A DEA-Based Complexity of Needs Approach for Hospital Beds Evacuation during the COVID-19 Outbreak

Abstract

Data envelopment analysis (DEA) is a powerful nonparametric engineering tool for estimating technical efficiency and production capacity of service units. Assuming an equally proportional change in the output/input ratio, we can estimate how many additional medical resource health service units would be required if the number of hospitalizations was expected to increase during an epidemic outbreak. This assessment proposes a two-step methodology for hospital beds vacancy and reallocation during the COVID-19 pandemic. The framework determines the production capacity of hospitals through data envelopment analysis and incorporates the complexity of needs in two categories for the reallocation of beds throughout the medical specialties. As a result, we have a set of inefficient healthcare units presenting less complex bed slacks to be reduced, that is, to be allocated for patients presenting with more severe conditions. The first results in this work, in collaboration with state and municipal administrations in Brazil, report 3772 beds feasible to be evacuated by 64% of the analyzed health units, of which more than 82% are moderate complexity evacuations. The proposed assessment and methodology can provide a direction for governments and policymakers to develop strategies based on a robust quantitative production capacity measure.

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

“…In turn, Moss et al [80] simulated clinical presentations and patient flows through the Australian health care system, including expansion of available acute care capacity and alternative clinical assessment pathways. Other interesting related studies are illustrated in Nepomuceno et al [81], Mehrotra et al [77], AbdelAziz et al [40], Aggarwal et al [41], and Araz et al [45].…”

“…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…”

Multicriteria Ranking for Police Efficient and Effective Analysis

“…Lo Storto [12], investigating the relationship between efficiency and effectiveness of public expenditure in 108 major Italian municipalities, suggests shreds of evidence for this trade-off involving indicators of public service quality (expenditure effectiveness) and DEA measures for cost-efficiencies. Nepomuceno et al [13] investigating 88 public and private health service units in Pernambuco, Brazil, also offer some support in addition to this discussion. According to the authors, most hospitalization-efficient units are crowded public hospitals working at full capacity most of the year, which can only meet all the demand for hospitalizations (the output in the analysis) by compromising the service's quality.…”

Mais De 600.000 Óbitos: Avaliação Da Eficiência Do Sistema De Saúde Pública Dos Municípios Brasileiros No Tratamento À Covid-19

“…No contexto específico da pandemia de COVID-19, foram realizados estudos com o objetivo de avaliar e comparar a eficiência relativa no combate à doença entre diferentes países (BREITENBACH et al, 2021;BREITENBACH et al, 2020;GHASEMI et al, 2020;MOURAD et al, 2021;SHIROUYEHZAD et al, 2020), entre hospitais de um mesmo país (KAMEL;MOUSA, 2021;NEPOMUCENO et al, 2020), entre estados de um mesmo país (MARIANO et al, 2020;MD HAMZAH et al, 2021), entre microrregiões de um mesmo país (FERRAZ et al, 2021) e entre municípios de um mesmo país (AROEIRA et al, 2020).…”