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Objectives: COVID 19 and increasing unmet needs of health technology had accelerated an adoption of digital health globally and the major categories are mobile-health, health information technology, telemedicine. Digital health interventions have various benefit on clinical efficacy, quality of care and reducing healthcare costs. The objective of the study is to identify new reimbursement policy trend of digital health medical devices in South Korea. Method(s): Official announcements published in national bodies and supplementary secondary research were used to capture policies, frameworks and currently approved products since 2019. Result(s): With policy development, several digital health devices and AI software have been introduced as non-reimbursement by utilizing new Health Technology Assessment (nHTA) pathway including grace period of nHTA and innovative medical devices integrated assessment pathway. AI based cardiac arrest risk management software (DeepCARS) and electroceutical device for major depressive disorders (MINDD STIM) have been approved as non-reimbursement use for about 3 years. Two digital therapeutics for insomnia and AI software for diagnosis of cerebral infarction were approved as the first innovative medical devices under new integrated assessment system, and they could be treated in the market. In addition, there is remote patient monitoring (RPM) reimbursement service fee. Continuous glucose monitoring devices have been reimbursed for type 1 diabetes patients by the National Health Insurance Service (NHIS) since January 2019. Homecare RPM service for peritoneal dialysis patients with cloud platform (Sharesource) has been reimbursed since December 2019, and long-term continuous ECG monitoring service fee for wearable ECG monitoring devices (ATpatch, MEMO) became reimbursement since January 2022. Conclusion(s): Although Korean government has been developed guidelines for digital health actively, only few products had been reimbursed. To introduce new technologies for improved patient centric treatment, novel value-based assessment and new pricing guideline of digital health medical devices are quite required.Copyright © 2023
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To better prepare for the next pandemic, the White House seeks a significant increase in discretionary funding for the Centers for Disease Control and Prevention (CDC), proposing a $1.6 billion increase to an $8.7 billion budget able to modernize data collection and boost support for local health departments. ARPA-H initially would focus on cancer, diabetes and Alzheimer's disease and is modeled after the military's Defense Advanced Research Projects Agency (DARPA), with project funding decisions made by program managers, as opposed to the peer-review process of NIH research institutes. In addition to funding genomic sequencing capacity on the state and federal level, the new program would build a National Bioinformatics Infrastructure throughout the public health system.
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During the formation of medical images, they are easily disturbed by factors such as acquisition devices and tissue backgrounds, causing problems such as blurred image backgrounds and difficulty in differentiation. In this paper, we combine the HarDNet module and the multi-coding attention mechanism module to optimize the two stages of encoding and decoding to improve the model segmentation performance. In the encoding stage, the HarDNet module extracts medical image feature information to improve the segmentation network operation speed. In the decoding stage, the multi-coding attention module is used to extract both the position feature information and channel feature information of the image to improve the model segmentation effect. Finally, to improve the segmentation accuracy of small targets, the use of Cross Entropy and Dice combination function is proposed as the loss function of this algorithm. The algorithm has experimented on three different types of medical datasets, Kvasir-SEG, ISIC2018, and COVID-19CT. The values of JS were 0.7189, 0.7702, 0.9895, ACC were 0.8964, 0.9491, 0.9965, SENS were 0.7634, 0.8204, 0.9976, PRE were 0.9214, 0.9504, 0.9931. The experimental results showed that the model proposed in this paper achieved excellent segmentation results in all the above evaluation indexes, which can effectively assist doctors to diagnose related diseases quickly and improve the speed of diagnosis and patients’quality of life. Author
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Physical inactivity is becoming an important threat to public health in today's society. The COVID-19 pandemic has also reduced physical activity (PA) levels given all the restrictions imposed worldwide. In this work, physical activity interventions supported by mobile devices and relying on control engineering principles were proposed. The model was constructed relying on previous studies that consider a fluid analogy of Social Cognitive Theory (SCT), which is a psychological theory that describes how people acquire and maintain certain behaviors, including health-promoting behaviors, through the interplay of personal, environmental, and behavioral factors. The obtained model was validated using secondary data (collected earlier) from a real intervention with a group of male subjects in Great Britain. The present model was extended with new technology for a better understanding of behavior change interventions. This involved the use of applications, such as phone-based ecological momentary assessments, to collect behavioral data and the inclusion of simulations with logical reward conditions for reaching the behavioral threshold. A goal of 10,000 steps per day is recommended due to the significant link observed between higher daily step counts and lower mortality risk. The intervention was designed using a Model Predictive Control (MPC) algorithm configured to obtain a desired performance. The system was tested and validated using simulation scenarios that resemble different situations that may occur in a real setting.
