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Forecasting the Demand for Emergency Medical Services
Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, Faculty of Science & Engineering.ORCID iD: 0009-0009-1747-3852
Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-3016-2778
Linköping University, Department of Science and Technology, Communications and Transport Systems. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-5868-2388
2019 (English)In: Proceedings of the 52nd Hawaii International Conference on System Sciences, 2019, Honolulu: University of Hawai'i at Manoa , 2019, Vol. January, p. 1855-1864Conference paper, Published paper (Refereed)
Abstract [en]

Accurate forecast of the demand for emergency medical services (EMS) can help in providing quick and efficient medical treatment and transportation of out-of-hospital patients. The aim of this research was to develop a forecasting model and investigate which factors are relevant to include in such model. The primary data used in this study was information about ambulance calls in three Swedish counties during the years 2013 and 2014. This information was processed, assigned to spatial grid zones and complemented with population and zone characteristics. A Zero-Inflated Poisson (ZIP) regression approach was then used to select significant factors and develop the forecasting model. The model was compared to the forecasting model that is currently incorporated in the EMS information system used by the ambulance dispatchers. The results show that the proposed model performs better than the existing one.

Place, publisher, year, edition, pages
Honolulu: University of Hawai'i at Manoa , 2019. Vol. January, p. 1855-1864
Series
Proceedings of the Annual Hawaii International Conference on System Sciences (HICSS), ISSN 1530-1605, E-ISSN 2572-6862 ; January
National Category
Transport Systems and Logistics Probability Theory and Statistics Communication Systems
Identifiers
URN: urn:nbn:se:liu:diva-157097DOI: 10.24251/HICSS.2019.225ISI: 000625294901112Scopus ID: 2-s2.0-85072773703ISBN: 9780998133126 (print)OAI: oai:DiVA.org:liu-157097DiVA, id: diva2:1318558
Conference
the 52nd Hawaii International Conference on System Sciences, 2019, January 8-11, Grand Wailea, Maui, Hawai'i
Available from: 2019-05-28 Created: 2019-05-28 Last updated: 2024-08-30Bibliographically approved
In thesis
1. An Operations Research Approach for Daily Emergency Management
Open this publication in new window or tab >>An Operations Research Approach for Daily Emergency Management
2019 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

Emergency services play a vital role in society by providing help to affected people and minimizing damage to public and private assets as well as the environment during emergencies. However, these organizations deal with problems of increasing demand uncertainty and resource shortage over time. These problems lead to the creation of many other problems, such as longer response times, lower survivability of victims and patients, and more severe damage to properties and the environment. Acquiring more information about future emergency demand, such as factors affecting this demand, can contribute to reduction of the effects of increasing demand uncertainty. The introduction of volunteers as a new type of emergency resource, which has gained attention in the past few years, can be a solution to the problem of increasing resource shortage.

The aim of this thesis is to provide operations research-based models and methods that can assist medical emergency services in daily emergency management. The aim is supported by two objectives: 1) to develop a forecasting model and 2) to develop models for the dispatch of volunteers. Three separate studies with a focus on these objectives are conducted, and the results are described in three papers.

In the first paper, a forecasting model for predicting the volume of ambulance calls per hour and geographic location for three counties in Sweden is presented. The model takes into consideration geographical zones with few or no population and very low call frequency. Comparative results based on the real data of ambulance calls show that the proposed model performs better than the model that is currently used in some parts of Sweden for operational and tactical planning of emergency medical services. In addition to performance improvement, the proposed model provides information about the factors affecting ambulance demand.

In the second paper, the use of volunteers in response to out-of-hospital cardiac arrest (OHCA) cases is considered, and a deterministic optimization model for their dispatch is provided. The model benefits from a survival function for determining dispatch decisions. The effect of arrival times of volunteers on the survivability of patients is also considered. The results show that, in terms of achieved survivability of patient based on the applied survival function, the proposed model performs better than simple decision rules used today.

The third paper presents a probabilistic method for the dispatch of volunteers to OHCA cases. This method considers the uncertainties associated with the actions of volunteers once they are assigned a task. The proposed method uses a survival function as the objective of dispatch decisions. The results of the method are compared to the static dispatch method that is currently used in an operational system in Sweden for the utilization of volunteers in OHCA cases. Comparative results based on real data show that, with respect to used survival function, the proposed method contributes to higher survivability of OHCA patients than the static dispatch method.

