The impact of the COVID-19 pandemic on referrals to musculoskeletal services from primary care and subsequent incidence of inflammatory rheumatic musculoskeletal disease: an observational study.

Objective: The aim was to describe the impact of the COVID-19 pandemic upon referral patterns and incident diagnosis of inflammatory rheumatic and musculoskeletal diseases (iRMDs). Methods: UK primary care data were used to describe referral patterns for patients with musculoskeletal conditions. Trends in referrals to musculoskeletal services and incident diagnoses of iRMDs (specifically, RA and JIA) were described using Joinpoint Regression and comparisons made between key pandemic time periods. Results: The incidence of RA and JIA reduced by -13.3 and -17.4% per month, respectively, between January 2020 and April 2020, then increased by 1.9 and 3.7% per month, respectively, between April 2020 and October 2021. The incidence of all diagnosed iRMDs was stable until October 2021. Referrals decreased between February 2020 and May 2020 by -16.8% per month from 4.8 to 2.4% in patients presenting with a musculoskeletal condition. After May 2020, referrals increased significantly (16.8% per month) to 4.5% in July 2020. The time from first musculoskeletal consultation to RA diagnosis and from referral to RA diagnosis increased in the early pandemic period [rate ratio (RR) 1.11, 95% CI 1.07, 1.15 and RR 1.23, 95% CI 1.17, 1.30, respectively] and remained consistently higher in the late pandemic period (RR 1.13, 95% CI 1.11, 1.16 and RR 1.27, 95% CI 1.23, 1.32, respectively), compared with the pre-COVID-19 pandemic period. Conclusion: Patients with underlying RA and JIA that developed during the pandemic might be yet to present or might be in the referral and/or diagnostic process. Clinicians should remain alert to this possibility, and commissioners should be aware of these findings, enabling the appropriate planning and commissioning of services.

Privacy-Preserving Fast Three-Factor Authentication and Key Agreement for IoT-Based E-Health Systems

Electronic healthcare (e-health) systems have received renewed interest, particularly in the current COVID-19 pandemic (e.g., lockdowns and changes in hospital policies due to the pandemic). However, ensuring security of both data-at-rest and data-in-transit remains challenging to achieve, particularly since data is collected and sent from less insecure devices (e.g., patients' wearable or home devices). While there have been a number of authentication schemes, such as those based on three-factor authentication, to provide authentication and privacy protection, a number of limitations associated with these schemes remain (e.g., (in)security or computationally expensive). In this study, we present a privacy-preserving three-factor authenticated key agreement scheme that is sufficiently lightweight for resource-constrained e-health systems. The proposed scheme enables both mutual authentication and session key negotiation in addition to privacy protection, with minimal computational cost. The security of the proposed scheme is demonstrated in the Real-or-Random model. Experiments using Raspberry Pi show that the proposed scheme achieves reduced computational cost (of up to 89.9% in comparison to three other related schemes).

The Past, Present and Future of the Healthcare Delivery System through Digitalization

The prevalence of chronic diseases and the recent global spread of deadly communicable diseases such as COVID-19 has resulted in changing global health needs which require new and adaptable approaches towards delivering healthcare. Healthcare digitization has aided in dealing with old and new healthcare issues and there is still enormous untapped power. Enough power to transform healthcare delivery systems when safe and accurate aggregation of individual health data is achieved. We explore a typical patient's healthcare pathway for two major chronic conditions, namely cardiovascular and mental diseases. The aim is to reveal healthcare delivery approach changes as used in the past, present, with a look to the future to manage these diseases. Further, we provide a holistic overview of the technologies behind the digital healthcare transformation. The study also offers a roadmap which depicts the evolution in the healthcare delivery system enabled by these technological health advancements and concludes with a critical evaluation of such systems. IEEE

SPOT: Secure and Privacy-Preserving PrOximiTy Protocol for e-Healthcare Systems

This paper introduces SPOT, a Secure and Privacy-preserving prOximity based protocol for e-healthcare systems. It relies on a distributed proxy-based approach to preserve users' privacy and a semi-trusted computing server to ensure data consistency and integrity. The proposed protocol ensures a balance between security, privacy and scalability. As far as we know, in terms of security, SPOT is the first one to prevent malicious users from colluding and generating false positives. In terms of privacy, SPOT supports both anonymity of users being in proximity of infected people and unlinkability of contact information issued by the same user. A concrete construction based on structure-preserving signatures and NIWI proofs is proposed and a detailed security and privacy analysis proves that SPOT is secure under standard assumptions. In terms of scalability, SPOT's procedures and algorithms are implemented to show its efficiency and practical usability with acceptable computation and communication overhead.

