Multi-Criteria Decision Analysis to prioritize hospital admission of patients affected by COVID-19 in low-resource settings with hospital-bed shortage

A Simple Risk Formula for the Prediction of COVID-19 Hospital Mortality

SARS-CoV-2 respiratory infection is associated with significant morbidity and mortality in hospitalized patients. We aimed to assess the risk factors for hospital mortality in non-vaccinated patients during the 2021 spring wave in the Czech Republic. A total of 991 patients hospitalized between January 2021 and March 2021 with a PCR-confirmed SARS-CoV-2 acute respiratory infection in two university hospitals and five rural hospitals were included in this analysis. After excluding patients with unknown outcomes, 790 patients entered the final analyses. Out of 790 patients included in the analysis, 282/790 (35.7%) patients died in the hospital; 162/790 (20.5) were male and 120/790 (15.2%) were female. There were 141/790 (18%) patients with mild, 461/790 (58.3%) with moderate, and 187/790 (23.7%) with severe courses of the disease based mainly on the oxygenation status. The best-performing multivariate regression model contains only two predictors-age and the patient's state; both predictors were rendered significant (p < 0.0001). Both age and disease state are very significant predictors of hospital mortality. An increase in age by 10 years raises the risk of hospital mortality by a factor of 2.5, and a unit increase in the oxygenation status raises the risk of hospital mortality by a factor of 20.

Artificial intelligence in emergency medicine. A systematic literature review

Motivation and objective: Emergency medicine is becoming a popular application area for artificial intelligence methods but remains less investigated than other healthcare branches. The need for time-sensitive decision-making on the basis of high data volumes makes the use of quantitative technologies inevitable. However, the specifics of healthcare regulations impose strict requirements for such applications. Published contributions cover separate parts of emergency medicine and use disparate data and algorithms. This study aims to systematize the relevant contributions, investigate the main obstacles to artificial intelligence applications in emergency medicine, and propose directions for further studies.

METHODS

The contributions selection process was conducted with systematic electronic databases querying and filtering with respect to established exclusion criteria. Among the 380 papers gathered from IEEE Xplore, ACM Digital Library, Springer Library, ScienceDirect, and Nature databases 116 were considered to be a part of the survey. The main features of the selected papers are the focus on emergency medicine and the use of machine learning or deep learning algorithms.

FINDINGS AND DISCUSSION

The selected papers were classified into two branches: diagnostics-specific and triage-specific. The former ones are focused on either diagnosis prediction or decision support. The latter covers such applications as mortality, outcome, admission prediction, condition severity estimation, and urgent care prediction. The observed contributions are highly specialized within a single disease or medical operation and often use privately collected retrospective data, making them incomparable. These and other issues can be addressed by creating an end-to-end solution based on human-machine interaction.

CONCLUSION

Artificial intelligence applications are finding their place in emergency medicine, while most of the corresponding studies remain isolated and lack higher generalization and more sophisticated methodology, which can be a matter of forthcoming improvements.

Use of a rapid triage assessment tool to discriminate the need for hospitalisation in patients with severe COVID-19 infection presenting to an outpatient clinic: a single-centre, prospective cohort study

OBJECTIVES

The WHO designated individuals with low oxygen saturation, SpO2<94%, as severe SARS-CoV2 infection (COVID-19) and recommendations to seek care in a hospital setting were advised. A rapid, office-based method to select patients with severe COVID-19 who need intensive care was necessary during the peak of the pandemic.

DESIGN, SETTING AND PARTICIPANTS

This is a prospective cohort study of patients with confirmed severe COVID-19 between September 2020 and April 2021.

OUTCOME MEASURES AND ANALYSIS

Oxygen saturation was obtained at rest (SpO2r), following exertion from a 20 m walk test (SpO2e), and the difference was calculated (SpO2Δ). Radiographs and laboratory values were obtained and recorded. Logistic regression models were used to determine variables associated with hospitalisation. A lung injury score was used to quantify pulmonary involvement.

RESULTS

Out of 103 patients enrolled with severe COVID-19 infection, 19 (18.4%) were admitted to the hospital (no deaths). Patients managed as outpatients had a standard treatment protocol. The SpO2Δ and SpO2e were associated with hospitalisation (p<0.005) while SpO2r was no different between non-hospitalised and hospitalised patients (90.7%±2.7% vs 90.8%±2.3%, p=0.87). By contrast, exertional SpO2e was significantly different between non-hospitalised and hospitalised (87.3%±2.6% vs 84.4%±3.4%, p=0.0005). The mean lung injury score was 11.0±3.5 (18-point scale) and did not discriminate against those who would need hospitalisation. Lower lung fields were significantly more involved than the upper (p<0.0001). All patients had elevated biomarkers of inflammation, C reactive protein (CRP) median 82.5 IQR (43-128.6) mg/L and evidence of elevated liver enzymes. A logistic regression model was constructed including SpO2Δ, CRP and alanine aminotransferase to predict hospitalisation. Only SpO2Δ was significant, p=0.012, 95% CI (1.128 to 2.704) and correctly classified 85.71% of patients who could remain at home or would need to receive treatment in the hospital.

CONCLUSION

An office-based, 20 m walk test can help diverge patients with severe COVID-19 who need escalated care. Further, an aggressive standardised treatment protocol can be used to successfully manage patients outside of hospitals despite having severe COVID-19.

Evaluation of an adjusted MEWS (Modified Early Warning Score) for COVID-19 patients to identify risk of ICU admission or death in the Kingdom of Bahrain

BACKGROUND

While most COVID-19 cases have uncomplicated infection, a small proportion has the potential to develop life-threatening disease, as such development of a prediction tool using patients baseline characteristics at the time of diagnosis should aid in early identification of high-risk groups and devise pertinent management. Hence, we set up this retrospective study to determine preadmission triaging tool to predict the development of severe COVID-19 in the Kingdom of Bahrain MATERIALS AND METHODS: A retrospective study was conducted from 1 September 2020 to 30 November 2020 with enrolment of all SARS-CoV-2 PCR-confirmed persons aged ≥ 14 years who attended Al-Shamil Field Hospital (SFH) in the Kingdom of Bahrain for triaging and assessment with recording of the following parameters: systolic blood pressure, heart rate, respiratory rate, temperature, the alert, verbal, pain, unresponsive neurological score, age, oxygen saturation, comorbidities, Body Mass Index (BMI), duration of symptoms and living with immunocompromised populations to develop our local adjusted MEWS as predictor for ICU admission & for consideration of suitable isolation at home. Follow up data of all patients was obtained from the electronic medical records system including CXR findings, treatments/medications received, need of oxygen supplements /intubation, needs of ICU care, and the outcome (death /discharged alive) IBM SPSS statistic version 21 program was used for data analysis.

