Operating Room Scheduling in Teaching Hospitals

Operating room scheduling is an important operational problem in most hospitals. In this paper, a novel mixed integer programming (MIP) model is presented for minimizing Cmax and operating room idle times in hospitals. Using this model, we can determine the allocation of resources including operating rooms, surgeons, and assistant surgeons to surgeries, moreover the sequence of surgeries within operating rooms and the start time of them. The main features of the model will include the chronologic curriculum plan for training residents and the real-life constraints to be observed in teaching hospitals. The proposed model is evaluated against some real-life problems, by comparing the schedule obtained from the model and the one currently developed by the hospital staff. Numerical results indicate the efficiency of the proposed model compared to the real-life hospital scheduling, and the gap evaluations for the instances show that the results are generally satisfactory

Cut-off Value for Stenosis Ratio and Zung Depression Scale in Successful Prediction of Posterior Spinal Fusion surgery

Background: The aim of this study was to investigate effect of some clinical attributes in prediction of satisfaction with posterior spinal fusion (PSF) surgery in patients with lumbar disc herniation (LDH) and lumbar spinal canal stenosis (LSCS) and determine a cut-off point for these attributes.Methods: The attributes such as stenosis ratio (SR) values (described by Lurencin), Japanese Orthopaedic Association (JOA), The Zung depression scale (ZDS), duration of symptoms (in months), were investigated for 329 patients with LSCS and 151 patients with LDH separately. Patient satisfaction was recorded based on the international standard questionnaire Swiss Spinal Stenosis Score (SSS). The sensitivity and specificity values and the optimal cut-off points were calculated for SR, JOA, ZDS and duration of symptoms using receiver operating characteristic (ROC) analysis.Results: One hundred fifty-one patients with LDH (39 male, 112 female; mean age 50.24 ± 9.21 years) and 329 patients with LSCS (111 male, 218 female; mean age 53.28 ±7.81 years) were followed–up for 6 months. Post-surgical satisfaction was 73.86% in patients with LSCS and 85.43% in patients with LDH. The cut-off point of SR for prediction of besting surgical outcome was estimated more than 0.46 with asymptotic significance less than 0.05, 60% sensitivity and 75% specificity in LSCS patients (AUC-0.705, 95% CI, 0.644–0.766; P < 0.001).Conclusion: The findings show that the SR with a cut off value of 0.46 cross sectional area, in patients with LSCS may be superior to JOA, duration of symptoms and ZDS for prediction of satisfaction with PSF surgery

سامانه مدیریت دانش، گزیری هوشمند برای بهبود در مدیریت خطاهای درمان

Errors in treatment process are of the main issues regarding patient safety in health centers and hospitals that have had a growing rate, causing concern in health industry. Using the knowledge associated with such incidents in hospitals has facilitated management as well as reporting process. It is also effective in managerial decisions. The purpose of this study is exploiting the main processes of knowledge management for improving the management and reporting medical errors for medical centers and hospitals, paving the way for achieving and recording the knowledge related to such incidents by presenting proper strategies. Afterwards, by disseminating and proper transferring of knowledge to right people, implementation and efficient utilization of this knowledge will be made possible. The present study is qualitative from the viewpoint of the implementation process and is applied from the perspective of studying the results. Next, to understand the role of knowledge management processes in hospitals, filed studies have been employed. In presenting the findings, we managed to achieve various methods and approaches of the four processes of knowledge management in management of documents related to errors in hospitals. The manner of doing different stages was considered from individual, group and inter organizational viewpoints. Then, the manner of gaining knowledge related to treatment errors was explained. At the stage of storing knowledge, the manner of coding knowledge and the benefits of reusing knowledge repositories in hospitals has been mentioned. At the stage of publishing and distributing knowledge, the importance of maintaining the security and privacy of individuals as well as respecting their privacy have been regarded. In the process of adoption and utilization, the method of using knowledge in making decisions and planning is expressed. The results reveal that knowledge management systems are capable of creating cohesion and sustainability. In explicit knowledge associated with treatment errors, they can also help preserve the knowledge and experience of experts and the elite in this field. In case they leave, this knowledge and experience will not vanish

