Information technology in emergency management of COVID-19 outbreak

Design and implementation of student management system of integrated programmable device programming system

With the continuous growth and development of society, the reform of higher education is gradually taking shape, and colleges and universities are taking more and more responsibilities in promoting education. The main element of university management is the student management system, which is very important to the development of the university. Under the objective environment of colleges and universities seeking to expand the scale of running schools and rapid economic and social development, the current student management system has been unable to meet the various needs of contemporary students. The integration of programmable device programming systems offers a student management system distinct advantages in terms of reliability, flexibility, and user-friendly operation. This study focuses on developing an effective and affordable student management system by incorporating a programmable device programming system. To evaluate the system's performance, this paper suggests the utilization of a BP neural network, renowned for its high nonlinear approximation capabilities and effectiveness in handling complex nonlinear functional relationships. The experimental findings highlight a significant contribution, demonstrating that the system achieved a throughput of 180 times per second, with a maximum CPU utilization of 99%. Notably, the system's stability exceeded 95%, contrasting sharply with the traditional student management system's stability at a mere 65%. These results underscore the substantial contribution of the proposed system, showcasing its enhanced stability compared to conventional student management systems.

Improving healthcare quality by unifying the American electronic medical report system: time for change

BACKGROUND

In recent years, innovation in healthcare technology has significantly improved the efficiency of the healthcare system. Advancements have led to better patient care and more cost-effective services. The electronic medical record (EMR) system, in particular, has enhanced interoperability and collaboration across healthcare departments by facilitating the exchange and utilization of patient data. The COVID-19 pandemic further accelerated this trend, leading to a surge in telemedicine services, which rely on electronic communication to deliver healthcare remotely.

MAIN BODY

Integrating artificial intelligence (AI) and machine learning (ML) in healthcare have been instrumental in analyzing vast data sets, allowing for identifying patterns and trends that can improve care delivery and pinpoint potential issues. The proposal of a unified EMR system in the USA aims to capitalize on these technological advancements. Such a system would streamline the sharing of patient information among healthcare providers, improve the quality and efficiency of care, and minimize the likelihood of errors in patient treatment.

CONCLUSION

A unified electronic medical record system represents a promising avenue for enhancing interoperability within the US healthcare sector. By creating a more connected and accessible network of patient information, it sets the stage for a transformation in healthcare delivery. This change is imperative for maintaining the momentum of progress in healthcare technology and realizing the full potential of recent advancements in patient care and system efficiency.

Designing a medical information diagnosis platform with IoT integration

In order to enhance the operational efficiency of the healthcare industry, this paper investigates a medical information diagnostic platform through the application of swarm and evolutionary algorithms. This paper begins with an analysis of the current development status of medical information diagnostic platforms based on Chat Generative Pre-trained Transformer (ChatGPT) and Internet of Things (IoT) technology. Subsequently, a comprehensive exploration of the advantages and disadvantages of swarm and evolutionary algorithms within the medical information diagnostic platform is presented. Further, the optimization of the swarm algorithm is achieved through reverse learning and Gaussian functions. The rationality and effectiveness of the proposed optimization algorithm are validated through horizontal comparative experiments. Experimental results demonstrate that the optimized model achieves favorable performance at the levels of minimum, average, and maximum algorithm fitness values. Additionally, preprocessing data in a 10 * 10 server configuration enhances the algorithm's fitness values. The minimum fitness value obtained by the optimized algorithm is 3.56, representing a 3 % improvement compared to the minimum value without sorting. In comparative experiments on algorithm stability, the optimized algorithm exhibits the best stability, with further enhancement observed when using sorting algorithms. Therefore, this paper not only provides a new perspective for the field of medical information diagnostics but also offers effective technical support for practical applications in medical information processing.

COVID-19 infection prevention and control for hospital workers in Indonesia

Introduction

The outbreak of SARS-CoV-2 in 2019 led to a global pandemic, posing unprecedented challenges to healthcare systems, particularly in hospitals.

