Browsing by Author "Ataman, Mustafa Gokalp"

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Citation - WoS: 20
Citation - Scopus: 26
An analysis of Emergency Medical Services demand: Time of day- day of the week- and location in the city
(ELSEVIER, 2017) Gorkem Sariyer; Mustafa Gokalp Ataman; Serhat Akay; Turhan Sofuoglu; Zeynep Sofuoglu; Ataman, Mustafa Gokalp; Sariyer, Gorkem; Sofuoglu, Turhan; Akay, Serhat; Sofuoglu, Zeynep
Objective: Effective planning of Emergency Medical Services (EMS) which is highly dependent on the analysis of past data trends is important in reducing response time. Thus we aimed to analyze demand for these services based on time and location trends to inform planning for an effective EMS. Materials and methods: Data for this retrospective study were obtained from the Izmir EMS 112 system. All calls reaching these services during first six months of 2013 were descriptively analyzed based on time and location trends as a heat-map form. Results: The analyses showed that demand for EMS varied within different time periods of day and according to day of the week. For the night period demand was higher at the weekend compared to weekdays whereas for daytime hours demand was higher during the week. For weekdays a statistically significant relation was observed between the call distribution of morning and evening periods. It was also observed that the percentage of demand changed according to location. Among 30 locations the five most frequent destinations for ambulances which are also correlated with high population densities accounted for 55.66% of the total. Conclusion: The results of this study shed valuable light on the areas of call center planning and optimal ambulance locations of Izmir which can also be served as an archetype for other cities. Copyright (C) 2016 The Emergency Medicine Association of Turkey. Production and hosting by Elsevier B.V. on behalf of the Owner. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Citation - WoS: 15
Citation - Scopus: 18
Analyzing main and interaction effects of length of stay determinants in emergency departments
(Kerman University of Medical Sciences j_mahdavi@kmu.ac.ir, 2020) Gorkem Sariyer; Mustafa Gökalp Ataman; İlker Kızıloğlu; Ataman, Mustafa Gokalp; Sariyer, Gorkem; Kiziloglu, Ilker
Background: Measuring and understanding main determinants of length of stay (LOS) in emergency departments (EDs) is critical from an operations perspective since LOS is one of the main performance indicators of ED operations. Therefore this study analyzes both the main and interaction effects of four widely-used independent determinants of ED-LOS. Methods: The analysis was conducted using secondary data from an ED of a large urban hospital in Izmir Turkey. Between-subject factorial analysis of variance (ANOVA) was used to test the main and interaction effects of the corresponding factors. P values <.05 were considered statistically significant. Results: While the main effect of gender was insignificant age mode of arrival and clinical acuity had significant effects whereby ED-LOS was significantly higher for the elderly those arriving by ambulance and clinically-categorized high-acuity patients. Additionally there was an interaction between the age and clinical acuity in that while ED-LOS increased with age for high acuity patients the opposite trend occurred for low acuity patients. When ED-LOS was modeled using gender age and mode of arrival there was a significant interaction between age and mode of arrival. However this interaction was not significant when the model included age mode of arrival and clinical acuity. Conclusion: Significant interactions exist between commonly used ED-LOS determinants. Therefore interaction effects should be considered in analyzing and modelling ED-LOS. © 2020 Elsevier B.V. All rights reserved.
