Open Access
Accumulating priority queues versus pure priority queues for managing patients in emergency departments
(Elsevier, 2019) Cildoz Esquíroz, Marta; Ibarra, Amaia; Mallor Giménez, Fermín; Institute of Smart Cities - ISC
Improving the quality of healthcare in emergency departments (EDs) is at the forefront of many hospital managers’ efforts, as they strive to plan and implement better patient flow strategies. In this paper, a new approach to manage the patient flow in EDs after triage is proposed. The new queue discipline, named accumulative priority queue with finite horizon and denoted by APQ-h, is an extension of the accumulative priority queue (APQ) discipline that considers not only the acuity level of patients and their waiting time but also the stage of the healthcare treatment. APQ disciplines have been studied in the literature from a queueing theory point of view, which requires assumptions rarely found in real EDs, such as homogeneity in the patient arrival pattern and only one service stage. The APQ-h discipline accumulates priority from the point of waiting for the first physician consultation until the moment the waiting time exceeds the upper time limit set to access the physician after the patient's arrival. A recent study shows that a management strategy of this type is applied in practice in several Canadian EDs. The main aim of this paper is to explore the implementation of APQ-h managing policies in a real ED. For this purpose, a simulation model replicating a real ED is developed. This simulation model is also used to obtain the optimal APQ type polices through a simulation-based optimization method that solves a multi-objective and stochastic optimization problem. Arrival to provider time and total waiting time in the ED are considered to be the key ED performance indicators. An extensive computational analysis shows the flexibility of the APQ-h and APQ discipline and their superiority over other pure priority disciplines in a real setting and in a variety of ED scenarios. In addition, no superiority over the APQ discipline is demonstrated. © 2019 The Authors
Open Access
Acuity-based rotational patient-to-physician assignment in an emergency department using electronic health records in triage
(SAGE, 2023) Cildoz Esquíroz, Marta; Ibarra Bolt, Amaya; Mallor Giménez, Fermín; Estadística, Informática y Matemáticas; Estatistika, Informatika eta Matematika; Institute of Smart Cities - ISC
Emergency department (ED) operational metrics generated by a new acuity-based rotational patient-to-physician assignment (ARPA) algorithm are compared with those obtained with a simple rotational patient assignment (SRPA) system aimed only at an equitable patient distribution. The new ARPA method theoretically guarantees that no two physicians’ assigned patient loads can differ by more than one, either partially (by acuity levels) or in total; whereas SRPA guarantees only the latter. The performance of the ARPA method was assessed in practice in the ED of the main public hospital (Hospital Compound of Navarra) in the region of Navarre in Spain. This ED attends over 140 000 patients every year. Data analysis was conducted on 9,063 ED patients in the SRPA cohort, and 8,892 ED patients in the ARPA cohort. The metrics of interest are related both to patient access to healthcare and physician workload distribution: patient length of stay; arrival-to-provider time; ratio of patients exceeding the APT target threshold; and range of assigned patients across physicians by priority levels. The transition from SRPA to ARPA is associated with improvements in all ED operational metrics. This research demonstrates that ARPA is a simple and useful strategy for redesigning front-end ED processes.
Open Access
Estimation of patient flow in hospitals using up-to-date data: application to bed demand prediction during pandemic waves
(Public Library of Science, 2023) García de Vicuña Bilbao, Daniel; López-Cheda, Ana; Jácome, María Amalia; Mallor Giménez, Fermín; Institute of Smart Cities - ISC
Hospital bed demand forecast is a first-order concern for public health action to avoid healthcare systems to be overwhelmed. Predictions are usually performed by estimating patients flow, that is, lengths of stay and branching probabilities. In most approaches in the literature, estimations rely on not updated published information or historical data. This may lead to unreliable estimates and biased forecasts during new or non-stationary situations. In this paper, we introduce a flexible adaptive procedure using only near-real-time information. Such method requires handling censored information from patients still in hospital. This approach allows the efficient estimation of the distributions of lengths of stay and probabilities used to represent the patient pathways. This is very relevant at the first stages of a pandemic, when there is much uncertainty and too few patients have completely observed pathways. Furthermore, the performance of the proposed method is assessed in an extensive simulation study in which the patient flow in a hospital during a pandemic wave is modelled. We further discuss the advantages and limitations of the method, as well as potential extensions.

Mallor Giménez, Fermín

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