Safety-Stock: Predicting the demand for supplies in Brazilian hospitals during the COVID-19 pandemic

Gonzatto, Oilson Alberto; Nascimento, Dejair Caitano do; Russo, Cibele M.; Henriques, Marcos Jardel; Tomazella, Caio Paziani; Santos, Maristela Oliveira dos; Neves, Denis; Assad, Diego; Guerra, Rafaela; Bertazo, Evelyn Keise; Cuminato, José Alberto; Louzada, Francisco

doi:10.1016/j.knosys.2022.108753

Cited by 5 publications

(4 citation statements)

References 11 publications

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“…These 3 countries were selected for this study because they have high cumulative COVID-19 death tolls [ 42 ], and they are among the top 4 nations in terms of Twitter users [ 43 ]; in addition, publicly accessible validation data on ICU bed demand are available for each country. Three main restrictions in terms of how many patients can be treated at a hospital during the pandemic are available PPE, available ICU beds, and available health care professionals per shift [ 44 ]. The number of available ICU beds was selected as the validation parameter for our model.…”

Section: Methodsmentioning

confidence: 99%

A Novel Approach for the Early Detection of Medical Resource Demand Surges During Health Care Emergencies: Infodemiology Study of Tweets

Kaur,

Cargill,

Hui

et al. 2024

JMIR Form Res

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Background The COVID-19 pandemic has highlighted gaps in the current handling of medical resource demand surges and the need for prioritizing scarce medical resources to mitigate the risk of health care facilities becoming overwhelmed. Objective During a health care emergency, such as the COVID-19 pandemic, the public often uses social media to express negative sentiment (eg, urgency, fear, and frustration) as a real-time response to the evolving crisis. The sentiment expressed in COVID-19 posts may provide valuable real-time information about the relative severity of medical resource demand in different regions of a country. In this study, Twitter (subsequently rebranded as X) sentiment analysis was used to investigate whether an increase in negative sentiment COVID-19 tweets corresponded to a greater demand for hospital intensive care unit (ICU) beds in specific regions of the United States, Brazil, and India. Methods Tweets were collected from a publicly available data set containing COVID-19 tweets with sentiment labels and geolocation information posted between February 1, 2020, and March 31, 2021. Regional medical resource shortage data were gathered from publicly available data sets reporting a time series of ICU bed demand across each country. Negative sentiment tweets were analyzed using the Granger causality test and convergent cross-mapping (CCM) analysis to assess the utility of the time series of negative sentiment tweets in forecasting ICU bed shortages. Results For the United States (30,742,934 negative sentiment tweets), the results of the Granger causality test (for whether negative sentiment COVID-19 tweets forecast ICU bed shortage, assuming a stochastic system) were significant (P<.05) for 14 (28%) of the 50 states that passed the augmented Dickey-Fuller test at lag 2, and the results of the CCM analysis (for whether negative sentiment COVID-19 tweets forecast ICU bed shortage, assuming a dynamic system) were significant (P<.05) for 46 (92%) of the 50 states. For Brazil (3,004,039 negative sentiment tweets), the results of the Granger causality test were significant (P<.05) for 6 (22%) of the 27 federative units, and the results of the CCM analysis were significant (P<.05) for 26 (96%) of the 27 federative units. For India (4,199,151 negative sentiment tweets), the results of the Granger causality test were significant (P<.05) for 6 (23%) of the 26 included regions (25 states and the national capital region of Delhi), and the results of the CCM analysis were significant (P<.05) for 26 (100%) of the 26 included regions. Conclusions This study provides a novel approach for identifying the regions of high hospital bed demand during a health care emergency scenario by analyzing Twitter sentiment data. Leveraging analyses that take advantage of natural language processing–driven tweet extraction systems has the potential to be an effective method for the early detection of medical resource demand surges.

