How, When, & Where Temporary Hospitals Fit in Turbulent Times: A Hybrid MADM Optimization in the Middle East.

Governments have been challenged to provide temporary hospitals and other types of facilities to face the COVID-19 pandemic. This research proposes a novel multi-attribute decision-making (MADM) model to help determine how, when, and where these temporary facilities should be installed based on a set of critical success factors (CSFs) mapped in an uncertain environment. We portray the available facilities for temporary hospitals based on the CSFs that must be considered to make critical decisions regarding the optimal position based on the government's strategic decision-making process, thus indirectly providing better services and maximizing resources. In relation to earlier work, this research builds upon hybrid Pythagorean fuzzy numbers to find weights in Best-Worst Methods and rank temporary facilities based on evaluation by an area-based method for ranking. Policy implications and future directions are derived.

Patient Voices in Hospital Safety during the COVID-19 Pandemic.

Hospitalized patients and their families may be reluctant to express safety concerns. We aimed to describe safety and quality concerns experienced by hospitalized patients and families and factors and outcomes surrounding decisions about voicing concerns, including those related to the COVID-19 pandemic. We conducted semi-structured interviews with 19 discharged inpatients or family members in a qualitative descriptive design. Some participants reported concern about staff competency or knowledge, communication and coordination, potential treatment errors, or care environment. Factors influencing feeling safe included healthcare team member characteristics, communication and coordination, and safe care expectations. Reasoning for voicing concerns often included personal characteristics. Reasons for not voicing concerns included feeling no action was needed or the concern was low priority. Outcomes for voicing a concern were categorized as resolved, disregarded, and unknown. These findings support the vital importance of open safety communication and trustworthy response to patients and family members who voice concerns.

Monitoring of Thermal Comfort and Air Quality for Sustainable Energy Management inside Hospitals Based on Online Analytical Processing and the Internet of Things.

There is a need to ensure comfortable conditions for hospital staff and patients from the point of view of thermal comfort and air quality so that they do not affect their performance. We consider the need for hospital employees and patients to enjoy conditions of greater well-being during their stay. This is understood as a comfortable thermal sensation and adequate air quality, depending on the task they are performing. The contribution of this article is the formulation of the fundamentals of a system and platform for monitoring thermal comfort and Indoor Air Quality (IAQ) in hospitals, based on an Internet of Things platform composed of a low-cost sensor node network that is capable of measuring critical variables such as humidity, temperature, and Carbon Dioxide (CO2). As part of the platform, a multidimensional data model with an On-Line Analytical Processing (OLAP) approach is presented that offers query flexibility, data volume reduction, as well as a significant reduction in query response times. The experimental results confirm the suitability of the platform's data model, which facilitates operational and strategic decision making in complex hospitals.

To introduce or not? Strategic analysis of hospital operations with telemedicine

Despite its efficiency in reducing the impact of pandemics (e.g., the COVID-19), whether to introduce telemedicine as an additional way to serve chronically ill patients remains controversial for hospitals in many countries. This paper builds a stylized model to investigate a hospital's telemedicine strategy and the corresponding impacts on its operations regarding outpatient management of chronic diseases. We implement our analysis from three key concerns of the hospital in the presence of a pandemic: the differences in medical consumption and reimbursement between in-person and telemedicine modalities and the effort cost of infection reduction resulting from the pandemic. We find that in the absence of the pandemic, the hospital prefers to introduce telemedicine when the differences in medical consumption and reimbursement are both small. In the presence of the pandemic, we find that the introduction of telemedicine does not always benefit the hospital and that it is better not to introduce telemedicine in some cases since it may exacerbate the negative influence of the pandemic on the hospital's total costs. Furthermore, we surprisingly find that the hospital may set greater in-person capacity but less telemedicine capacity in response to the outbreak of the pandemic under certain conditions, which contradicts public beliefs. Finally, we show that social welfare can be improved by introducing telemedicine when the effort cost of infection reduction and the difference in reimbursement are both of moderate size. The condition under which social welfare is improved tightens with a greater difference in medical consumption. © 2021 Elsevier B.V.

