key: cord-1046033-8k7b39p6 authors: Garaix, Thierry; Gaubert, Stéphane; Josse, Julie; Vayatis, Nicolas; Véber, Amandine title: Decision-making tools for healthcare structures in times of pandemic date: 2022-03-02 journal: Anaesth Crit Care Pain Med DOI: 10.1016/j.accpm.2022.101052 sha: e9608387fc1968bd3bdb2efe2024439043859716 doc_id: 1046033 cord_uid: 8k7b39p6 nan From the early stages of the pandemic, many organisational issues arose to cope with the wave of patients, which overwhelmed many hospitals and care structures. Emergency call centres faced a sharp increase in the number of calls, which had to be efficiently sorted according to the urgency of the patient's condition. The saturation of critical care services led to the need to re-orient incoming patients to other hospitals with available beds, or to organise the transfer of patients from a heavily burdened region to a region under a weaker capacity strain. Hospital services had to find humanly manageable ways to deal with the surplus of work, in conditions where health workers were not only at risk of physical and psychological exhaustion, but also at risk of being infected with SARS-CoV-2. In this context, already existing collaborations between mathematicians or computer scientists and medical doctors or emergency call centres gave rise to the extraordinary quick emergence of tools to help operators and medical staff with logistic decisions. Below, we present four such initiatives, which arose during the first COVID-19 wave in France in 2020. They illustrate what close collaborations between medical staff and researchers from the fields of operations research, modelling of complex dynamics or data sciences can bring to the management of critical situations in health services. Beyond the current pandemic, they also pose the question of how to improve our preparedness to future crises. The French Grand Est region was severely affected by COVID-19 from the end of February 2020. The capacity to receive mechanically ventilated patients in intensive care, saturating quickly, proved to be a crucial issue in the management of the sanitary crisis. As the circulation of information on the location or capacity of resuscitation beds was difficult, resuscitators expressed the need for a field IT tool dedicated to the management of resuscitation beds. To J o u r n a l P r e -p r o o f respond to this request, the ICUBAM application (Intensive Care Unit Bed Activity Monitor) was developed by a consortium of engineers, applied mathematicians and computer scientists from École polytechnique, INRIA and the University Hospital of Nancy. ICUBAM enables the network of resuscitators to add information to a database in real time (in particular on the reception capacity of their unit) via their mobile phone, and to turn it into a cartographic visualisation (Figure 1 ). Its efficiency results from the direct involvement of the concerned medical staff, keeping the tool up-to-date, and from the possibility of analysing and visualising the data collected in real time to anticipate the needs in resuscitation beds. The project is open-source, available on GitHub [1] , and can be deployed easily. ICUBAM was launched at the end of March 2020 in the Grand Est region with the agreement of the Regional Health Agency and in two weeks, it was used by 130 services and a network of 300 resuscitators in 40 departments, which represents more than 2,000 resuscitation beds that can accommodate a COVID+ patient. ICUBAM collected data during the first three waves of the epidemic and helped resuscitators on a day-to-day basis by measuring and nowcasting the load imposed on resuscitation services during the pandemic [2] . The medical classification was exploited: some patients call at early stages of the disease (needing only medical advice), and this can be used to anticipate the load over a horizon of one week for medical emergency services [5] and of around two weeks for ICU admissions [6] . A daily cartography of the epidemic based on the calls to SAMU was thus produced. These staffing methods are useful independently of the pandemic context. For instance, they may be used to compare different mechanisms of treatment of calls proposed by practitioners. Despite the rapid dissemination of tools able to monitor resource availability and to forecast demands, early in the COVID-19 crisis health care managers realised they lacked adapted prepared response plans and decision-support tools for patient flow and medical resource management. The research team of the CNRS lab LIMOS and Mines Saint-Étienne developed several methods and tools to evaluate and support patient flow and resource management decisions. This research is rooted in several studies conducted on emergency management during epidemics or floods [7, 8, 9, 10] , home health care services [11] and cancer treatments [12] . All these works do not consider some specificities of pandemics like COVID-19, with the burden put on the complete health care network and the high risk of contamination for patients and caregivers. The current projects aim to extend these tools to larger networks with more complex care pathways and caregivers acceptance of reorganisation, and to compute standardised guidelines for decision makers on several pandemic scenarios. The ONADAP project revealed that, while it was difficult to translate the state-of-the-art of epidemic modelling in specific prevention and control action at the level of state policies during the pandemic, it was possible to setup an efficient detection system at the level of a single care unit within a few weeks with immediate impact. Furthermore, this initiative also illustrated how fruitful the retro-transfer from field experts dealing with issues in the real world to academic researchers may be, since ONADAP nourished a series of works developed by researchers from the consortium on various topics related to mathematical models in the context of the pandemic such as graph inference based on epidemic models [14] , modelling the phylogeny of SARS-CoV-2 [15] , methodological review on epidemic models used for SARS-CoV-2 [16] , exit policies with differential screening of the population [17] , symptoms-serology correlation at Percy hospital [18] and accurate estimation of the reproduction number [19] . Lines coloured in red indicate that more than 80% of beds are occupied, orange corresponds to 50-80% of occupied beds, and green corresponds to less than 50% ICUBAM -ICU Bed Availability Monitor (GitHub repository) ICUBAM: ICU Bed Availability Monitoring and analysis in the Grand Est region of France during the COVID-19 epidemic Piecewise Affine Dynamical Models of Timed Petri Nets --Application to Emergency Call Centers Feedback on the Regulation of Samu de Paris during the COVID-19 Crisis Understanding and monitoring the evolution of the Covid-19 epidemic from medical emergency calls: the example of the Paris area Early indicators of intensive care unit bed requirement during the COVID-19 epidemic: A retrospective study in Ile-de-France region A stochastic optimization model for shift scheduling in emergency departments Proactive on-call scheduling during a seasonal epidemic Dynamic Insertion of Emergency Surgeries With Different Waiting Time Targets Hospital flood emergency management planning using Markov models and discrete-event simulation Staff dimensioning in homecare services with uncertain demands Daily outpatient chemotherapy appointment scheduling with random deferrals Capacity Planning in Intensive Care Unit During a Pandemic Crisis Network Reconstruction Problem for an Epidemic Reaction-Diffusion A New Bayesian Structured Coalescent Approximation That Incorporates Dynamic Epidemiological Data Epidemic Models for COVID-19 during the First Wave from Epidemic Models for Personalised COVID-19 Isolation and Exit Policies Using Clinical Risk Predictions A retrospective analysis of the relation between self-declared Covid-19 symptoms within hospital staff members and their SARS-CoV-2 serological status prediction Computing the daily reproduction number of COVID-19 by inverting the renewal equation using a variational technique