Location selection of field hospitals amid COVID-19 considering effectiveness and fairness: A case study of Florida.

The world has experienced an unprecedented global health crisis since 2020, the COVID-19 pandemic, which inflicted massive burdens on countries' healthcare systems. During the peaks of the pandemic, the shortages of intensive care unit (ICU) beds illustrated a critical vulnerability in the fight. Many individuals suffering the effects of COVID-19 had difficulty accessing ICU beds due to insufficient capacity. Unfortunately, it has been observed that many hospitals do not have enough ICU beds, and the ones with ICU capacity might not be accessible to all population strata. To remedy this going forward, field hospitals could be established to provide additional capacity in helping emergency health situations such as pandemics; however, location selection is a crucial decision ultimately for this purpose. As such, we consider finding new field hospital locations to serve the demand within certain travel-time thresholds, while accounting for the presence of vulnerable populations. A multi-objective mathematical model is proposed in this paper that maximizes the minimum accessibility and minimizes the travel time by integrating the Enhanced 2-Step Floating Catchment Area (E2SFCA) method and travel-time-constrained capacitated p-median model. This is performed to decide on the locations of field hospitals, while a sensitivity analysis addresses hospital capacity, demand level, and the number of field hospital locations. Four counties in Florida are selected to implement the proposed approach. Findings can be used to identify the ideal location(s) of capacity expansions concerning the fair distribution of field hospitals in terms of accessibility with a specific focus on vulnerable strata of the population.

Service Quality Analysis of a Field Hospital during COVID-19 in Thailand

The objectives of this research are to explore dimensions of service quality and evaluate service quality expectations, perceptions and satisfactions of healthcare workers in a field hospital. Data were collected from 126 medical personnel who were caring for COVID-19 patients. The questionnaire was developed from guidelines for setting up field hospitals in Thailand. Exploratory Factor Analysis (EFA) extracted 7 dimensions, Service quality was analyzed with service gap analysis, Important Performance Analysis (IPA) and Priority nonconformity index (PNCI). The Gap analysis found that overall service quality was satisfactory. Infrastructure was a most satisfied dimension. Social responsibility was a most dissatisfaction. IPA showed logistics with risk management and administrative procedure were strength. The PNCI suggested to transfer resources from infrastructure medical service, occupational health and safety to improve personnel quality and social responsibility. © 2022 ACM.

Infection Prevention Considerations for a Multi-Mission Convention Center Field Hospital in Baltimore, Maryland, During the COVID-19 Pandemic.

The state of Maryland identified its first case of coronavirus disease 2019 (COVID-19) on March 5, 2020. The Baltimore Convention Center (BCCFH) quickly became a selected location to set up a 250-bed inpatient field hospital and alternate care site. In contrast to other field hospitals throughout the United States, the BCCFH remained open throughout the pandemic and took on additional COVID-19 missions, including community severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) diagnostic testing, monoclonal antibody infusions for COVID-19 outpatients, and community COVID-19 vaccinations.To prevent the spread of pathogens during operations, infection prevention and control guidelines were essential to ensure the safety of staff and patients. Through multi-agency collaboration, use of infection prevention best practices, and answering what we describe as PPE-ESP, an operational framework was established to reduce infection risks for those providing or receiving care at the BCCFH during the COVID-19 pandemic.

Recruitment, Readiness, and Retention of Providers at a Field Hospital during the Pandemic.

In response to the coronavirus disease 2019 (COVID-19) pandemic, the State of Maryland established a 250-bed emergency response field hospital at the Baltimore Convention Center to support the existing healthcare infrastructure. To operationalize this hospital with 65 full-time equivalent (FTE) clinicians in less than four weeks, more than 300 applications were reviewed, 186 candidates were interviewed, and 159 clinicians were credentialed and onboarded. The key steps to achieve this undertaking involved employing multidisciplinary teams with experienced personnel, mass outreach, streamlined candidate tracking, pre-interview screening, utilizing all available expertise, expedited credentialing, and focused onboarding. To ensure staff preparedness, the leadership developed innovative team models, applied principles of effective team building, and provided 'just in time' training on COVID-19 and non-COVID-19 related topics to the staff. The leadership focused on staff safety and well-being, offered appropriate financial remuneration and provided leadership opportunities that allowed retention of staff.

