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Center za pomoč uporabnikom rešitev eZdravja je ključna komponenta sistema eZdravje v Sloveniji, ki je namenjena vsem uporabnikom tega sistema. Center izvaja tri osnovne naloge. Splošna podpora vsem uporabnikom rešitev eZdravja je namenjena zdravstvenim delavcem, administrativnemu osebju, informatikom, ponudnikom programskih rešitev, pacientom in vsem drugim uporabnikom rešitev eZdravja, ki želijo prijaviti motnje v delovanju, potrebujejo pomoč ali zahtevajo informacije v zvezi z delovanjem rešitev eZdravja. Storitev elektronskega naročanja na zdravstvene storitve pomaga pacientom pri naročanju. Podpora pri priklopu v zNET je namenjena izvajalcem zdravstvene dejavnosti pri postopku vključitve v omrežje zNET. Dostopanje do pomoči je možno s spletnim obrazcem, preko elektronske pošte, pogosto zastavljenih vprašanj ali telefona. Na svoji spletni strani Center za pomoč uporabnikom objavlja obvestila, povezana z delovanjem rešitev eZdravja, in semafor o delovanju rešitev. V zadnjih dveh letih je bilo v delo Centra vključenih več novih rešitev. Uporaba je pospešeno narasla - v letu 2021 smo beležili več kot sedemkratno povečanje glede na leto 2020. Prispevek analizira delovanje Centra skozi dinamiko in vsebino obravnavanih zahtevkov in opravljenih storitev. Delovanje Centra je pomembna komponenta uspešne uporaba rešitev eZdravja v Sloveniji, kar se je še posebej izkazalo v času epidemije COVID-19.Alternate :The eHealth Service Desk is a key component of the eHealth system in Slovenia, which is intended for the all users of the system. The Service Desk performs three main tasks. General support for all users of eHealth solutions addresses health care professionals, administrative staff, information technology specialists, software solution providers, patients and all other users of eHealth solutions who wish to report malfunctions, need assistance or require information related to the functioning of the eHealth solutions. The electronic appointment for health care services helps patients to make an eAppointment for health care services. The zNET Connection Support offers help to health care providers in the process of joining the zNET network. Assistance can be accessed in several ways: via an online form, email, FAQs or phone. The Service Desk publishes notifications related to the eHealth solutions on its website and maintains a simple indicator that displays the status of eHealth solutions. During the last two years, several new eHealth solutions have been added to the Service Desk portfolio. The use has been growing rapidly in recent years, namely in 2021 we recorded a more than sevenfold increase compared to 2020. The paper analyses the operation of the eHealth Service Desk through the dynamics and content of the requests handled or services provided. The Service Desk is an important component for the successful use of eHealth solutions in Slovenia, which was particularly evident during the COVID-19 epidemic.
ABSTRACT
The global extent of COVID-19 mutations and the consequent depletion of hospital resources highlighted the necessity of effective computer-assisted medical diagnosis. COVID-19 detection mediated by deep learning models can help diagnose this highly contagious disease and lower infectivity and mortality rates. Computed tomography (CT) is the preferred imaging modality for building automatic COVID-19 screening and diagnosis models. It is well-known that the training set size significantly impacts the performance and generalization of deep learning models. However, accessing a large dataset of CT scan images from an emerging disease like COVID-19 is challenging. Therefore, data efficiency becomes a significant factor in choosing a learning model. To this end, we present a multi-task learning approach, namely, a mask-guided attention (MGA) classifier, to improve the generalization and data efficiency of COVID-19 classification on lung CT scan images. The novelty of this method is compensating for the scarcity of data by employing more supervision with lesion masks, increasing the sensitivity of the model to COVID-19 manifestations, and helping both generalization and classification performance. Our proposed model achieves better overall performance than the single-task (without MGA module) baseline and state-of-the-art models, as measured by various popular metrics.
