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کاربرد نوع شرط:
- جایگاه : پژوهشی
- مجله: Journal of Industrial and Systems Engineering
- نوع مقاله: Journal Article
- کلمات کلیدی: Simulation,Hospital,discrete-event modeling,patient flow,surgical suite
- چکیده:
- چکیده انگلیسی: Surgical suits allocate a large amount of expenses to hospitals; on the other hand, they constitute a huge part of hospital revenues. Patient flow optimization in a surgical suite by omitting or reducing bottlenecks which cause loss of time is one of the key solutions in minimizing the patients’ length of stay[1] (LOS) in the system, lowering the expenses, increasing efficiency, and also enhancing patients’ satisfaction. In this paper, an analytical model based on simulation aiming at patient flow optimization in the surgical suite has been proposed. To achieve such a goal, first, modeling of patients' workflow was created by using discrete-event simulation. Afterward, improvement scenarios were applied in the simulated model of surgical suites. Among defined scenarios, the combination scenario consisting of the omission of the waiting time between the patients’ entrance to the surgical suite and beginning of the admission procedure, being on time for the first operation, and adding a resource to the resources of the transportation and recovery room, was chosen as the best scenario. The results of the simulation indicate that performing this scenario can decrease patients’ LOS in such a system to 22.15%.
- انتشار مقاله: 25-05-1397
- نویسندگان: Roghaye Khasha,Mohammad Mehdi Sepehri,Toktam Khatibi
- مشاهده
- جایگاه : پژوهشی
- مجله: Journal of Health Management and Informatics
- نوع مقاله: Journal Article
- کلمات کلیدی:
- چکیده:
- چکیده انگلیسی: Objective: The high prevalence of cardiovascular diseases has caused many health problems in countries. Cardiac Rehabilitation Programs (CRPs) is a complementary therapy for Percutaneous Coronary Intervention (PCI) patients. However, PCI patients hardly attend CRPs. This study aims to decipher the reasons why PCI patients rarely participate in CRPs after PCI.Methods: The parameters affecting the attendance of the patients at CRPs were identified by using the previous studies and opinions of experts. A questionnaire was designed based on the identified parameters and distributed among PCI patients who were referred to Tehran Heart Center Hospital.Results: According to data mining approach, 184 samples were collected and classified with three algorithms (Decision Trees, k-Nearest Neighbor (kNN), and Naïve Bayes). The obtained results by decision trees were superior with the average accuracy of 82%, while kNN and Naïve Bayes obtained 81.2% and 78%, respectively. Results showed that lack of physician’s advice was the most significant reason for non-participation of PCI patients in CRPs (P< .0001). Other factors were family and friends’ encouragement, paying expenses by insurance, awareness of the benefits of the CRPs, and comorbidity, respectively.Conclusion: Results of the best model can enhance the quality of services, promote health and prevent additional costs for patients. Keywords: Cardiovascular Disease, Percutaneous Coronary Intervention, Cardiac Rehabilitation Programs, Data Mining, Classification
- انتشار مقاله: 23-07-1397
- نویسندگان: Tara Zamir,Mohammad Mehdi Sepehri,Hassan Aghajani,Morteza Khakzar Bafruei,Toktam Khatibi
- مشاهده
- جایگاه : پژوهشی
- مجله: International Journal of Hospital Research
- نوع مقاله: Journal Article
- کلمات کلیدی: Data mining,Feature Selection,clustering,Patient Satisfaction
- چکیده:
- چکیده انگلیسی: Background and Objective: The health industry is a competitive and lucrative industry that has attracted many investors. Therefore, hospitals must create competitive advantages to stay in the competitive market. Patient satisfaction with the services provided in hospitals is one of the most basic competitive advantages of this industry. Therefore, identifying and analyzing the factors affecting the increase of patient satisfaction is an undeniable necessity that has been addressed in this study.
