long-Term Care

Every country in the world is facing serious demographic aging, since the average life expectancy is consistently increasing. Agencies involved in the implementation of caregiving through long-term care institutions can develop more convenient approaches using information and communication technology to enhance overall efficiency. Communication technology has enabled the strengthening of physiological instruments, improving the efficiency and quality of services, while integrating management systems for optimum efficiency. This work conducted empirical studies, collecting responses to questionnaires from residents and caregivers in five institutions located in the south of Taiwan. The PZB model, proposed by Parasuraman, Zeithaml, and Berry, was used to construct the questionnaire to analyze the service quality following the incorporation of information and communication technology. The results of the empirical study show that 34% and 63% of the relatives of the residents agreed and strongly agreed that the system was practical and convenient, respectively. As for the caregivers, 77% of them agreed or strongly agreed that the system was mobile, practical, and convenient, and they agreed that the system could significantly increase working efficiency, reduce waiting time, and improve administration for chronic diseases among care-home residents.

Research on the Satisfaction Degree Characteristics of Residential Public Resources under Lockdowns for Pandemic Prevention and Control: A Case Study in the Changchun

In order to improve the ability of residential disaster prevention, control, and governance, it is important to objectively measure how nearby residents’ needs match the public resources of the residential area, and to understand the factors affecting the satisfaction of residents’ needs at the time of lockdowns. Taking Changchun City as an example, this paper used Structural Equation Modeling (SEM)-logit and Importance–Satisfaction (I–S) evaluation methods to discuss the impact mechanism and improvement strategies of residential public resource elements on the satisfaction of residents’ needs during the lockdown period. The results showed the influencing factors and the degree of importance of the satisfaction of residents’ needs under different types of settlements have obvious differentiation characteristics. The level of resource management can better affect the overall evaluation of residents in newly built settlements, and the quality of the conditions of the space environment is more important for the old residential communities. The satisfaction of residents in settlements has a more significant impact. Finally, the study explained the renovation proposals and their priority levels that meet the needs of residents to provide beneficial support for the resilience of urban settlements.

Using refined kano model and decision trees to discover learners' needs for teaching videos

With the advancement of technology and the spread of the COVID19 epidemic, learning can no longer only be done through face-to-face teaching. Numerous digital learning materials have appeared in large numbers, changing people's learning mode. In the era of information explosion, how to capture the learners' attention to teaching videos and improve learning effectiveness is the common goal of every designer of e-leaning teaching content. Previous researches focused on the analysis of learning effectiveness and satisfaction. Instructional designers only provided design elements with high learning effectiveness or high satisfaction, and lacked in-depth analysis of the learners' perspectives. The opinions of these e-learning users are often the key to the success of online teaching videos. Therefore, this study aims at the design elements that will be used in the teaching film. The operation mode of the piano mechanism will be employed as the content of the teaching film. Based on eight elements including arrow cueing, dynamic arrow cueing, spreading-color cueing, contrary to cueing, font style, color application, anthropomorphic, and audiovisual complementarity, we use Refined Kano Model to analyze learners' needs of categorization of each element, and discover learners' expectations for teaching videos. In addition, this study also conducts in-depth data analysis through decision trees algorithm, and stratification analyses using different variables (such as design expertise, using frequency, and usage experience, etc.) to find out the key design factors that affect learners' learning. Depending on the learner's background, the use of e-learning experience, using frequency, and the length of the learning video, our results could provide for reference when designing teaching videos. Instructional designers can better understand how to effectively use design elements, so that the teaching videos can achieve the best learning effect.