Purpose

This study explores the intricacies of strategies employed for preventing and controlling COVID-19 in Indonesian hospitals, with a particular focus on the protocols, challenges, and solutions faced by healthcare professionals.

Methods

Using a cross-sectional analysis, we examined 27 hospitals and uncovered disparities in their preparedness levels. During our investigation, we observed the robust implementation of infection prevention measures, which encompassed stringent protocols, adequate ventilation, and proper use of personal protective equipment. However, shortcomings were identified in areas such as surveillance, mental health support, and patient management.

Discussion

This study underscores the importance of addressing these gaps, suggesting tailored interventions, and continuous training for healthcare staff. Effective leadership, positive team dynamics, and adherence to comprehensive policies emerge as pivotal factors. Hospitals should strengthen weak areas, ensure the ethical execution of emergency protocols, and integrate technology for tracking and improving standard operating procedures. By enhancing the knowledge and skills of healthcare workers and maintaining strong management practices, hospitals can optimize their efforts in COVID-19 prevention and control, thereby safeguarding the wellbeing of professionals, patients, and communities.

A practice-based nursing emergency management system model for public health emergencies: A descriptive qualitative study

AIM

This study aims to explore nursing emergency management system under public health emergencies.

DESIGN

A descriptive qualitative study.

METHOD

Semi-structured interviews were conducted during March-November 2020 with 11 nursing emergency management administrators from 11 COVID-19 designated hospitals and infectious disease hospitals under four administrative divisions in China, who were recruited through convenience and purposive sampling. Interviews were audio-recorded and transcribed. Data were analysed with Braun and Clarke's inductive thematic analysis. The study adhered to the Consolidated Criteria for Reporting Qualitative Research.

RESULTS

Three main themes emerged from data analysis: (1) Five core elements of nursing emergency management system: Structure, Staff, System, Setting, and Supply (5S); (2) Four phases of nursing emergency management system: Reduction, Readiness, Response and Recovery (4R); and (3) operational process of nursing emergency management system (5S*4R). The findings generated a conceptual model of a nursing emergency management system.

PUBLIC CONTRIBUTION

This study provides a theoretical basis for nursing emergency management system for public health emergencies in the future. Nurse administrators could refer to this model to either review or develop their system and develop viable interventions to prepare for future public health emergencies.

Characteristics and specifications of dashboards developed for the COVID-19 pandemic: a scoping review

Aim

The use of information-based solutions such as dashboards is on the rise for taking fact-based actions against the COVID-19 crisis. This scoping review aimed to comprehensively investigate COVID-19 dashboards from different technical perspectives.

Subject and methods

Three main bibliographic databases, PubMed, Web of Science, and Scopus, were searched on 28 August 2021 to retrieve relevant studies. Arksey and O'Malley's (Int J Soc Res Methodol 8(1):19-32, 2005) methodological framework and the enhanced version of this methodology developed by Levac et al. (Implement Sci 5(1):1-9, 2010) were adopted for conducting this review.

Results

In total, 26 articles were included. The COVID-19 dashboards mainly focused on the infected (n = 25), deceased (n = 17), and recovered cases (n = 13), as well as the performed test (n = 10). Most of the dashboards were interactive, with public accessibility targeting various user groups. While some dashboards were both informative and supportive (38%), most were mainly informative (92%). The dashboard data were generally analyzed using simple techniques (58%) and delivered through web-based applications (88%).

Conclusion

Dashboards can help immediately manage, analyze, and summarize a huge amount of information about a COVID-19 outbreak. The findings revealed that the developed COVID-19 dashboards share more or less analogous characteristics that could lay the groundwork for designing and developing dashboards for any other pandemic.

