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Operating room (OR) planning has gradually become an important element of Hospital administration in recent years. The OR performance plays a significant role in enhancing the quality of the care provided to the patients, and reducing patients' length of stay (LOS), which is the function of the OR performance, decreases the costs. Following a survey of the existing research and conducted interviews with clinical and non-clinical OR staff, there are several factors influencing patients’ length of stay (LOS) in OR, and consequently the costs of OR. In this work, first, we implemented the DEMATEL to define the relationship between OR factors, next, employed ANP to identify the most influential factors on the LOS in the OR. The results show the most influential factors are operative time, operative setup, patient waiting time, anesthesia, and patient preparation. Running two rooms concurrently and minimizing turnover time (TOT) reduce patients' LOS by 10% and 5.6%, respectively, and improve the patients' and clinical personnel's satisfaction. Reducing the LOS not only leads to reduced OR costs but also helps increase the number of operations and thereby hospitals' revenue. [ABSTRACT FROM AUTHOR]

In this article, we offer and test a nonsurvey-based method to characterize learner emotions. Our method, instead of using surveys, uses logs of learner behaviors in learning management systems (LMS) to reason about the emotional state of the e-learner. We use the control value theory (CVT) as the theoretical base of measuring emotions. Using this theory, learner emotions are directly tied to their achievements. We develop two fuzzy inference systems, one to measure academic self-efficacy (ASE), that we call ASEMEL, and another to measure task value, TAVAMEL. These two factors, according to the CVT, can identify the prospective outcome emotions in a learning environment. We conducted our experiment in an LMS with a sample of 30 students and validated the performance of our nonsurvey-based systems by comparing the results with the measures of an equivalent survey-based method. Finally, by linking our ASEMEL and TAVAMEL results, our system anticipated "hopelessness," "anticipated relief" and "no emotion" with 97% accuracy, "hope/anxiety" with 77% accuracy, and "anticipatory joy" with 87% accuracy compared with the self-reports of the students.

Background: Infertility treatment methods that are used today have a limited (or little) success rate, and patients bear a lot of financial and emotional burden to get pregnant. Recently, artificial intelligence has been proposed to evaluate gametes better and choose the best embryo for transfer to the uterus. This study investigated the financial benefit of using artificial intelligence for infertility treatment. Materials and Methods: We aim to evaluate the effectiveness of AI in IVF, comparing AI model performance with standard methods and introducing a novel method to measure financial benefits in healthcare resource allocation. Results: Achieving 75% accuracy, AI significantly outperformed standard methods, reducing the likelihood of discarding viable embryos. This technology streamlines the IVF process, leading to shorter treatment cycles and a cost reduction of 1500 dollars per cycle. Conclusion: The integration of AI in IVF represents a paradigm shift, improving success rates, cost-efficiency, and patient experiences. Further research and adoption of AI-driven embryo selection can revolutionize infertility treatments, benefiting both patients and healthcare systems. [ABSTRACT FROM AUTHOR]

Purpose: High efficiency in the operating room may significantly improve the overall performance of the hospital and the service quality provided to patients. The operating room is the de facto financial hub of the hospital and maximizing its efficiency may lead to considerable improvements. To this end, we seek ways of accelerating the patient flow in order to save time and cost in healthcare facilities. Methodology: In this study, we use agent-based simulation to simulate patient care in the operating room. After performing the required validations, a number of improvement scenarios were developed and evaluated. Findings: A hybrid scenario including modifications to the referral time of the patient by the surgeon, transfer time of the surgical set and supplies to the operating room, and the timing of anesthesia proved to have the most positive impact on the criteria i.e. activities, reducing the average Length of Stay (LOS) by 9.69 minutes. The second-most effective scenario involved modifying the referral time of the patient by the surgeon, reduced the LOS by 7.31 minutes. Originality/Value: Through this research, it became apparent that minimizing the patients' LOS improves the efficiency of the operating room as it helps reduce the overall idle time and increases the number of operations carried out in each shift. Making time even for one additional operation per day significantly increases the operating room income. Moreover, a shorter LOS means less fatigue for the medical staff and reduces the total cost of running the operating room by reducing the staffs overtime hours. [ABSTRACT FROM AUTHOR]