RESULTS

Our study showed that using the locally developed adjusted MEWS score, there was an significant association between high value of this adjusted MEWS score and abnormal radiographic finding (49.7 % Vs. 17 % for patients with high score Vs. those with low score respectively). Out of the 181 patients with high scores on adjusted MEWS; 38.7 % required oxygen via nasal cannula, 14.4 % required face mask and 8.3 % non-rebreather mask; this proportion was significantly higher than their counterpart patients who score low on adjusted MEWS (20.9 %, 7.7 %, 4.8 %respectively) with statistically significance difference between the two groups (p value of 0.00, 0.00,.004 respectively) Requirement of ICU admission was significantly higher among patients with high score in comparison to those with low score (14.4 % vs. 3 %) with significant p value (0.00) But higher score value was not associated significantly with increase mortality rate among COVID patients.

CONCLUSION

Development of our new Adjusted MEWS score system by adding the additional elements of age, oxygen saturation, comorbidities, Body Mass Index (BMI) and duration of symptoms found to be very useful predictor tool for preadmission triaging of COVID patients based on their risk assessment to help clinician to decide on the appropriate placement to different level of isolation facilities.

Closing the Gap Between Decision Analysis and Policy Analysts Before the Next Pandemic

Decision analysis (DA) is an explicitly prescriptive discipline that separates beliefs about uncertainties from value preferences in modeling to support decision making. Researchers have been advancing DA tools for the last 60 years to support decision makers handling complex decisions requiring subjective judgments. Recently, some DA researchers and practitioners wondered whether the difficult decisions made during the COVID-19 pandemic regarding testing, masking, closing and reopening businesses, allocating ventilators, and prioritizing vaccines would have been improved with more DA involvement. With its focus on quantifying uncertainties, value trade-offs, and risk attitudes, DA should have been a valuable tool for decision makers during the pandemic. To influence decisions, DA applications require interactions with policymakers and experts to construct formal representations of the decision frame, elicit uncertainties, and assess risk tolerances and trade-offs among competing objectives. Unfortunately, such involvement of decision analysts in the process of decision making and policy setting did not occur during much of the COVID-19 pandemic. This lack of participation may have been partly because many decision makers were unaware of when DA could be valuable in helping with the challenges of the COVID-19 pandemic. In addition, decision analysts were perhaps not sufficiently adept at inserting themselves into the policy process at critical junctures when their expertise could have been helpful.

Funding: This research was partially supported by the U.S. Department of Homeland Security through the Center for Accelerating Operational Efficiency at Arizona State University.

A study about management of drugs for leprosy patients under medical monitoring: A solution based on AHP-Electre decision-making methods

Leprosy, also known as Hansen's, is one of the listed neglected tropical diseases as a major health problem global. Treatment is one of the main alternatives, however, the scarcity of medication and its poor distribution are important factors that have driven the spread of the disease, leading to irreversible and multi-resistant complications. This paper uses a distribution methodology to optimize medication administration, taking into account the most relevant attributes for the epidemiological profile of patients and the deficit in treatment via Polychemotherapy. Multi-criteria Decision Methods were applied based on AHP-Electre model in a database with information from patients in the state of Para between 2015 and 2020. The results pointed out that 84% of individuals did not receive any treatment and, among these, the method obtained a gain in the distribution of 68% in patients with positive diagnosis for leprosy.

Systematic review of MCDM approach applied to the medical case studies of COVID-19: trends, bibliographic analysis, challenges, motivations, recommendations, and future directions

When COVID-19 spread in China in December 2019, thousands of studies have focused on this pandemic. Each presents a unique perspective that reflects the pandemic's main scientific disciplines. For example, social scientists are concerned with reducing the psychological impact on the human mental state especially during lockdown periods. Computer scientists focus on establishing fast and accurate computerized tools to assist in diagnosing, preventing, and recovering from the disease. Medical scientists and doctors, or the frontliners, are the main heroes who received, treated, and worked with the millions of cases at the expense of their own health. Some of them have continued to work even at the expense of their lives. All these studies enforce the multidisciplinary work where scientists from different academic disciplines (social, environmental, technological, etc.) join forces to produce research for beneficial outcomes during the crisis. One of the many branches is computer science along with its various technologies, including artificial intelligence, Internet of Things, big data, decision support systems (DSS), and many more. Among the most notable DSS utilization is those related to multicriterion decision making (MCDM), which is applied in various applications and across many contexts, including business, social, technological and medical. Owing to its importance in developing proper decision regimens and prevention strategies with precise judgment, it is deemed a noteworthy topic of extensive exploration, especially in the context of COVID-19-related medical applications. The present study is a comprehensive review of COVID-19-related medical case studies with MCDM using a systematic review protocol. PRISMA methodology is utilized to obtain a final set of (n = 35) articles from four major scientific databases (ScienceDirect, IEEE Xplore, Scopus, and Web of Science). The final set of articles is categorized into taxonomy comprising five groups: (1) diagnosis (n = 6), (2) safety (n = 11), (3) hospital (n = 8), (4) treatment (n = 4), and (5) review (n = 3). A bibliographic analysis is also presented on the basis of annual scientific production, country scientific production, co-occurrence, and co-authorship. A comprehensive discussion is also presented to discuss the main challenges, motivations, and recommendations in using MCDM research in COVID-19-related medial case studies. Lastly, we identify critical research gaps with their corresponding solutions and detailed methodologies to serve as a guide for future directions. In conclusion, MCDM can be utilized in the medical field effectively to optimize the resources and make the best choices particularly during pandemics and natural disasters.

Nanomaterials to combat SARS-CoV-2: Strategies to prevent, diagnose and treat COVID-19

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and the associated coronavirus disease 2019 (COVID-19), which severely affect the respiratory system and several organs and tissues, and may lead to death, have shown how science can respond when challenged by a global emergency, offering as a response a myriad of rapid technological developments. Development of vaccines at lightning speed is one of them. SARS-CoV-2 outbreaks have stressed healthcare systems, questioning patients care by using standard non-adapted therapies and diagnostic tools. In this scenario, nanotechnology has offered new tools, techniques and opportunities for prevention, for rapid, accurate and sensitive diagnosis and treatment of COVID-19. In this review, we focus on the nanotechnological applications and nano-based materials (i.e., personal protective equipment) to combat SARS-CoV-2 transmission, infection, organ damage and for the development of new tools for virosurveillance, diagnose and immune protection by mRNA and other nano-based vaccines. All the nano-based developed tools have allowed a historical, unprecedented, real time epidemiological surveillance and diagnosis of SARS-CoV-2 infection, at community and international levels. The nano-based technology has help to predict and detect how this Sarbecovirus is mutating and the severity of the associated COVID-19 disease, thereby assisting the administration and public health services to make decisions and measures for preparedness against the emerging variants of SARS-CoV-2 and severe or lethal COVID-19.

Health Decision Analysis: Evolution, Trends, and Emerging Topics

Health remains one of the most challenging realms for decision makers and policy making while critical for the well-being of humans, the stability of societies, and the development of economies. Decision making in this field ranges from medical doctors identifying the best treatments for patients, healthcare companies selecting the most promising drugs for development, healthcare providers deciding for adequate levels of resourcing, health regulators deciding whether to approve a new medicine or health technology, to regional and national health departments identifying how to increase the health security of regions and countries. In this positioning paper, and introduction to this Special Issue, we present the history, evolution, and trends of health decision analysis and suggest that these developments and news trends can be conceptualized as an emerging field of applied research for our discipline: Health Decision Analysis.