Designing humanitarian logistics network for managing epidemic outbreaks in disasters using Internet-of-Things. A case study: An earthquake in Salas-e-Babajani city

Along with the destructive effects of catastrophes throughout the world, the COVID-19 outbreak has intensified the severity of disasters. Although the global aid organizations and philanthropists aim to alleviate the adverse impacts, many employed actions are not impactful in dealing with the epidemic outbreak in disasters. However, there is a gap in controlling the epidemic outbreak in the aftermath of disasters. Therefore, this paper proposes a novel humanitarian location-allocation-inventory model by focusing on preventing COVID-19 outbreaks with IoT-based technology in the response phase of disasters. In this study, IoT-based systems enable aid and health-related organizations to monitor people remotely, suspect detection, surveillance, disinfection, and transportation of relief items. The presented model consists of two stages; the first is defining infected cases, transferring patients to temporary hospitals promptly, and accommodating people in evacuation centers. Next, distribution centers are located in the second stage, and relief items are transferred to temporary hospitals and evacuation centers equally regarding shortage minimization. The model is solved by the LP-metric method and applied in a real case study in Salas-e-Babajani city, Kermanshah province. Then, sensitivity analysis on significant model parameters pertaining to the virus, relief items, and capacity has been conducted. Using an IoT-based system in affected areas and evacuation centers reduces the number of infected cases and relief item's shortages. Finally, several managerial insights are obtained from sensitivity analyses provided for healthcare managers.

The development of new remote technologies in disaster medicine education: A scoping review

Background

Remote teaching and online learning have significantly changed the responsiveness and accessibility after the COVID-19 pandemic. Disaster medicine (DM) has recently gained prominence as a critical issue due to the high frequency of worldwide disasters, especially in 2021. The new artificial intelligence (AI)-enhanced technologies and concepts have recently progressed in DM education.

Objectives

The aim of this article is to familiarize the reader with the remote technologies that have been developed and used in DM education over the past 20 years.

Literature scoping reviews

Mobile edge computing (MEC), unmanned aerial vehicles (UAVs)/drones, deep learning (DL), and visual reality stimulation, e.g., head-mounted display (HMD), are selected as promising and inspiring designs in DM education.

Methods

We performed a comprehensive review of the literature on the remote technologies applied in DM pedagogy for medical, nursing, and social work, as well as other health discipline students, e.g., paramedics. Databases including PubMed (MEDLINE), ISI Web of Science (WOS), EBSCO (EBSCO Essentials), Embase (EMB), and Scopus were used. The sourced results were recorded in a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flowchart and followed in accordance with the PRISMA extension Scoping Review checklist. We included peer-reviewed articles, Epubs (electronic publications such as databases), and proceedings written in English. VOSviewer for related keywords extracted from review articles presented as a tabular summary to demonstrate their occurrence and connections among these DM education articles from 2000 to 2022.

Results

A total of 1,080 research articles on remote technologies in DM were initially reviewed. After exclusion, 64 articles were included in our review. Emergency remote teaching/learning education, remote learning, online learning/teaching, and blended learning are the most frequently used keywords. As new remote technologies used in emergencies become more advanced, DM pedagogy is facing more complex problems.

Discussions

Artificial intelligence-enhanced remote technologies promote learning incentives for medical undergraduate students or graduate professionals, but the efficacy of learning quality remains uncertain. More blended AI-modulating pedagogies in DM education could be increasingly important in the future. More sophisticated evaluation and assessment are needed to implement carefully considered designs for effective DM education.

Guidelines for the development of a critical software under emergency

CONTEXT

During the first wave of the COVID-19 pandemic, an international and heterogeneous team of scientists collaborated on a social project to produce a mechanical ventilator for intensive care units (MVM). MVM has been conceived to be produced and used also in poor countries: it is open-source, no patents, cheap, and can be produced with materials that are easy to retrieve.

OBJECTIVE

The objective of this work is to extract from the experience of the MVM development and software certification a set of lessons learned and then guidelines that can help developers to produce safety-critical devices in similar emergency situations.

METHOD

We conducted a case study. We had full access to source code, comments on code, change requests, test reports, every deliverable (60 in total) produced for the software certification (safety concepts, requirements specifications, architecture and design, testing activities, etc.), notes, whiteboard sketches, emails, etc. We validated both lessons learned and guidelines with experts.

