An Analysis of Integrating Machine Learning in Healthcare for Ensuring Confidentiality of the Electronic Records

Abstract

The adoption of sustainable electronic healthcare infrastructure has revolutionized healthcare services and ensured that E-health technology caters efficiently and promptly to the needs of the stakeholders associated with healthcare. Despite the phenomenal advancement in the present healthcare services, the major obstacle that mars the success of E-health is the issue of ensuring the confidentiality and privacy of the patients' data. A thorough scan of several research studies reveals that healthcare data continues to be the most sought after entity by cyber invaders. Various approaches and methods have been practiced by researchers to secure healthcare digital services. However, there are very few from the Machine learning (ML) domain even though the technique has the proactive ability to detect suspicious accesses against Electronic Health Records (EHRs). The main aim of this work is to conduct a systematic analysis of the existing research studies that address healthcare data confidentiality issues through ML approaches. B.A. Kitchenham guidelines have been practiced as a manual to conduct this work. Seven well-known digital libraries namely IEEE Xplore, Science Direct, Springer Link, ACM Digital Library, Willey Online Library, PubMed (Medical and Bio-Science), and MDPI have been included to perform an exhaustive search for the existing pertinent studies. Results of this study depict that machine learning provides a more robust security mechanism for sustainable management of the EHR systems in a proactive fashion, yet the specified area has not been fully explored by the researchers. K-nearest neighbor algorithm and KNIEM implementation tools are mostly used to conduct experiments on EHR systems' log data. Accuracy and performance measure of practiced techniques are not sufficiently outlined in the primary studies. This research endeavour depicts that there is a need to analyze the dynamic digital healthcare environment more comprehensively. Greater accuracy and effective implementation of ML-based models are the need of the day for ensuring the confidentiality of EHRs in a proactive fashion.

Securing electronic health records against insider-threats: A supervised machine learning approach

“…Machine learning for this application has, to-date, been relatively under-used for EHR security despite its success in other cyber security applications (). This is confirmed in the detailed findings by Adil et al who conduct an SLR-based analysis of existing applications of machine learning for safeguarding EHR (Seh et al, 2021). Their analysis covers 7 digital libraries, and findings indicate that there are 19 related articles in this domain (3 of which are by the authors of this article), from which the K-nearest neighbour algorithm is predominantly employed.…”

“…Their approach is also tested by means of the Cooja simulator rather than real-world data, as employed in this article. Crucially, as Adil et al discuss, there are very few articles adopting a machine learning approach for EHR security despite the success of its application in related security domains (Seh et al, 2021). This is also confirmed by Qayyum et al who question the levels of robustness for the use of machine learning within healthcare-based security applications (Adnan Qayyum et al, 2020).…”

A comprehensive literature review of Islam-oriented scales on religiosity, spirituality, and morality

“…Internal validity. This determines the quality of the assessment used to conduct the study and ensures the efficacy of the literature review process (Kaur et al, 2020;Seh et al, 2022). For this purpose, a search mechanism was created, as detailed in the "Methodology" section.…”

“…External validity. This assures the results are generalizable, or in other words, it determines the applicability of the results (Kaur et al, 2020;Seh et al, 2022). For this purpose, the search engines of specific digital libraries were used to obtain the results.…”

We may Still not be Ready for Newer Healthcare Technologies: An Ethical Perspective of Privacy Concerns

“…Concern over the privacy and confidentiality of patient data is rising as electronic health records become more widely used and technology is incorporated into healthcare systems on a larger scale [17] . Issues of dependability, security, and privacy are particularly important because healthcare information is sensitive and there is a lot of reliance on reliable records [18] .…”