Which countries need COVID-19 vaccines the most? Development of a prioritisation tool

Background

The COVID-19 pandemic and associated non-pharmaceutical interventions (NPIs) have affected all countries. With a scarcity of COVID-19 vaccines there has been a need to prioritize populations, but assessing relative needs has been challenging. The COVAX Facility allocates vaccines to cover 20% of each national population, followed by a needs assessment that considers five quantitative metrics alongside a qualitative assessment. The objective of this study was to identify the most important factors for assessing countries’ needs for vaccines, and to weight each, generating a scoring tool for prioritising countries.

Methods

The study was conducted between March and November 2021. The first stage involved an online Delphi survey with a purposive and snowball sample of public health experts, to reach consensus on country-level factors for assessing relative needs for COVID-19 vaccines. The second stage involved a discrete choice experiment (DCE) to determine weights for the most important factors.

Results

Responses were received from 28 experts working across 13 different countries and globally. The most common job titles reported were director and professor, with most based in national public health institutes (n = 9) and universities (n = 8). The Delphi survey found 37 distinct factors related to needs. Nine of the most important factors were included in the DCE. Among these, the most important factor was the ‘proportion of overall population not fully vaccinated’ (with a mean weight of 19.5), followed by ‘proportion of high-risk population not fully vaccinated’ (16.1), ‘health system capacity’ (14.2), ‘capacity to purchase vaccines’ (11.9) and the ‘proportion of the population clinically vulnerable’ (11.3).

Conclusions

Several factors exist, extending beyond those currently used, which may lead to some countries having a greater need for vaccines compared to others. By assessing relative needs, this scoring tool can build on existing methods to further the role of equity in global COVID-19 vaccine allocation.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-022-13948-6.

The lung ultrasound "Rule of 7" in the prognosis of COVID-19 patients: Results from a prospective multicentric study

Purpose

There is growing evidence regarding the imaging findings of coronavirus disease 2019 (COVID-19) in lung ultrasound (LUS), however the use of a combined prognostic and triage tool has yet to be explored.To determine the impact of the LUS in the prediction of the mortality of patients with highly suspected or confirmed COVID-19.The secondary outcome was to calculate a score with LUS findings with other variables to predict hospital admission and emergency department (ED) discharge.

Material and methods

Prospective study performed in the ED of three academic hospitals. Patients with highly suspected or confirmed COVID-19 underwent a LUS examination and laboratory tests.

Results

A total of 228 patients were enrolled between March and September 2020. The mean age was 61.9 years (Standard Deviation - SD 21.1). The most common findings in LUS was a right posteroinferior isolated irregular pleural line (53.9%, 123 patients). A logistic regression model was calculated, including age over 70 years, C-reactive protein (CRP) over 70 mg/L and a lung score over 7 to predict mortality, hospital admission and discharge from the ED. We obtained a predictive model with a sensitivity of 56.8% and a specificity of 87.6%, with an AUC of 0.813 [p < 0.001].

Conclusions

The combination of LUS, clinical and laboratory findings in this easy to apply "rule of 7" showed excellent performance to predict hospital admission and mortality.

Knowledge Mapping of Multicriteria Decision Analysis in Healthcare: A Bibliometric Analysis

Objective

Multicriteria decision analysis (MCDA) is a useful tool in complex decision-making situations, and has been used in medical fields to evaluate treatment options and drug selection. This study aims to provide valuable insights into MCDA in healthcare through examining the research focus of existing studies, major fields, major applications, most productive authors and countries, and most common journals in the domain.

Methods

A bibliometric analysis was conducted on the publication related to MCDA in healthcare from the Web of Science Core Collection (WoSCC) database on 14 July 2021. Three bibliometric software (VOSviewer, R-bibliometrix, and CiteSpace) were used to conduct the analysis including years, countries, institutes, authors, journals, co-citation references, and keywords.

Results

A total of 410 publications were identified with an average yearly growth rate of 32% (1999–2021), from 196 academic journals with 23,637 co-citation references by 871 institutions from 70 countries/regions. The United States was the most productive country (n = 80). Universiti Pendidikan Sultan Idris (n = 16), Université de Montréal (n = 13), and Syreon Research Institute (n = 12) were the top productive institutions. A A Zaidan, Mireille Goetghebeur and Zoltan Kalo were the biggest nodes in every cluster of authors' networks. The top journals in terms of the number of articles (n = 17) and citations (n = 1,673) were Value in Health and Journal of Medical Systems, respectively. The extant literature has focused on four aspects, including the analytic hierarchy process (AHP), decision-making, health technology assessment, and healthcare waste management. COVID-19 and fuzzy TOPSIS received careful attention from MCDA applications recently. MCDA in big data, telemedicine, TOPSIS, and fuzzy AHP is well-developed and an important theme, which may be the trend in future research.

Conclusion

This study uncovers a holistic picture of the performance of MCDA-related literature published in healthcare. MCDA has a broad application on different topics and would be helpful for practitioners, researchers, and decision-makers working in healthcare to advance the wheel of medical complex decision-making. It can be argued that the door is still open for improving the role of MCDA in healthcare, whether in its methodology (e.g., fuzzy TOPSIS) or application (e.g., telemedicine).

Decision support analysis for risk identification and control of patients affected by COVID-19 based on Bayesian Networks

In the context of the outbreak of coronavirus disease (COVID-19), this paper proposes an innovative and systematic decision support model based on Bayesian networks (BNs) to identify and control the risk of COVID-19 patients spreading the virus, which requires the following three steps. First, by consulting the related literature and combining this with expert knowledge, we identify and classify the characteristics (risk factors) of COVID-19 and obtain a conceptual framework for COVID-19 Risk Assessment Bayesian Networks (CRABNs). Second, data on COVID-19 patients with expert scoring results on patient risk levels were collected from hospitals in Hubei Province of China and are used as the training set, and the structure and parameters of the CRABNs model are obtained through machine learning. Finally, we propose two indicators, namely, Model Bias and Model Accuracy, and use the remaining data to verify the feasibility and effectiveness of the CRABNs model to ensure that there are no significant differences between the predicted results of the model and the actual results provided by experts who have relevant experience in treating COVID-19. At the same time, we compared the CRABNs model with the support vector machine (SVM), random forest (RF), and k-nearest neighbour (KNN) models through four indicators: accuracy, sensitivity, specificity, and F-score. The results suggest the reliability of the model and show that it has promising application potential. The proposed model can be used globally by doctors in hospitals as a decision support tool to improve the accuracy of assessing the severity of COVID-19 symptoms in patients. Furthermore, with the further improvement of the model in the future, it can be used for risk assessments in the field of epidemics.