FINDINGS

We contribute a set of validated lessons learned and a set of validated guidelines, together with a discussion of benefits and risks of each guideline.

CONCLUSION

In this work we share our experience in certifying software for healthcare devices produced under emergency, i.e. with strict and pressing time constraints and with the difficulty of establishing a heterogeneous development team made of volunteers. We believe that the guidelines will help engineers during the development of critical software under emergency.

Analysis of the Government and Capital Market’s Response to Normalized Epidemic Prevention and Control Based on the Development of Smart Health Care

With the continuous prominence of medical problems, the construction of a smart medical system has received more and more attention. With coronavirus disease 2019 in a state of regular prevention and control, the epidemic prevention and control in China had produced favorable effects. Therefore, a comprehensive evaluation of the emergency governance mechanism in China was crucial to epidemic prevention and control worldwide. This paper empirically analyzed the effects of epidemic prevention and control based on the dual perspective of epidemic governance and capital market, taking the daily outbreak data at the provincial level in China as the sample. The findings were as follows: (1) The accumulation of epidemic medical treatment experience in each province and city produced a positive emergency effect on preventing and controlling the dissemination of epidemic. The effective medical security mechanism was one of the most important mechanisms for the significant emergency effect, with the effective material security as a critical supplement. (2) Companies and corporations with stronger capabilities for emergency medical treatment received more positive responses from the capital market comparatively. Aiming at diversifying directions and measurements for the improvement of emergency governance, this paper provided theoretical bases for improving the emergency governance system against major public health emergencies by using the Chinese characteristics.

National IoMT platform strategy portfolio decision model under the COVID-19 environment: based on the financial and non-financial value view

The Internet of Medical Things (IoMT) is an emerging technology in the healthcare revolution which provides real-time healthcare information communication and reasonable medical resource allocation. The COVID-19 pandemic has had a significant effect on people's lives and has affected healthcare capacities. It is important for integrated IoMT platform development to overcome the global pandemic challenges. This study proposed the national IoMT platform strategy portfolio decision-making model from the non-financial (technology, organization, environment) and financial perspectives. As a solution to the decision problem, initially, the decision-making trial and evaluation laboratory (DEMATEL) technology were employed to capture the cause-effect relationship based on the perspectives and criteria obtained from the insight of an expert team. The analytic network process (ANP) and pairwise comparisons were then used to determine the weights for the strategy. Simultaneously, this study incorporated IoMT platform resource limitations into the zero-one goal programming (ZOGP) method to obtain an optimal portfolio selection for IoMT platform strategy planning. The results showed that the integrated MCDM method produced reasonable results for selecting the most appropriate IoMT platform strategy portfolio when considering resource constraints such as system installation costs, consultant fees, infrastructure costs, reduction of medical staff demand, and improvement rates for diagnosis efficiency. The decision-making model of the IoMT platform in this study was conclusive and significantly compelling to aid government decision makers in concentrating their efforts on planning IoMT strategies in response to various pandemic and medical resource allocations.

An extended multi-criteria group decision-making method with psychological factors and bidirectional influence relation for emergency medical supplier selection