Blockchain-based Electronic Healthcare Information System Optimized for Developing Countries

Healthcare is the most crucial sector in people’s life. Many applications and systems have been proposed to improve the healthcare area. The outbreak of the novel coronavirus Covid19 turns more focus on healthcare applications. To manage medical data, healthcare professionals in developed countries have adopted several electronic healthcare information systems and technologies in recent years. However, these technologies show serious privacy risks and security issues, especially in the transfer of data and the recording of data transactions. Furthermore, the high cost of these technologies acquisition, as well as the complexity of their management, make their application in underdeveloped nations extremely problematic. This article proposes a solution based on a decentralized Blockchain architecture to reinforce the security of health information systems. This solution is particularly recommended for developing countries which lack high-tech infrastructures and suffer from poor interoperability between existing information systems. Various researches and works that implement blockchainbased solutions in the security of electronic health information systems (eHIS) are discussed in this article. A new approach based on a hyperledger fabric, implementing smart contracts and several other components is proposed. The suggested architecture involves many actors who can interact with medical records such as patients, doctors, pharmacists, laboratories and insurance companies. Data privacy is guaranteed because there is minimal risk of unauthorized access entities, and by design, the smart contract is the sole way to manipulate participant data. Various optimization and measurement experiments were carried on. The results covering various key parameters of system performance such as throughput, latency, CPU usage, memory consumption and network usage are presented © 2022,IAENG International Journal of Computer Science.All Rights Reserved

Considerations About Algorithmic Literacy

In recent years, digital media is becoming increasingly popular as a source of health information. The widespread use of digital media, such as social media platforms, has become a part of everyday life, especially when it comes to COVID-19-related information. Extensive academic research compounded by general societal discord suggests that the future looks worrisome, especially when people use their mobile phones to seek health-related information. Relying on the environment of digital sources that are often processed by imperfect or biased algorithms augments the risk of being misinformed about COVID-19. This environment propelled the COVID-19 pandemic to explode with new waves of fake news and misinfodemics. The urgent challenge of health communication is to provide timely science-based health information to help people resist misinfodemics. This article explores disinformation and misinfodemics and the extent to which algorithmic literacy can contribute to eHealth literacy and manage societal discord on important medical topics.

QDS-COVID: A visual analytics system for interactive exploration of millions of COVID-19 healthcare records in Brazil.

COVID-19 is responsible for the deaths of millions of people around the world. The scientific community has devoted its knowledge to finding ways that reduce the impact and understand the pandemic. In this work, the focus is on analyzing electronic health records for one of the largest public healthcare systems globally, the Brazilian public healthcare system called Sistema Único de Saúde (SUS). SUS collected more than 42 million flu records in a year of the pandemic and made this data publicly available. It is crucial, in this context, to apply analysis techniques that can lead to the optimization of the health care resources in SUS. We propose QDS-COVID, a visual analytics prototype for creating insights over SUS records. The prototype relies on a state-of-the-art datacube structure that supports slicing and dicing exploration of charts and Choropleth maps for all states and municipalities in Brazil. A set of analysis questions drives the development of the prototype and the construction of case studies that demonstrate the potential of the approach. The results include comparisons against other studies and feedback from a medical expert.

Automated risk assessment of covid-19 patients at diagnosis using electronic healthcare records

COVID-19 causes significant morbidity and mortality and early intervention is key to minimizing deadly complications. Available treatments, such as monoclonal antibody therapy, may limit complications, but only when given soon after symptom onset. Unfortunately, these treatments are often expensive, in limited supply, require administration within a hospital setting, and should be given before the onset of severe symptoms. These challenges have created the need for early triage of patients likely to develop life-threatening complications. To meet this need, we developed an automated patient risk assessment model using a real-world hospital system dataset with over 17,000 COVID-positive patients. Specifically, for each COVID-positive patient, we generate a separate risk score for each of four clinical outcomes including death within 30 days, mechanical ventilator use, ICU admission, and any catastrophic event (a superset of dangerous outcomes). We hypothesized that a deep learning binary classification approach can generate these four risk scores from electronic healthcare records data at the time of diagnosis. Our approach achieves significant performance on the four tasks with an area under receiver operating curve (AUROC) for any catastrophic outcome, death within 30 days, ventilator use, and ICU admission of 86.7%, 88.2%, 86.2%, and 87.8%, respectively. In addition, we visualize the sensitivity and specificity of these risk scores to allow clinicians to customize their usage within different clinical outcomes. We believe this work fulfills a clear clinical need for early detection of objective clinical outcomes and can be used for early screening for treatment intervention. © 2021 IEEE