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In recent years, there has been a significant growth in the development of machine learning algorithms towards better experience in patient care. In this paper, a contemporary survey on the deep learning and machine learning techniques used in multimodal signal processing for biomedical applications is presented. Specifically, an overview of the preprocessing approaches and the algorithms proposed for five major biomedical applications are presented, namely detection of cardiovascular diseases, retinal disease detection, stress detection, cancer detection and COVID-19 detection. In each case, processing on each multimodal data type, such as an image or a text is discussed in detail. A list of various publicly available datasets for each of these applications is also presented. © 2023 IEEE.
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The advance of digitalization is constantly bringing new solutions to various areas of life in our society. The COVID-19 pandemic, among other things, brought increased attention to the application and support of treatments through digital solutions in the healthcare sector due to contact restrictions. However, the development of digital solutions comes at a high cost in terms of time and expenses. Mobile app development requires the development of two separate apps for the two respective market-leading mobile operating systems iOS and Android. Cross-platform frameworks make it possible to develop apps for both operating systems on a single code base, thus saving the development and maintenance of two separate codes. Flutter is currently the most popular cross-platform framework for the development of mobile apps. This paper has evaluated Flutter based on an existing criteria catalogue. As a usage context for the evaluation, a prototype for Cancer Counselling App of the University Medical Center Freiburg was implemented. According to the gained own prototyping experience with Flutter and a thorough literature analysis in this area, the criteria catalogue was filled out and the result was compared with other mobile App development paradigms. Copyright © 2023 by SCITEPRESS - Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)
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Objective Based on text mining technology and biomedical database, data mining and analysis of coronavirus disease 2019 (COVID-19) were carried out, and COVID-19 and its main symptoms related to fever, cough and respiratory disorders were explored. Methods The common targets of COVID-19 and its main symptoms cough, fever and respiratory disorder were obtained by GenCLiP 3 website, Gene ontology in metascape database (GO) and pathway enrichment analysis, then STRING database and Cytoscape software were used to construct the protein interaction network of common targets, the core genes were screened and obtained. DGIdb database and Symmap database were used to predict the therapeutic drugs of traditional Chinese and Western medicine for the core genes. Results A total of 28 gene targets of COVID-19 and its main symptoms were obtained, including 16 core genes such as IL2, IL1B and CCL2. Through the screening of DGIdb database, 28 chemicals interacting with 16 key targets were obtained, including thalidomide, leflunomide and cyclosporine et al. And 70 kinds of Chinese meteria medica including Polygonum cuspidatum, Astragalus membranaceus and aloe. Conclusion The pathological mechanism of COVID-19 and its main symptoms may be related to 28 common genes such as CD4, KNG1 and VEGFA, which may participate in the pathological process of COVID-19 by mediating TNF, IL-17 and other signal pathways. Potentially effective drugs may play a role in the treatment of COVID-19 through action related target pathway.Copyright © 2022 Tianjin Press of Chinese Herbal Medicines. All Rights Reserved.
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For many years, proper biomedical waste (BMW) management in line with the rules was among the overlooked components of health care. Biomedical waste presents a series of environmental contamination. As a result, it must be treated using extreme caution and disposed of properly. There are a few gaps in the handling of biological generated waste, and the coronavirus epidemic has made it very hard. The virus's quick breakout resulted in a massive increase in the amount of biohazardous matter. The COVID-19 epidemic's devastation has altered global waste generation trends, needing special attention. Sudden variations in trash generation and volume need a dynamic response from authorities. This study highlights the problems that the collection and recycling business faces even after a pandemic, as well as the basic possibility to eliminate current framework faults. The study covers specific situations for handling medical waste, polymeric garbage, and recycling bins, which were all major causes of concerns all through this time period. We also go over successful stakeholder involvement and teamwork.The existence of illness sewage treatment in regular effluent created offers significant dangers and liabilities to hygiene workers. Small metal usage is predicted to recover as a result of rising hygiene concerns, particularly from items used for safety precautions and medication. The research further underlines the significance of creating localized, streamlined supply channels to deal with these kinds of situations in the case of unanticipated devastating catastrophes. Despite presenting unique solutions to existing recycling challenges, the paper also presents numerous crucial recommendations to regulators to enable them to cope with any potential outbreaks in a holistic way. © 2023 Author(s).