The COVID-19 pandemic outbreak spread rapidly worldwide, posing a severe threat to human life. Due to its unpredictability and destructiveness, the emergency has aroused great common in society. At the same time, the selection of emergency medical supplier is one of the critical links in emergency decision-making, so undertaking appropriate decision-making using scientific tools becomes the primary challenge when an emergency outbreak occurs. The multi criteria group decision-making (MCGDM) method is an applicable and common method for choosing supplier. Nevertheless, because emergency medical supplier selection should consider regarding many aspects, it is difficult for decision makers (DMs) to develop a comprehensive assessment method for emergency medical supplier. Therefore, few academics have focused on emergency situation research by the MCGDM method, and the existing MCGDM method has some areas for improvement. In view of this situation, in this study, we propose a new MCGDM method, which considers the bidirectional influence relation of the criteria, consensus and the psychological factors of DMs. It providers a good aid in emergency decision-making and it could apply to other types of MCGDM research. Firstly, DMs give their assessment in interval type-2 fuzzy sets (IT2FSs). Secondly, an extended IT2FSs assessment method and a novel ISM-BWM-Cosine Similarity-Max Deviation Method (IBCSMDM) are used for weighing all alternatives. The TODIM (an acronym for interactive and multi-criteria decision-making in Portuguese) can obtain the ranking results under different risk attenuation factors. Eventually, this extended IT2FSs-IBCSMDM-TODIM method is applied in a real case in Wuhan in the context of COVID-19 to illustrate the practicability and usefulness.

A systematic review: the dimensions to evaluate health care performance and an implication during the pandemic

BACKGROUND

The balanced scorecard (BSC) has been implemented to evaluate the performance of health care organizations (HCOs). BSC proved to be effective in improving financial performance and patient satisfaction.

AIM

This systematic review aims to identify all the perspectives, dimensions, and KPIs that are vital and most frequently used by health care managers in BSC implementations.

METHODS

This systematic review adheres to PRISMA guidelines. The PubMed, Embase, Cochrane, and Google Scholar databases and Google search engine were inspected to find all implementations of BSC at HCO. The risk of bias was assessed using the nonrandomized intervention studies (ROBINS-I) tool to evaluate the quality of observational and quasi-experimental studies and the Cochrane (RoB 2) tool for randomized controlled trials (RCTs).

RESULTS

There were 33 eligible studies, of which we identified 36 BSC implementations. The categorization and regrouping of the 797 KPIs resulted in 45 subdimensions. The reassembly of these subdimensions resulted in 13 major dimensions: financial, efficiency and effectiveness, availability and quality of supplies and services, managerial tasks, health care workers' (HCWs) scientific development error-free and safety, time, HCW-centeredness, patient-centeredness, technology, and information systems, community care and reputation, HCO building, and communication. On the other hand, this review detected that BSC design modification to include external and managerial perspectives was necessary for many BSC implementations.

CONCLUSION

This review solves the KPI categorization dilemma. It also guides researchers and health care managers in choosing dimensions for future BSC implementations and performance evaluations in general. Consequently, dimension uniformity will improve the data sharing and comparability among studies. Additionally, despite the pandemic negatively influencing many dimensions, the researchers observed a lack of comprehensive HCO performance evaluations. In the same vein, although some resulting dimensions were assessed separately during the pandemic, other dimensions still lack investigation. Last, BSC dimensions may play an essential role in tackling the COVID-19 pandemic. However, further research is required to investigate the BSC implementation effect in mitigating the pandemic consequences on HCO.

A framework for information technology-based management against COVID-19 in Iran

Background

The COVID-19 pandemic has become a global concern. Iran is one of the countries affected most by the SARS-CoV-2 outbreak. As a result, the use of information technology (IT) has a variety of applications for pandemic management. The purpose of this study was to develop a conceptual framework for responding to the COVID-19 pandemic via IT management, based on extensive literature review and expert knowledge.

Methods

The conceptual framework is developed in three stages: (1) a literature review to gather practical experience with IT applications for managing the COVID-19 pandemic, (2) a study of Iranian documents and papers that present Iran’s practical experience with COVID-19, and (3) developing a conceptual framework based on the previous steps and validating it through a Delphi approach in two rounds, and by 13 experts.

