The Trajectory of IT in Healthcare at HICSS: A Literature Review, Analysis, and Future Directions

Samhan, Bahae; Crampton, Tara; Ruane, Regina

doi:10.17705/1cais.04341

Cited by 8 publications

(4 citation statements)

References 415 publications

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“…However, privacy and security concerns can arise when enormous amounts of data are incorrectly handled due to limited resources and energy [14,35]. Data sharing with a second and third party and storage on a cloud server and a healthcare server demonstrate that users no longer own their data [70,92,118].…”

Section: Iotmentioning

confidence: 99%

Security and Privacy of Wearable Wireless Sensors in Healthcare: A Systematic Review

Kaur,

Shahrestani,

Ruan

2024

Comput. Networks Commun.

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Wearable Wireless Sensor Network (WWSN) devices are widely used in healthcare to monitor health data. However, when WWSN users transmit their data to healthcare professionals or third parties over wireless connections, they face privacy and security vulnerabilities. This paper aims to identify the unsolved privacy and security challenges in wearable sensor devices in healthcare, especially the aspects overlooked by previous research. The main research question is: What are the unsolved privacy and security challenges in wearable sensor devices in healthcare, and what are their implications for users and healthcare professionals? This systematic review employs specific keywords to search for relevant publications on bibliographic databases, including Google Scholar, Scopus, IEEE Xplore, and Web of Science. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) charts helped in screening and summarising the selected papers. The results highlight the critical areas that can make WWSNs vulnerable to security attacks. The findings examine the security and privacy issues of wearable sensor devices in cloud computing, fog computing, the Internet of Things (IoT) and the like. Many studies examine IoT due to its privacy and security challenges, especially regarding handling extensive data, using public channels, deploying advanced technologies, managing sharing policies alongside the growing number of wireless devices, and protecting data from hackers. These challenges seriously threaten the confidentiality, integrity, and availability of health data transmitted by WWSN users to healthcare professionals or third parties in cloud-based environments and IoT and are exacerbated by limited resources. The significant findings thereby focus on unresolved areas in IoT. This paper aims to safeguard against cyber-attacks on healthcare and increase users' adoption rate of WWSN devices.

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Section: Iotmentioning

confidence: 99%

Security and Privacy of Wearable Wireless Sensors in Healthcare: A Systematic Review

Kaur,

Shahrestani,

Ruan

2024

Comput. Networks Commun.

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“…Because the disease can develop over time and patient dynamics might alter [ 14 , 15 ], medical specialists' available time is often constrained [ 16 , 17 ]. Additionally, diagnostics can be a very difficult procedure [ 18 , 19 ]. The concept of AI typically refers to the ability of a machine to engage in cognitive processes that are commonly associated with human intelligence, including but not limited to perception, reasoning, learning, environmental interaction, problem-solving, decision-making, and potentially even creative expression [ 20 ].…”

Section: Reviewmentioning

confidence: 99%

Perspective of Artificial Intelligence in Disease Diagnosis: A Review of Current and Future Endeavours in the Medical Field

Umapathy,

Rajinikanth B,

Samuel Raj

et al. 2023

Cureus

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Artificial intelligence (AI) has demonstrated significant promise for the present and future diagnosis of diseases. At the moment, AI-powered diagnostic technologies can help physicians decipher medical pictures like X-rays, magnetic resonance imaging, and computed tomography scans, resulting in quicker and more precise diagnoses. In order to make a prospective diagnosis, AI algorithms may also examine patient information, symptoms, and medical background. The application of AI in disease diagnosis is anticipated to grow as the field develops. In the future, AI may be used to find patterns in enormous volumes of medical data, aiding in disease prediction and prevention before symptoms appear. Additionally, by combining genetic data, lifestyle data, and environmental variables, AI may help in the diagnosis of complicated diseases. It is crucial to remember that while AI can be a powerful tool, it cannot take the place of qualified medical personnel. Instead, AI ought to support and improve diagnostic procedures, enhancing patient care and healthcare results. Future research and the use of AI for disease diagnosis must take ethical issues, data protection, and ongoing model validation into account.

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“…Additionally, another important aspect of AI is robotic process automation (RPA), which refers to the use of software robots to automate repetitive and routine tasks and physical robots, which are physical machines that can be programmed to perform tasks in the physical world [6,7] (Figure 1). Te application of AI as a support system in healthcare has gained special relevance in recent decades, mainly as a result of growing data ecosystems in healthcare systems [8,9]. In general terms, AI-based support systems ofer a potential resource for reducing the cost of healthcare, increasing the efciency of said services, and creating a highly valued support system for the well-being of patients and the healthcare sector in general, contributing to the satisfaction and clinical safety of patients and their family members [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Applications of Artificial Intelligence in Nursing Care: A Systematic Review

Martinez-Ortigosa,

Martinez-Granados,

Gil-Hernández

et al. 2023

Journal of Nursing Management

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Aim. To synthesise the available evidence on the applicability of artificial intelligence in nursing care. Background. Artificial intelligence involves the replication of human cognitive abilities in machines, allowing to perform tasks that conventionally necessitate human cognition. However, its application in health sciences is a recent one, and its use is currently limited to supporting the diagnosis and prognosis of hospitalised patients, among others. Evaluation. A systematic review was conducted in the PubMed-Medline, Scopus, CINAHL, Web of Science, and Nursing & Allied Health databases until September 2022, following the PRISMA guidelines. Key Issues. A total of 21 articles were selected for the review. The different applications of artificial intelligence in nursing identified comprised (i) advances in early disease detection and clinical decision making; (ii) artificial intelligence-based support systems in nursing for patient monitoring and workflow optimisation; and (iii) artificial intelligence insights for nursing training and education. Conclusion. Artificial intelligence-based systems demonstrated increased autonomy of patients and professionals in care processes such as wound management through guided instructions, improved workflows, and efficiency in terms of time, materials, and human resources. Implications for Nursing Management. Artificial intelligence applied to nursing practice can be a very useful resource for professionals, managers, and supervisors. It has the potential to change current working flow systems and may serve as a down-to-earth resource to support nursing professionals in their decision-making process that ensures high quality and patient safety care.

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The Trajectory of IT in Healthcare at HICSS: A Literature Review, Analysis, and Future Directions

Cited by 8 publications

References 415 publications

Security and Privacy of Wearable Wireless Sensors in Healthcare: A Systematic Review

Security and Privacy of Wearable Wireless Sensors in Healthcare: A Systematic Review

Perspective of Artificial Intelligence in Disease Diagnosis: A Review of Current and Future Endeavours in the Medical Field

Applications of Artificial Intelligence in Nursing Care: A Systematic Review

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