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Section: Methodsmentioning

confidence: 99%

A Novel Approach for the Early Detection of Medical Resource Demand Surges During Health Care Emergencies: Infodemiology Study of Tweets

Kaur,

Cargill,

Hui

et al. 2024

JMIR Form Res

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“…Another focus of the studies analyzed corresponds to macro logistical processes: 8% correspond to procurement, where studies on PPE forecasting (Best and Williams, 2021;Gonzatto et al, 2022), supplier selection (Pamucar et al, 2022), and analysis on public procurement during the pandemic (Harland et al, 2021). About 3% of the studies are related to storage, location of inventory (Rastegar et al, 2021), and PPE inventory tracking and control (Means et al, 2020), 12% are related to distribution, formulating scheduling models to optimize transportation of PPE, vaccines, and wastes (Ash et al, 2022;Bala et al, 2021;Göçmen, 2022;Goentzel et al, 2022;Gonzatto et al, 2022;Kargar et al, 2020;Tirkolaee et al, 2021); use of drones to deliver drugs (Banik et al, 2022;Koshta et al, 2021) and a study (2%) related to internal production logistics focusing on the implementation of process modularity at HSCM (Saïah et al, 2022).…”

Section: Approachesmentioning

confidence: 99%

“…The internal HSC serves Doctors, nurses, and clinical support staff. The most common methods for distributing supplies range from requisition-based systems that rely on clinical staff participation to methods in which the hospital's central stores manage inventory and replenishment (Gonzatto et al, 2022;Leite et al, 2021). Par level systems, automated cabinets, and Kanban replenishment methods are popular storage and distribution methods.…”

Section: Chain Classificationmentioning

confidence: 99%

Hospital Supply Chain Management: Cross-Disciplinarity Opportunities in the Post-Pandemic Era and Research Agenda

Orjuela

Rincon-Guevara

Jaimes

2023

OSCM: An Int. Journal

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The COVID-19 pandemic has impacted various aspects of society, and research has been published across different sectors beyond healthcare. This article presents the results of a literature review in Hospital Supply Chain management (HSCM) to identify current and future research areas. The literature review identified four main research themes associated with the scope of HSCM, the flow type, and the approaches employed. The results of this study suggest that COVID-19 is a catalyst for short-and long-term changes that require the attention of researchers, strategies to incorporate 4.0 technologies, and integration of different stakeholders to improve the resilience and sustainability of the Supply Chain (SC) in future pandemic scenarios. This paper provides relevant information to the implementation for future research regarding the management of the HSCM for the post-pandemic period.

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“…With the global COVID-19 pandemic, public health emergencies around the world occur frequently, and the demand for emergency supplies has increased rapidly, particularly for medical protective equipment [ 25 ]. For example, Shanghai, Wuhan, and Chengdu, which are three large, modern cities in China, exposed the lack of emergency supply management capacity due to the massive spread of the COVID-19 epidemic [ 26 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Extended Multicriteria Group Decision Making with a Novel Aggregation Operator for Emergency Material Supplier Selection

Liu

Zhu

Yang

et al. 2023

Entropy

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How to ensure the normal production of industries in an uncertain emergency environment has aroused a lot of concern in society. Selecting the best emergency material suppliers using the multicriteria group decision making (MCGDM) method will ensure the normal production of industries in this environment. However, there are few studies in emergency environments that consider the impact of the decision order of decision makers (DMs) on the decision results. Therefore, in order to fill the research gap, we propose an extended MCGDM method, whose main steps include the following: Firstly, the DMs give their assessment of all alternatives. Secondly, we take the AHP method and entropy weight method to weight the criteria and the DMs. Thirdly, we take the intuitionistic fuzzy hybrid priority weight average (IFHPWA) operator we proposed to aggregate evaluation information and take the TOPSIS method to rank all the alternatives. Finally, the proposed method is applied in a case to prove its practicability and effectiveness. The proposed method considers the influence of the decision order of the DMs on the decision results, which improves the accuracy and efficiency of decision-making results.

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Safety-Stock: Predicting the demand for supplies in Brazilian hospitals during the COVID-19 pandemic

Cited by 5 publications

References 11 publications

A Novel Approach for the Early Detection of Medical Resource Demand Surges During Health Care Emergencies: Infodemiology Study of Tweets

A Novel Approach for the Early Detection of Medical Resource Demand Surges During Health Care Emergencies: Infodemiology Study of Tweets

Hospital Supply Chain Management: Cross-Disciplinarity Opportunities in the Post-Pandemic Era and Research Agenda

Extended Multicriteria Group Decision Making with a Novel Aggregation Operator for Emergency Material Supplier Selection

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