A survey of healthcare sector digitization strategies: Vulnerabilities, countermeasures and opportunities

Nyakina, Judith Nyakanga; Taher, Bahaa Hussein

doi:10.30574/wjaets.2023.8.1.0050

Cited by 2 publications

(2 citation statements)

References 189 publications

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“…Developing 4. Ethical, Security, and Privacy Considerations: Amid the sensitive nature of healthcare data, the architecture strongly emphasizes ethical regulations, security, and privacy [60] Robust security measures ensure that patient information remains confidential and protected from unauthorized access [62].…”

Section: Implementation Challengesmentioning

confidence: 99%

EHealth Innovation for Chronic Obstructive Pulmonary Disease: A Context-Aware Comprehensive Framework

Iqbal,

Qureshi,

Chishti

2024

SCPE

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Chronic Obstructive Pulmonary Disease (COPD) poses a significant global healthcare challenge. It is a progressive lung disease that causes breathing difficulties and can significantly impact a person's quality of life. COPD is primarily caused by smoking, but other factors, such as air pollution and genetic predisposition, can also contribute to its development. This paper introduces a novel Context-Aware Framework for the Diagnosis and Personalized Management of COPD. We discuss the limitations of traditional COPD management, highlighting the importance of early detection and remote monitoring. Early detection and remote monitoring are crucial in managing COPD as they allow for timely interventions and better disease management. In this paper, we propose a framework based mostly on contextual data and other parameters of COPD as put forth by the World Health Organization (WHO) in the form of the International Classification of Functioning, Disability, and Health. Ontologies drive this architecture and incorporate dynamic contextual information from patient environments, user profiles, and sensor data. In addition to the various obvious data items like patient personal details (gender, contact, medical history) and COPD risks and symptoms, the COPD ontology also considers the details about the caregiver and healthcare professional. This is in addition to the contextual data processed separately using the Context Ontology. The ontology we constructed using Protégé serves as the framework for the structured representation and logical inference of contextual information. By harnessing dynamic contextual data, our ontology enables real-time decision-making tailored to individual patient requirements. It empowers healthcare professionals to make informed choices and deliver timely interventions, enhancing healthcare services by offering proactive care to detect early signs of health deterioration and suggest preventive measures. This approach improves patient experiences and optimizes resource allocation within the healthcare system. To uphold ethical standards and prioritize the needs of patients, we emphasize the significance of safeguarding data, obtaining informed permission, and recognizing data ownership. The ontology-based approach presented in this study offers a scalable and flexible framework that can be readily incorporated into existing healthcare systems, redefining the management of COPD in response to evolving demands. Security poses one of the biggest threats in context-based environments due to the different data formats acquired by the diverse sensors. Another essential consideration is confidentiality because the data in hand is sensitive patient information.

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Section: Implementation Challengesmentioning

confidence: 99%

EHealth Innovation for Chronic Obstructive Pulmonary Disease: A Context-Aware Comprehensive Framework

Iqbal,

Qureshi,

Chishti

2024

SCPE

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“…However, complex encryption and authentication techniques cannot be implemented on these devices because of their limited computational capacity, memory, and power, which may result in increasing resource consumption and add extensive latency. As a result, these devices are susceptible to network and system attacks [33].…”

Section: Ioht Device Operating System Vulnerabilitymentioning

confidence: 99%

Privacy-Enhancing Technologies in Federated Learning for the Internet of Healthcare Things: A Survey

et al. 2023

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Advancements in wearable medical devices using the IoT technology are shaping the modern healthcare system. With the emergence of the Internet of Healthcare Things (IoHT), efficient healthcare services can be provided to patients. Healthcare professionals have effectively used AI-based models to analyze the data collected from IoHT devices to treat various diseases. Data must be processed and analyzed while avoiding privacy breaches, in compliance with legal rules and regulations, such as the HIPAA and GDPR. Federated learning (FL) is a machine learning-based approach allowing multiple entities to train an ML model collaboratively without sharing their data. It is particularly beneficial in healthcare, where data privacy and security are substantial concerns. Even though FL addresses some privacy concerns, there is still no formal proof of privacy guarantees for IoHT data. Privacy-enhancing technologies (PETs) are tools and techniques designed to enhance the privacy and security of online communications and data sharing. PETs provide a range of features that help protect users’ personal information and sensitive data from unauthorized access and tracking. This paper comprehensively reviews PETs concerning FL in the IoHT scenario and identifies several key challenges for future research.

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A survey of healthcare sector digitization strategies: Vulnerabilities, countermeasures and opportunities

Cited by 2 publications

References 189 publications

EHealth Innovation for Chronic Obstructive Pulmonary Disease: A Context-Aware Comprehensive Framework

EHealth Innovation for Chronic Obstructive Pulmonary Disease: A Context-Aware Comprehensive Framework

Privacy-Enhancing Technologies in Federated Learning for the Internet of Healthcare Things: A Survey

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