Methods: Because patient satisfaction characteristics used in hospitals may have a hidden relationship with each other, data mining approaches and tools to analyze patient satisfaction according to the questionnaire used We used the hospital. After preparing the data, the characteristics mentioned in the questionnaire for patients, classification models were applied to the collected and cleared data, and with the feature selection methods, effective characteristics Patients were identified and analyzed for satisfaction or dissatisfaction.
Results: Based on the findings of the present study, it can be concluded that the factors of patient mentality of the physician's expertise and skill, appropriate and patient behavior of the physician and food quality (hoteling) respectively have a higher chance of increasing patient satisfaction with Establish services provided in the hospital.
Conclusion: Comparing the approach used in this study with other studies showed that due to the hidden effects of variables on each other and the relatively large number of variables studied, one of the best options for analyzing patient satisfaction questionnaire data, Use of data mining tools and approaches- انتشار مقاله: 21-07-1399
- نویسندگان: Toktam Khatibi,Rouhangiz Asadi,Mohammad Mehdi Sepehri,Pejman Shadpour
- مشاهده
- جایگاه : پژوهشی
- مجله: International Journal of Hospital Research
- نوع مقاله: Journal Article
- کلمات کلیدی: The Hospital Discharge Process,Workflow Pattern,Process Detection,Process Improvement
- چکیده:
- چکیده انگلیسی: Background and Objectives: Patient discharge process starts from the point of the initial order of the physician order and continues to the discharge time of a patient and the release of the bed that was allocated to him/her. Lengthening the patient discharge process is regarded as a negative factor in the management of beds; this lengthy process leads to delay in accepting new patients, increases the waiting time for the patients who demand an empty bed (especially in emergencies), imposes extra costs on the hospital, and creates some other problems. Therefore, discharge process pattern extraction and analysis can be helpful to shorten this process, accelerate the process of admission, reduce the costs of hospital, etc.
Methods:In the present study, first, the fuzzy model of the hospital's discharge process and the hospital's workflow pattern have been drawn according to the most frequent patterns in the data and based on the experts' opinions. Afterwards, the dotted charts of the different sectors have been extracted and analyzed using the process mining tools.
Findings: After analyzing the dotted charts, the delayed segments were specified on the pattern of the workflow and finally, some suggestions have been offered through separating the four areas of the human resources, the system, the environment.
Conclusions:The discharge process of a hospital is associated with almost all its sectors and to improve it, a huge part of the organization is involved- انتشار مقاله: 18-04-1396
- نویسندگان: Toktam Khatibi,Nasim Nejadjafari
- مشاهده
- جایگاه : پژوهشی
- مجله: International Journal of Hospital Research
- نوع مقاله: Journal Article
- کلمات کلیدی: Laparoscopy,Image Segmentation,Surgical instrument detection,Generalized Near-set Theory
- چکیده:
- چکیده انگلیسی: Background and Objectives: Identification of surgical instruments in laparoscopic video images has several biomedical applications. While several methods have been proposed for accurate detection of surgical instruments, the accuracy of these methods is still challenged high complexity of the laparoscopic video images. This paper introduces a Surgical Instrument Detection Framework (SIDF) for accurate identification of surgical instruments in complex laparoscopic video frames. Methods: Based on the Generalized Near-Set Theory, a novel image segmentation algorithm, termed Generalized Near-Set Theory-based Image Segmentation Algorithm (GNSTISA) was developed. According to SIDF, first GNSTISA is executed to segment the laparoscopic images. Next, the segments generated by GNSTISA are filtered based on their color and texture. The remaining segments would then indicate surgical instruments. Findings: Using the laparoscopic videos of varicocele surgeries obtained from Hasheminezhad Kidney Center, the performance of GNSTISA was compared with previous image segmentation methods. The results showed that GNSTISA outperforms the earlier algorithms in term of accurate segmentation of laparoscopic images. Moreover, the accuracy of SIDF in identifying the surgical instruments was found superior to that of other methods. Conclusions: SIDF eliminates the limitations of previous image segmentation methods, and can be used for precise identification of surgical instrument detection.