Results

The proposed conceptual framework demonstrates that during pandemics, 22 different types of technologies were used for various purposes, including virtual education, early warning, rapid screening and diagnosis of infected individuals, and data management. These objectives were classified into six categories, with the following applications highlighted: (1) Prevention (M-health, Internet search queries, telehealth, robotics, Internet of things (IoT), Artificial Intelligence (AI), big data, Virtual Reality (VR), social media); (2) Diagnosis (M-health, drones, telehealth, IoT, Robotics, AI, Decision Support System (DSS), Electronic Health Record (EHR)); (3) Treatment (Telehealth, M-health, AI, Robotic, VR, IoT); (4) Follow-up (Telehealth, M-health, VR), (5) Management & planning (Geographic information system, M-health, IoT, blockchain), and (6) Protection (IoT, AI, Robotic and automatic vehicles, Augmented Reality (AR)). In Iran, the use of IT for prevention has been emphasized through M-health, internet search queries, social media, video conferencing, management and planning objectives using databases, health information systems, dashboards, surveillance systems, and vaccine coverage.

Conclusions

IT capabilities were critical during the COVID-19 outbreak. Practical experience demonstrates that various aspects of information technologies were overlooked. To combat this pandemic, the government and decision-makers of this country should consider strategic planning that incorporates successful experiences against COVID-19 and the most advanced IT capabilities.

Use of Big Data in Disaster Recovery: An Integrative Literature Review

OBJECTIVE

Disasters of all varieties have been steadily increasing in frequency. Simultaneously, "big data" has seen explosive growth as a tool in business and private industries while opportunities for robust implementation in disaster management remain nascent. To more explicitly ascertain the current status of big data as applied to disaster recovery, we conducted an integrative literature review.

METHODS

Eleven databases were searched using iteratively developed keywords to target big data in a disaster recovery context. All studies were dual-screened by title and abstract followed by dual full-text review to determine if they met inclusion criteria. Articles were included if they focused on big data in a disaster recovery setting and were published in the English-language peer-reviewed literature.

RESULTS

After removing duplicates, 25,417 articles were originally identified. Following dual title/abstract review and full-text review, 18 studies were included in the final analysis. Among those, 44% were United States-based and 39% focused on hurricane recovery. Qualitative themes emerged surrounding geographic information systems (GIS), social media, and mental health.

CONCLUSIONS

Big data is an evolving tool for recovery from disasters. More research, particularly in real-time applied disaster recovery settings, is needed to further expand the knowledge base for future applications.

Tecnologia digital para o enfrentamento da Covid-19: um estudo de caso na atenção primária

RESUMO Em países desenvolvidos, a utilização de recursos tecnológicos digitais é considerada uma ferramenta potente para o enfrentamento da Covid-19. No entanto, no Brasil, a implantação desses recursos nas unidades de saúde ainda é bastante deficitária e não priorizada pela gestão. O propósito deste trabalho é relatar a experiência de implantação do aplicativo InfoSaúde, iniciada em julho de 2020, na atenção primária, para a otimização das ações direcionadas ao controle da Covid-19 em uma comunidade vulnerável. A definição dos requisitos do aplicativo e dos fluxos operacionais foram obtidos por meio de questionários padronizados para os profissionais de saúde e usuários, resultando em três macroprocessos: Prevenção, Atendimento e Monitoramento, testados e validados pelos profissionais e usuários, e com interface ao sistema de informação da unidade. Os resultados encontrados foram: a) Retorno às ações de prevenção com informação a distância; b) Agilização das intervenções pela integração de setores de: Assistência, Laboratório e Vigilância; redução da sobrecarga de trabalho e risco ocupacional com atividades a distância; e c) Melhoria no sistema de informação e capacidade de intervenção precoce a distância. A implantação de recurso tecnológico simples na atenção primária é factível, contribuindo para integralidade do cuidado, redução do risco ocupacional e carga de trabalho.

Central COVID-19 Coordination Centers in Germany: Description, Economic Evaluation, and Systematic Review

Background

During the COVID-19 pandemic, Central COVID-19 Coordination Centers (CCCCs) have been established at several hospitals across Germany with the intention to assist local health care professionals in efficiently referring patients with suspected or confirmed SARS-CoV-2 infection to regional hospitals and therefore to prevent the collapse of local health system structures. In addition, these centers coordinate interhospital transfers of patients with COVID-19 and provide or arrange specialized telemedical consultations.