Citation - WoS: 10
Citation - Scopus: 13
Big data analytics and the effects of government restrictions and prohibitions in the COVID-19 pandemic on emergency department sustainable operations
(SPRINGER, 2023) Gorkem Sariyer; Mustafa Gokalp Ataman; Sachin Kumar Mangla; Yigit Kazancoglu; Manoj Dora; Ataman, Mustafa Gokalp; Dora, Manoj; Sariyer, Gorkem; Mangla, Sachin Kumar; Kazancoglu, Yigit
Grounded in dynamic capabilities this study mainly aims to model emergency departments' (EDs) sustainable operations in the current situation caused by the COVID-19 pandemic by using emerging big data analytics (BDA) technologies. Since government may impose some restrictions and prohibitions in coping with emergencies to protect the functioning of EDs it also aims to investigate how such policies affect ED operations. The proposed model is designed by collecting big data from multiple sources and implementing BDA to transform it into action for providing efficient responses to emergencies. The model is validated in modeling the daily number of patients the average daily length of stay (LOS) and daily numbers of laboratory tests and radiologic imaging tests ordered. It is applied in a case study representing a large-scale ED. The data set covers a seven-month period which collectively means the periods before COVID-19 and during COVID-19 and includes data from 238152 patients. Comparing statistics on daily patient volumes average LOS and resource usage both before and during the COVID-19 pandemic we found that patient characteristics and demographics changed in COVID-19. While 18.92% and 27.22% of the patients required laboratory and radiologic imaging tests before-COVID-19 study period these percentages were increased to 31.52% and 39.46% during-COVID-19 study period. By analyzing the effects of policy-based variables in the model we concluded that policies might cause sharp decreases in patient volumes. While the total number of patients arriving before-COVID-19 was 158347 it decreased to 79805 during-COVID-19. On the other hand while the average daily LOS was 117.53 min before-COVID-19 this value was calculated to be 16503 min during-COVID-19 study period. We finally showed that the model had a prediction accuracy of between 80 to 95%. While proposing an efficient model for sustainable operations management in EDs for dynamically changing environments caused by emergencies it empirically investigates the impact of different policies on ED operations.
Citation - WoS: 7
Citation - Scopus: 7
Data-driven decision making for modelling covid-19 and its implications: A cross-country study
(Elsevier Inc., 2023) Gorkem Sariyer; Sachin Kumar Kumar Mangla; Yigit Kazancoglu; Vranda Jain; Mustafa Gökalp Ataman; Ataman, Mustafa Gokalp; Sariyer, Gorkem; Jain, Vranda; Mangla, Sachin Kumar; Kazancoglu, Yigit
Grounded in big data analytics capabilities this study aims to model the COVID-19 spread globally by considering various factors such as demographic cultural health system economic technological and policy-based. Classified values on each country's case death and recovery numbers (per 1000000 population) were used to represent COVID-19 spread. Data sets also included 29 input variables for the corresponding six factors containing data from 159 countries. The proposed model used a Multilayer Perceptron algorithm. The results show that each of the pre-mentioned factors significantly affects disease spread. Urban population median age life expectancy numbers of medical doctors and nursing personnel current health expenditure as a % of GDP international health regulations capacity score continent literacy rate governmental response stringency index testing policy internet usage % human development index and GDP per capita were identified as significant. Taking early measures and adopting open public testing policies were recommended to policymakers in fighting pandemic diseases since the created scenarios on policy-based factors revealed their importance. © 2023 Elsevier B.V. All rights reserved.
Citation - WoS: 11
Citation - Scopus: 10
Factors Relating to Decision Delay in the Emergency Department: Effects of Diagnostic Tests and Consultations
(Dove Medical Press Ltd, 2023) Mustafa Gökalp Ataman; Gorkem Sariyer; Caner Saǧlam; Arif Karagöz; Erden Erol Ünlüer; Ataman, Mustafa Gokalp; Sariyer, Gorkem; Karagoz, Arif; Unluer, Erden Erol; Saglam, Caner
Purpose: The purpose of this study is to investigate the factors increasing waiting time (WT) and length of stay (LOS) in patients which may cause delays in decision-making in the emergency departments (ED). Patients and Methods: Patients who arrived at a training hospital in the central region of Izmir City Turkey during the first quarter of 2020 were retrospectively analyzed. WT and LOS were the outcome variables of the study and gender age arrival type triage level determined based on the clinical acuity diagnosis encoded based on International Classification of Diseases-10 (ICD-10) the existence of diagnostic tests or consultation status were the identified factors. The significance of the differences in WT and LOS values based on each level of these factors was analyzed using independent sample t-tests and ANOVA. Results: While patients for which no diagnostic testing or consultation was requested had a significantly higher WT in EDs their LOS values were substantially lower than those for which at least one diagnostic test or consultation was ordered (p≤0.001). Besides elderly and red zone patients and those who arrived by ambulance had significantly lower WT and higher LOS values than other levels for all groups of patients for which laboratory-type or imaging-type diagnostic test or consultation was requested (p≤0.001 for each comparison). Conclusion: Besides ordering diagnostic tests or consultation in EDs different factors may extend patients’ WT and LOS values and cause significant decision-making delays. Understanding the patient characteristics associated with longer waiting times and LOS values and thus delayed decisions will enable practitioners to improve operations management in EDs. © 2023 Elsevier B.V. All rights reserved.