طراحی چارچوب مدیریت دانش هم‌افزا در بیمارستان‌ها و مراکز درمانی

Improving quality and reducing the expenses in Department of Health has always been one of the main priorities of different stakeholders and value seekers in this very prominent section of communities and governments. Knowledge asset management in this field has had an undeniable effect on the aspects of quality improvement and cost reduction strategies as well as playing an effective role in medical decision making and clinical processes. The purpose behind the present study was achieving a true understanding of the actual position knowledge management processes in health centers and hospitals. Moreover, it was aimed at proposing technology-driven solutions, based on knowledge management, to manage such valuable medical assets. The current study was qualitative from the viewpoint of administrative process, and falls into applied research from the perspective of results. In addition, in order to describe and discover the phenomena relating to knowledge management in hospitals, field studies have been drawn upon. To represent the findings, different approaches and methods regarding knowledge management in hospitals were expressed. In knowledge acquisition level, controlled and uncontrolled activities to elicit knowledge have been considered. Knowledge storage process, knowledge encoding, and benefits of reusing knowledge repertoires in hospitals have been regarded as well. Also, in publishing and distributing sector of knowledge, access was personalized and maintaining security has been expressed. Meanwhile, in the implementation and operation phase, the manner of using proved explicit knowledge has been mentioned. The results show that tools and indices of knowledge management are capable of helping the hospitals in acquisition, storage, retrieval, and learning assets relating to knowledge, both tangibly and non-tangibly. Applying knowledge process in order to utilize tacit and explicit knowledge in hospital environment is recommended. Knowledge management will be able to establish consistency in the knowledge structure resources in hospitals, assuring that by experts and elites leaving the field, their knowledge will remain stable and intact

Intelligent Sales Prediction for Pharmaceutical Distribution Companies: A Data Mining Based Approach

One of the problems of pharmaceutical distribution companies (PDCs) is how to control inventory levels in order to prevent costs of excessive inventory and to prevent losing customers due to drug shortage. Consequently, the purpose of this study is to propose a novel method to forecast sales of PDCs. The presented method is a combination of network analysis tools and time series forecasting methods. Due to lack of enough past sales records of each drug, an explorative network based analysis is conducted to find clique sets and group members and to use comembers’ sales data in their sales prediction. Afterwards, time series sales forecasting models were built with three different approaches including ARIMA methodology, neural network, and an advanced hybrid neural network approach. The offered hybrid method by applying each drug and its comembers past records facilitates capturing both linear and nonlinear patterns of sales accurately. The performance of the proposed method was evaluated by a real dataset provided by one of the leading PDCs in Iran. The results indicated that the proposed method is able to cope with low number of past records while it forecasts medicines sales accurately

The Readiness of Hospitals to Implement the RFID Technology

Introduction: This study is implemented with the aim of a systematic collecting and reviewing of conducted researches in connection with the implementation of the Radio-frequency identification (RFID) technology. Methods: This study has examined the existent literatures in databases such as Google Scholar, ISI Web of Knowledge and Science Direct by using the qualitative research methods as a systematical review. The statistical society in this study consisted of archival sources that were collected and classified by the systematic review protocol and were analyzed by using the Marshall and Rasman model. Results: There were many concerns for the use of this technology to optimize the use of RFID technology, especially in the developing country. Using the experiences of developed countries in the use of this technology and changing it according to the conditions could be a major contribution to the use of this technology. Conclusion: Studies done in this area are limited but they will have growth, yet need culture.  It is said that the use of this technology is evident in various industry and society sectors especially in the healthcare sector

Predicting IVF Pregnancy Outcome and Analyzing its Cost Factors: An Artificial Intelligence Approach

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

Exact and Heuristic Solutions to Minimize Total Waiting Time in the Blood Products Distribution Problem

This paper presents a novel application of operations research to support decision making in blood distribution management. The rapid and dynamic increasing demand, criticality of the product, storage, handling, and distribution requirements, and the different geographical locations of hospitals and medical centers have made blood distribution a complex and important problem. In this study, a real blood distribution problem containing 24 hospitals was tackled by the authors, and an exact approach was presented. The objective of the problem is to distribute blood and its products among hospitals and medical centers such that the total waiting time of those requiring the product is minimized. Following the exact solution, a hybrid heuristic algorithm is proposed. Computational experiments showed the optimal solutions could be obtained for medium size instances, while for larger instances the proposed hybrid heuristic is very competitive