Design of Uninterrupted Oxygen Supply for Field Hospital in Jordan

Field hospitals were a great help in global pandemics and catastrophes such as earthquakes and the spread of airborne viruses. This study focused on the design of an interrupted oxygen supply since continuous oxygen provision for covid-19 patients is a huge problem facing field hospitals around the world, three methods to avoid any oxygen supply interruptions are discussed, where the outlet of the oxygen concentrator is lowered to 4.5 bar, and the outlet of the liquid oxygen vaporizer is regulated at 4.25 bar, and the outlet of the oxygen cylinders is set to 4 bars, a final one-way valve connecting the three lines of oxygen which are set to 4 bars. © 2022 IEEE.

Implementation of Q-Learning Algorithm on Arduino : Application to Autonomous Mobile Robot Navigation in COVID-19 Field Hospitals

The recourse to Mobile Robots (MRs) in fighting against Coronavirus pandemic (COVID-19) has today become a necessity in almost all hospitals worldwide. Indeed, for example, the Ultraviolet Disinfection (UVD) robot has been very useful, since COVID-19 pandemic began, to destroy viruses in Wuhan hospitals. However, MRs are equipped with a locomotion system, which should be capable of navigating through, generally, an unknown work environment. In such situation, MR should also have learning mechanism. In this paper, we propose a low-cost solution to deal with the problem of autonomous navigation and the trajectory optimization for MR in Field Hospitals (FHs). The architecture of these latter may be varying from one to another according to where it should be installed. Thus, a rapidly adapting to the new trajectory is then necessary to help save lives in pandemic situation. So, to conduct a successful autonomous navigation of MR particularly in COVID-19 FHs, a practical low-cost solution must then be found. To do so, a MR equipped with TCS230 Color Sensor, is used to read colored sticky notes fixed on the ground, to identify room number of the FH that serves for learning and correcting robot trajectory keypoints. Furthermore, an Android application is also developed in this work to remotely control, via a Bluetooth wireless connection, an Arduino-based MR in some specific situation. Some experiments were carried out to verify the performance of the proposed autonomous navigation approach of MR type ELEGOO Smart Robot Car V3.0. Moreover, the simulation results have confirmed that the robot can reach the goal with optimal trajectory by only exploiting the popular Q-Learning algorithm with the use of a low-cost color sensor, and sticky notes which could be easily deployed according to the architecture of the new installed FH.

The Response by International Emergency Medical Teams Following the Beirut Harbor Explosion in 2020 - Who Were They, When Did They Arrive, What Did They Do, and Were They Needed?

INTRODUCTION: On August 4, 2020, a massive explosion struck the Beirut Harbor in Lebanon. Approximately 220 people were killed and around 7,000 were injured, of which 12% were hospitalized. Despite being weakened by economic crisis and increasing numbers of coronavirus disease 2019 (COVID-19) cases, the national health care system responded promptly. Within a day, international health care assistance in the form of International Emergency Medical Teams (I-EMTs) started arriving. Previous studies have found that I-EMTs have arrived late and have not been adapted to the context and dominating health care needs. The aim of this study was to document the organization, type, activity, and timing of I-EMTs deployed to Beirut and to discuss their relevance in relation to medical needs. METHODS: Data on all deployed I-EMTs were retrieved from all available sources, including internet searches, I-EMT contacts, and from the World Health Organization (WHO) EMT coordination cell (EMT CC) in Lebanon. The WHO EMT classification was used to categorize deployed teams. Information on characteristics, timing, and activities was retrieved and systematically assessed. RESULTS: Nine I-EMTs were deployed to Beirut following the explosion. Five were equivalent to EMT Type 2 (field hospitals), out of which three were military. The first EMT Type 2 arrived within 24 hours, while the last EMT set up one month after the explosion. Four civilian I-EMTs provided non-clinical support as EMT Specialized Care Teams. A majority of the I-EMTs were focused on trauma care. Three of the four I-EMT Specialized Care Teams were rapidly re-tasked to support COVID-19 care in public hospitals. CONCLUSION: A majority of the deployed I-EMT Type 2 were military and focused on trauma care rather than the normal burden of disease including COVID-19. Re-tasking of EMTs requires flexible EMTs. To be better adapted, the I-EMT response should be guided by a systematic assessment of both health care capacities in the affected country as well as the varying health effects of hazards before deployment.