ABSTRACT
Tradicionalno srečanje članov Sekcije za informatiko v zdravstveni negi (SIZN), ki deluje pri Slovenskem društvu za medicinsko informatiko (SDMI), je potekalo 11. novembra 2022 v Termah Zreče kot "hibridni" dogodek. Slišali smo zanimive vsebine, ki jih navajamo po vrstnem redu v programu: * Vladislav Rajkovič: Digitalne kompetence v zdravstvene nege v luči umetne inteligence * Jožica Čehovin Zajc, Marija Milavec Kapun: SmartNurse: kaj se lahko naučimo, ko učimo * Tina Kamenšek, Marija Milavec Kapun: GenoNurse - mednarodno partnerstvo za izboljšanje kompetenc študentov zdravstvene nege na področju genomike * Nino Fijačko, Gregor Štiglic, Lucija Gosak: Percepcija uporabe navidezne resničnosti za učenje temeljnih postopkov oživljanja odrasle osebe pri študentih zdravstvene nege: študija uporabnosti * Jerneja Meža, Barbara Smrke: Obrnjeno učenje v okviru študijskega programa Zdravstvena nega * Maja Drešček Dolinar, Nataša Mlinar Reljić, Barbara Donik, Gregor Štiglic: E-učenje študentov zdravstvene nege v času pandemije COVID-19 * Darja Fridau, Cvetka Krel, Sebastjan Bevc: Vključitev negovalnih diagnoz NANDA-I v elektronski zdravstveni zapis * Cvetka Krel, Dominika Vrbnjak, Gregor Štiglic, Sebastjan Bevc: Vsebinska veljavnost slovenske različice vprašalnika »Vprašalnik za merjenje uporabe, kakovosti in zadovoljstva z elektronskim zdravstvenim zapisom z vidika medicinskih sester« * Samanta Mikuletič, Boštjan Žvanut: Informacijska varnostna kultura med zaposlenimi v zdravstveni negi: rezultati preliminarne študije * Aljaž Bajc, Neža Jarc: Organizacija informatike v Covid enoti intenzivne terapije * Mihael Nedeljko, Boris Miha Kaučič: Uporaba informacijsko komunikacijske tehnologije pri starejših odraslih zmanjšuje socialno izolacijo in izboljšuje kakovost življenja * Anže Mihelič, Boštjan Žvanut, Simon Vrhovec: Pametne naprave v pomoč starejšim odraslim * Nives Matko, Marizela Nuhanović, Megi Trojar: Ocena tveganja za nastanek razjede zaradi pritiska * Tjaša Ulčnik, Rok Škedelj: Model za oceno ustreznosti perfuzorja * Maša Zapušek, Klara Vrtačnik: Ocena ogroženosti dializnega bolnika * Alen Lončar, Marija Milavec Kapun: Uporabnost aplikacije NurseCal za izračun odmerkov in pretočnosti zdravil * Uroš Višič: Digitalizacija zdravstveno-vzgojnih vsebin Sole za starše v času pandemije Covid-19 Povzetki in prispevki predstavitev so objavljeni v zborniku srečanja. Zaključek Ob zaključku rednega letnega strokovnega srečanja je potekal sestanek članov SIZN, kjer je predsednik sekcije izr. prof. dr. Boštjan Žvanut podal poročilo o delu SIZN za leto 2022, prav tako so bile opredeljeni načrti in usmeritve za nadaljnje delo SIZN. Program srečanja je bil tudi v tem v letu posredovan v evalvacijo za licenčne točke Zbornice zdravstvene in babiške nege Slovenije - Zveze strokovnih društev medicinskih sester, babic in zdravstvenih tehnikov Slovenije (Zbornice-Zveze). Komisija za oceno ustreznosti stalnega strokovnega izpopolnjevanja, imenovana s strani Zbornice-Zveze, je programu SIZN 2022 dodelila 6 licenčnih točk za pasivne udeležence in 10 licenčnih točk za aktivne udeležence. Zaključki srečanja in ključne ugotovitve se nanašajo na dopolnjevanje kompetenc s področja zdravstvene nege, ki jih zahteva uporaba sodobnih IKT rešitev, in pomen pedagoškega dela na tem področju ter razvoj IKT rešitev za učenje in poučevanje (navidezna resničnost, obrnjeno učenje), izpostavili pa smo tudi pomen usposabljanja pacientov na področju informacijske in zdravstvene pismenosti. Zahvala Hvaležni smo vsem članom SIZN za skupno rast, podporo in zaupanje.