Rise of multiattribute decision-making in combating COVID-19: A systematic review of the state-of-the-art literature

Considering the coronavirus disease 2019 (COVID-19) pandemic, the government and health sectors are incapable of making fast and reliable decisions, particularly given the various effects of decisions on different contexts or countries across multiple sectors. Therefore, leaders often seek decision support approaches to assist them in such scenarios. The most common decision support approach used in this regard is multiattribute decision-making (MADM). MADM can assist in enforcing the most ideal decision in the best way possible when fed with the appropriate evaluation criteria and aspects. MADM also has been of great aid to practitioners during the COVID-19 pandemic. Moreover, MADM shows resilience in mitigating consequences in health sectors and other fields. Therefore, this study aims to analyse the rise of MADM techniques in combating COVID-19 by presenting a systematic literature review of the state-of-the-art COVID-19 applications. Articles on related topics were searched in four major databases, namely, Web of Science, IEEE Xplore, ScienceDirect, and Scopus, from the beginning of the pandemic in 2019 to April 2021. Articles were selected on the basis of the inclusion and exclusion criteria for the identified systematic review protocol, and a total of 51 articles were obtained after screening and filtering. All these articles were formed into a coherent taxonomy to describe the corresponding current standpoints in the literature. This taxonomy was drawn on the basis of four major categories, namely, medical (n = 30), social (n = 4), economic (n = 13) and technological (n = 4). Deep analysis for each category was performed in terms of several aspects, including issues and challenges encountered, contributions, data set, evaluation criteria, MADM techniques, evaluation and validation and bibliography analysis. This study emphasised the current standpoint and opportunities for MADM in the midst of the COVID-19 pandemic and promoted additional efforts towards understanding and providing new potential future directions to fulfil the needs of this study field.

Evaluating Interventions in Response to COVID-19 Outbreak by Multiple-Criteria Decision-Making Models

The COVID-19 outbreak has currently led to serious social and economic consequences. In poor and developing countries, there are more challenges and barriers to tackling the pandemic. The study’s aim is to propose a hybrid approach to multiple-criteria decision-making (MCDM) models for determining the most efficient intervention strategies. The methodology is a combination between the Best-Worst Model (BWM) and Group Best-Worst Model (GBWM) to estimate the efficiency score of intervention. Based on the background of knowledge, five groups of stakeholders including Academicians, Entrepreneurs, Commons Residents, Social Workers, Health Workers are considered decision-makers (DMs). A set of nine potential strategies was evaluated and prioritized by all DMs. The findings have shown that different groups of stakeholders prioritized differently the importance of criteria due to their interests. In the context of Vietnam, however, the Availability of Health Systems is prioritized as the most important intervention. The results and proposed model of this paper contributed to MCDM literature as well as a good reference to apply practically in many different countries.

Model to support intervention prioritization for the control of Aedes aegypti in Brazil: a multi-criteria approach

Background

Despite continuous strategic investments to mitigate the complexity involving arboviruses control, it is still necessary to further research methods and techniques to achieve in depth knowledge and shorter response times in the application of intervention activities. Consequently, the current work focused its efforts on the development of a multicriteria decision support model for the prioritization of prompt response activities for Aedes aegypti control, based on a case study in the city of Natal/RN.

Method

The research was carried out in three stages: a) preliminary; b) modelling and choice; and c) finalization; the second stage was made possible by the Flexible and Interactive Tradeoff (FITradeoff) method for ranking problematic. Furthermore, the research encompassed ten actors who were involved in the model construction, eight internal and two external to the Natal Zoonoses Control Center (ZCC-Natal) as well as the observation of four operating scenarios for arboviruses control, based on transmission levels; and, evaluation of eleven alternatives from six different criteria perspectives.

Results

Rankings of the interventions evaluated in each of the four control operation scenarios present in the city of Natal/RN were obtained, considering technical criteria guided by the Pan American Health Organization (PAHO).

Conclusions

As a result, it was developed a structured decision-making model that could help decision makers to minimize the effects and risks associated with the proliferation of the vector.

Coping strategies that motivated frontline nurses while caring for the covid-19 patients during the pandemic: A Scoping Review

Background

The world faced a great health crisis during the COVID‐19 pandemic. Consequently, the health care providers struggled and faced tremendous difficulties in treating high‐load critical patients. This was particularly true in low‐ and middle‐income countries where the work and patient loads are always higher and nurses at the forefront must deal with emergencies while being at high risk of exposure. However, little is known about the survival strategies of frontline nurses as dealt with the pandemic.

Objectives

This study catalogued the coping strategies of frontline nurses to deal with caring for the COVID‐19 patients during the pandemic.

Methods

The Arksey O'Malley framework was followed to conduct a scoping review. A systematic literature search was conducted using three databases: Google Scholar, Scopus and PubMed; and out of the 192 studies, 12 met the inclusion criteria set for this review study.

Results

A total of 44 strategies were identified that motivated nurses to deal with the COVID‐19 situation, and these strategies could be categorized into five main themes: nurses' self‐strategies, nurses' strategies at the ethical level, employers' strategies, nursing leaders' strategies and supplementary strategies.

Conclusions

The findings of this study will provide guidance for health care workers, employers, policymakers, regulators and other stakeholders to adopt and promote different strategies in managing difficult emergency situations in future.

Implications for nursing management

This study emphasizes the importance of learning how to deal with adversity by health care workers and organizations in an emergency.

Human Centered Decision-Making for COVID-19 Testing Center Location Selection: Tamil Nadu—A Case Study

This paper proposes a blend of three techniques to select COVID-19 testing centers. The objective of the paper is to identify a suitable location to establish new COVID-19 testing centers. Establishment of the testing center in the needy locations will be beneficial to both public and government officials. Selection of the wrong location may lead to lose both health and wealth. In this paper, location selection is modelled as a decision-making problem. The paper uses fuzzy analytic hierarchy process (AHP) technique to generate the criteria weights, monkey search algorithm to optimize the weights, and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method to rank the different locations. To illustrate the applicability of the proposed technique, a state named Tamil Nadu, located in India, is taken for a case study. The proposed structured algorithmic steps were applied for the input data obtained from the government of India website, and the results were analyzed and validated using the government of India website. The ranks assigned by the proposed technique to different locations are in aligning with the number of patients and death rate.