Objective

This study describes the establishment and management of a CCCC at a German university hospital.

Methods

We performed economic analyses (cost, cost-effectiveness, use, and utility) according to the CHEERS (Consolidated Health Economic Evaluation Reporting Standards) criteria. Additionally, we conducted a systematic review to identify publications on similar institutions worldwide. The 2 months with the highest local incidence of COVID-19 cases (December 2020 and January 2021) were considered.

Results

During this time, 17.3 requests per day were made to the CCCC regarding admission or transfer of patients with COVID-19. The majority of requests were made by emergency medical services (601/1068, 56.3%), patients with an average age of 71.8 (SD 17.2) years were involved, and for 737 of 1068 cases (69%), SARS-CoV-2 had already been detected by a positive polymerase chain reaction test. In 59.8% (639/1068) of the concerned patients, further treatment by a general practitioner or outpatient presentation in a hospital could be initiated after appropriate advice, 27.2% (291/1068) of patients were admitted to normal wards, and 12.9% (138/1068) were directly transmitted to an intensive care unit. The operating costs of the CCCC amounted to more than €52,000 (US $60,031) per month. Of the 334 patients with detected SARS-CoV-2 who were referred via EMS or outpatient physicians, 302 (90.4%) were triaged and announced in advance by the CCCC. No other published economic analysis of COVID-19 coordination or management institutions at hospitals could be found.

Conclusions

Despite the high cost of the CCCC, we were able to show that it is a beneficial concept to both the providing hospital and the public health system. However, the most important benefits of the CCCC are that it prevents hospitals from being overrun by patients and that it avoids situations in which physicians must weigh one patient’s life against another’s.

Innovation in Coping with the COVID-19 Pandemic: The Best Practices from Five Smart Cities in Indonesia

In relation to innovations which help to cope with the COVID-19 pandemic, the best practices of several smart cities become an important input to organize and create strategies for future cities. This research aims to identify the responses of cities to the COVID-19 pandemic; analyze their innovation in tackling the COVID-19 pandemic; and create strategies and planning for the future of the cities. This study analyzes aspects such as the use of information and communication technology (ICT), smart city implementation, the biological disaster of the COVID-19 pandemic, the environment, and spatial plans. This research was carried out in five smart cities using a case study. The results indicated that each case had innovations for coping with the COVID-19 pandemic. They showed a uniqueness and local innovations adapted to the problems faced in the five case areas. The innovations were demonstrated by the use of ICT-based applications in several public services as part of smart city implementation. The concept of a smart city, which addresses the biological disaster of the COVID-19 pandemic through the existence of ship-based isolation centers and sociotechnical innovations, was then adapted in various cities throughout Indonesia. In terms of the smart environment concept, this is translated through technological and social innovation approaches to improve medical and domestic waste management, public service systems, and the socialization of environmental protection programs in cities during the pandemic. The COVID-19 pandemic is allegedly accelerating the implementation of the smart city concept in spatial planning. There is a tendency for the use of public space in the city center to shift to local-scale service centers. Additionally, other activities are increasingly occupying the digital space so that it affects the arrangement of spatial organization and increases the need for ICT infrastructure. The efficient and flexible use of applications for supporting the implementation of smart cities needs to be broadened for the public services provided by both the government and private sectors. Meanwhile, in relation to the dimensions of a smart environment, it is necessary to take into account the waste management as a result of COVID-19. The same case is the aspect of spatial planning in which it is necessary to redesign open spaces for public use. City planning in the future also needs to be capable of the smart mitigation of non-physical disasters, such as the COVID-19 pandemic.

Development, Implementation, and User Evaluation of COVID-19 Dashboard in a Third-Level Hospital in Iran

INTRODUCTION

The implementation of a dashboard enables managers to make informed and evidence-based decisions through data visualization and graphical presentation of information. This study aimed to design and implement a COVID-19 management dashboard in a third-level hospital in Mashhad, Iran.

