Pankaj Khatiwada

The evolution of Patient-Generated Health Data (PGHD) represents a major shift in healthcare, fueled by technological progress. The advent of PGHD, with technologies such as wearable devices and home monitoring systems, extends data collection beyond clinical environments, enabling continuous monitoring and patient engagement in their health management. Despite the growing prevalence of PGHD, there is a lack of clear understanding among stakeholders about its meaning, along with concerns about data security, privacy, and accuracy. This article aims to thoroughly review and clarify PGHD by examining its origins, types, technological foundations, and the challenges it faces, especially in terms of privacy and security regulations. The review emphasizes the role of PGHD in transforming healthcare through patient-centric approaches, their understanding, and personalized care, while also exploring emerging technologies and addressing data privacy and security issues, offering a comprehensive perspective on the current state and future directions of PGHD. The methodology employed for this review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and Rayyan, AI-Powered Tool for Systematic Literature Reviews. This approach ensures a systematic and comprehensive coverage of the available literature on PGHD, focusing on the various aspects outlined in the objective. The review encompassed 36 peer-reviewed articles from various esteemed publishers and databases, reflecting a diverse range of methodologies, including interviews, regular articles, review articles, and empirical studies to address three RQs exploratory, impact assessment, and solution-oriented questions related to PGHD. Additionally, to address the future-oriented fourth RQ for PGHD not covered in the above review, we have incorporated existing domain knowledge articles. This inclusion aims to provide answers encompassing both basic and advanced security measures for PGHD, thereby enhancing the depth and scope of our analysis.

Internet of Things (IoT) devices have changed how billions of people in the world connect and interact with each other. But, as more people use IoT devices, many questions arise about how these devices handle private data and whether they properly ask for permission when using it. Due to information privacy regulations such as the EU’s General Data Protection Regulation (GDPR), which requires companies to seek permission from data subjects (DS) before using their data, it is crucial for IoT companies to obtain this permission correctly. However, this can be really challenging in the IoT world because people often find it difficult to interact with and manage multiple IoT devices under their control. Also, the rules about privacy are not always clear. As such, this paper proposes a new model to improve how consent is managed in the world of IoT. The model seeks to minimize “consent fatigue” (when people get tired of always being asked for permission) and give DS more control over how their data are shared. This includes having default permission settings, being able to compare similar devices, and, in the future, using AI to give personalized advice. The model allows users to easily review and change their IoT device permissions if previous conditions are not met. It also emphasizes the need for easily understandable privacy rules, clear communication with users, and robust tracking of consent for data usage. By using this model, companies that provide IoT services can do a better job of protecting user privacy and managing DS consent. In addition, companies can more easily comply with data protection laws and build stronger relationships with their customers.

This paper provides a comprehensive exploration of the threats and risk associated with the use of digital technology for remote healthcare. Through our "DigiRemote" research project, we aim to find out the challenges and risks involved in scaling remote health care, with specific focus on its technical and security dimensions. The article delves into the use of digital technologies, including Bluetooth-enabled IoMT devices, for monitoring health metrics in remote care settings. Emphasis is placed on understanding the threats in the generation, storage, and transfer of health data. By, utilizing the STRIDE (Spoofing, Tampering, Repudiation, Information disclosure, Denial of service, and Elevation of privilege) model, we identified major threats, which are visually represented via heatmaps and bar graphs for ease of analysis. The DREAD (Damage, Reproducibility, Exploitability, Affected Users, and Discoverability) model assists in quantifying these threats, categorizing them into distinct risk levels, with detailed description of each threat category. In addition, we also suggest countermeasures for the highest-risk threats. This article serves as a pivotal resource for healthcare organizations and security professionals, shedding light on the risks of remote healthcare and providing mitigation strategies for both the implementation and post-implementation stages.

In the contemporary wave of digital transformation, the implementation of electronic health records (EHRs) has become a pivotal undertaking for numerous nations. However, amidst this technological advancement, a critical facet deserving heightened attention is the security and privacy of these electronic health systems. Regrettably, this crucial concern often finds itself eclipsed by other aspects of digitalization. Consequently, these oversight lapses create vulnerabilities within the EHR framework, leaving them open and exposed to an array of malicious cyber intrusions. In response to this pressing issue, our study delves into a comprehensive evaluation of security measures within the ambit of African digital health strategies. Remarkably, among the number of approximately 42 nations that have embarked on digital health strategy formulation, a mere 2 countries have taken cognizance of the imperative to integrate robust security and privacy policies into their healthcare-oriented digital transformation initiatives. In light of this disconcerting revelation, we present an actionable roadmap that endeavours to fortify EHR security, aligning with the progressive "shift-left" paradigm. By advocating for the proactive integration of security measures from the inception of EHR development, we strive to curtail vulnerabilities and enhance the overall resilience of these systems. Our proposed roadmap stands as a clarion call for governments, healthcare authorities, and technology stakeholders to collectively prioritize security in tandem with digital health advancement, thereby fostering a safeguarded and privacy-respecting electronic healthcare landscape.