The Architecture of Multifunctional Prefabricated Hospitals

The paper discusses the principles of planning infectious diseases hospitals of a new type, the need for which arose in connection with the global epidemiological situation and the announcement by the WHO (World Health Organization) of the SARS-CoV-2 pandemic. Using the examples of the projects built in the Russian Federation, the prerequisites are analyzed for the decision-making as to functional zoning, architecture, space-planning and design solutions, as well as within the framework of the SDP (Site Development Plan). Options for the subsequent use of the developed solutions in new projects are proposed. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Community Reintegration of Long-Stay Hospitalised Mental Health Consumers During COVID-19 Pandemic

Mandatory restrictions on social activities and access to services during the COVID-19 pandemic prevented discharge into the community of consumers from an extended-stay, hospital-based rehabilitation program. This article describes a revised discharge process that overcame the pandemic-related restrictions by replacing the graded and slow discharge process with a one-stage, full-time transition that facilitated the discharge of 16 adult consumers. No adverse mental health outcomes occurred during the process. The change of practice with its positive results reflected therapeutic risk-taking. The COVID-19 pandemic provided scope for creativity and innovation in mental health care and psychiatric rehabilitation. IMPLICATIONS Innovation and collaboration by service providers can promote consumers' positive risk-taking to return to a life in mainstream society after a prolonged stay in an inpatient rehabilitation unit. Community reintegration of consumers with enduring severe mental illness can occur under adverse public health and social situations like the COVID-19 pandemic with innovative hospital discharge planning.

A sequential space syntax approach for healthcare facility layout design

This paper presents a two-phase approach for solving the facility layout problem in a physical rehabilitation hospital. The first phase solves the block layout problem, where the relative location and size of the departments in the facility are determined. The model used in this phase is based on Space Syntax which offers a series of tools that can be used to analyze and quantify spatial relations that are useful when modeling block layouts. Two Space Syntax-based metrics are introduced to model proximity and ease of access in layout designs, critical qualities in health care settings. A tabu search algorithm based on a novel nested-bay encoding is used to find the block layouts. A set of test cases from a large provider of rehabilitation hospitals shows the ability of this approach to handle healthcare-specific design requirements. An important concern for physical rehabilitation hospitals, where a large portion of the patient population is especially vulnerable to infectious diseases, and which has gained greater attention due to the COVID pandemic, is infection control. The approach herein is more capable of addressing control of infectious disease than existing metrics by providing designers with more granular control of space separation. Results show that the Space Syntax approach provides powerful, but easy to use, modeling capabilities, and that the resulting block layouts are more realistic. The second phase model is a mixed integer program for constructing corridor networks on a block layout that minimize travel distance, number of intersections, and maximum traffic on a turn. Both models are configurable so that facility designers can generate different designs according to their goals by changing the model parameters.

Short-course remdesivir for healthcare-associated COVID-19: Case series from a non-acute care hospital

Healthcare-associated COVID-19 among vulnerable patients leads to disproportionate morbidity and mortality. Early pharmacologic intervention may reduce negative sequelae and improve survival in such settings. This study aimed to describe outcome of patients with healthcare-associated COVID-19 who received early short-course remdesivir therapy. We reviewed the characteristics and outcome of hospitalized patients who developed COVID-19 during an outbreak that involved two wards at a non-acute care hospital in Japan and received short-course remdesivir. Forty-nine patients were diagnosed with COVID-19, 34 on a comprehensive inpatient rehabilitation ward and 15 on a combined palliative care and internal medicine ward. Forty-seven were symptomatic and 46 of them received remdesivir. The median age was 75, and the median Charlson comorbidity index was 6 among those who received it. Forty-one patients had received one or two doses of mRNA vaccines, while none had received a third dose. Most patients received 3 days of remdesivir. Of the patients followed up to 14 and 28 days from onset, 41/44 (95.3%) and 35/41(85.4%) were alive, respectively. Six deaths occurred by 28 days in the palliative care/internal medicine ward and two of them were possibly related to COVID-19. Among those who survived, the performance status was unchanged between the time of onset and at 28 days. © 2022 Japanese Society of Chemotherapy and The Japanese Association for Infectious Diseases

Recommendations for the transformation of patient rooms into isolated patient rooms in the process of the COVID-19 pandemic