MATERIALS AND METHODS

This descriptive developmental applied study was conducted in the second half of 2020 in three stages, using user-centered design methodology in four phases: (1) specification of the application context, (2) specification of requirements, (3) creation of design solutions, and (4) evaluation of designs. Data collection in each phase was performed through holding group discussions with the main users, nominal group techniques, interviews, and questioners. The dashboard prototype for the data display was designed using the Power BI Desktop software. Subsequently, users' comments were obtained using the focus group method and included in the dashboard.

RESULTS

In total, 25 indicators related to input, process, and output areas were identified based on the findings of the first stage. Moreover, eight items were introduced by participants as dashboard requirements. The dashboard was developed based on users' feedback and suggestions, such as the use of colors, reception of periodic and specific reports based on key performance indicators, and rearrangement of the components visible on the page. The result of the user satisfaction survey indicated their satisfaction with the developed dashboard.

CONCLUSION

The selection of proper criteria for the implementation of an effective dashboard is critical for the health care organization since they are designed with a high-tech and content-based environment. The dashboard in the present study was a successful combination of clinical and managerial indicators. Future studies should focus on the design and development of dashboards, as well as benchmarking by using data from several hospitals.

The most places at risk surrounding the COVID-19 treatment hospitals in an urban environment- case study: Tehran city

Investigations on the spatial patterns of COVID-19 spreading indicate the possibility of the virus transmission by moving infected people in an urban area. Hospitals are the most susceptible locations due to the COVID-19 contaminations in metropolises. This paper aims to find the riskiest places surrounding the hospitals using an MLP-ANN. The main contribution is discovering the influence zone of COVID-19 treatment hospitals and the main spatial factors around them that increase the prevalence of COVID-19. The innovation of this paper is to find the most relevant spatial factors regarding the distance from central hospitals modeling the risk level of the study area. Therefore, eight hospitals with two service areas for each of them are computed with [0-500] and [500-1000] meters distance. Besides, five spatial factors have been considered, consist of the location of patients' financial transactions, the distance of streets from hospitals, the distance of highways from hospitals, the distance of the non-residential land use from the hospitals, and the hospital patient number. The implementation results revealed a meaningful relation between the distance from the hospitals and patient density. The RMSE and R measures are 0.00734 and 0.94635 for [0-500 m] while these quantities are 0.054088 and 0.902725 for [500-1000 m] respectively. These values indicate the role of distance to central hospitals for COVID-19 treatment. Moreover, a sensitivity analysis demonstrated that the number of patients' transactions and the distance of the non-residential land use from the hospitals are two dominant factors for virus propagation. The results help urban managers to begin preventative strategies to decrease the community incidence rate in high-risk places.

A scope of mobile health solutions in COVID-19 pandemics

Background and aim

COVID-19 has become an international emergency. The use of digital solutions can be effective in managing, preventing, and overcoming the further spread of infectious disease outbreaks. Accordingly, the use of mobile-health (m-health) technologies has the potential to promote public health. This review aimed to study the application of m-health solutions for the management of the COVID-19 outbreak.

Methods

The search strategy was done in Medline (PubMed), Embase, IEEE, and Google Scholar by using related keywords to m-health and COVID-19 on July 6, 2020. English papers that used m-health technologies for the COVID-19 outbreak were included.

Results

Of the 2046 papers identified, 16 were included in this study. M-health had been used for various aims such as early detection, fast screening, patient monitoring, information sharing, education, and treatment in response to the COVID-19 outbreak. M-health solutions were classified into four use case categories: prevention, diagnosis, treatment, and protection. The mobile phone-based app and short text massaging were the most frequently used modalities, followed by wearables, portable screening devices, mobile-telehealth, and continuous telemetry monitor during the pandemics.

Conclusion

It appears that m-health technologies played a positive role during the COVID-19 outbreak. Given the extensive capabilities of m-health solutions, investigation and use of all potential applications of m-health should be considered for combating the current Epidemics and mitigating its negative impacts.