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The epidemic caused by a new mutation of the coronavirus family called Covid-19 has created a global crisis involving all the world's countries. This disease has become a severe danger to everyone due to its unknown nature, high spread, and inability to detect the infected. In this regard, one of the important issues facing patients with Covid-19 is the prescription of Drugs according to the severity of the disease and considering the records of underlying diseases in people. In recent years, recommender systems have been developed significantly along with the advancement in information technology and artificial intelligence, which is one of its applications in various fields of medical sciences. Among them, we can refer to recommending systems for the prevention, control, and treatment of diseases. In this research, using the collaborative filtering approach as one of the types of recommender systems as well as the K-means clustering algorithm, a Drug recommendation system for patients with Covid-19 in the treatment stage of the disease is presented. The results of this research show that this recommender system has an acceptable performance based on the evaluation criteria of precision, recall, and F1-score compared to the opinions of experts in this field. © 2023 IEEE.
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As a side-effect of the Covid-19 pandemic, significant decreases in medical procedures for noncommunicable diseases have been observed. This calls for a decision support assisting in the analysis of opportunities to relocate procedures among hospitals in an efficient or, preferably, optimal manner. In the current paper we formulate corresponding decision problems and develop linear (mixed integer) programming models for them. Since solving mixed integer programming problems is NP-complete, we verify experimentally their usefulness using real-world data about urological procedures. We show that even for large models, with millions of variables, the problems' instances are solved in perfectly acceptable time.
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The pandemic is changing the clinical needs and potential for AI-driven computer-assisted diagnoses (CDS). Since the beginning, rapid identification of COVID-19 patients has been a significant difficulty, especially in areas with limited diagnostic testing capacity. Intelligent Information System (IIS) represents the knowledge progression of available data. It has been directed by recent technological integration, data processing, and distribution in multiple computational environments. Intelligent Information Systems are aimed to work like an advanced human brain, where, as per the requirement of changing circumstances, the optimal decision can be evolved. IIS tools are expected to be adaptive, which may vary according to their processing data. As a result, the goal of this study was to provide a complete analysis of various technologies for combating COVID-19, with a focus on their features, problems, and domiciliation nation. Our findings demonstrate the performance of developing technologies. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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The future of the healthcare sector and its systems are beginning to take shape by combining technological innovations with traditional techniques to generate new and efficient solutions for patient care and data storage. Most conventional medical information management and storage systems are centralized, which means there is a risk of data loss in the event of malicious attacks or even natural disasters because of the single point of failure that centralized systems have. Blockchain technology is decentralized and emerging, offering the potential to significantly revolutionize how data is stored and managed in the healthcare industry. This paper presents the advantages of using blockchain in the healthcare sector, as this technology has a strategic role in both the management of stored data and the creation of predictive systems that could prevent a pandemic like the one generated by the SARS-Cov-2 virus. We present different existing studies and the method addressed in the ongoing STAMINA project. © 2022 IEEE.
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Beginning in 2020, the new coronavirus began to expand globally. Due to Covid-19, millions of individuals are infected. Initially, the availability of corona test kits was problematic. Researchers examined the present scenario and developed the Covid-19 X-ray scan detection system. In terms of Covid-19 detection, artificial intelligence (AI)-based solutions give superior outcomes. Many AI-based models can not provide optimum results because of the issue of overfitting, which has a direct impact on model efficiency. In this work, we developed the CNN-based classification method based on the pre-trained Inception-v3 for normal, viral pneumonia, lung opacity, and Covid-19 samples. In the suggested model, we employed transfer learning to produce promising results for binary class classification. The presented model attained impressive outcomes with an accuracy of 99.42% for Covid-19 vs. Normal, 99.01% for Covid-19 vs. Lung Opacity, and 99.8% for Covid-19 vs. Viral Pneumonia, and 99.93% for Lung Opacity vs. Viral Pneumonia. Comparing the suggested model to existing deep learning-based systems indicated that ours was better. © 2022 IEEE.