In order to prevent the spread of Covid-19 and improve the treatment process, interest in hospital design and in-hospital transformation has increased worldwide. Since Covid-19 is not believed to be the last infectious health threat for communities around the world, it is of great importance to study existing hospital adaptations and work on obtaining more planning and design strategies for treatment and wellbeing areas. In this study it is aimed to make constructive recommendations on the basis of in-space planning and mechanical ventilation, which can be applied in hospital transformations for pandemic processes. Published guidelines and literature studies for hospitals to prevent the spread of infection have been examined, structured and unstructured interviews were conducted with architects and engineers specialized in hospital design. Also, a survey was conducted with 35 hospital workers and 4 people from the hospital management to analyze the changes made in the hospital during the Covid-19 process. Afterwards, the design plans of one of the two private hospitals studied in Turkey and the steps that can be applied in the transformation of patient rooms were explained in this article with the case study. Interdisciplinary work was carried in order to ensure infection control in hospital transformations, since it is required to make in-hospital space planning (separation/ transformation of spaces, interior zoning) and ventilation (filtration, zoning of mechanical ventilation) in harmony. Suggestions were developed that could play a role in ensuring the consistency of mechanical and architectural planning in order to successfully complete hospital transformation practices to prevent the spread of infections in pandemic processes. As the transformation scenarios in this study were performed through an exemplary private hospital room in accordance with the criteria of the Turkish Ministry of Health, ventilation plans on the standard room project were detailed and shown in the conclusion section, where the transformation steps can also be adapted to other hospitals. © 2023 Gazi Universitesi Muhendislik-Mimarlik. All rights reserved.

The role of open space and green areas in the design of an inclusive nursing home environment during Covid-19 period

The paper highlights the importance of high-quality outdoor spaces in improving physical and social well-being in the residential environment of the elderly. The historical Swedish practice of green areas design was analysed as a role model to gain knowledge and understanding of the importance of open space and green areas in residential environments. During the Covid-19 pandemic, nursing homes were among the most critical living environments, as evidenced by a disproportionate number of infections and deaths among nursing home residents worldwide. The main objective of this study is to confirm the importance of architectural design quality with special attention to outdoor space, especially in situations comparable to Covid-19 pandemics. The methods used for the research were a literature review, selection of three case studies based on two dependent and one independent variable, on-site work analysis and use of the previously developed "Safe & Connected” assessment tool. The main findings show the positive influence of open space and green areas existence on reducing the risk of infections of SARS-CoV-2 in nursing homes at three different levels. In this context, it appears that the appropriate design of outdoor space can help contain the spread of viral infections while allowing nursing home residents to live with dignity and maximum social integration. © 2022 The Author(s)

Application of machine learning approaches to predict the impact of ambient air pollution on outpatient visits for acute respiratory infections

With a remarkable increase in industrialization among fast-developing countries, air pollution is rising at an alarming rate and has become a public health concern. The study aims to examine the effect of air pollution on patient's hospital visits for respiratory diseases, particularly Acute Respiratory Infections (ARI). Outpatient hospital visits, air pollution and meteorological parameters were collected from March 2018 to October 2021. Eight machine learning algorithms (Random Forest model, K-Nearest Neighbors regression model, Linear regression model, LASSO regression model, Decision Tree Regressor, Support Vector Regression, X.G. Boost and Deep Neural Network with 5-layers) were applied for the analysis of daily air pollutants and outpatient visits for ARI. The evaluation was done by using 5-cross-fold confirmations. The data was randomly divided into test and training data sets at a scale of 1:2, respectively. Results show that among the studied eight machine learning models, the Random Forest model has given the best performance with R2 = 0.606, 0.608 without lag and 1-day lag respectively on ARI patients and R2 = 0.872, 0.871 without lag and 1-day lag respectively on total patients. All eight models did not perform well with the lag effect on the ARI patient dataset but performed better on the total patient dataset. Thus, the study did not find any significant association between ARI patients and ambient air pollution due to the intermittent availability of data during the COVID-19 period. This study gives insight into developing machine learning programs for risk prediction that can be used to predict analytics for several other diseases apart from ARI, such as heart disease and other respiratory diseases. © 2022 Elsevier B.V.

