A model of workflow in the hospital during a pandemic to assist management

Abstract

We present a computational model of workflow in the hospital during a pandemic. The objective is to assist management in anticipating the load of each care unit, such as the ICU, or ordering supplies, such as personal protective equipment, but also to retrieve key parameters that measure the performance of the health system facing a new crisis. The model was fitted with good accuracy to France’s data set that gives information on hospitalized patients and is provided online by the French government. The goal of this work is both practical in offering hospital management a tool to deal with the present crisis of COVID-19 and offering a conceptual illustration of the benefit of computational science during a pandemic.

Application of Digital Twin and Heuristic Computer Reasoning to Workflow Management: Gastroenterology Outpatient Centers Study

“…This set of parameter can include indicator of each provider and its team performances as well as duration of processes as a function of the distribution of staff in the peri-operative area. To build a digital twin of the workflow, the unknown vector parameter is obtained by fitting the model output to the observation; the fitting is solution of a (stochastic) minimization process as in [3, 7, 4]: for a set of initial condition , known control conditions and duration T . In our notation the digital twin gives the simulation operator: The predictive capability of this operator is essential to the success of computational heuristic reasoning; For robustness one has to: verify in real time that the digital twin runs properly and sends alert and/or makes correction automatically based on redundancy of input channel of information. double check the digital twin prediction off line to prevent errors due to delay in communication feed of input channels. verify and validate the prediction against observation by computing . calibrate as needed the digital twin with the minimizing process as described above. exercise heuristic reasoning as described in the next section to support the decision of the manager: in the short time scale (that requires restarting the digital twin in the middle of the day with hybrid inputs from EHR and sensor data) in the long time scale that can start form a clean initial state before clinic start. The digital twin uses a data base of observation with a large number of clinical days and time range that keeps track of all input channels.…”

Process Improvement Approaches for Increasing the Response of Emergency Departments against the COVID-19 Pandemic: A Systematic Review

“…Single Tang et al [93] Discrete event simulation Nepomuceno et al [81] Data envelopment analysis (DEA) Mehrotra et al [77] Stochastic optimization AbdelAziz et al [40] Multi-objective pareto optimization Peng et al [85]; Moss et al [80] Simulation Aggarwal et al [41] Additive utility assumption Araz et al [45] System dynamics Hybrid Garbey et al [62] Markov chains, stochastic optimization Albahri et al [42] Entropy, TOPSIS De Nardo et al [58] Potentially all pairwise ranking of all possible alternatives (PAPRIKA), multi-criteria decision making (MCDM) Parker et al [84] Linear programming, mixed-integer programming Zeinalnezhad et al [98] Colored petri nets, discrete event simulation Zhang & Cheng. [100] Logistic regression, Markov chains Abadi et al [39] Hybrid salp swarm algorithm and genetic algorithm (HSSAGA) Haddad et al [67] Simulation, optimization…”

“…The proposed framework provides ED managers with an intelligent automated framework capable of eliminating exposed shifts while mitigating low nursing staff commitment and stress. Other highlighted interventions are reported in Garbey et al [62], Albahri et al [42], De Nardo et al [58], Parker et al [84], and Zhang & Cheng [100].…”

Gastroenterology Procedures Generate Aerosols: an Air Quality Turnover Solution to Mitigate the Risk

“…We have specifically developed mathematical and cyber-physical tools to address some of the workflow issues via simulation calibrated on unbiased, real-time, autonomous measurement of clinic performances [10–13,16,19].…”