Factors Relating to Decision Delay in the Emergency Department: Effects of Diagnostic Tests and Consultations.

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.

Fiscal responses to COVID-19 outbreak for healthy economies: Modelling with big data analytics.

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.

How machine learning facilitates decision making in emergency departments: Modelling diagnostic test orders.

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.

Big data analytics and COVID-19: investigating the relationship between government policies and cases in Poland, Turkey and South Korea.

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'.

Predicting waiting and treatment times in emergency departments using ordinal logistic regression models.

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 37,711 patients arriving at the ED of a large urban hospital were examined.

Analyzing Main and Interaction Effects of Length of Stay Determinants in Emergency Departments.

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.

The likelihood of requiring a diagnostic test: Classifying emergency department patients with logistic regression.

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).

An analysis of Emergency Medical Services demand: Time of day, day of the week, and location in the city.

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.