Multi-criteria decision-making for coronavirus disease 2019 applications: a theoretical analysis review

The influence of the ongoing COVID-19 pandemic that is being felt in all spheres of our lives and has a remarkable effect on global health care delivery occurs amongst the ongoing global health crisis of patients and the required services. From the time of the first detection of infection amongst the public, researchers investigated various applications in the fight against the COVID-19 outbreak and outlined the crucial roles of different research areas in this unprecedented battle. In the context of existing studies in the literature surrounding COVID-19, related to medical treatment decisions, the dimensions of context addressed in previous multidisciplinary studies reveal the lack of appropriate decision mechanisms during the COVID-19 outbreak. Multiple criteria decision making (MCDM) has been applied widely in our daily lives in various ways with numerous successful stories to help analyse complex decisions and provide an accurate decision process. The rise of MCDM in combating COVID-19 from a theoretical perspective view needs further investigation to meet the important characteristic points that match integrating MCDM and COVID-19. To this end, a comprehensive review and an analysis of these multidisciplinary fields, carried out by different MCDM theories concerning COVID19 in complex case studies, are provided. Research directions on exploring the potentials of MCDM and enhancing its capabilities and power through two directions (i.e. development and evaluation) in COVID-19 are thoroughly discussed. In addition, Bibliometrics has been analysed, visualization and interpretation based on the evaluation and development category using R-tool involves; annual scientific production, country scientific production, Wordcloud, factor analysis in bibliographic, and country collaboration map. Furthermore, 8 characteristic points that go through the analysis based on new tables of information are highlighted and discussed to cover several important facts and percentages associated with standardising the evaluation criteria, MCDM theory in ranking alternatives and weighting criteria, operators used with the MCDM methods, normalisation types for the data used, MCDM theory contexts, selected experts ways, validation scheme for effective MCDM theory and the challenges of MCDM theory used in COVID-19 studies. Accordingly, a recommended MCDM theory solution is presented through three distinct phases as a future direction in COVID19 studies. Key phases of this methodology include the Fuzzy Delphi method for unifying criteria and establishing importance level, Fuzzy weighted Zero Inconsistency for weighting to mitigate the shortcomings of the previous weighting techniques and the MCDM approach by the name Fuzzy Decision by Opinion Score method for prioritising alternatives and providing a unique ranking solution. This study will provide MCDM researchers and the wider community an overview of the current status of MCDM evaluation and development methods and motivate researchers in harnessing MCDM potentials in tackling an accurate decision for different fields against COVID-19.

Multi-Criteria Decision Analysis to Prioritize People for COVID-19 Vaccination When Vaccines Are in Short Supply

COVID-19 pandemic underscored the need for a rapid tool supporting decision-makers in prioritizing patients in the immediate and overwhelming context of pandemics, where shortages in different healthcare resources are faced. We have proposed Multi-Criteria Decision Analysis (MCDA) to create a system of criteria and weights to prioritize uses of COVID-19 vaccines in groups of people at significantly higher risk of severe COVID-19 disease or death, when vaccines are in short supply, for use in Tunisia. The prioritization criteria and the levels within each criterion were identified based on available COVID-19 evidence with a focus on the criteria selected by Tunisian scientific committees. To determine the weights for the criteria and levels, reflecting their relative importance, a panel of frontline physicians treating COVID-19 were invited to participate in an online survey using 1,000 minds MCDA software (www.1000minds.com) which implements the PAPRIKA (Potentially All Pairwise RanKings of all possible Alternatives) method. Ten criteria and twenty-three levels have been selected for prioritizing the uses of COVID-19 vaccines in groups of people at significantly higher risk of severe disease or death. Among the invited physicians, sixty have completed the survey. The obtained scores were, in decreasing order of importance (mean weights in parentheses, summing to 100%). Obesity (16.2%), Age (12.7%), Chronic pulmonary diseases (10.8%), Chronic cardiovascular conditions (10.3%), Bone marrow or organ transplantation (10.1%), Immunodeficiency or Immunosuppression (9.6%), Diabetes (9%), Renal failure (8.4%), evolutive cancer (6.9%), and high blood pressure (6%). MCDA-based prioritization scoring system comprising explicit criteria and weights provides an adaptable and multicriteria approach that can assist policy-makers to prioritize uses of COVID-19 vaccines.

Fermatean fuzzy set extensions of SAW, ARAS, and VIKOR with applications in COVID-19 testing laboratory selection problem

The multiple attribute decision-making models are empowered with the support of fuzzy sets such as intuitionistic, q-rung orthopair, Pythagorean, and picture fuzzy sets, and also neutrosophic sets, etc. These concepts generate varying representation opportunities for the decision-maker's preferences and expertise. Pythagorean and Fermatean fuzzy sets are special cases of q-rung orthopair fuzzy set when q = 2 and q = 3, respectively. From a geometric perspective, the latter provides a broader representation domain than the former does. In this study, the emerging concept of Fermatean fuzzy set is studied in detail and three well-known multi-attribute evaluation methods, namely SAW, ARAS, and VIKOR are extended under Fermatean fuzzy environment. In this manner, the decision-makers will have more freedom in specifying their preferences, thoughts, and expertise, and the abovementioned decision approaches will be able to handle this new type of data. The applicability of the propositions is shown in determining the best Covid-19 testing laboratory which is an important topic of the ongoing global health crisis. To validate the proposed methods, a benchmark analysis covering the results of the existing Fermatean fuzzy set-based decision methods, namely TOPSIS, WPM, and Yager aggregation operators is presented.

Critical country-level determinants of death rate during Covid-19 pandemic

The COVID-19 pandemic has already led to over 94 million confirmed cases and over 2 million deaths globally (John Hopkins CSSE, 2021). Due to the magnitude of the socio-economic damage of COVID-19 all over the world, we analyzed the critical country-level determinants of the death rate during the COVID-19 pandemic. We have examined the effects of GDP (allocated to pandemics and health), education, gender, cultural factors, number of physicians (per 1000 of the population) on the death rate. A correlation between the death rate and socio-economic conditions has been observed. The finding shows that power distance, individualism, gender, and age affect the death rate more than other socio-economic factors we use. We have also performed the same analysis by using Lockdown levels as a moderator. Lockdown levels have a more significant moderating effect on cultural factors rather than the other socio-economic factors. However, due to the topic's sensitivity, we still need to pay attention to the socio-economic factors that may have lower levels of significant relationship with the death rate, since even 0.1 % of changes in coefficients of our other socio-economic variables could mean thousands of lives. The study results will help health organizations, administration, and policymakers take the necessary steps to combat and manage the pandemic.

Fuzzy case-based reasoning approach for finding COVID-19 patients priority in hospitals at source shortage period

In this research article, we introduced an algorithm to evaluate COVID-19 patients admission in hospitals at source shortage period. Many researchers have expressed their conclusions from different perspectives on various factors such as spatial changes, climate risks, preparedness, blood type, age and comorbidities that may be contributing to COVID-19 mortality rate. However, as the number of people coming to the hospital for COVID-19 treatment increases, the mortality rate is likely to increase due to the lack of medical facilities. In order to provide medical assistance in this situation, we need to consider not only the extent of the disease impact, but also other important factors. No method has yet been proposed to calculate the priority of patients taking into account all the factors. We have provided a solution to this in this research article. Based on eight key factors, we provide a way to determine priorities. In order to achieve the effectiveness and practicability of the proposed method, we studied individuals with different results on all factors. The sigmoid function helps to easily construct factors at different levels. In addition, the cobweb solution model allows us to see the potential of our proposed algorithm very clearly. Using the method we introduced, it is easier to sort high-risk individuals to low-risk individuals. This will make it easier to deal with problems that arise when the number of patients in hospitals continues to increase. It can reduce the mortality of COVID-19 patients. Medical professionals can be very helpful in making the best decisions.

The lung ultrasound "Rule of 7" in the prognosis of COVID-19 patients: Results from a prospective multicentric study

Purpose

There is growing evidence regarding the imaging findings of coronavirus disease 2019 (COVID-19) in lung ultrasound (LUS), however the use of a combined prognostic and triage tool has yet to be explored.