Citation - WoS: 10
Citation - Scopus: 16
Fiscal responses to COVID-19 outbreak for healthy economies: Modelling with big data analytics
(ELSEVIER, 2023) Gorkem Sariyer; Serpil Kahraman; Mert Erkan Sozen; Mustafa Gokalp Ataman; Ataman, Mustafa Gokalp; Sariyer, Gorkem; Sözen, Mert Erkan; Kahraman, Serpil
Fiscal responses to the COVID-19 crisis have varied a lot across countries. Using a panel of 127 countries over two separate subperiods between 2020 and 2021 this paper seeks to determine the extent that fiscal responses contributed to the spread and containment of the disease. The study first documents that rich countries which had the largest total and health-related fiscal responses achieved the lowest fatality rates defined as the ratio of COVID-related deaths to cases despite having the largest recorded numbers of cases and fatalities. The next most successful were less developed economies whose smaller total fiscal responses included a larger health-related component than emerging market economies. The study used a promising big data analytics technology the random forest algorithm to determine which factors explained a country's fatality rate. The findings indicate that a country's fatality ratio over the next period can be almost entirely predicted by its economic development level fiscal expenditure (both total and health-related) and initial fatality ratio. Finally the study conducted a counterfactual exercise to show that had less developed economies implemented the same fiscal responses as the rich (as a share of GDP) then their fatality ratios would have declined by 20.47% over the first period and 2.59% over the second one.
Citation - WoS: 3
Citation - Scopus: 4
How machine learning facilitates decision making in emergency departments: Modelling diagnostic test orders
(WILEY, 2021) Gorkem Sariyer; Mustafa Gokalp Ataman; Ataman, Mustafa Gokalp; Sariyer, Gorkem
Objectives Since emergency departments (EDs) are responsible for providing initial care for patients who may need urgent medical care they are highly sensitive to increased patient delays. A key factor that increases patient delays is ordering diagnostic tests. Therefore understanding the factors increasing diagnostic test orders and proposing efficient models may facilitate decision making in EDs. Methods Month and week of the year day of the week and daily numbers of patients encoded based on 21 different ICD-10 codes were used as input variables. Daily test frequencies of patients requiring tests from laboratory and imaging services were modelled separately by linear regression models. Although significance of the input variables was identified based on these models obtained forecasts and residuals were further processed by machine learning techniques to obtain hybrid models. Results Day of the week and number of patients with ICD-10 codes of 'A00-B99' 'I00-I99' 'J00-J99' 'M00-M99' and 'R00-R99' were significant in both test types. In addition to these although daily patient frequencies with 'H60-H95' 'N00-N99' and 'O00-O9A' were significant for laboratory services 'L00-L99' 'S00-T88' and 'Z00-Z99' were significant for imaging services. Although prediction accuracies of regression models were respectively as 93.658% and 95.028% for laboratory and imaging services modelling they increased to 99.997% and 99.995% with the machine learning-integrated hybrid model. Conclusion The significant factors identified here can predict increases in use of laboratory and imaging services. This could enable these services to be prepared in advance to reduce ED patient delays thereby reducing ED overcrowding. The proposed model may also be efficiently used for decision making.
