Article Preview
Top1. Introduction
The novel coronavirus disease 2019 (COVID-19), a highly contagious disease caused by the new coronavirus (SARS-CoV-2), has spread worldwide and put the health systems of several countries under pressure, with a demand for assistance exceeding in-country capacities, especially regarding critical care (Guan et al., 2020; Zhou et al., 2020; Daeho & Neumann 2020, Leung et al. 2020, Prem et al. 2020).
In this critical scenario, objective criteria must be established to assess the need for patients to be admitted to and released from hospital beds, considering not only their clinical conditions, but also the best possible use of limited resources. Triage, as part of the health care process, is used to support decisions on resource allocation in such situations (Ghanbari et al. 2019).
Models of triage have been proposed to support rapid sorting and categorizing of patients based on their conditions and the available resources. The development of proper triage protocols is important for dealing with a health crisis. The ethical concept of utilitarianism, which seeks the greatest good for the greatest number of people, has often supported the practice of triage in disaster/emergency situations (Christian et al 2006, Ghanbari et al. 2019, White et al 2009).
In this paper, a decision model was developed based on the utilitarian principle, to support the triage of suspected/confirmed COVID-19 patients in a scenario of resource scarcity. Three alternatives are considered, representing the possible recommendations for a patient: intensive care, hospital stay, and home isolation. The model is based on Multi-Attribute Utility Theory (MAUT - Kenney & Raiffa 1976), an approach that it is useful for dealing with this type of decision situation, since this is clearly a multi-attribute decision problem that involves both the life of the patient and the overall cost to the health system (which may include subjective factors such as the life of other patients, as later discussed). Moreover, this is a stochastic decision problem, in which the probabilities of survival of the patient for each treatment alternative should be considered.
However, assessing a patient’s probability of survival in each of the possible treatment alternatives can be a difficult task for health professionals involved in the care of suspected/confirmed COVID-19 patients. Addressing this issue, this paper presents a structured method that considers objective information to infer the patient’s chances of survival for each treatment alternative. The chances of survival are defined as ranges of possible values for the patient's probabilities of survival: this approach is appropriate for dealing with the uncertainties inherent in this problem. The method was developed together with experts (physicians and researchers involved in the care of suspected/confirmed COVID-19 patients), considering the knowledge acquired from their experience and from papers published in scientific journals.