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The COVID-19 pandemic has significantly augmented the urgency for service providers to identify and develop clinically urgent system alterations into healthcare systems to facilitate antibody testing and treatment interventions. However, it has been difficult to determine how users assess the value of an information system in terms of its functionality and features. Conversely, the system development process to address urgent user requirements, for example, developing new functionality for COVID antibody testing, has been beset by a myriad of difficulties as research to understand the value of specific aspects of clinical information systems has been elusive. This study addresses this knowledge gap by identifying specific aspects of a national clinical information system in Wales, UK. Through a series of semi-structured interviews, a quantitative study of 559 clinical users and a focus group, the study deconstructs system-related value into 14 unique attributes that have been found to vary according to different types of user roles and geographic location. Attribution theory is identified in this study as a novel and effective way to study this multifaceted concept of system value. The identification of component attributes of the value of a clinical information system provides insights for service users, system developers, and organization managers to prioritize and focus their system development activity by using an importance ranking identified through this study. © 2021 Elsevier Ltd
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Over the course of the COVID-19 pandemic, large volumes of biomedical information concerning this new disease have been published on social media. Some of this information can pose a real danger to people's health, particularly when false information is shared, for instance recommendations on how to treat diseases without professional medical advice. Therefore, automatic fact-checking resources and systems developed specifically for the medical domain are crucial. While existing fact-checking resources cover COVID-19-related information in news or quantify the amount of misinformation in tweets, there is no dataset providing fact-checked COVID-19-related Twitter posts with detailed annotations for biomedical entities, relations and relevant evidence. We contribute CoVERT, a fact-checked corpus of tweets with a focus on the domain of biomedicine and COVID-19-related (mis)information. The corpus consists of 300 tweets, each annotated with medical named entities and relations. We employ a novel crowdsourcing methodology to annotate all tweets with fact-checking labels and supporting evidence, which crowdworkers search for online. This methodology results in moderate inter-annotator agreement. Furthermore, we use the retrieved evidence extracts as part of a fact-checking pipeline, finding that the real-world evidence is more useful than the knowledge indirectly available in pretrained language models. © European Language Resources Association (ELRA), licensed under CC-BY-NC-4.0.
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Crisis management is witnessing rapid changes due to advances in information technology, and the development of information systems in various fields, especially in the field of higher education during the spread of the Corona pandemic, has led to the need to study the relationship between information systems and crisis management. This study aims to identify the degree of relationship between the application of the six procedures and controls for information systems and the five stages of crisis management from the point of view of workers in the information and documentation centers at the University of Benghazi, University of Tripoli and the Libyan International Medical University, Libya. The descriptive-analytical method was applied to this study in addition to analyzing the results using a statistical application called the Statistical Package for Social Sciences (SPSS), which was used to calculate the frequencies for each procedure and create tables and charts. To analyze the data, we used Mean and Standard deviations, standard error, Cronbach's alpha coefficient, and Pearson's correlation coefficient as a statistical relationship. The study determined whether or not there is a correlation between the independent variables, i.e., the six procedures and controls on the information systems scale (data, physical requirements, software requirements, networks and communications, data and information security, human resources) and the dependent variable, i.e., the five stages on the crisis management scale (the stage of discovery of warning signals, the stage of Preparedness and prevention, damage containment stage, activity recovery stage, learning stage). © 2022 IEEE.
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With the development of telecare medical information system (TMIS), doctors and patients are able to access useful medical services via 5G wireless communications without visiting the hospital in person. Unfortunately, TMIS should have the essential security properties, such as anonymity, mutual authentication, and privacy, since the patient’s data is transmitted via a public channel. Moreover, the sensing devices deployed in TMIS are resource-limited in terms of communication and computational costs. Thus, we design a physically secure privacy-preserving scheme using physical unclonable functions (PUF) in TMIS, called PUF-PSS to resolve the security requirements and efficiency of the existing related schemes. PUF-PSS prevents the security threats and also guarantees anonymity, key freshness, and authentication. We evaluate the security of PUF-PSS by performing formal and informal security analyses, including AVISPA implementation and ROR oracle model. We perform the test bed experiments utilizing well-known MIRACL based on a Raspberry PI 4 and compare the communication and computational costs of PUF-PSS with the previous schemes for TMIS. Consequently, PUF-PSS guarantees better efficiency and security than previous schemes and can be applied to TMIS environments.
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This article analyzes the development status, development trend and prospects of China's Internet of Medical Things (IoMT) industry from a macro perspective. Our survey mainly includes: analyzing the necessity and urgency of China's medical system reform from the various dilemmas faced by China's medical system, and analyzing the development of the IoMT industry based on the current basic conditions of development of the Internet of Things (IoT), information technology and background of COVID-19 epidemic. Opportunities and the evolution of China's IoMT policy were also analyzed. Moreover, from the five aspects of medical industry informatization, Internet hospitals, smart wearable devices, medical AI industry and medical industry digitization, the development status and trends of China's IoMT industry are analyzed. Finally, it looks forward to the development prospects and directions of IoMT industry for health care in China. © 2022 The authors and IOS Press.
