Coordination between hospitals and insurers in developing economies: an interpretive structural modeling approach

Digital health care services in post COVID-19 scenario: modeling the enabling factors

Purpose

The recent pandemic of COVID-19 has posed challenges for delivering essential and desirable health-care services for the masses. Digital health-care services initiated by several hospitals and health practitioners promise efficient and safe health care in the new normal post-COVID era but need a supportive enabling ecosystem. Therefore, this study aims toward identifying and modeling the key enabling factors for digital health-care services.

Design/methodology/approach

A total of nine factors were identified from the literature review and verified by the domain experts which can enable the wider acceptance of digital health-care services. The identified factors were then modeled with the help of the total interpretive structural modeling (TISM) approach and fuzzy Matrices d’Impacts Croises Multiplication Appliquée à un Classement (MICMAC) and a meaningful contextual relationship were developed for the factors.

Findings

This study reflects that the trust of patients is required for the acceptance of digital health care. Quality of patient care and affordability cum accessibility of online services will define mass engagement. Hospital staff resilience, hospital care service capacity, strategic partnerships and collaborations supported by technology and regulatory structure are the major factors defining the enabling ecosystem.

Originality/value

This study has its uniqueness in the way the TISM approach and fuzzy MICMAC are used for modeling the enabling factors toward growth and acceptance of digital health-care services in the days to come in developing nations. The focus of this study can be considered as relevant for the study interested in investigating the role of cognitive dimensions in influencing actors’ behaviors and decisions.

Logistic planning for pharmaceutical supply chain using multi-objective optimization model

Purpose

Appropriate logistic planning for the pharmaceutical supply chain can significantly improve many financial and performance aspects. To this aim, a multi-objective optimization model is proposed in this paper that considers different types of pharmaceuticals, different vehicles with determining capacities and multi-period logistic planning. This model can be updated based on new information about resources and newly identified requests.

Design/methodology/approach

The main objective function of the proposed model in this paper is minimizing the unsatisfied prioritized requests for pharmaceuticals in the network. Besides, the total transportation activities of different types of vehicles and related costs are considered as other objectives. Therefore, these objectives are optimized hierarchically in the proposed model using the Lexicographic method. This method finds the best value for the first objective function. Then, it tries to optimize the second objective function while maintaining the optimality of the first objective function. The third objective function is optimized based on the optimality of other objective functions, as well. A non-dominated sorting genetic algorithm II-multi-objective particle swarm optimization heuristic method is designed for this aim.

Findings

The performances of the proposed model were analyzed in different cases and its results for different problems were shown within the framework of a case study. Besides, the sensitivity analysis of results shows the logical behavior of the proposed model against various factors.

Practical implications

The proposed methodology can be applied to find the best logistic plan in real situations.

Originality/value

In this paper, the authors have tried to use a multi-objective optimization model to guide and correct the pharmaceutical supply chain to deal with the related requests. This is important because it can help managers to improve their plans.