International Journal of Clinical Case Reports 2024, Vol.14, No.6, 339-350 http://medscipublisher.com/index.php/ijccr 345 features are equally relevant in mental health care, where continuous monitoring and timely interventions can significantly improve patient outcomes. 6 Challenges in the Application of Smart Health Devices 6.1 Technical and device limitations Smart health devices face several technical and device limitations that hinder their effectiveness and reliability. Issues such as device accuracy, data reliability, and durability are prevalent. For instance, smart wearable devices used in cardiovascular care often struggle with accuracy and clinical validity, which can lead to misdiagnoses or inappropriate treatments (Bayoumy et al., 2021). Additionally, the performance of these devices can be compromised by technical difficulties, especially among older and less educated users, as seen in the use of smart devices by community health workers (Greuel et al., 2023). The transition of wearable data from clinical trials to practical medical applications is also burdened with challenges related to energy efficiency, real-time data processing, and network security (Ajakwe et al., 2022). 6.2 User acceptance and adherence User acceptance and adherence are significant challenges, particularly among the elderly and groups with difficulty using technology. The adoption of IoT applications in healthcare is generally low, with major barriers including social influence, personal inattentiveness, and perceived privacy risks (Al-rawashdeh et al., 2022). Elderly users and those with cognitive impairments often find it difficult to interact with smart health devices, which can lead to poor adherence to prescribed health regimens (Martin et al., 2008). Moreover, the temptation to replace educational conversations with passive video watching can negatively impact the quality of interactions between health workers and clients (Greuel et al., 2023). 6.3 Regulatory and policy issues Regulatory and policy issues present another layer of complexity in the deployment of smart health devices. The lack of standardized regulatory policies and concerns for patient privacy are major hindrances to the widespread adoption of these technologies (Bayoumy et al., 2021). The integration of IoT in healthcare also raises significant legal and ethical concerns, particularly around data management, security, and patient consent (Farahani et al., 2018). The need for comprehensive evaluation frameworks and pragmatic regulatory policies is crucial to address these challenges and ensure the safe and effective use of smart health devices (Peralta-Ochoa et al., 2023). 6.4 Network and communication infrastructure The successful operation of smart health devices heavily relies on robust network and communication infrastructure. Current connectivity solutions face challenges such as support for a massive number of devices, standardization, energy efficiency, and security (Ahad et al., 2020). The deployment of 5G networks is expected to address some of these issues by providing enhanced mobile broadband and ultra-reliable low latency communications, which are essential for real-time health monitoring and remote surgeries (Peralta-Ochoa et al., 2023). However, the existing network infrastructure in many regions is still inadequate, leading to inconsistent device performance and limited coverage (Talal et al., 2019). The need for improved network performance and enhanced cellular coverage is critical for the effective implementation of smart health devices in community care (Ahad et al., 2020). 7 Future Prospects for the Application of Smart Health Devices in Community Care 7.1 Application of artificial intelligence and big data The integration of artificial intelligence (AI) and big data with smart health devices holds significant promise for precision care. AI can process vast amounts of data generated by these devices to provide personalized health insights and predictive analytics. For instance, the synergy between AI and the Internet of Things (IoT) in healthcare, often referred to as the Artificial Intelligence of Things (AIoT), enables the collection and analysis of large datasets to optimize patient outcomes (Baker and Xiang, 2023). AI's ability to interpret and make decisions based on big data can enhance the precision of health interventions, tailoring them to individual patient needs. This convergence is particularly beneficial in identifying unique patient phenotypes and providing personalized treatment plans (Johnson et al., 2020).
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