The models and method in this thesis focus on solving real-world problems and use real data for that purpose when available. Some simplifications were considered in the development process. Nevertheless, these models and method have the potential to be beneficial for medical emergency services in practice and can be used as a base for dynamic resource management systems. Such systems can be helpful for both tactical and operational planning of emergency resources.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2019. p. 47
Series
Linköping Studies in Science and Technology. Licentiate Thesis, ISSN 0280-7971 ; 1842
National Category
Transport Systems and Logistics Computer and Information Sciences Communication Studies
Identifiers
urn:nbn:se:liu:diva-157099 (URN)10.3384/lic.diva-157099 (DOI)9789176850589 (ISBN)
Presentation
2019-06-10, K3, Kåkenhus, Campus Norrköping, Linköpings universitet, Norrköping, 13:15 (English)
Opponent
Supervisors
Available from: 2019-05-28 Created: 2019-05-28 Last updated: 2022-01-14Bibliographically approved
2. Models for Dispatch of Volunteers in Daily Emergency Response
Open this publication in new window or tab >>Models for Dispatch of Volunteers in Daily Emergency Response
2022 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Sufficient emergency resources are essential for emergency services to provide timely help to affected people and to minimize damage to public and private assets and the environment. Emergency services, however, face resource shortages and increasing demand over time. As a result, their response times increase, resulting in lower survival chances of affected people and more severe damage to properties and the environment. Thus, emergency services need to utilize and effectively manage all their available resources. These can be divided into traditional resources, such as ambulances, and new and emerging resources, such as volunteers. Models and methods developed using operations research (OR) methodologies can facilitate the management of these resources. However, despite a rich literature on OR-based models and methods focusing on traditional resources, the literature on new and emerging resources, and specifically volunteers, is scarce.

The aim of this thesis is to develop models and methods for task assignment and dispatch of volunteers to daily medical emergencies. This also includes forecasting models for future emergencies. The developed models and methods consider volunteer programs in Sweden and the Netherlands, employing real historical data.

The aim has been addressed through three studies, one main study and two sub-studies, the results of which are presented in the six included papers. The main study focuses on the development of models, methods, and strategies for task assignment and dispatch of volunteers to out-of-hospital cardiac arrest (OHCA) cases using OR. To evaluate the survival rates of these patients, the most important health outcome of a response process, survival functions have been used in the development of these models and strategies. The results of this study are presented in Papers II–V. The first sub-study investigates different types of new and emerging resources used in daily medical emergency response, and the results are presented as an overview of the literature in Paper I. The second sub-study focuses on the forecast of medical emergency demand, and its outcomes are presented in Paper VI.

The overall conclusion is that the use of OR-based models and methods can contribute to improved outcomes and increased survival probabilities compared to the strategies and techniques used in the existing systems.

Abstract [sv]

För att räddningsorganisationer som till exempel ambulanssjukvården och den kommunala räddningstjänsten snabbt ska kunna hjälpa drabbade människor, byggnader och miljö, krävs att de har tillräckligt med resurser. Dock ökar hela tiden antalet utryckningar, samtidigt som budgetnedskärningar och rekryteringsproblem påverkar resurstillgången. Detta leder till längre insatstider, vilket ger ökad dödlighet och ökade kostnader. Därför är det av högsta vikt att de befintliga räddningsresurserna används så effektivt som möjligt. Dessa kan delas in i traditionella resurser, som till exempel ambulanser, och nya resurser, som frivilliga personer. Matematiska modeller, som optimerings- och simuleringsmodeller, har länge använts för att stödja planeringen och resurshanteringen av traditionella räddningsresurser, men för frivilliga resurser är det ett mer outforskat område.

Syftet med forskningen som presenteras i denna avhandling är att utveckla modeller och metoder för tilldelning av arbetsuppgifter och utlarmning av frivilliga insatspersoner till akuta sjukdomsfall. I detta ingår också modeller för att prognosticera dylika händelser. De utvecklade modellerna och metoderna baseras på, och är framtagna för att stödja, verkliga frivilliginitiativ i Sverige och Nederländerna.

Syftet uppfylls genom tre olika studier som presenteras via sex forskningsartiklar i avhandlingen. I den första studien görs en litteraturöversikt över nya typer av räddningsresurser som används som komplement till ambulanssjukvård. Den andra studien fokuserar på utvecklingen av modeller, metoder och strategier för uppgiftstilldelning och utlarmning av frivilliga insatspersoner vid hjärtstopp. I den tredje studien presenteras en ny prognosmodell för att prediktera ambulansuppdrag.

Den generella slutsatsen är att beslutsstöd baserat på matematisk modellering kan bidra till bättre utfall, och en ökad överlevnadsgrad, jämfört med de strategier för uppgiftstilldelning, utlarmning och prognostisering som används idag.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2022. p. 72
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 2203
National Category
Transport Systems and Logistics Information Systems
Identifiers
urn:nbn:se:liu:diva-182309 (URN)10.3384/9789179291853 (DOI)9789179291846 (ISBN)9789179291853 (ISBN)
Public defence
2022-02-18, Online through Zoom (contact viveka.nilson@liu.se) and K3, Kåkenhus, Campus Norrköping, Norrköping, 13:15 (English)
Opponent
Supervisors
Funder
Swedish Civil Contingencies Agency
Available from: 2022-01-14 Created: 2022-01-14 Last updated: 2022-01-14Bibliographically approved

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Steins, KrisjanisMatinrad, NikiAndersson Granberg, Tobias

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