- انتشار مقاله: 23-05-1392
- نویسندگان: Toktam Khatibi,Mohammad Mehdi Sepehri,Pejman Shadpour
- مشاهده
- جایگاه : پژوهشی
- مجله: International Journal of Hospital Research
- نوع مقاله: Journal Article
- کلمات کلیدی: Risk Management,Fuzzy set theory,Failure Modes and Effect Analysis,Surgical Cancellation
- چکیده:
- چکیده انگلیسی: Background and Objectives: Surgical cancelation is a significant source of time and resource waste, patient safety risk, and stress for patients and their families. In this study, a risk management-based approach is developed to prioritize factors contributing to surgical cancellation. Methods: Factors leading to surgical cancellation were comprehensively classified based on literature review. A Fuzzy Failure Mode and Effect Analysis were developed for identifying the relative importance of the potential surgical cancellation factors. Validity of the results was examined by obtaining experts’ opinions. Findings: Our analysis identified inadequacy of recovery beds, inadequacy of ICU beds, high-risk surgery, and high blood pressure and diabetes as the most important factors contributing to surgical cancelation. Conclusions: According to our results, the Fuzzy Failure Mode and Effect Analysis can successfully rank the factors contributing to surgical cancellation. Our results encourage further use of the risk management theory and tools combined with fuzzy set theory to support and facilitate the clinical decision-making process.
- انتشار مقاله: 07-08-1391
- نویسندگان: Roghayeh Khasha,Mohammad Mehdi Sepehri,Toktam Khatibi
- مشاهده
- جایگاه : پژوهشی
- مجله: Asian Pacific Journal of Cancer Prevention
- نوع مقاله: Journal Article
- کلمات کلیدی: Prediction,Acute lymphoblastic leukemia (ALL),childhood blood cancer,Cranial Radiotherapy,Stacked ensemble
- چکیده:
- چکیده انگلیسی: Background: Acute Lymphoblastic Leukemia (ALL) is the most common blood disease in children and is responsible for the most deaths amongst children. Due to major improvements in the treatment protocols in the 50-years period, the survivability of this disease has witnessed dramatic rise until this date which is about 90 percent. There are many investigations tending to indicate the efficiency of cranial radiotherapy found out that without that, outcome of the patients did not change and even it improved at some cases. Methods: the main aim of this study is predicting cranial radiotherapy treatment in pediatric acute lymphoblastic leukemia patients using machine learning. Scope of this paper is intertwined with predicting the necessity of one of the treatment modalities that has been used for many years for this group of patients named Cranial Radiotherapy (CRT). For this purpose, a case study is considered at Mahak charity hospital. In this paper, our focus is on ALL patients aged 0 to 17 treated at Mahak hospital, one of the best centers for treatment of childhood malignancies in Iran. Dataset analyzed in this study is gathered by the research team from patient’s paper-based files. Our dataset consists of 241 observations on patients with 31 attributes after the data cleaning process. Our designed machine learning model for predicting cranial radiotherapy treatment in pediatric acute lymphoblastic leukemia patients is a stacked ensemble classifier of independently strong models with a meta-learner to tune the weights and parameters of the base classifiers. Results: The stacked ensemble classifier show highly reasonable performance with AUC of 87.52%. Moreover, the attributes are ranked based on their predictive power and the most important variable for CRT necessity prediction is the disease relapse. Conclusion: In order to conclude, derived from previous studies regarding CRT it is not only cost-effective but also more healthy to eradicate the use of CRT for the treatment of childhood ALL. Furthermore, it is valuable to increase the clinical databases by creating more synthetic health databases not only for research purposes but also for physicians to keep track of their patient’s status.
- انتشار مقاله: 24-02-1399
- نویسندگان: Amirarash Kashef,Toktam Khatibi,Azim Mehrvar
- مشاهده