Supporting staff at a low secure mental health setting during the COVID-19 pandemic

Why you should read this article: • To learn about some adaptations to practice adopted by a low secure mental health unit during the COVID-19 pandemic • To be aware of interventions used to enable staff to continue to support patients during the COVID-19 pandemic • To appreciate that staff's experience of the adaptations to practice and the well-being support offered to them during the pandemic can be used to inform post-pandemic practice The coronavirus disease 2019 (COVID-19) pandemic significantly disrupted the work of healthcare professionals in forensic mental health settings, but also prompted them to adopt new ways of working that have benefits and challenges. A low secure mental health unit in the UK adapted its ways of working during the pandemic by providing staff who could work remotely with the necessary equipment. The unit also implemented a stepped psychological response, as recommended by The British Psychological Society, to assist its staff to continue to support patients. This article describes the unit's response to the pandemic and discusses the findings of two evaluations – staff experiences of remote working and of a group relaxation session. The findings could help inform post-pandemic practice in forensic mental health settings.

Interacting With COVID-19: How Population Behavior, Feedback and Memory Shaped Recurrent Waves of the Epidemic

Until the approval of vaccines at the end of 2020, societies relied on non-pharmaceutical interventions (NPIs) in order to control the COVID-19 pandemic. Spontaneous changes in individual behavior might have contributed to or counteracted epidemic control due to NPIs. For example, the population compliance to NPIs may have varied over time as people developed 'epidemic fatigue' or altered their perception of the risk and severity of COVID-19. Whereas official measures are well documented, the behavioral response of the citizens is harder to capture. We propose a mathematical model of the societal response, taking into account three main effects: the citizen response dynamics, the authorities' NPIs, and the occurrence of unpreventable events that significantly alter the virus transmission rate. A key assumption is that a society has a waning memory of the epidemic effects, which reflects on both the severity of the authorities' NPIs and on the citizens' compliance to the prescribed rules. This, in turn, feeds back onto the transmission rate of the disease, such that a higher number of hospitalizations decreases the probability of transmission. We show that the model is able to reproduce the COVID-19 dynamics in terms of hospital admissions for several European countries during 2020 over surprisingly long time scales. Also, it is capable of capturing the effects of disturbances (for example the emergence of new virus variants) and can be exploited for implementing control actions to limit such effects. A possible application, illustrated in this letter, consists of exploiting the estimations based on the data of one country, to predict and control the evolution in another country, where the virus spreading is still in an earlier phase. © 2017 IEEE.

A partial coverage hierarchical location allocation model for health services

We consider a hierarchical maximal covering location problem (HMCLP) to locate health centres and hospitals so that the maximum demand is covered by two levels of services in a successively inclusive hierarchy. We extend the HMCLP by introducing the partial coverage and a new definition of the referral. The proposed model may enable an informed decision on the healthcare system when dynamic adaptation is required, such as a COVID-19 pandemic. We define the referral as coverage of health centres by hospitals. A hospital may also cover demand through referral. The proposed model is solved optimally for small problems. For large problems, we propose a customised genetic algorithm. Computational study shows that the GA performs well, and the partial coverage substantially affects the optimal solutions. © 2023 Inderscience Enterprises Ltd.

Hybridizing adaptive large neighborhood search with kernel search: a new solution approach for the nurse routing problem with incompatible services and minimum demand

The average age of the population has grown steadily in recent decades along with the number of people suffering from chronic diseases and asking for treatments. Hospital care is expensive and often unsafe, especially for older individuals. This is particularly true during pandemics as the recent SARS-CoV-2. Hospitalization at home has become a valuable alternative to face efficiently a huge increase in treatment requests while guaranteeing a high quality of service and lower risk to fragile patients. This new model of care requires the redefinition of health services organization and the optimization of scarce resources (e.g., available nurses). In this paper, we study a Nurse Routing Problem that tries to find a good balance between hospital costs reduction and the well-being of patients, also considering realistic operational restrictions like maximum working times for the nurses and possible incompatibilities between services jointly provided to the same patient. We first propose a Mixed Integer Linear Programming formulation for the problem and use some valid inequalities to strengthen it. A simple branch-and-cut algorithm is proposed and validated to derive ground benchmarks. In addition, to efficiently solve the problem, we develop an Adaptive Large Neighborhood Search hybridized with a Kernel Search and validate its performance over a large set of different realistic working scenarios. Computational tests show how our matheuristic approach manages to find good solutions in a reasonable amount of time even in the most difficult settings. Finally, some interesting managerial insights are discussed through an economic analysis of the operating context. © 2022 The Authors. International Transactions in Operational Research published by John Wiley & Sons Ltd on behalf of International Federation of Operational Research Societies.

