Implementation of Intrahospital Transfer Strategy During COVID-19 and Identify Success Factors Based on DEMATEL Technique.

BACKGROUND AND OBJECTIVES: The COVID-19 pandemic caused a significant strain on world health care systems. The lack of trained and experienced staff was a complicated issue during the pandemic. To overcome insufficient staffing problems, the intrahospital transfer (IHT) strategy was implemented at Milad Hospital in Tehran during COVID-19. We evaluated the effectiveness of the IHT strategy in order to determine whether the strategy should be continued post-COVID. METHODS: Six supervisors with experience in COVID-19 wards and the IHT strategy were consulted to identify the advantages of continuing the IHT strategy and to evaluate the success and continuation of IHT factors. Then, the decision-making trial and evaluation laboratory (DEMATEL) method was used to establish a network of influence relationships among IHT strategy factors' success. RESULTS: The result showed that all criteria except increasing patient satisfaction (C1) and reducing waste of time (C8) are cause-and-effect criteria that affected other criteria. CONCLUSION: The research findings have implications for improving the day-to-day experience of staff navigating transfers of patients between wards and paraclinic units. This study also highlights the theoretical value of the cross-disciplinary integration of medical decision issues and multiple-attribute decision-making methodologies.

Implementation of Clean Hospital Strategy and Prioritizing Covid-19 Prevention Factors Using Best-Worst Method.

Many nations have suffered the catastrophe of COVID-19, and one of the first countries affected by the pandemic was Iran; all industries and individuals have been adversely affected by the pandemic. Health care systems and patients' conditions, in particular, were disrupted due to canceling elective surgery. To put it more sharply, a delay in performing elective surgery may potentially impact patients' survival and the quality of their lives. To cope with the new situation, in the first stage, the Clean Hospital strategy was proposed in order to minimize the effects of this pandemic on elective surgical services. The mentioned strategy is a try to provide a solution and resume elective surgeries in the pandemic period. In the second stage, panel discussion, Delphi method, and the best-worst method (BWM) were employed to prioritize the factors that inhibit Coronavirus transmission. The proposed strategy and the results of this study could be used by policymakers and health departments to resume elective surgeries and control the infection to maintain a hospital or a section of it clear. The overall result of the study showed that the most important Covid-19 prevention factors in Clean Hospitals were personal protection (w = 0.212), screening checklist (w = 0.182), and check body temperature (w = 0.126), respectively (C1 > C2 > C3). According to the financial, time, and human resource limitations, first, resources were allocated to higher priority criteria, and in order of priority, all items (C1, C2, …., C9) were used in the Clean hospital strategy.

Measuring the customer satisfaction of public transportation in Tehran during the COVID-19 pandemic using MCDM techniques.

The expeditiously spreading of coronavirus disease 2019 (COVID-19) has affected every facet of human lives, including transportation. Due to some characteristics of COVID-19, like high infectivity, people prefer to use their private cars more than before. On the one hand, this circumstance caused public transportation to face an unprecedented decrease in demand and, consequently, revenue. On the other hand, it could intensify traffic congestion during rush hours. This study provides a computational framework to assess public transportation's customer satisfaction in Tehran during the COVID-19 pandemic. To this end, a combined multi-criteria decision-making (MCDM) approach based on the best-worst method (BWM) and fuzzy technique for order performance by similarity to ideal solution (fuzzy TOPSIS) is introduced, which benefits from all the advantages of BWM and fuzzy TOPSIS procedure and consequently provides consistent and reliable outcomes. Outcomes of the implemented model provide precious insight for improving service quality during and after the pandemic; for example, it reveals the performance of each transport mode about each criterion which can help policymakers and transit agencies to allocate resources more intelligently. Final results indicate that during the pandemic, taxis had a better performance compared to other transportation modes.