There is an increasing number of assisted living technologies and health monitoring systems to provide electronic health services and help patients and the elderly stay at home longer. It ensures users’ safety and reduces caregivers’ workload. Regardless of the benefits that health monitoring systems have brought to users, they are in the face of privacy challenges, which can influence users’ adoption of these technologies. One of the challenges is multi-party privacy policy management. As different parties may have different considerations in the same scenario, they can make different privacy decisions. Therefore, a privacy policy management scheme is required to deal with conflicts. In this paper, we present the work of a user study with the scope of collecting several participants’ opinions on multi-party privacy policy management in the context of health monitoring. One focus group and eight semi-structured interviews were conducted. Our questions for participants adopt strategies from online social networks. After that, we discuss participants’ views on privacy decision conflict resolution in different health conditions and whether a recommendation system is needed to help with multi-party privacy policy management. We try to make prioritization based on participants’ views and the emergency level of events in different scenarios, increase technology adoption, and ensure health services are better delivered. The results reflect the importance of adopting flexible strategies like majority weighted voting in different health conditions, which serves as a basis for multi-party privacy policy scheme development.

The Internet of Medical Things (IoMT) emerges with new trendsetter device applications, where it defines the incorporation of medical devices with the Internet of Things (IoT). The healthcare sector continues to encounter challenging obstacles that have an impact on the quality of treatment provided to patients. To get rid of this problem, IoMT is being deployed to achieve the high reliability and efficiency of the health system. The IoMT devices are superimposed with clinical information as they contain the details of patient health data, address, and other patient identifiers. By containing such amount of sensitive information, it becomes cumbersome to preserve data privacy and security. Due to inadequate security and privacy precautions, patient health data is susceptible to leakage, which has a direct impact on the patient’s life. In addition, the majority of medical devices are susceptible to cyberattacks, putting patient information at risk. Inadequate control of life-support equipment can have a devastating effect on patient outcomes. Thus, this survey intends to review the various security models of IoMT devices using standard techniques to support health care systems. It provides a wide range of literature reviews regarding IoMT systems and compares them with traditional methodologies. This review work exhibits the motivation for current technologies to maintain the security and privacy of patients’ data with IoMT devices. The systematic review entails background on security in IoMT devices, techniques for security, usage of diverse validation measures, and also discusses the problems and motivation for future research work.

The Internet of Thigs is mainly considered as the key technology tools which enables in connecting many devices through the use of internet, this has enabled in overall exchange of data and information, support in receiving the instruction and enable in acting upon it in an effective manner. With the advent of IoT, many devices are connected to the internet which enable in assisting the individuals to operate the devise virtually, share data and program required actions. This study is focused in understanding the key determinants of creating smart homes by applying natural language processing (NLP) through IoT. The major determinants considered are Integrating voice understanding into devices; Ability to control the devices remotely and support in reducing the energy bills. The present study focuses in sourcing the data from both sources and information are collected through the usage of questionnaire and the information are quantitatively applied through 5-point Likert scale. The researchers use extensive data analysis tools using AMOS for understanding the path diagram using structural equation model.

The introductory section of the research mainly deals with the benefits of using a data security system for keeping all medical records safe and secure. However, the medical authorities need to implement an effective data security management system by using IoT and a wireless networking system for data management. All the essential benefits, as well as the importance of using a data security management system, is necessary for keeping all the records related to medical facilities safe and secure. The literature review section of the research mainly discusses all the benefits of using a data security management system for medical records safety. However, the entire essential benefits, as well as technologies, needs to be focused on managing all the data security systems. All the necessary details for keeping medical data safe and secure is important for accessing all the patients' information from an effective perspective. However, designing this data security management is important for keeping all medical records safe and secure. In the data analysis part two tests have been occurred one is regression test and another is one sample T test where two independent variable are chosen and those are year and use of cryptography in health sectors. The dependent variable is enhanced level of protection of medical records. The derived values have been compared with. 05 that is the level of significance.