Citation - WoS: 1
Mode of Arrival Aware Models for Forecasting Flow of Patient and Length of Stay in Emergency Departments
(GALENOS PUBL HOUSE, 2022) Mustafa Gokalp Ataman; Gorkem Sariyer; Ataman, Mustafa Gokalp; Sariyer, Gorkem
Aim: Flow of patients to emergency departments (EDs) and their stays in EDs (ED-LOS) depend significantly on their arrival modes. In this study developing effective models for forecasting patient flow and length of stay (LOS) in EDs by considering arrival modes led better planning of ED operations. Materials and Methods: In this study by categorizing the mode of arrival into two self-arrived in and by ambulance autoregressive integrative moving average (ARIMA) models are applied for forecasting four time series: daily number of patients self arrived/arrived by an ambulance and average LOS of patients self-arrived/arrived by an ambulance. The models are validated with real-life data received from a large-scaled urban ED in Izmir Turkey. Results: While seasonal ARIMA is proper for forecasting the daily number of patients on both modes non-seasonal models are proper for forecasting the average LOS. The mean absolute percentage errors (MAPE) for the models of four time series are 5432% 13085% 9955% and 10.984% respectively. Thus daily arrivals to the EDs show seasonality patterns. Conclusion: By emphasizing the impact of mode of arrival in ED context this study can be used to aid the strategic decision making in the EDs for capacity planning to enable efficient use of the ED resources.
Citation - WoS: 6
Citation - Scopus: 5
The likelihood of requiring a diagnostic test: Classifying emergency department patients with logistic regression
(SAGE PUBLICATIONS INC, 2022) Gorkem Sariyer; Mustafa Gokalp Ataman; Ataman, Mustafa Gokalp; Sariyer, Gorkem
Background: Emergency departments (EDs) play an important role in health systems since they are the front line for patients with emergency medical conditions who frequently require diagnostic tests and timely treatment. Objective: To improve decision-making and accelerate processes in EDs this study proposes predictive models for classifying patients according to whether or not they are likely to require a diagnostic test based on referral diagnosis age gender triage category and type of arrival. Method: Retrospective data were categorised into four output patient groups: not requiring any diagnostic test (group A), requiring a radiology test (group B), requiring a laboratory test (group C), requiring both tests (group D). Multivariable logistic regression models were used with the outcome classifications represented as a series of binary variables: test (1) or no test (0), in the case of group A no test (1) or test (0). Results: For all models age triage category type of arrival and referral diagnosis were significant predictors whereas gender was not. The main referral diagnosis with high model coefficients varied by designed output groups (groups A B C and D). The overall accuracies of the logistic regression models for groups A B C and D were respectively 74.11% 73.07% 82.47% and 85.79%. Specificity metrics were higher than the sensitivities for groups B C and D meaning that these models were better able to predict negative outcomes.
Citation - WoS: 2
Citation - Scopus: 2
The Power of Governments in Fight Against COVID-19: High-Performing Health Systems or Government Response Policies?
(WALTER DE GRUYTER GMBH, 2023) Gorkem Sariyer; Mert Erkan Sozen; Mustafa Gokalp Ataman; Ataman, Mustafa Gokalp; Sariyer, Gorkem; Sozen, Mert Erkan
Due to the pandemic situation caused by COVID-19 disease there have been tremendous efforts worldwide to keep the spread of the virus under control and protect the functioning of health systems. Although governments take many actions in fighting this pandemic it is well known that health systems play an undeniable role in this fight. This study aimed to investigate the role of health systems and government responses in fighting COVID-19. By purposively sampling Finland Denmark the UK and Italy and analyzing their health systems' performances governments' stringency indexes and COVID-19 spread variables this study showed that high-performing health systems were the main power of states in managing pandemic environments. This study also measured relations between short and medium-term measures and COVID-19 case and death numbers in all study countries. It showed that medium-term measures had significant effects on death numbers.
Citation - Scopus: 1
Utilizing mHealth applications in emergency medical services of Turkey
(Springer International Publishing, 2018) Gorkem Sariyer; Mustafa Gökalp Ataman; Ataman, Mustafa Gokalp; Sariyer, Görkem
[No abstract available]