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Though the effect of the coronavirus has known to be a catastrophic pandemic since a 100 years ago, severe acute respiratory syndrome-2 coronavirus (SARS2-CoV) was first claimed to be emerged in December 2019 at the city of Wuhan, China. Abruptly, the virus dominated more than 218 countries with 157,566,607 confirmed cases and the death figure has reached nearly 3,284,551 till time. Recently the pandemic is getting worse day-by-day, people are suffering from hypoxia and severe respiratory problems despite the continuous services provided by the healthcare sector. Prior concern behind this emergency is that, till date, researchers and scientists failed to invent any productive pharmaceutical treatment to weed out the infection completely. Although vaccination is publicly available, it is applicable only for precautionary purposes and not evident of preventive measures. This review focuses on the therapeutic status to control the severity of SAS2-CoV agent. The approach aims at implicating a low toxic metabolite anti-malarial drug, hydroxychloroquine combined with an antibiotic called azithromycin for the treatment of acute respiratory disturbance and hypoxia. This article briefly demonstrates the phramaco-potential of both these medications, their effects on patients based on a clinical observation and ongoing status of dosage to validate its implication.
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The COVID-19 outbreak has fashioned to severe threat to each and every individual in social and economic aspects in the country. This required improved wisdom to know how it is different and dominant, to diagnose and determine effective vaccines to avoid the transmission of these deadly causative agents. From this review, the probable property of these deadly transmissible viruses is related to that of SARS-CoV-2 as a fright zone of viruses. It also provides some sparks about effective and accurate diagnosis and treatment strategies. The effective management and control of panic zone of virus (PZV) and SARS-CoV-2 are more important to reduce the pandemic situation.
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This research developed a multinomial classification model that predicts the prevalent mode of transmission of the coronavirus from person to person within a geographic area, using data from the World Health Organization (WHO). The WHO defines four transmission modes of the coronavirus disease 2019 (COVID-19);namely, community transmission, pending (unknown), sporadic cases, and clusters of cases. The logistic regression was deployed on the COVID-19 dataset to construct a multinomial model that can predict the prevalent transmission mode of coronavirus within a geographic area. The k-fold cross validation was employed to test predictive accuracy of the model, which yielded 73% accuracy. This model can be adopted by local authorities such as regional, state, local government, and cities, to predict the prevalent transmission mode of the virus within their territories. The outcome of the prediction will determine the appropriate strategies to put in place or re-enforced to curtail further transmission.
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The recent studies have indicated the requisite of computed tomography scan analysis by radiologists extensively to find out the suspected patients of SARS-CoV-2 (COVID-19). The existing deep learning methods distribute one or more of the subsequent bottlenecks. Therefore, a straight forward method for detecting COVID-19 infection using real-world computed tomography scans is presented. The detection process consists of image processing techniques such as segmentation of lung parenchyma and extraction of effective texture features. The kernel-based support vector machine is employed over feature vectors for classification. The performance parameters of the proposed method are calculated and compared with the existing methodology on the same dataset. The classification results are found outperforming and the method is less probabilistic which can be further exploited for developing more realistic detection system.