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COVID-19, chronic obstructive pulmonary disease (COPD), heart failure (HF), and pneumonia can lead to acute respiratory deterioration. Prompt and accurate diagnosis is crucial for effective clinical management. Chest X-ray (CXR) and chest computed tomography (CT) are commonly used for confirming the diagnosis, but they can be time-consuming and biased. To address this, we developed a computationally efficient deep feature engineering model called Hybrid-Patch-Alex for automated COVID-19, COPD, and HF diagnosis. We utilized one CXR dataset and two CT image datasets, including a newly collected dataset with four classes: COVID-19, COPD, HF, and normal. Our model employed a hybrid patch division method, transfer learning with pre-trained AlexNet, iterative neighborhood component analysis for feature selection, and three standard classifiers (k-nearest neighbor, support vector machine, and artificial neural network) for automated classification. The model achieved high accuracy rates of 99.82%, 92.90%, and 97.02% on the respective datasets, using kNN and SVM classifiers.
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In recent years, COVID-19 has impacted all aspects of human life. As a result, numerous publications relating to this disease have been issued. Due to the massive volume of publications, some retrieval systems have been developed to provide researchers with useful information. In these systems, lexical searching methods are widely used, which raises many issues related to acronyms, synonyms, and rare keywrds. In this paper, we present a hybrid relation retrieval system, CovRelex-SE, based on embeddings to provide high-quality search results. Our system can be accessed through the following URL: https://www.jaist.ac.jp/is/labs/nguyen-lab/systems/covrelex-se/. © 2023 Association for Computational Linguistics.
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A highly transmissible and pathogenic Coronavirus SARS-CoV-2 has caused the COVID-19 pandemic, which severely affected human health and impacted negatively on the environment. In this review, we discuss the extent of the generation of COVID waste, and how its disposal can influence the environment. We have especially emphasized the COVID-related biomedical waste management. An attempt has also been made to identify several challenges encountered in India. Studies have indicated an altered water usage pattern, which increased megacities' water footprint in India. Enhanced domestic sewage discharge resulted in higher fecal coliform count in water bodies. Disposal of COVID biomedical waste (CBW) and personal protective equipment (PPE) resulted in a huge amount of single-use plastics (SUPs);which in turn cause the long-term risk of micro- and nano-plastic in the environment. This review also aims to put up the need for well-equipped infrastructure, efficient treatment facility, and public availability of CBW data in India to make effective policies and sustainable solutions for long-term goals. © 2023 Elsevier Inc. All rights reserved.
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Coinciding with the global pandemic of SARS-CoV-2 and the resulting global public health crisis caused by COVID-19, artificial intelligence methods started playing an ever more important role in Infectious Medicine. On one hand this was a result of a continuous digital transformation of Infectious Medicine-a trend started decades ago. On the other hand, the pandemic catalyzed the adoption of artificial intelligence and other digital and quantitative techniques by Infectious Medicine. In this chapter we review recent works touching upon aspects of COVID-19 patient journey and how it interconnects with big data and artificial intelligence. These include early and clinical research, epidemiology and detection, diagnostics, clinical care and decision support, as well as long-term care and prevention. We cross-compare the published works and assess their maturity. Finally, we provide a conclusion on the state of artificial intelligence in the Infectious Medicine of COVID-19 and attempt a future perspective. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.
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Purpose: The main objective of this paper is to analyze the Brazilian Ministry of Health (MoH) efforts in the management of medical equipment, with a specific approach for lung ventilators in the pandemic scenario of COVID-19. Methods: The methodology included a review of the normative framework and literature on technological management and research on the database of the Ministry of Health. Results: As a promoter for acquiring medical equipment, the MoH role is highlighted and added to this competence; its function as the coordinator of the National Policy on Health Technology Management (PNGTS). According to the PNGTS the MoH has to support health managers in the implementing, monitoring, and maintaining health technologies. The scenario of lung ventilators in the pandemic was discussed, with research to verify demands, offers, installed capacity, and investments. In less than one year, the Ministry of Health acquired several pulmonary ventilators, 8.55 times greater than the annual averages of equipment acquired from 2016 to 2019. So far, there is still no maintenance plans or strategy of management for that equipment, especially in a post-pandemic scenario. Conclusion: It is possible to conclude that the Ministry of Health needs to improve health technology management systems. On the scale of the Policy, it is necessary to commit to permanent and long-term actions to ensure sustainability and reduce the technological vulnerabilities of the SUS.
