Towards Model-Based Policy Elaboration on City Scale Using Game Theory: Application to Ambulance Dispatching

Kovalchuk, Sergey V.; Moskalenko, Mariia A.; Yakovlev, A. N.

doi:10.1007/978-3-319-93698-7_31

Cited by 4 publications

(4 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To confirm or deny the hypothesis that those factors are essential for AH assessment and that our method reproduces AH+ distribution with satisfactory accuracy, additional spatially explicit datasets should be used. We plan to use the hospital data on emergency calls in Saint Petersburg in 2015 which were related to acute coronary syndrome (ACS) [11]. Since ACS is one of the most widespread consequences of AH [1], our idea is to compare different neighborhoods by the value of the ratio of total number of ACS cases to the assessed number of AH+ individuals.…”

Section: Resultsmentioning

confidence: 99%

Analyzing the spatial distribution of individuals predisposed to arterial hypertension in Saint Petersburg using synthetic populations

Leonenko

Kovalchuk

2020

ITM Web Conf.

Self Cite

View full text Add to dashboard Cite

Arterial hypertension (AH) is one of the most common cardiovascular diseases, and it can lead to serious complications. To optimize the delivery of patients exposed to AH to medical institutions and thus to curtail mortality in Russian cities caused by the consequences of hypertension, it is necessary to estimate the number of potential patients, along with their spatial distribution. This paper presents a method which uses synthetic population data to assess the spatial distribution of individuals potentially prone to arterial hypertension. The risk of arterial hypertension of an individual is calculated based on its demographic characteristics (age and gender). Using Saint Petersburg as a case study, we demonstrate that the mentioned approach makes it possible to perform predictions of AH cases distribution in absence of real data on hypertension status of the individuals. The results of the study will be used to assess the input flows of patients to healthcare facilities and optimize their workflow. Synthetic populationA "synthetic population" is a synthesized, spatially explicit human agent database (essentially, a simulated census) representing the population of a city, region or country. By its *

show abstract

Section: Resultsmentioning

confidence: 99%

Analyzing the spatial distribution of individuals predisposed to arterial hypertension in Saint Petersburg using synthetic populations

Leonenko

Kovalchuk

2020

ITM Web Conf.

Self Cite

View full text Add to dashboard Cite

show abstract

“…However, the optimal strategy combination is not always applied for a non-cooperative environment. To find out which strategy combination is realistic, we conduct a two-dimensional simulation to predict the stationary equilibrium [6]. Thus, the two-dimensional GT-DES model is designed for a city environment.…”

Section: D Model-based Ambulance Dispatchingmentioning

confidence: 99%

“…The proposed approach is based on the combination of discrete event simulation (DES) and an equilibrium-based framework using game theory (GT) to simulate the patient flow and hospitals' responding strategies in order to understand the dynamics in the ambulance dispatching process. The work is based on the idea [6] showing that the global optimal behaviour of healthcare agents may differ from the equilibrated and real behaviours. This work develops this idea with a multi-agent hybrid (the coupled game-theoretical model and discrete event simulation (GT-DES)) model and applies it to the real-world example of Saint Petersburg to evaluate the proposed approach.…”

Section: Introductionmentioning

confidence: 99%

Modelling Hospital Strategies in City-Scale Ambulance Dispatching

Fu¹,

Krzhizhanovskaya²,

Yakovlev³

et al. 2022

Preprint

View full text Add to dashboard Cite

The optimisation in the ambulance dispatching process is significant for patients who need early treatments. However, the problem of dynamic ambulance redeployment for destination hospital selection has rarely been investigated. The paper proposes an approach to model and simulate the ambulance dispatching process in multi-agents healthcare environments of large cities. The proposed approach is based on using the coupled game-theoretic (GT) approach to identify hospital strategies (considering hospitals as players within a non-cooperative game) and performing discrete-event simulation (DES) of patient delivery and provision of healthcare services to evaluate ambulance dispatching (selection of target hospital). Assuming the collective nature of decisions on patient delivery, the approach assesses the influence of the diverse behaviours of hospitals on system performance with possible further optimisation of this performance. The approach is studied through a series of cases starting with a simplified 1D model and proceeding with a coupled 2D model and real-world application. The study considers the problem of dispatching ambulances to patients with the Acute Coronary Syndrome (ACS) directed to the percutaneous coronary intervention (PCI) in the target hospital. A real-world case study of data from Saint Petersburg (Russia) is analysed showing the better conformity of the global characteristics (mortality rate) of the healthcare system with the proposed approach being applied to discovering the agents' diverse behaviour..

show abstract

“…The EMS data we used in the research contain 5125 ACS-related EMS calls registered in Saint Petersburg from January to November 2015 [7]. The backof-the-envelope analysis of the time series corresponding to daily number of calls (Fig.…”

Section: Fig 3 the Spatial Distribution Of Acs Emergency Service Calls In Saint Petersburgmentioning

confidence: 99%

Analyzing the Spatial Distribution of Acute Coronary Syndrome Cases Using Synthesized Data on Arterial Hypertension Prevalence

Leonenko

2020

Lecture Notes in Computer Science

View full text Add to dashboard Cite

In the current study, the authors demonstrate the method aimed at analyzing the distribution of acute coronary syndrome (ACS) cases in Saint Petersburg. The employed approach utilizes a synthetic population of Saint Petersburg and a statistical model for arterial hypertension prevalence. The number of ACS-related emergency services calls in an area is matched with the population density and the prospected number of individuals with arterial hypertension, which makes it possible to find locations with excessive ACS incidence. Three categories of locations, depending on the joint distribution of the above-mentioned indicators, are proposed as a result of data analysis. The method is implemented in Python programming language, the visualization is made using QGIS open software. The proposed method can be used to assess the prevalence of certain health conditions in the population and to match them with the corresponding severe health outcomes.

show abstract

Towards Model-Based Policy Elaboration on City Scale Using Game Theory: Application to Ambulance Dispatching

Cited by 4 publications

References 12 publications

Analyzing the spatial distribution of individuals predisposed to arterial hypertension in Saint Petersburg using synthetic populations

Analyzing the spatial distribution of individuals predisposed to arterial hypertension in Saint Petersburg using synthetic populations

Modelling Hospital Strategies in City-Scale Ambulance Dispatching

Analyzing the Spatial Distribution of Acute Coronary Syndrome Cases Using Synthesized Data on Arterial Hypertension Prevalence

Contact Info

Product

Resources

About