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The COVID-19 pandemic has led to the reorganisation and creation of new hospitals, shocking healthcare workers' routines. This study investigates nurses' stress perception in COVID-19 time and how some antecedents (i.e., narcissism and age) impact it. The paper focuses on two facets of narcissism, i.e., Leadership/Authority and Entitlement/Exploitativeness. We recruited 281 nurses who completed an online survey investigating their stress perception levels and personalities. Data are analysed using hierarchical linear modelling and simple slope analyses. Results show that Leadership/Authority negatively influences stress perception, while Entitlement/Exploitativeness positively influences it. Furthermore, nurses' age moderates the above relationships. The study contributes to narcissism, stress and nursing literature, showing some positive facets of narcissism that might be useful for reducing stress perception and facilitating human relationships in the work environment, both in emergency and non-emergency contexts.
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The World Health Organization (WHO) has declared the novel coronavirus as global pandemic on 11 March 2020. It was known to originate from Wuhan, China and its spread is unstoppable due to no proper medication and vaccine. The developed forecasting models predict the number of cases and its fatality rate for coronavirus disease 2019 (COVID-19), which is highly impulsive. This paper provides intrinsic algorithms namely - linear regression and long short-term memory (LSTM) using deep learning for time series-based prediction. It also uses the ReLU activation function and Adam optimiser. This paper also reports a comparative study on existing models for COVID-19 cases from different continents in the world. It also provides an extensive model that shows a brief prediction about the number of cases and time for recovered, active and deaths rate till January 2021.
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ObjectiveDigital health (DH) is the integration of technologies to tackle challenges in healthcare. Its applications include mobile health, remote & wireless healthcare, artificial intelligence, and robotics. Digital technologies are increasingly being used to deliver routine care, whilst simultaneously patients are increasing their uptake of DH solutions (e.g. wearables).With the adoption of DH increasing across the NHS, there is a growing need for a digitally literate workforce. However, there are no national standards on DH education for UK medical students. Consequently, this study sought to assess the current provisions, perceptions and challenges regarding DH education in the undergraduate medical curriculum.MethodsAn anonymous cross-sectional online survey was developed following a literature search and by collecting iterative feedback from both researchers and external collaborators. The survey consisted of questions in 6 areas: (a) understanding of DH;(b) existing provision of DH education;(c) interest in DH education;(d) preferred means of delivering and assessing DH education;(e) impact of the COVID-19 pandemic on DH;and (f) demographic information.The survey was administered via Qualtrics from March to October 2021, and disseminated to UK medical students via university mailing lists, social media and student representatives. Quantitative and qualitative data were collected pertaining to demographics, attitudes, preferences, and current provisions regarding DH education. Qualitative responses underwent thematic analysis. For quantitative analysis, R (version 3.5.0) and R Studio (version 1.1a) were used.Results514 complete responses were received from 39 UK medical schools in 2021. 57.2% of respondents were female, with a mean age of 22.9 ± 3.2. 65.8% of students considered DH ‘extremely important’ to future clinical practice, particularly the domains of electronic patient records, telehealth and smartphone applications. However, only 18.1% felt aware of the DH competencies required in clinical medicine. 70.2% of students reported receiving some DH education, with the highest proportion being in the form of lectures or seminars (30.5%, n=157), e-learning modules (28.6%, n=147) and ad hoc teaching during clinical placements (22.8%, n=117). However, only 25.7% felt satisfied with these provisions. Themes for student satisfaction related to a practical teaching approach, delivery of content appropriate for their training stage and coverage of topics in student interest. Conversely, student dissatisfaction originated from inadequate teaching, and subsequent fears of falling behind. 56.1% preferred DH education to be mandatory rather than elective, ideally through hands-on workshops (75.8%) and lectures and seminars (60.4%). 65.4% thought DH proficiency should be assessed in some capacity, of which 75.6% preferred formative assessment.ConclusionThis study represents the first national survey of UK medical students on DH education. Overwhelmingly, the results indicate that medical students recognise the significance of DH and would appreciate better formal integration into their curriculum;which is supported by previous similar studies in the literature. This study also identified how students would prefer to be taught and assessed on DH, in particular that they would prefer it be mandatory yet remain formative at present. Given the increasing ubiquity of DH in clinical practice, it is therefore crucial that universities and wider medical education organisations work to improve and standardise DH education, to better prepare medical students to adapt to the continuously developing digital landscape. This rings especially true in light of the recent COVID-19 pandemic which has highlighted the quintessential nature of DH to medical practice. Our intended future research from this study includes undergraduate focus groups for greater qualitative depth of information, and Delphi panels from wider medical education stakeholders into what should be included in DH education, with the eventual goal of devel ping a comprehensive and standardised national DH curriculum.
