Welcome to the Grand Rounds Further Reading List.
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PRIVACY MATTERS
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Cilliers, L. (2020). "Wearable devices in healthcare: Privacy and information security issues." Health Information Management Journal 49(2-3): 150-156 Request Article https://journals.sagepub.com/doi/abs/10.1177/1833358319851684
Background: Mobile health has provided new and exciting ways for patients to partake in their healthcare. Wearable devices are designed to collect the user’s health data, which can be analysed to provide information about the user’s health status. However, little research has been conducted that addresses privacy and information security issues of these devices. Objective: To investigate the privacy and information security issues to which users are exposed when using wearable health devices. Method: The study used a cross-sectional survey approach to collect data from a convenience sample of 106 respondents. Results: Half of the respondents did not understand the need to protect health information. There also appeared to be a general lack of awareness among respondents about the information security issues surrounding their data collected by wearable devices. Conclusion: Users were not knowledgeable about the privacy risks that their data are exposed to or how these data are protected once collected. Implications: Users of wearable devices that collect personal information about health need to be educated about privacy and information security issues to which they are exposed when using these devices.
Khalid, N., et al. (2023). "Privacy-preserving artificial intelligence in healthcare: Techniques and applications." Computers in biology and medicine 158: 106848 Request Article https://www.sciencedirect.com/science/article/pii/S001048252300313X
There has been an increasing interest in translating artificial intelligence (AI) research into clinically-validated applications to improve the performance, capacity, and efficacy of healthcare services. Despite substantial research worldwide, very few AI-based applications have successfully made it to clinics. Key barriers to the widespread adoption of clinically validated AI applications include non-standardized medical records, limited availability of curated datasets, and stringent legal/ethical requirements to preserve patients’ privacy. Therefore, there is a pressing need to improvise new data-sharing methods in the age of AI that preserve patient privacy while developing AI-based healthcare applications. In the literature, significant attention has been devoted to developing privacy-preserving techniques and overcoming the issues hampering AI adoption in an actual clinical environment. To this end, this study summarizes the state-of-the-art approaches for preserving privacy in AI-based healthcare applications. Prominent privacy-preserving techniques such as Federated Learning and Hybrid Techniques are elaborated along with potential privacy attacks, security challenges, and future directions.
Pika, A., et al. (2020). "Privacy-Preserving Process Mining in Healthcare." International Journal of Environmental Research and Public Health 17(5): 1612 Request Article https://www.mdpi.com/1660-4601/17/5/1612
There has been an increasing interest in translating artificial intelligence (AI) research into clinically-validated applications to improve the performance, capacity, and efficacy of healthcare services. Despite substantial research worldwide, very few AI-based applications have successfully made it to clinics. Key barriers to the widespread adoption of clinically validated AI applications include non-standardized medical records, limited availability of curated datasets, and stringent legal/ethical requirements to preserve patients’ privacy. Therefore, there is a pressing need to improvise new data-sharing methods in the age of AI that preserve patient privacy while developing AI-based healthcare applications. In the literature, significant attention has been devoted to developing privacy-preserving techniques and overcoming the issues hampering AI adoption in an actual clinical environment. To this end, this study summarizes the state-of-the-art approaches for preserving privacy in AI-based healthcare applications. Prominent privacy-preserving techniques such as Federated Learning and Hybrid Techniques are elaborated along with potential privacy attacks, security challenges, and future directions.
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Healthcare Information Security and Privacy by 