Browsing by Author "Ataman, Mustafa Gökalp"

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Citation - Scopus: 2
An (s S) inventory optimization problem: A case study for a hospital
(IGI Global, 2019) Gizem Sağol; Gorkem Sariyer; Banu Yetkin Yetkin Ekren; Mustafa Gökalp Ataman; Ataman, Mustafa Gökalp; Sariyer, Görkem; Sağol, Gizem; Ekren, Banu Yetkin
Inventory management is one essential lever to use the resources efficiently. However managing inventories in hospitals is a challenging task because of the several issues: a high service level of medical supplies is required under the unpredictable demand medical products constitute a significant portion of the overall costs and the management of these supplies requires considerable effort to check the levels to track usage and to distribute them. Therefore it is pertinence to apply operations research tools to cope with the managerial issues of the hospital inventory system. In this chapter the authors implement an (s S) inventory model by using simulation in a case study of a hospital in Izmir Turkey. They aim to analyze the unpredictable nature of demand of medical supplies in this hospital and its implications on the developed inventory policy. © 2022 Elsevier B.V. All rights reserved.
Citation - WoS: 12
Citation - Scopus: 16
Big data analytics and COVID-19: investigating the relationship between government policies and cases in Poland- Turkey and South Korea
(OXFORD UNIV PRESS, 2022) Mert Erkan Sozen; Gorkem Sariyer; Mustafa Gokalp Ataman; Ataman, Mustafa Gökalp; Sarlyer, Görkem; Sariyer, Gorkem; Sözen, Mert Erkan
We used big data analytics for exploring the relationship between government response policies human mobility trends and numbers of coronavirus disease 2019 (COVID-19) cases comparatively in Poland Turkey and South Korea. We collected daily mobility data of retail and recreation grocery and pharmacy parks transit stations workplaces and residential areas. For quantifying the actions taken by governments and making a fairness comparison between these countries we used stringency index values measured with the `Oxford COVID-19 government response tracker'. For the Turkey case we also developed a model by implementing the multilayer perceptron algorithm for predicting numbers of cases based on the mobility data. We finally created scenarios based on the descriptive statistics of the mobility data of these countries and generated predictions on the numbers of cases by using the developed model. Based on the descriptive analysis we pointed out that while Poland and Turkey had relatively closer values and distributions on the study variables South Korea had more stable data compared to Poland and Turkey. We mainly showed that while the stringency index of the current day was associated with mobility data of the same day the current day's mobility was associated with the numbers of cases 1 month later. By obtaining 89.3% prediction accuracy we also concluded that the use of mobility data and implementation of big data analytics technique may enable decision-making in managing uncertain environments created by outbreak situations. We finally proposed implications for policymakers for deciding on the targeted levels of mobility to maintain numbers of cases in a manageable range based on the results of created scenarios.
Citation - WoS: 18
Citation - Scopus: 19
Factors affecting length of stay in the emergency department: A research from an operational viewpoint
(ROUTLEDGE JOURNALS TAYLOR & FRANCIS LTD, 2020) Gorkem Sariyer; Mustafa Gokalp Ataman; Ilker Kiziloglu; Ataman, Mustafa Gökalp; Sarıyer, Görkem; Kızıloğlu, İlker
Background: Due to the persistent increase inpatient volumes of emergency departments improving the timeliness of emergency care delivery has become more important from an operational viewpoint. Objectives: To determine the main factors affecting length of stay (LOS) in an ED of a large-scale training hospital. Methods: This was a retrospective study set in an urban ED. The outcome variable of the study was LOS, demographic status-based and time-based predictor variables were gender age arrival type diagnosis month day of the week and period of the day. The descriptive statistics are presented. The hypotheses of this study were tested with an independent group t-test and ANOVA. A multivariate linear regression model was built to identify the dependence of LOS on the predictor variables. Results: LOS significantly differed based on diagnosis day of the week and period of the day. Weekends and evening periods had higher ED volumes and a decrease in mean LOS. In the regression model with the exception of month all predictor variables were observed to be significant. As a result it is concluded that understanding time based factors and preparing the staffing schedule according to these could improve the timeliness of emergency care delivery.
Citation - WoS: 32
Citation - Scopus: 35
Predicting waiting and treatment times in emergency departments using ordinal logistic regression models
(W B SAUNDERS CO-ELSEVIER INC, 2021) Mustafa Gokalp Ataman; Gorkem Sariyer; Ataman, Mustafa Gökalp; Sarıyer, Görkem
Background: Since providing timely care is the primary concern of emergency departments (EDs) long waiting times increase patient dissatisfaction and adverse outcomes. Especially in overcrowded ED environments emergency care quality can be significantly improved by developing predictive models of patients' waiting and treatment times to use in ED operations planning. Methods: Retrospective data on 37711 patients arriving at the ED of a large urban hospital were examined. Ordinal logistic regression models were proposed to identify factors causing increased waiting and treatment times and classify patients with longer waiting and treatment times. Results: According to the proposed ordinal logistic regression model for waiting time prediction age arrival mode and ICD-10 encoded diagnoses are all significant predictors. The model had 52.247% accuracy. The model for treatment time showed that in addition to age arrival mode and diagnosis triage level was also a significant predictor. The model had 66.365% accuracy. The model coefficients had negative signs in the corresponding models indicating that waiting times are negatively related to treatment times. Conclusion: By predicting patients' waiting and treatment times ED workloads can be assessed instantly. This enables ED personnel to be scheduled to better manage demand supply deficiencies increase patient satisfaction by informing patients and relatives about expected waiting times and evaluate performances to improve ED operations and emergency care quality. (c) 2021 Elsevier Inc. All rights reserved.