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ObjectiveArtificial intelligence (AI) predictive tools can help inform the clinical decision-making process by, for example, detecting early signs of patient deterioration or predicting the likelihood of a patient developing a particular disease or complications postsurgery. However, it is unclear how acceptable or useful clinicians find these tools in practice. This project aims to explore healthcare staff’ perceptions on the benefits and challenges of using AI tools to inform clinical decision-making in practice.MethodsHealthcare staff (physicians, pharmacists and nurses) working in different departments at one large teaching hospital in the North East were invited to participate in semi-structured interviews. Interviews were conducted between August and November 2021 by zoom videoconferencing, with questions focused on what AI predictive tools they currently use, how they guide daily tasks around diagnosis, management, prevention, prognosis and screening, and what challenges they face with their use. All transcribed files were checked for accuracy. Thematic saturation guided the volume of qualitative data collection. Qualitative data analysis and development of themes was performed for each interview using Nvivo 12 software. Ethical approval was obtained (20/EM/0183, IRAS 280077).ResultsTen healthcare staff were interviewed (physicians (n=7), pharmacists (n=1), surgeons (n=2)) from different medical specialities (e.g., Oncology, Endocrinology, Cardiology, Head and Neck, and transplant surgery). Five themes emerged, including the meaning of the term AI, the usefulness of AI predictive tools in informing clinical decision-making, features that healthcare staff found helpful, and challenges around their use. Healthcare staff recognised the benefits of AI predictive tools in being able to ‘detect deterioration quicker than you would currently do’(05-ID), which informed decisions around patient discharge: ‘can you safely send them home (...) or do you want to keep them, in case they do deteriorate’ (05-ID). They found AI predictive tools useful when explaining the potential risk of cardiovascular events to patients and encouraging medication adherence ‘it does help so much convincing the patient to actually adhere to the medication’ (07-Endo).During COVID-19, AI prediction tools helped identify patients that might potentially need mechanical ventilation and ICU admission. Healthcare staff also felt it was important that AI predictive tools provided reliable information, that was easy to understand, and integrated with the current systems. A concern raised around the use of AI predictive tools was whether they might ‘mislead junior doctors or doctors who would not have that much of a clinical sense and would totally depend on it’ (07-Endo).ConclusionThis study demonstrated opportunities for the application of AI predictive tools in clinical practice. Concerns raised around the use of these tools should be considered by developers. We recognise that the perceptions of only a small number of clinicians were included mainly due to the increased time pressures on staff during the COVID-19 pandemic. Healthcare staff described essential features that will guide the future development of AI predictive tools with higher potential for application in real practice.