Challenges of Internet of Medical Things for Electronic Healthcare

The internet of medical things is one of the greatest marvels of the 21st century. Research indicates that after the covid-19 pandemic IoMT has gained a lot of popularity due to its demand in E-health and more particularly in telehealth and telemedicine. However, all the existing IoMT initiatives are at their early stage of development and require a more advanced approach within their domain. More significant, concerning the use of IoT in both the software and hardware arena, the lack of knowledge and experience to manufacture IoMT devices is observed. Thus, the health system is aware that there are substantial challenges to implementing IoMT software and hardware. In this paper, we aim to provide a high-level review of existing IoMT data interoperability, product design, product's market adoption, data challenges. Also, we are providing practical suggestions through implementing semi-automated systems using cloud computing, and artificial intelligence via digital health platforms. Knowing these provided high-level suggestions will enhance the process of IoMT production and provide better and more reliable healthcare and remote monitoring system. © 2021 IEEE.

Privacy-Preserving Fast Authentication and Key Agreement for E-health Systems in IoT, based on Three-Factor Authentication

Electronic healthcare (e-health) systems have received renewed interest, particularly in the current COVID-19 pandemic (e.g., lockdowns and changes in hospital policies due to the pandemic). However, ensuring security of both data-at-rest and data-in-transit remains challenging to achieve, particularly since data is collected and sent from less insecure devices (e.g., patients wearable or home devices). While there have been a number of authentication schemes, such as those based on three-factor authentication, to provide authentication and privacy protection, a number of limitations associated with these schemes remain (e.g., (in)security or computationally expensive). In this study, we present a privacy-preserving three-factor authenticated key agreement scheme that is sufficiently lightweight for resource-constrained e-health systems. The proposed scheme enables both mutual authentication and session key negotiation in addition to privacy protection, with minimal computational cost. The security of the proposed scheme is demonstrated in the Real-or-Random model. Experiments using Raspberry Pi show that the proposed scheme achieves reduced computational cost (of up to 89.9\% in comparison to three other related schemes). IEEE

MEC and D2D as Enabling Technologies for a Secure and Lightweight 6G eHealth System

The COVID-19 pandemic has changed the world. Today, the use of Information and Communications Technology (ICT) in support of education, medicine, business and administration has become a reality practically everywhere. In particular, the eHealth (digital Health) sector is on the cusp of a revolution, fueled by the worldwide health emergency due to the spread of the new coronavirus. With a view to developing new sixth generation (6G)-oriented architectures, advanced eHealth services like telemonitoring would benefit from the support of technologies that guarantee secure data access, ultra-low latency and very-high reliability targets, which are hardly achievable by the fifth generation (5G). This is the reason why this work proposes an eHealth system architecture, in which low-latency enabling technologies like Device-to-Device (D2D) communications and Multi-access Edge Computing (MEC) are integrated and supported by security mechanisms for an optimal management of sensitive health data collected by Internet of Medical Things (IoMT) devices. A preliminary evaluation of the proposed framework is provided that shows promising results in terms of data security and latency reduction. IEEE

MedBlock: An AI-enabled and Blockchain-driven Medical Healthcare System for COVID-19

An Artificial Intelligence (AI)-enabled and blockchain-driven Electronic Health Record (EHR) maintenance system has a tremendous potential to facilitate reliable, secure, and robust storage systems for EHRs. Such an EHR system would also facilitate researchers, doctors, and government authorities to access data for research, perform analytics, and help in making well-informed decisions. The Artificial Neural Network (ANN) is employed to classify the patients as potentially COVID-19 positive and potentially COVID-19 negative based on the clinical reports and reports of CT-scan. The data of potentially COVID-19 positive patients is stored on blockchain employing InterPlanetary File System (IPFS) protocol. The accessibility of EHR can be done by authorized entities post verification and validation of entities. We analyze the performance of various AI-based algorithms employing metrics such as loss curve, accuracy, etc. for the task of predicting the patient's potential COVID-19 infection. The 6G network significantly mitigates the network latency and reliability issues and also facilitates the real-time transmission of information. The amount of data generated is pretty high amidst this pandemic and so we employed IPFS protocol which suffices to be a cost-effective solution, moreover satisfying all are stringent requirements. At last, we evaluate the network, security, and storage performance of our architecture MedBlock, which outperformed other state-of-the-art systems.