To determine the impact of the LUS in the prediction of the mortality of patients with highly suspected or confirmed COVID-19.The secondary outcome was to calculate a score with LUS findings with other variables to predict hospital admission and emergency department (ED) discharge.

Material and methods

Prospective study performed in the ED of three academic hospitals. Patients with highly suspected or confirmed COVID-19 underwent a LUS examination and laboratory tests.

Results

A total of 228 patients were enrolled between March and September 2020. The mean age was 61.9 years (Standard Deviation – SD 21.1). The most common findings in LUS was a right posteroinferior isolated irregular pleural line (53.9%, 123 patients). A logistic regression model was calculated, including age over 70 years, C-reactive protein (CRP) over 70 mg/L and a lung score over 7 to predict mortality, hospital admission and discharge from the ED. We obtained a predictive model with a sensitivity of 56.8% and a specificity of 87.6%, with an AUC of 0.813 [p < 0.001].

Conclusions

The combination of LUS, clinical and laboratory findings in this easy to apply “rule of 7” showed excellent performance to predict hospital admission and mortality.

Evaluation of criteria and COVID-19 patients for intensive care unit admission in the era of pandemic: A multi-criteria decision making approach

BACKGROUND AND OBJECTIVE

The COVID-19 pandemic results in an intense flow of patients to hospitals especially to the intensive care units (ICUs) to be treated. The ICUs will therefore be confronted with a massive influx of patients (e.g. Spain and Italy). However, if the number of patients is higher than the resources available in ICUs, rationing decisions such as determining and evaluating the criteria for ICU admission becomes essential. In this case, the decision of which patients will be admitted to the ICUs may put significant pressure on healthcare personnel. The goal of this paper is to determine the criteria to be used in the decision of admission of COVID-19 patients to the ICUs.

METHODS

A three-step methodology is applied. In the first step, the evaluation criteria are determined, and then the criteria are prioritized using a fuzzy analytical hierarchy process (AHP) in an uncertain and multiple-criteria environment choice. Finally, COVID-19 patients are ranked using the Multi-Objective Optimization Method by Ratio Analysis to find out which patient is more urgent.

RESULTS

According to experts' evaluation of ICU admission criteria, "increment of >2 in SOFA score" seems the most dominant factor among others. The proposed methodology is tested on 10 anonymous COVID-19 positive patients being treated in a public hospital and the ICU admission results are discussed.

CONCLUSIONS

Obtained priorities and ranking is in line with the hospitals' behavior that potentially depicts the usefulness and validity of the proposed approach.

Assessment of COVID-19 progression on day 5 from symptoms onset

BACKGROUND

A major limitation of current predictive prognostic models in patients with COVID-19 is the heterogeneity of population in terms of disease stage and duration. This study aims at identifying a panel of clinical and laboratory parameters that at day-5 of symptoms onset could predict disease progression in hospitalized patients with COVID-19.

METHODS

Prospective cohort study on hospitalized adult patients with COVID-19. Patient-level epidemiological, clinical, and laboratory data were collected at fixed time-points: day 5, 10, and 15 from symptoms onset. COVID-19 progression was defined as in-hospital death and/or transfer to ICU and/or respiratory failure (PaO2/FiO2 ratio < 200) within day-11 of symptoms onset. Multivariate regression was performed to identify predictors of COVID-19 progression. A model assessed at day-5 of symptoms onset including male sex, age > 65 years, dyspnoea, cardiovascular disease, and at least three abnormal laboratory parameters among CRP (> 80 U/L), ALT (> 40 U/L), NLR (> 4.5), LDH (> 250 U/L), and CK (> 80 U/L) was proposed. Discrimination power was assessed by computing area under the receiver operating characteristic (AUC) values.

RESULTS

A total of 235 patients with COVID-19 were prospectively included in a 3-month period. The majority of patients were male (148, 63%) and the mean age was 71 (SD 15.9). One hundred and ninety patients (81%) suffered from at least one underlying illness, most frequently cardiovascular disease (47%), neurological/psychiatric disorders (35%), and diabetes (21%). Among them 88 (37%) experienced COVID-19 progression. The proposed model showed an AUC of 0.73 (95% CI 0.66-0.81) for predicting disease progression by day-11.

CONCLUSION

An easy-to-use panel of laboratory/clinical parameters computed at day-5 of symptoms onset predicts, with fair discrimination ability, COVID-19 progression. Assessment of these features at day-5 of symptoms onset could facilitate clinicians' decision making. The model can also play a role as a tool to increase homogeneity of population in clinical trials on COVID-19 treatment in hospitalized patients.

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

The COVID-19 pandemic has strongly affected the dynamics of Emergency Departments (EDs) worldwide and has accentuated the need for tackling different operational inefficiencies that decrease the quality of care provided to infected patients. The EDs continue to struggle against this outbreak by implementing strategies maximizing their performance within an uncertain healthcare environment. The efforts, however, have remained insufficient in view of the growing number of admissions and increased severity of the coronavirus disease. Therefore, the primary aim of this paper is to review the literature on process improvement interventions focused on increasing the ED response to the current COVID-19 outbreak to delineate future research lines based on the gaps detected in the practical scenario. Therefore, we applied the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to perform a review containing the research papers published between December 2019 and April 2021 using ISI Web of Science, Scopus, PubMed, IEEE, Google Scholar, and Science Direct databases. The articles were further classified taking into account the research domain, primary aim, journal, and publication year. A total of 65 papers disseminated in 51 journals were concluded to satisfy the inclusion criteria. Our review found that most applications have been directed towards predicting the health outcomes in COVID-19 patients through machine learning and data analytics techniques. In the overarching pandemic, healthcare decision makers are strongly recommended to integrate artificial intelligence techniques with approaches from the operations research (OR) and quality management domains to upgrade the ED performance under social-economic restrictions.

A decision support system to solve the problem of health care priority-setting

Purpose

The purpose of this study is to provide a decision support tool to deal with the problem of seting priorites among patients competing for limited health care resources. Limited resources and unlimited demands prevent health-care services to be provided to all those in need. This became publicity evident with the current Covid-19 pandemic. Although controversial, health care rationing has always existed and is now inevitable. Setting priorities becomes then inevitable. How to define those priorities is a complex and yet irresolvable issue mainly because it involves several and conflicting criteria, translated into efficiency and equity considerations. This is why multi-criteria decision analysis (MCDA) was introduced to health care as an appropriate decision-support framework for solving complex problems.

Design/methodology/approach

This paper proposes the application of two combined approaches – analytic hierarchy process (AHP)-Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and AHP-VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR), as decision support tools to rank patients with competing needs in a more effective and equitable way. A rationing scenario involving four patients, differentiated by personal characteristics and health conditions, is used to illustrate, test and compare the applicability of both approaches. After extraction of the relative weights of the prioritization criteria involved in the hypothetical scenario from paired wise comparison methods, TOPSIS and VIKOR priority setting methods were designed.