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ObjectiveThere is increasing interest in remote monitoring of patients within the comfort and safety of their homes or care homes and became more pertinent during the COVID-19 pandemic to reduce hospital footfall and staff risk. While specifically designed medical devices exist, commercial wearable activity trackers (WAT), such as FitBits, are cheap, easy to use, and patients may already use them for lifestyle advice so their value in clinical intervention is of interest.The feasibility of using commercial WAT for daily monitoring within a tertiary oncology centre was investigated, including limitations of non-medical devices, such as data collection and synchronisation errors.MethodsParticipants were recruited for a study that investigated if remote monitoring of step counts was feasible and acceptable. Patients with advanced lung, upper and lower gastrointestinal cancer, or mesothelioma who were starting a new line of systemic anti-cancer treatment were recruited between December 2020 and December 2021.Once recruited, participants were provided with a FitBit Inspire HR or Inspire 2 and asked to wear it every day for a 16-week monitoring period. Pseudo-anonymous accounts were created to register the FitBits without sharing patient identifiable data and the devices were set up to automatically synchronise data to the cloud-based platform, Fitabase, via their smartphone.Steps were monitored on every workday and the ability to record heart rate was used as a proxy marker for compliance as it confirmed that the device was being worn. A day was considered complaint if the device was worn for >70% of waking hours, assumed for purpose of trial to be 7am to 10pm.The manufacturer or age of the participant’s smartphone was not recorded. Previous discussions with FitBit regarding synchronisation issues had highlighted potential clashes with other Bluetooth devices preventing automatic synchronisation so use of other such devices was documented.ResultsForty-seven patients were recruited and 43 were eligible for ongoing monitoring. Average age was 66 (SD 9) and majority were men (72%). Twenty-nine patients completed the maximum 112 days of monitoring.Patients were eligible for monitoring on 3855 days. Of these, synchronisation errors occurred on 482 days (13%) and all data from the previous 24 hours was missing on 275 days (7%) due to synchronisation not occurring on the day on monitoring. Only 5 (11%) of participants did not have synchronisation errors during their monitoring period. The median number of synchronisation errors per patient was 8 and maximum of 49, which accounted for 64% of that participant’s monitored days. One participant was withdrawn due to 100% synchronisation error over the first seven monitored days.Twenty-two participants (47%) used other Bluetooth devices but there was no correlation between their use and synchronisation errors (r=-0.32), nor significant difference in synchronisation error rate (p=0.08).562 days (15%) were considered non-compliant as heart rate was documented for less than 70% of the waking hour period. When synchronisation errors were removed, however, only 216 days (7%) were truly non-compliant due to the patient not wearing the device, rather than not having access to the data.ConclusionThis study has revealed a potential limitation of using commercial wearable activity trackers, such as FitBits, for clinical monitoring. While compliance with monitoring was good and matched previous reports on compliance at over 80%, the loss of data due to synchronisation errors reduced perceived compliance and, importantly for clinical interventions, reduced data available for immediate action.Correcting these issues and restarting the automatic synchronisation was not a complex procedure but did necessitate a telephone call with the participant to manually synchronise the device, restart their smartphone or occasionally reinstall the app, which added to the participant burden of the investigation and overwhelmed the technological abilities of some participants. Currently, it is not clear wha causes these synchronisation errors and, therefore, it is not possible to select patients who would be more suitable for this intervention.The frequency of synchronisation errors mean that it is not feasible to use commercially available WAT for remote monitoring of patients and caution is needed if the results are used to guide clinical intervention, rather than simply offer lifestyle advice.
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ObjectiveIt has been recognised that the Covid -19 pandemic positively accelerated digital adoption (Greenway et al., 2021;Issa, 2020). However, rapid deployments of technology do not often assess and understand patient safety risks;resulting in harm, which have ethical and legal considerations (HEE, 2019). The NHS has received caution of the potential risks of the use of new digital solutions during the pandemic (Hutchings, 2020). To nurture digital health safety, clinical safety risk management practice is worthy of study. Further, identifying factors that support the promising adoption and implementation of safety guidelines will develop maturity of the professional practice.MethodsConducted for a Master’s Dissertation in Digital Health Leadership with The Institute of Global Health Innovation Imperial College, this study uses a promising practice model to identify assets of the Australian healthcare system to achieve patient safety when deploying digital health technologies. The question guiding the study is: what are the factors that need to be evaluated to support the scaled adoption and implementation of digital health safety guidelines as a professional practice in Australia? Taking into consideration the socio-technological factors of digital health safety, the research strategy uses a mixed method to generate a creative and innovative study. Qualitative