TY - JOUR
T1 - Ambulance dispatching during a pandemic
T2 - Tradeoffs of categorizing patients and allocating ambulances
AU - Rautenstrauss, Maximiliane
AU - Martin, Layla
AU - Minner, Stefan
N1 - Publisher Copyright:
© 2021 Elsevier B.V.
PY - 2023/1/1
Y1 - 2023/1/1
N2 - Amidst a pandemic, operators of emergency medical service (EMS) systems aim at upholding service at sufficiently low response times while reducing the infection probability of their personnel. Designating ambulances to serve only infected patients and suspected cases may reduce the outage probabilities of ambulances and consequently the response times of the EMS. We investigate the benefits that EMS personnel and patients can gain from such a split. As a solution method to quantify these benefits, we apply a two-stage approach. First, we run a first-stage optimization model to pre-select ambulance splits with the highest emergency call coverage. Second, we solve the approximate Hypercube Queuing Model (AHQM) to evaluate the performance of the pre-selected ambulance splits at the second stage. We contribute to the existing literature by including multiple incident categories and outages of ambulances in the AHQM and combining it with the first-stage optimization model. Further, we conduct a case study for the Coronavirus Disease 2019 (Covid-19) pandemic to draw conclusions on the benefits of splitting. We observe that an ambulance split would not reduce the average response time for the examined data set since the Covid-related call volume in Munich and the infection probability are too low. However, a sensitivity analysis shows that long isolation times and high infection probabilities make an ambulance split beneficial for patients and EMS personnel, as an ambulance split reduces the average response time without significantly increasing the mean infection probability for EMS personnel.
AB - Amidst a pandemic, operators of emergency medical service (EMS) systems aim at upholding service at sufficiently low response times while reducing the infection probability of their personnel. Designating ambulances to serve only infected patients and suspected cases may reduce the outage probabilities of ambulances and consequently the response times of the EMS. We investigate the benefits that EMS personnel and patients can gain from such a split. As a solution method to quantify these benefits, we apply a two-stage approach. First, we run a first-stage optimization model to pre-select ambulance splits with the highest emergency call coverage. Second, we solve the approximate Hypercube Queuing Model (AHQM) to evaluate the performance of the pre-selected ambulance splits at the second stage. We contribute to the existing literature by including multiple incident categories and outages of ambulances in the AHQM and combining it with the first-stage optimization model. Further, we conduct a case study for the Coronavirus Disease 2019 (Covid-19) pandemic to draw conclusions on the benefits of splitting. We observe that an ambulance split would not reduce the average response time for the examined data set since the Covid-related call volume in Munich and the infection probability are too low. However, a sensitivity analysis shows that long isolation times and high infection probabilities make an ambulance split beneficial for patients and EMS personnel, as an ambulance split reduces the average response time without significantly increasing the mean infection probability for EMS personnel.
KW - Ambulance dispatching
KW - Approximate hypercube queuing model
KW - OR in health services
KW - Pandemic
UR - http://www.scopus.com/inward/record.url?scp=85122209146&partnerID=8YFLogxK
U2 - 10.1016/j.ejor.2021.11.051
DO - 10.1016/j.ejor.2021.11.051
M3 - Article
AN - SCOPUS:85122209146
SN - 0377-2217
VL - 304
SP - 239
EP - 254
JO - European Journal of Operational Research
JF - European Journal of Operational Research
IS - 1
ER -