Findings

Results suggest that patients ranking from both combination approaches are similar and in accordance with the order made directly by health-care professionals. Therefore, the relative weights computed by AHP in combination with TOPSIS and/or VIKOR methods could be used with suitable applicability by health-care decision-makers.

Originality/value

This study is the first attempt to apply a combination of MCDA methods to patients’ prioritization context and the first to cross previous studies to deepen and consolidate the research.

Development of a core evaluation framework of value-added medicines: report 2 on pharmaceutical policy perspectives

BACKGROUND

A core evaluation framework that captures the health care and societal benefits of value added medicines (VAMs, also often called repurposed medicines) was proposed in Report 1, aiming to reduce the heterogeneity in value assessment processes across countries and to create incentives for manufacturers to invest into incremental innovation. However, this can be impactful only if the framework can be adapted to heterogeneous health care financing systems in different jurisdictions, and the cost of evidence generation necessitated by the framework takes into account the anticipated benefits for the health care system and rewards for the developers.

AREAS COVERED

The framework could potentially improve the pricing and reimbursement decisions of VAMs by adapting it to different country specific decision-contexts such as deliberative processes, augmented cost-effectiveness frameworks or formal multi-criteria decision analysis (MCDA); alternatively, some of its domains may be added to current general evaluation frameworks of medicines. The proposed evaluation framework may provide a starting point for practices based on which VAMs can be exempted from generic pricing mechanisms or can be integrated into the reimbursement and procurement system, allowing for price differentiation according to their added value. Besides evidence from RCTs, pricing and reimbursement decision processes of VAMs should allow for ex-ante non-RCT evidence for certain domains. Alternatively, relying on ex-post evidence agreements-such as outcome guarantee or coverage with evidence development-can also reduce decision uncertainty.

CONCLUSIONS

The core evaluation framework for VAMs could trigger changes in the existing pricing, reimbursement and procurement practices by improving the appraisal of the added value created by incremental innovation.

Public preferences for allocating absolute scarce critical healthcare resources during the COVID-19 pandemic

PURPOSE

This paper aims to investigate the Portuguese general public views regarding the criteria that should guide critical COVID-19 patients to receive medical devices (ventilators and IUC beds) during the current pandemic context. Based on rationing principles and protocols proposed in ethical and medical literature the authors explore how Portuguese general public evaluates the fairness of five allocation principles: "prognosis", "severity of health condition", "patients age", "instrumental value" (frontline healthcare professionals should be prioritized during the pandemic) and "lottery".

DESIGN/METHODOLOGY/APPROACH

An online questionnaire was used to collect data from a sample of 586 Portuguese citizens. Descriptive statistics and non-parametric tests were used to define a hierarchy of prioritization criteria and to test for the association between respondents support to them and their socio-demographic and health characteristics.

FINDINGS

Respondents gave top priority to prognosis when faced with absolute scarcity, followed closely by the severity of health condition, patient's age with instrumental value receiving lowest support, on average. However, when the age of the patients was confronted with survival, younger-first principle prevailed over recovery. In a pandemic context, lottery was considered the least fair allocation method. The findings suggest that respondents' opinions are aligned with those of ethicists but are partially in disagreement with the protocol suggested for Portugal.

ORIGINALITY/VALUE

This study represents the first attempt to elicit public attitudes towards distributive criteria during a pandemic and, therefore, in a real context where the perception is that life and death decisions have to be made.

Rapid Development of a Tool for Prioritizing Patients with Coronavirus Disease 2019 for Intensive Care

Supplemental Digital Content is available in the text.

Objectives:

To explain and demonstrate a new approach for rapidly developing a decision-support tool for prioritizing patients with coronovirus 2019 disease for admission to ICUs.

Design:

An expert group used multi-criteria decision analysis methods to specify criteria and weights, representing their relative importance, for prioritizing patients with coronovirus 2019 disease with respect to likely clinical benefit. Specialized multi-criteria decision analysis software, implementing the “Potentially All Pairwise RanKings of all possible Alternatives” method to determine the weights, was used. Social equity considerations for prioritizing patients were also identified as important.

Setting:

The prioritization tool was developed in New Zealand.

Subjects:

An expert group comprising specialists from intensive care medicine and nursing, Māori (New Zealand’s indigenous population) health, infectious diseases, and neonatology was formed. The group’s work was supported by health economists and decision analysts and overseen by an ethicist and a senior representative from the New Zealand Ministry of Health.

Interventions:

Multi-criteria decision analysis to create a prioritization tool.

Measurements and Main Results:

The prioritization tool comprised eight criteria with respect to likely clinical benefit. In decreasing order of importance (weights in parentheses): Sequential Organ Failure Assessment score (15.7%), preexisting cardiovascular conditions (15.7%), functional capacity (15.7%), age (12.4%), preexisting respiratory conditions (11.1%), immunocompromised (11.1%), body mass index (9.2%), and other relevant medical conditions (9.2%). Two social equity considerations were also included in the overarching decision framework to be used alongside the clinical criteria: prioritizing Māori and Pacific people (and, potentially, other at-risk groups), and healthcare and other frontline workers.

Conclusions:

The criteria and weights in the prioritization tool can be easily revised as new evidence emerges. The approach for developing the tool could be used in other countries whose ICUs are at risk of being overwhelmed by the coronavirus disease 2019 pandemic to rapidly develop their own prioritization tools. In the event that future crises threaten to overload ICUs, other prioritization tools could also be rapidly developed.

Reduction of Human Mobility Matters during Early COVID-19 Outbreaks: Evidence from India, Japan and China

Mobility restrictions have been a heated topic during the global pandemic of coronavirus disease 2019 (COVID-19). However, multiple recent findings have verified its importance in blocking virus spread. Evidence on the association between mobility, cases imported from abroad and local medical resource supplies is limited. To reveal the association, this study quantified the importance of inter- and intra-country mobility in containing virus spread and avoiding hospitalizations during early stages of COVID-19 outbreaks in India, Japan, and China. We calculated the time-varying reproductive number (Rt) and duration from illness onset to diagnosis confirmation (Doc), to represent conditions of virus spread and hospital bed shortages, respectively. Results showed that inter-country mobility fluctuation could explain 80%, 35%, and 12% of the variance in imported cases and could prevent 20 million, 5 million, and 40 million imported cases in India, Japan and China, respectively. The critical time for screening and monitoring of imported cases is 2 weeks at minimum and 4 weeks at maximum, according to the time when the Pearson's Rs between Rt and imported cases reaches a peak (>0.8). We also found that if local transmission is initiated, a 1% increase in intra-country mobility would result in 1430 (±501), 109 (±181), and 10 (±1) additional bed shortages, as estimated using the Doc in India, Japan, and China, respectively. Our findings provide vital reference for governments to tailor their pre-vaccination policies regarding mobility, especially during future epidemic waves of COVID-19 or similar severe epidemic outbreaks.

Use of alternative care sites during the COVID-19 pandemic in the city of Buenos Aires, Argentina

Objectives

In large cities, where a large proportion of the population live in poverty and overcrowding, orders to stay home to comply with isolation requirements are difficult to fulfil. In this article, the use of alternative care sites (ACSs) for the isolation of patients with confirmed COVID-19 or persons under investigation (PUI) in the City of Buenos Aires during the first wave of COVID-19 are described.