data has been collected from stakeholders including the Australasia Institute of Digital Health (AIDH) members and Certified Health Informatician Australasia (CHIA) Alumni via surveys, interviews and focus group. This will be analysed alongside data mined from existing documents and artifacts to understand trends, implications and what is grounded in national policy and strategy. It is expected data mining of resources will provide further insights into the maturity digital health safety practices.ResultsThe promising practice investigation is related to the larger problem of the adoption of safety standards to ensure innovative new ways of working do not compromise patient safety. The presentation will share results from the international literature review and early insights of the first phase of data analysis. Evidence from the literature has exposed the current healthcare information technology safety practice challenges. There were few studies that focused on the factors influencing the adoption of digital health safety standards. However, the review surfaced six key areas that need to be understood to improve safety practice and culture, which will be summarised in the presentation. A comparison of safety frameworks from England and Australia will be presented. In addition, a review of the unique assets of the Australian healthcare system will be provided. Finally, a maturity model to guide the professional practice to assist organisations determining their status in adopting digital health safety into governance, policy, process, culture, and other facets of operations will be shared (Rowlands, Zelcer & Williams, 2017).ConclusionAs a science, measuring the impact digital health and patient safety remains rudimentary (Singh & Sittig, 2016). The health science community recognises digital health safety is challenging and international efforts are being made to understand the socio-technical dynamics to ensure patient safety (Sittig et al., 2020). Given the national focus ‘to embed digital clinical safety across health and care’ (NHS X, 2021, p. 25), it is timely to look beyond to source exemplar organisations and best practice to participate in research (Gandhi et al., 2016). In contrast to the approach taken by the NHS Digital to mandate digital clinical safety standards, in Australia the Patient Safety Electronic Health (E-Health) Professional Practice Guidelines empowers organisations to establish ‘best fit’ with their strategic and operating context. This study is framed alongside the NHS Digital Clinical Safety Strategy and searches for evidence of a promising practice related to the Australian healthcare system and patient safety cult re. This presentation will be beneficial for Digital Clinical Safety Officers and Chief Clinical Information Officers developing a clinical safety risk management process, investing in team building, recourses, and capability.
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Telemedicine, the provision of healthcare services at a distance, has gained the spotlight in recent years for the provision of medical care, medical advice and monitoring of patients in their homes. In Asia, telemedicine has flourished due to motivations such as limited personal resources, scarce healthcare facilities and a growing demand for more and better healthcare. The COVID-19 pandemic has posed many challenges to healthcare delivery, some of which are answered by telemedicine. Asia, one of the first areas of the world to be hit by the new coronavirus, quickly understood the benefits of telemedicine in this scenario. Before engaging in large-scale telemedicine Asian countries still need to resolve several issues related to the users and providers of healthcare, the organisation of healthcare systems, the availability of technology and the existing legal framework. Even so, Asia might experience a dynamic boost in telemedicine due to the pandemic.
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The annual ConV2X is a leading international health tech symposium driving real world evidence, strategy, research, operations and trends to create a blueprint for a new digital health era. The 2021 symposium featured a scientific program of academic/research presentations in addition to business and industry talks. The research track focused on exploring and sharing developments in blockchain and emerging technologies in health and clinical medicine. Submissions were based on original research, conceptual frameworks, proposed applications, position papers, case studies, and real-world implementation. Selection was based on a peer-review process. Faculty, students, and industry researchers were encouraged to submit s to present ideas before an informed and knowledgeable audience of industry leaders, policy makers, funders, and researchers. This presentation was selected by the scientific review committee. Submission Review Committee Dave Kochalko, CEO of ARTiFACTS Anjum Khurshid, UT Austin Carlos Caldas, UT Engineering Gil Alterovitz, Harvard Medical School Kayo Fujimoto, UT Health Houston Lei Zhang, University of Glasglow Sean Manion, CSciO of ConsenSys Health Vijayakuman Varadarajan, University of South Wales Vikram Dhillon, Wayne State University Yuichi Ikeda, Kyoto University
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How is blockchain technology empowering patients to verify provenance and quality of COVID-19 vaccines and other lifesaving drugs through Asia's first blockchain solution for supply chain connectivity and traceability for pharmaceuticals and vaccines? With two million products on the blockchain network, this ground breaking scalable initiative controls "pack" vs "batch." Authorities are now seizing counterfeits and consumers are benefitting by verifying their drug(s) are effective and can be trusted as safe and efficacious. Why use blockchain and what are the vaccine management issues along the pharma supply chain?