Covid-19 Modular Construction

Modular construction methods have been widely used in the civil engineering industry as they provide ease of assembly, convenience of design, and allowing for flexibility in terms of placement. With the increasing number of COVID-19 cases, hospitals’ capacity is decreasing as more intensive care units (ICU) rooms are allocated to those cases. The limited capacity can be overcome by using modular construction to provide field hospitals, to accommodate more patients. This paper adopts transient Lagrangian computational fluid dynamics simulations to investigate the importance of having an appropriate ventilation system in place to achieve containment of contaminants within a modular construction room. The model was validated using the results from the experimental simulation of aerosol in an airconditioned space. The performance of having 10, 20, and 40 air changes per hour (ACH) was examined for a room with a geometry of 6.1 × 2.5 × 3.0 m. It was observed that the rate at which the mouth-generated aerosols were filtered towards the ventilation system (outlet) increased by 137% by increasing the ACH from 10 to 40. Aerosol particles landing on equipment decreased by 25% when the rate was increased to 40 ACH. © 2022, Canadian Society for Civil Engineering.

Selecting the field hospital location for earthquakes: an application for Ankara Province in Turkey

Purpose>The authors present a location selection model for the field hospital to build after a possible earthquake in Ankara, Turkey using the VIKOR method.Design/methodology/approach>Companies or governments that make location selection decisions to improve their performance in new investment decisions for different service industries. On the other hand, disasters, especially earthquakes, force the governments to evaluate their existing potentialities and develop action plans to improve their middle and long-term preparations. This paper proposes a VIKOR method-based location selection model for the field hospital to build after a possible earthquake. Also, the authors present a methodology using the VIKOR method that how government agencies take action for the field hospital's location selection process via VIKOR methodology.Findings>The modeling and application results show that the field hospital's location selection decision-making process improves considerably using the VIKOR model. This paper shows that the proposed VIKOR-based model can rank alternatives suitability at various criteria targeting to minimize the possible earthquake's impact and obtains a single overall ranking score to select the best alternative.Research limitations/implications>The study does not consider the uncertain nature of the field hospital selection problem. The application part is restricted to the Ankara case. But the proposed model can easily extend for different locations in the world.Originality/value>This paper presents the multi-criteria decision-making (MCDM) framework study of the establishment of field hospitals and demonstrates its importance when criteria diversity is restricted.

Development of Tele-Operated Mobile Robots for COVID-19 Field Hospitals

In this article, a mobile robot for item delivery with tele-operation capability is developed and used in the field hospital. The user is able to control the robot and communicate with the patients via an web-App on a cloud server. The robots are deployed and tested on-site in a large size field hospital, while the workload of the robot is studied and planned by using simulation approach. However, the difficulties on actual implementation have arise due to the working condition and risk of infection. These take into account in the development and deployment phases of the system. © 2021 IEEE.

A Pathfinding Algorithm for Lowering Infection Exposure of Healthcare Personnel Working in Makeshift Hospitals.