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With the prevailing COVID-19 pandemic, the lack of digitally-recorded and connected health data poses a challenge for analysing the situation. Virus outbreaks, such as the current pandemic, allow for the optimisation and reuse of data, which can be beneficial in managing future outbreaks. However, there is a general lack of knowledge about the actual flow of information in health facilities, which is also the case in Uganda. In Uganda, where this case study was conducted, there is no comprehensive knowledge about what type of data is collected or how it is collected along the journey of a patient through a health facility. This study investigates information flows of clinical patient data in health facilities in Uganda. The study found that almost all health facilities in Uganda store patient information in paper files on shelves. Hospitals in Uganda are provided with paper tools, such as reporting forms, registers and manuals, in which district data is collected as aggregate data and submitted in the form of digital reports to the Ministry of Health Resource Center. These reporting forms are not digitised and, thus, not machine-actionable. Hence, it is not easy for health facilities, researchers, and others to find and access patient and research data. It is also not easy to reuse and connect this data with other digital health data worldwide, leading to the incorrect conclusion that there is less health data in Uganda. The a FAIR architecture has the potential to solve such problems and facilitate the transition from paper to digital records in the Uganda health system. © 2022 Chinese Academy of Sciences. Published under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.
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Ischemic stroke is a medical emergency that requires hospitalization and occasionally, specialized care at the Intensive Care Unit. Mortality prediction in the ICUs has been a challenge for intensivists, since prompt identification could impact medical clinical practices and allow efficient allocation of health resources in the ICUs, which are extremely restricted, especially in the era of COVID-19 pandemic. Clinical decision support systems based on machine learning algorithms are taking advantage of the vast amount of information available in the ICUs and are becoming popular in the medical predictive analysis. This study aims to explore the feasibility of interpretable machine learning models to predict mortality in critically-ill patients suffering from stroke. To do so, a vast variety of clinical and laboratory information stored in the electronic health record, are pre-processed to allow taking into account the temporal characteristics of a patient's stay. An 8-hour sliding observation window was utilized. For the experimental evaluation we used the Medical Information Mart for Intensive Care Database (MIMIC-IV). Results indicate sufficient ability to predict mortality at the end of a given day during the patient's stay. Moreover, attribute evaluation highlights the important indicators to consider when following up with a patient. © 2022 ACM.
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In Japan, there is no special law for the comprehensive utilization of personal data in the medical field, and a comprehensive legal system has been lacking for decades. This paper examines the background and issues of the cancer registration law specific to cancer and the legal structure of medical information in Japan and points out that one of the problems that has become clear in dealing with COVID-19 is the difficulty of handling the complicated system of anonymously processed information and the difficulty of using it for the purpose. The report also points out that the Medical Big Data Law, which is also based on the premise of anonymous use, has a complex mechanism in addition to that of the Personal Information Protection Law. A system to certify certification bodies and other protection measures will be introduced yesterday, but before examining the specifics of anonymization, it is necessary to establish a fundamental system that enables the use of more basic information in a way that can be easily understood by healthcare professionals. In conclusion, this paper points out that in the future, while protecting personal information, it will be useful to reorganize it into a special law that is easier to understand from a comprehensive point of view, and to construct a law that makes medical information available to contribute to medical research and development, to reduce the increase in medical costs. © 2022, IFIP International Federation for Information Processing.
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During the COVID-19 pandemic, humanity faced various health problems. One of the most common diseases is pneumonia. The life of every person depends on the correct and effective diagnosis of the disease. Currently, a large number of software applications with elements of artificial intelligence are being developed, which can reduce the time of patient care, improve the methodology and efficiency of disease diagnosis. With our research, we strive to contribute to the development of such software applications, namely, to develop software tools with elements of fuzzy logic. To develop a decision-making system, scales and algorithms in order to assess we considered the prognosis of the severity of community-acquired pneumonia PORT(PSI), CURB/CRB-65 and SMART-COP/SMART-CO. To improve the quality of processing fuzzy production rules of knowledge base, the logic programming language Prolog was used. The created application is planned to be integrated into medical information systems. © 2022 CURRAN-CONFERENCE. All rights reserved.
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Efforts to deal with several risks have been regulated by law, which certain programs should be initiated by the organization especially for the prevention and control of patient care in the context of hospital management system. The availability of service facilities and medical equipment is important in handling Covid-19 patients as many drawbacks have been found due to new circumstances that many do not predict before, such as the allocation room and time management for separating the patients within treatment. Indeed, the shortcomings in managing the process often related to the utilization of technology infrastructure in containing patients' data and the medicine effectively. It is better to identify the issues resides in the hospital before coming up with the solution. Thus, this study want to assess the maturity level of the Information System using the Healthcare Information System Maturity Model (HISMM) as the framework. In the detail, to increase the flexibility of the environment for the sake of study, the investigation was conducted focusing on several attributes as a reference by analysing six dimensions that were relevant namely Data Analysis, Strategy, People, Electronic Medical Records, Information Security, and IT Infrastructure System. The results of this study indicated that State Hospital Jakarta has reached the maturity level at position 4 (four), which is equal to democratic cooperation. The recommendations given in this study are in the form of a roadmap that contains several steps in respected attributes that can support hospitals to reach the next level of maturity in the future. © 2022 IEEE.
