Challenges of mobile device use in healthcare

Vrhovec, Simon

doi:10.1109/mipro.2016.7522357

Cited by 8 publications

(4 citation statements)

References 14 publications

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“… Secure Mobile Devices: Many healthcare providers use mobile devices in their work, and also the patient uses the mobile devices to collect PGHD these can be a potential security risk if not properly secured. Vrhovec et al [ 82 ] examines the proliferation of mobile devices in healthcare and the consequent security risks, noting that 44% of data breaches in healthcare are linked to mobile device use. It highlights the benefits of mobile devices, such as improved healthcare worker coordination and reduced data redundancy, while also addressing the significant security challenges posed by rapid technology adoption, including device theft, insecure applications, and inadequate security management.…”

Section: Resultsmentioning

confidence: 99%

“…RQS1 [76][77][78][79][80][81][82][83][84][85] explores several important strategies that can help safeguard the security and privacy of PGHD. These include restricting data access to authorized personnel only, implementing robust data usage controls, maintaining audit trails to monitor data access, encrypting data at rest and in transit, securing mobile devices used to collect PGHD, mitigating risks associated with connected devices, conducting regular risk assessments to identify vulnerabilities, educating healthcare staff about security protocols, and carefully considering how to integrate PGHD into electronic medical record systems.…”

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confidence: 99%

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Patient-Generated Health Data (PGHD): Understanding, Requirements, Challenges, and Existing Techniques for Data Security and Privacy

Khatiwada,

Yang,

Lin

et al. 2024

JPM

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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.

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Section: Resultsmentioning

confidence: 99%

mentioning

confidence: 99%

Patient-Generated Health Data (PGHD): Understanding, Requirements, Challenges, and Existing Techniques for Data Security and Privacy

Khatiwada,

Yang,

Lin

et al. 2024

JPM

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“…Therefore, there is a gap in the motivational theories that would focus on individuals outside of the organizational context. Such insights would also benefit the research on the motivation of individuals in organizational contexts as people tend to behave similarly at work and at home [22]. This paper focuses on the effect of privacy concerns and perceived threats of individual social network users on intention for engaging in self-protective behavior.…”

Section: Introductionmentioning

confidence: 99%

Social Network Self-Protection Model:What Motivates Users to Self-Protect?

Fujs

Mihelič

Vrhovec

2019

JCSM

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Social networks are an indispensable activity for billions of users making them an attractive target for cyberattacks. There is however only scarce research on self-protection of individuals outside the organizational context. This study aims to address this gap by explaining what motivates individuals to self-protect on social networks. A survey (N = 274) has been conducted among Slovenian Facebook users to test the proposed social network selfprotection model. The results show that privacy concerns and perceived threats significantly affect user's intention to self-protect. Descriptive norm only affects intention indirectly through perceived threats appearing to contradict a large body of research on behavioral intentions. "If others protect themselves, there must be a serious threat." On the other hand, it also helps to explain why the direct effect of descriptive norm on security-related behavior is relatively small in other studies. Surveillance concerns, regulation and information sensitivity all significantly affect privacy concerns. Although privacy concerns are currently high due to the recent high-profile privacy-related scandals (e.g., Cambridge Analytica, Facebook, Google+), it may not affect the motivation of users to self-protect as they dealt with issues far beyond their control.

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“…On the other hand 44 percent of data breaches happen in healthcare alone [8,49]. In an evaluation of mHealth solutions and corresponding studies McKay et al [37] point out the "lack of information in any of these studies about readability, privacy or security".…”

Section: Introductionmentioning

confidence: 99%

mHealth Applications for Goal Management Training - Privacy Engineering in Neuropsychological Studies

Gabel

Schiering

Müller

et al. 2018

IFIP Advances in Information and Communication Technology

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HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

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Challenges of mobile device use in healthcare

Cited by 8 publications

References 14 publications

Patient-Generated Health Data (PGHD): Understanding, Requirements, Challenges, and Existing Techniques for Data Security and Privacy

Patient-Generated Health Data (PGHD): Understanding, Requirements, Challenges, and Existing Techniques for Data Security and Privacy

Social Network Self-Protection Model:What Motivates Users to Self-Protect?

mHealth Applications for Goal Management Training - Privacy Engineering in Neuropsychological Studies

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