Due to the recent COVID-19 outbreak, makeshift (MS) hospitals have become an important feature in healthcare systems worldwide. Healthcare personnel (HCP) need to be able to navigate quickly, effectively, and safely to help patients, while still maintaining their own well-being. In this study, a pathfinding algorithm to help HCP navigate through a hospital safely and effectively is developed and verified. Tests are run using a discretized 2D grid as a representation of an MS hospital plan, and total distance traveled and total exposure to disease are measured. The influence of the size of the 2D grid units, the shape of these units, and degrees of freedom in the potential movement of the HCP are investigated. The algorithms developed are designed to be used in MS hospitals where airborne illness is prevalent and could greatly reduce the risk of illness in HCP. In this study, it was found that the quantum-based algorithm would generate paths that accrued 50-66% less total disease quantum than the shortest path algorithm with also about a 33-50% increase in total distance traveled. It was also found that the mixed path algorithm-generated paths accrued 33-50% less quantum, but only increased total distance traveled by 10-20%.

Strategic analysis for the installation of field hospitals for COVID-19 control: An approach based on P-median model

Concerning the expansion of the coronavirus in the world, the search for the development of solutions related to the control and mitigation of the pandemic situation became constant. The paper addresses an analysis of localities for the installation of field hospitals, highly requested as a point of treatment for COVID-19. In this scenario, a framework based on the P-median approach and mathematical programming is proposed, enabling an optimization model as an analysis format for the problematic situation. To support the implementation of the model, a computational tool for data processing was developed, integrating an optimization model to the geographical evaluation, exploring in the analysis numerical and graphical resources. As a validation of the study, a case study in the city of Rio de Janeiro - Brazil is presented, analyzing 162 neighborhoods and determining seven favorable localities for the installation of field hospitals. © 2021 The authors and IOS Press.

Alternate Care Sites for COVID-19 Patients: Experience from the H144 Hospital of the Health Service of the Principality of Asturias, Spain.

Coronavirus disease 2019 (COVID-19) temporary hospitals, also called "alternate care sites" (ACS), as support to the health network have had uneven use. The World Health Organization (WHO) has published different recommendations in this regard. World-wide, many health services have improved their surge capacity with the implementation of new temporary hospital structures, but there have been few experiences of use over time despite representing an important element as support to the hospital network in the management of COVID-19 patients. In this article, the experiences are explained in the design, execution, and use of the temporary COVID-19 Hospital H144 of the Health Service of the Principality of Asturias (Sespa), with 144 beds, which was in operation from April 1 through July 1, 2020 (without admitting patients) and from November 12, 2020 through March 5, 2121, admitting a total of 334 COVID-19 patients (66% women; 34% men) and generating 3,149 hospital stays. Maximum occupancy was 74 patients. Mean stay was 9.42 days (MD = 3.99; [1-34]). At discharge, 126 patients (38%) went to a nursing home, 112 (33%) to their home, 40 (12%) were transferred to another hospital, and 56 (17%) died. The mean age of the admitted patients was 82.79 years (MD = 8.68; [29-104]) and was higher in women (85.09; MD = 7.57; P = .000) than in men (78.28; MD = 9.22). Some aspects to consider for future experiences of use have been: teamwork from different fields of knowledge (ie, architecture, engineering, medicine, and nursing) is essential for success; integration in the health system must be fully developed from different perspectives (ie, information system, logistics, medical records, or clinical procedures, among others); clear procedures for patient admission from different structures (ie, home, hospitals, nursing homes, or primary health care network) must combine with flexibility of use to adapt to new and unknown circumstances; and they must not compromise specialized staff availability in other health facilities.

Adapting a Federal Disaster Medical Assistance Team to Operate During a Pandemic.

After Hurricane Laura struck the southeast coast of Louisiana in August 2020, the National Disaster Medical System (NDMS), a component of the US Department of Health and Human Services, Office of the Assistant Secretary for Preparedness and Response, deployed several 35-person disaster medical assistance teams in response to requests for medical support at 3 hospital locations that had been severely damaged in the storm. This was the first natural disaster medical deployment for NDMS during the coronavirus disease (COVID-19) pandemic. This article describes the modifications to the standard operating procedures that were made at 1 site to reduce the risk of infection to our patients and NDMS responders, including changes to the physical layout of the tenting, and alterations to the triage and treatment process.