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Amid COVID-19, world has gone under environmental reformation in terms of clean rivers and blue skies, whereas, generation of biomedical waste management has emerged as a big threat for the whole world, especially in the developing nations. Appropriate biomedical waste management has become a prime concern worldwide in the pandemic era of COVID-19 as it may affect environment and living organisms up to a great extent. The problem has been increased many folds because of unexpected generations of hazardous biomedical waste which needs extraordinary attentions. In this paper, the impacts and future challenges of solid waste management especially the biomedical waste management on environment and human beings have been discussed amid COVID-19 pandemic. The paper also recommends some guidelines to manage the bulk of medical wastes for the protection of human health and environment. The paper summarizes better management practices for the wastes including optimizing the decision process, infrastructure, upgrading treatment methods and other activities related with the biological disasters like COVID-19. As achieved in the past for viral disinfection, use of UV- rays with proper precautions can also be explored for COVID-19 disinfection. For biomedical waste management, thermal treatment of waste can be an alternative, as it can generate energy along with reducing waste volume by 80-95%. The Asian Development Bank observed that additional biomedical waste was generated ranged from 154 to 280 tons/day during the peak of COVID-19 pandemic in Asian megacities such as Manila, Jakarta, Wuhan, Bangkok, Hanoi, Kuala Lumpur.
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Introduction: Research participation during undergraduate years has a powerful influence on career selection and attitudes toward scientific research. Most undergraduate research programs in academic health centers are oriented toward basic research or address a particular disease focus or research discipline. Undergraduate research programs that expose students to clinical and translational research may alter student perceptions about research and influence career selection. Methods: We developed an undergraduate summer research curriculum, anchored upon a clinical and translational research study developed to address a common unmet needs in neonatal nurseries (e.g., assessment of neonatal opioid withdrawal syndrome). Program topics reflected the cross-disciplinary expertise that contributed to the development of this "bedside to bench" study, including opioid addiction, vulnerable populations, research ethics, statistics, data collection and management, assay development, analytical laboratory analysis, and pharmacokinetics. The curriculum was delivered through three offerings over 12 months, using Zoom video-conferencing due to restrictions imposed by the COVID-19 pandemic. Results: Nine students participated in the program. Two-thirds reported the course enhanced their understanding of clinical and translational research. Over three-quarters reported the curriculum topics were very good or excellent. In open-ended questions, students reported that the cross-disciplinary nature of the curriculum was the strongest aspect of the program. Conclusion: The curriculum could be readily adapted by other Clinical and Translational Science Award programs seeking to provide clinical and translational research-oriented programs to undergraduate students. Application of cross-disciplinary research approaches to a specific clinical and translational research question provides students with relevant examples of translational research and translational science.
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The COVID-19 pandemic has had significant impacts on the health of individuals and communities around the world. While the immediate health impacts of the virus itself are well-known, there are also a number of post-pandemic health issues that have emerged as a result of the pandemic. The pandemic has caused increased levels of anxiety, depression, and other mental health issues among people of all ages. The isolation, uncertainty, and grief caused by the pandemic have taken a toll on people's mental well-being, and there is a growing concern that the long-term effects of the pandemic on mental health could be severe. Many people have delayed or avoided medical care during the pandemic, which could lead to long-term health problems. Additionally, people who have contracted COVID-19 may experience ongoing symptoms, such as fatigue, shortness of breath, and muscle weakness, which could impact their long-term health. Machine learning (ML) can be a powerful tool to analyze the health impact of the post-pandemic period. With the vast amounts of data available from electronic health records, public health databases, and other sources, this article is making use of ML methods which can help identify patterns and insights to conclude the study. The proposed ML models can analyze health data to identify trends and patterns that may indicate future health problems. By monitoring patterns in medical records and public health data, the proposed ML model can help public health officials detect and respond to outbreaks more quickly. The survey outcome reveals that the level of physical activities has been decreased by 22% during COVID-19-outbreak. The variance is shown at 49% during COVID-19 outbreak. The absence of physical activity (PA) and perceived stress (PS) are observed to be suggestively correlated with the QoL (quality of life) of adults. Deteriorated mental health also disrupts the normal lives and impacts the sleeping quality of people. The analysis of the data is performed using statistical analytical tools to depict the consequences of pandemic on the health of individuals aged between 50 to 80 years.