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This research document evidences the design and implementation of an entry control system with body temperature taking and disinfection with information registration and cloud storage for the Health Center - San Andres, the work is oriented to control the entry to the health center for both medical staff who meet their workday and users who receive medical care, as this becomes a bridge of contagion when having physical contact between patient and doctor, in order to prevent the spread of COVID-19. © 2022 IEEE Computer Society. All rights reserved.
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The COVID-19 pandemic has pushed hospital organizations to digitally transform in providing health services to patients. Health information systems and integrated electronic medical records must be empowered as a means of communication and providing information to patients, increasing patient involvement and collaboration with health practitioners in hospitals. We adopt a conceptual approach that aims to gain an understanding of digital transformation in facilitating information availability and patient engagement in healthcare. We have explored important extant studies to capture the roots and dynamics at the base of the diffusion of patient-centered healthcare innovations. The results show that eight knowledge-based aspects of digital transformation and the patient-centered healthcare ecosystem form the basis of the proposed conceptual framework, namely: patient and family involvement, information availability, interaction between systems, Omnichannel devices, cloud utilization, information security, and patient privacy, value creation, and patient safety. This research has implications in the form of value creation as a continuous innovation process in the organizational structure of the hospital and patient safety as a benefit that is felt directly by hospital customers. © 2022 ACM.
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Day in day out, data are turned out in various medical laboratories which are adequately documented and used for surveillance in various diseases of concern in public health. Though data mining seems to be new in healthcare, medical laboratory services as very important component of healthcare need serious data mining for diagnosis of ailments and numerous public health diseases. This chapter was carried out based on review of literatures and practices available in Nigeria which contributes to healthcare quality improvement. The chapter examined origin, basic principles, advantages and disadvantages, uses, and challenges of data mining in relation to medical laboratory information management system (MLIMS) while looking at data management from hard to soft copies, possible applications, ethico-legal perspectives, implications of data mining, disease surveillance, and data mining toward quality improvement as used in medical laboratories. It is evident that most of decisions taken in healthcare and public health are based on information provided by data mining from medical laboratory services based on the diseases of interest. Data mining in medical laboratory services is a tool that aids in monitoring trends in the diagnosis of cancer, HIV, COVID-19, malaria, diabetes, and other diseases based on various parameters of assessment with all demographic variables well documented and analyzed. The interested agencies or ministries may apply data mining techniques based on medical laboratory results to find trends in disease outbreaks or deaths, per hospital, state, region, or country through which policies could be formulated and implemented toward surveillance and quality healthcare improvement. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
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The article presents analysis of the Arkhangelsk City Clinical Hospital â 7 capacity and medical personnel staffing. The new coronavirus infection incidence, cases of temporary disability due to disease and quarantine of COVID-19 were analyzed in the Solombalsky district of Arkhangelsk. The issues of primary health care support and readiness of medical personnel to work with medical information systems in in conditions of COVID-19 pandemic were studied. The management decision-makings improving quality and accessibility of medical services using information technologies are demonstrated.
Subject(s)
COVID-19 , Health Workforce , Information Technology , Primary Health Care , Delivery of Health Care , Humans , Pandemics , Personnel Staffing and Scheduling , QuarantineABSTRACT
Medical information systems (MIS) play a vital role in managing and scheduling medical resources to underpin healthcare services, which has become more critically important during major public health emergencies. During the Covid-19 pandemic, MIS is facing significant challenges to cope with the surge in demands of medical resources, resulting in more deaths and wider spreading of the disease. Our research examines how to allocate and utilize the medical resources across hospitals in a more accurate, and effective way to mitigate medical resource shortages and sustain the resource provisions. This paper mainly investigated the hospital's supply-and-demand problems for medical resources under major public health emergencies by analyzing the allocation of medical staff resources. Furthermore, a formal method based on the Colored Petri Nets (CPN) has been proposed to model and characterize the medical business process and resource scheduling tasks. The experiments demonstrate that our approach can correctly and efficiently complete the dynamical scheduling process for surging requests. © 2021 IEEE.