Healthcare waste management protocols challenged by Covid-19: a case study

The resilience of a new national healthcare waste management protocol was compared in hospital and community settings in Ethiopia during the coronavirus disease 2019 (Covid-19) pandemic. The aim was also to evaluate the flexibility and implementation status of the guidelines for healthcare waste management in the context of a pandemic. The study was performed in a cross-sectional design by using a structured questionnaire and checklists and through a systematic review. Primary and secondary data were collected, including those from municipal sites, Yekatit 12 Hospital and other relevant stakeholders. Furthermore, the data were analysed and compared to the global practices and standards. The study revealed there was a comparable difference in the level of care taken in healthcare waste management between healthcare institutions and in the community in Addis Ababa city. The provision of awareness training to waste handlers in both the clinical and municipal setting was significantly deficient, which has to be addressed together with many other variables, starting with the formulation of a resilient waste management protocol. Furthermore, waste management protocols have to be expert-revised periodically. © 2023 ICE Publishing. All rights reserved.

Predicting model of mild and severe types of COVID-19 patients using Thymus CT radiomics model: A preliminary study

Objective: To predict COVID-19 severity by building a prediction model based on the clinical manifestations and radiomic features of the thymus in COVID-19 patients. Method: We retrospectively analyzed the clinical and radiological data from 217 confirmed cases of COVID-19 admitted to Xiangyang NO.1 People's Hospital and Jiangsu Hospital of Chinese Medicine from December 2019 to April 2022 (including 118 mild cases and 99 severe cases). The data were split into the training and test sets at a 7:3 ratio. The cases in the training set were compared in terms of clinical data and radiomic parameters of the lasso regression model. Several models for severity prediction were established based on the clinical and radiomic features of the COVID-19 patients. The DeLong test and decision curve analysis (DCA) were used to compare the performances of several models. Finally, the prediction results were verified on the test set. Result: For the training set, the univariate analysis showed that BMI, diarrhea, thymic steatosis, anorexia, headache, findings on the chest CT scan, platelets, LDH, AST and radiomic features of the thymus were significantly different between the two groups of patients (P < 0.05). The combination model based on the clinical and radiomic features of COVID-19 patients had the highest predictive value for COVID-19 severity [AUC: 0.967 (OR 0.0115, 95%CI: 0.925-0.989)] vs. the clinical feature-based model [AUC: 0.772 (OR 0.0387, 95%CI: 0.697-0.836), P < 0.05], laboratory-based model [AUC: 0.687 (OR 0.0423, 95%CI: 0.608- 0.760), P < 0.05] and model based on CT radiomics [AUC: 0.895 (OR 0.0261, 95%CI: 0.835-0.938), P < 0.05]. DCA also confirmed the high clinical net benefits of the combination model. The nomogram drawn based on the combination model could help differentiate between the mild and severe cases of COVID-19 at an early stage. The predictions from different models were verified on the test set. Conclusion: Severe cases of COVID-19 had a higher level of thymic involution. The thymic differentiation in radiomic features was related to disease progression. The combination model based on the radiomic features of the thymus could better promote early clinical intervention of COVID-19 and increase the cure rate. © 2023 American Institute of Mathematical Sciences. All rights reserved.

Adopting electronic healthcare services during COVID-19 and the nations responding: evidence from Jordan

This research aims to examine the national responding of healthcare services and the existing initiatives among adoption of electronic service during the COVID-19 in Jordan. The study model is extending to the theory of acceptance technology and use, which includes effort expectancy, performance expectancy, and facilitating position factors. A quantitative approach survey questionnaire was conducted towards employees working in private hospitals in Jordan. A total of 315 valid and completed responses were retrieved. The sample was randomly selected to ensure fair representation of the population society. The conceptual model of this research was empirically examined using hierarchical regression. The results showed that all independent predictors conceptualised in the model were significant predictors with 80% of the variance in the adoption of electronic healthcare. The study provides some new findings that would help in understanding the role of technology over the emerging situations and crisis particularly in the COVID-19 pandemic. Copyright © 2023 Inderscience Enterprises Ltd.