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The corona virus disease (COVID-19) is a pandemic that facilitate a confrontation space for scientific and social existence of human frontiers. The rapid spread and mortality rate of COVID-19 and the preventive measures including social distancing and its impact on economy, developed an unprecedented consciousness around the globe. It has created an effect on the mental health of individuals employed across various sectors and is outlined in this study. There is currently an inadequate theoretical model that focuses on the comprehensive understanding of the psychology of preventive behaviour during the outbreak of pandemics. In this study, a transnational model is delineated for assessing the adoption of preventive behavioural practices associated with COVID-19 pandemic. It uses the components derived from the theories of situational awareness and health belief model and literatures related to impact of containment strategies on various sectors. The contribution includes policy recommendations that can be helpful for the healthcare professionals and government to control the disease spread.
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Due to the spread of COVID-19 all around the world, there is a need of automatic system for primary tongue ulcer cancerous cell detection since everyone do not go to hospital due to the panic and fear of virus spread. These diseases if avoided may spread soon. So, in such a situation, there is global need of improvement in disease sensing through remote devices using non-invasive methods. Automatic tongue analysis supports the examiner to identify the problem which can be finally verified using invasive methods. In automated tongue analysis image quality, segmentation of the affected region plays an important role for disease identification. This paper proposes mobile-based image sensing and sending the image to the examiner, if examiner finds an issue in the image, the examiner may guide the user to go for further treatment. For segmentation of abnormal area, K-mean clustering is used by varying its parameters.
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In early March 2020, the World Health Organization (WHO) proclaimed the novel COVID-19 as a global pandemic. The coronavirus went on to be a life-threatening infection and is still wreaking havoc all around the globe. Though vaccines have been rolled out, a section of the population (the elderly and people with comorbidities) still succumb to this deadly illness. Hence, it is imperative to diagnose this infection early to prevent a potential severe prognosis. This contagious disease is usually diagnosed using a conventional technique called the Reverse Transcription Polymerase Chain Reaction (RT-PCR). However, this procedure leads to a number of wrong and false-negative results. Moreover, it might also not diagnose the newer variants of this mutating virus. Artificial Intelligence has been one of the most widely discussed topics in recent years. It is widely used to tackle various issues across multiple domains in the modern world. In this extensive review, the applications of Artificial Intelligence in the detection of coronavirus using modalities such as CT-Scans, X-rays, Cough sounds, MRIs, ultrasound and clinical markers are explored in depth. This review also provides data enthusiasts and the broader health community with a complete assessment of the current state-of-the-art approaches in diagnosing COVID-19. The key issues and future directions are also provided for upcoming researchers.
ABSTRACT
To break the chain of COVID-19, a powerful and fast screening system is required which identifies the COVID-19 affected cases quickly such that the appropriate measures like quarantine or treatment can be taken. The traditional RT-PCR test is found to have larger misclassification rate. It also consumes more time to get the result. To solve this problem, in this paper, we have introduced a new model for COVID-19 detection based on chest X-ray (CXR) images and convolutional neural networks (CNNs). The proposed model is an automatic detection model, which considers the CXR image as input and performs an in-depth analysis to discover the COVID-19. The proposed CNN model is a very simple and effective, which is composed of five convolutional layers and three pooling layers. Every convolutional layer has different sized filters and different number of filters, which extracts all the possible features from CXR image. Simulation experiments are conducted over a newly constructed dataset based on the publicly available CXR (both COVID-19 and non-COVID-19) images. Simulation is done under two phases;3-class and 2-class and obtained an average accuracy of 92.22% and 94.44% respectively. Thus, the average accuracy is measured as 93.33%.