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Coronavirus outbreaks during the last couple of years created a huge health disaster for human lives. Diagnosis of COVID-19 infections is, thus, very important for the medical practitioners. For a quick detection, analysis of the COVID-19 chest X-ray images is inevitable. Therefore, there is a strong need for the development of a multiclass segmentation method for the purpose. Earlier techniques used for multiclass segmentation of images are mostly based on entropy measurements. Nonetheless, entropy methods are not efficient when the gray-level distribution of the image is nonuniform. To address this problem, a novel adaptive class weight adjustment-based multiclass segmentation error minimization technique for COVID-19 chest X-ray image analysis is investigated. Theoretical investigations on the first-hand objective functions are presented. The results on both the biclass and multiclass segmentation of medical images are enlightened. The key to our success is the adjustment of the pixel counts of different classes adaptively to reduce the error of segmentation. The COVID-19 chest X-ray images are taken from the Kaggle Radiography database for the experiments. The proposed method is compared with the state-of-the-art methods based on Tsallis, Kapur's, Masi, and Renyi entropy. The well-known segmentation metrics are used for an empirical analysis. Our method achieved a performance increase of around 8.03% in the case of PSNR values, 3.01% for FSIM, and 4.16% for SSIM. The proposed technique would be useful for extracting dots from micro-array images of DNA sequences and multiclass segmentation of the biomedical images such as MRI, CT, and PET.
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COVID-19 crisis has led to an outburst of information that needs to be organized, validated, and made available to the seekers. Despite the rapid growth and success of BERT models in the last 3 years, COVID QA is a difficult task due to the lack of applicable datasets and a relevant language representation. Therefore, this study proposes a transformer-based Question Answering (QA) model for COVID-19 questions from the biomedical domain. Further, explored several datasets, and models required for question type prediction, no-Answer prediction, and answer extraction and transfer learning strategies. It has been demonstrated that the exact match score can be significantly improved with limited amounts of training data from the biomedical domain. Finally, the findings of the study have been summarized as Factoid QA Finetuning Framework (FQFF), which can provide initial direction for domain-specific QA tasks with a limited amount of data. © 2023 IEEE.
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The Covid-19 global pandemic has represented a challenge for education, which had to migrate to virtual environments. Universities adopted different teaching methods to keep contributing to the growth of the professionals in various fields. In this context, the Biomedical Engineering program of the Pontificia Universidad Catolica del Peru and the Universidad Peruana Cayetano Heredia had to change or adapt the methodology of the courses included in its curriculum in order to reach the learning objectives. This paper presents a methodology for an innovative approach of simulated scenarios using digital tools for the virtual teaching of Clinical Engineering. The learning results achieved in two semesters of implementation of the methodology, during 2020 and 2021, were measured by means of a survey applied to the students at the end of the course. Obtaining achievement results above 76 % and improvement opportunities that would be useful for the next version of this course and for the replication of the methodology in other universities. © 2023 IEEE.
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The main causes of death in the world are cardiovascular diseases, strokes and respiratory diseases, among which the following stand out: chronic obstructive pulmonary disease and respiratory system infections. Regarding pulmonary function measurement technology, spirometry is the reference standard for the diagnosis and evaluation. This test requires specialized equipment that does not allow it to be performed on an outpatient basis or for constant monitoring. For this reason, the doctor must systematically look for the presence of symptoms that may go unnoticed by the patient and that can be attributed to age, sedentary lifestyle, or the fact of smoking. This is why it would be important to be able to constantly monitor breathing in order to identify irregularities in breathing rates that could be indicative of a respiratory condition.The solution proposed in this article is focused on the design of a prototype of a wearable device that allows the monitoring of the respiratory rate. For this prototype feasibility is analyzed using signals from a database. This device will allow this biometric variable to be identified and will notify when it is outside the normal ranges to suggest an airflow test (spirometry) at a possible early stage of a respiratory condition. This instrumentation system will be integrated into the frame of a pair of glasses, specifically positioning the sensor on the nasal platelets.