Study design

This is a cross-sectional study.

Methods

All patients with COVID-19 and PUI with insufficient housing resources who could not comply with orders to stay home and who were considered at low clinical risk in the initial triage were referred to refurbished hotels in the City of Buenos Aires (Ciudad Autónoma de Buenos Aires [CABA]). ACSs were divided into those for confirmed COVID-19 patients and those for PUI.

Results

From March to August 2020, there were 58,143 reported cases of COVID-19 (13,829 of whom lived in slums) in the CABA. For COVID-19 positive cases, 62.1% (n = 8587) of those living in slums and 21.4% (n = 9498) of those living outside the slums were housed in an ACS. In total, 31.1% (n = 18,085) of confirmed COVID-19 cases were housed in ACSs. In addition, 7728 PUI were housed (3178 from the slums) in an ACS. The average length of stay was 9.0 ± 2.5 days for patients with COVID-19 and 1.6 ± 0.7 days for PUI. For the individuals who were housed in an ACS, 1314 (5.1%) had to be hospitalised, 56 were in critical care units (0.22%) and there were 27 deaths (0.1%), none during their stay in an ACS.

Conclusions

Overall, about one-third of all people with COVID-19 were referred to an ACS in the CABA. For slum dwellers, the proportion was >60%. The need for hospitalisation was low and severe clinical events were rare. This strategy reduced the pressure on hospitals so their efforts could be directed to patients with moderate-to-severe disease.

Early Warning Scores in Patients with Suspected COVID-19 Infection in Emergency Departments

Early warning scores (EWSs) help prevent and recognize and thereby act as the first signs of clinical and physiological deterioration. The objective of this study is to evaluate different EWSs (National Early Warning Score 2 (NEWS2), quick sequential organ failure assessment score (qSOFA), Modified Rapid Emergency Medicine Score (MREMS) and Rapid Acute Physiology Score (RAPS)) to predict mortality within the first 48 h in patients suspected to have Coronavirus disease 2019 (COVID-19). We conducted a retrospective observational study in patients over 18 years of age who were treated by the advanced life support units and transferred to the emergency departments between March and July of 2020. Each patient was followed for two days registering their final diagnosis and mortality data. A total of 663 patients were included in our study. Early mortality within the first 48 h affected 53 patients (8.3%). The scale with the best capacity to predict early mortality was the National Early Warning Score 2 (NEWS2), with an area under the curve of 0.825 (95% CI: 0.75–0.89). The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) positive patients presented an area under the curve (AUC) of 0.804 (95% CI: 0.71–0.89), and the negative ones with an AUC of 0.863 (95% CI: 0.76–0.95). Among the EWSs, NEWS2 presented the best predictive power, even when it was separately applied to patients who tested positive and negative for SARS-CoV-2.

A Multi-Objective Optimization Method for Hospital Admission Problem—A Case Study on Covid-19 Patients

The wide spread of Covid-19 has led to infecting a huge number of patients, simultaneously. This resulted in a massive number of requests for medical care, at the same time. During the first wave of Covid-19, many people were not able to get admitted to appropriate hospitals because of the immense number of patients. Admitting patients to suitable hospitals can decrease the in-bed time of patients, which can lead to saving many lives. Also, optimizing the admission process can minimize the waiting time for medical care, which can save the lives of severe cases. The admission process needs to consider two main criteria: the admission time and the readiness of the hospital that will accept the patients. These two objectives convert the admission problem into a Multi-Objective Problem (MOP). Pareto Optimization (PO) is a common multi-objective optimization method that has been applied to different MOPs and showed its ability to solve them. In this paper, a PO-based algorithm is proposed to deal with admitting Covid-19 patients to hospitals. The method uses PO to vary among hospitals to choose the most suitable hospital for the patient with the least admission time. The method also considers patients with severe cases by admitting them to hospitals with the least admission time regardless of their readiness. The method has been tested over a real-life dataset that consisted of 254 patients obtained from King Faisal specialist hospital in Saudi Arabia. The method was compared with the lexicographic multi-objective optimization method regarding admission time and accuracy. The proposed method showed its superiority over the lexicographic method regarding the two criteria, which makes it a good candidate for real-life admission systems.

Development of a Multi-Criteria Model for Sustainable Reorganization of a Healthcare System in an Emergency Situation Caused by the COVID-19 Pandemic

Healthcare systems worldwide are facing problems in providing health care to patients in a pandemic caused by the SARS-CoV-2 virus (COVID-19). The pandemic causes an extreme disease to spread with fluctuating needs among patients, which significantly affect the capacity and overall performance of healthcare systems. In addition, its impact on the sustainability of the entire economic and social system is enormous and certain sustainable management strategies need to be selected. To meet the challenges of the COVID-19 pandemic and ensure sustainable performance, national healthcare systems must adapt to new circumstances. This paper proposes an original multi-criteria methodology for the sustainable selection of strategic guidelines for the reorganization of a healthcare system under the conditions of the COVID-19 pandemic. The selection of an appropriate strategic guideline is made on the basis of defined criteria and depending on infection capacity and pandemic spread risk. The criteria for the evaluation of strategic guidelines were defined on the basis of a survey in which the medical personnel engaged in the crisis response team during the COVID-19 pandemic in the Republic of Serbia participated. The Level-Based Weight Assessment (LBWA) model and Measuring Attractiveness by a Categorical-Based Evaluation Technique (MACBETH) method were used to determine the weight coefficient criteria, while a novel fuzzy Ranking of Alternatives through Functional Mapping of Criterion Subintervals into a Single Interval (RAFSI) model was used to evaluate the strategic guidelines. The proposed multi-criteria methodology was tested in a case study in the Republic of Serbia. The validity of the proposed methodology is shown through the simulation of changes in input parameters of Bonferroni aggregation functions and through a comparison with other multi-criteria methodologies.

Waste Segregation FMEA Model Integrating Intuitionistic Fuzzy Set and the PAPRIKA Method

Segregation is an important step in health care waste management. If done incorrectly, the risk of preventable infections, toxic effects, and injuries to care and non-care staff, waste handlers, patients, visitors, and the community at large, is increased. It also increases the risk of environmental pollution and prevents recyclable waste from being recovered. Despite its importance, it is acknowledged that poor waste segregation occurs in most health care organizations. This study therefore intends to produce, for the first time, a classification of failure modes related to segregation in the Nuclear Medicine Department of a health care organization. This will be done using Failure Mode and Effects Analysis (FMEA), by combining an intuitionistic fuzzy hybrid weighted Euclidean distance operator, and the multicriteria method Potentially All Pairwise RanKings of all possible Alternatives (PAPRIKA). Subjective and objective weights of risk factors were considered simultaneously. The failure modes identified in the top three positions are: improper storage of waste (placing items in the wrong bins), improper labeling of containers, and bad waste management (inappropriate collection periods and bin set-up).