7 General Discussion 143 The first way, the machine way, entails leveraging advancements in artificial intelligence (AI), using big data and sophisticated algorithms to provide personalized behavior change mechanisms or lifestyle recommendations. This method has been utilized effectively in entertainment and social media, as seen with platforms like Netflix and TikTok. Although AI has made considerable advances in healthcare, we are still grappling with severe issues around safety and privacy that must be resolved before this can become a widespread practice (Jiang et al., 2017). These issues and the already existing challenges in designing equitable interventions suggest that, while AI holds potential, its implementation for personalized eHealth solutions requires careful consideration. The second way, the human way, offers a more immediate and practical solution. By using eHealth platforms with human interaction, we can combine the best of both worlds. This “blended care” approach could offer the flexibility and constant availability of digital platforms and the healthcare professionals’ personal touch and human interaction. Within this model, barriers that would typically be present when people with a low SEP interact with either healthcare or eHealth systems could be mitigated. For example, interpreting data from eHealth systems could be challenging for some patients with low health literacy. Healthcare providers could help patients understand the data and make informed decisions about their health or select the desired behavior change techniques. Alternatively, eHealth systems could offer online advice, providing patients with information they can review at their own pace. This empowers them to understand their health information better, communicate more effectively with their healthcare providers, and play a more active role in their health decision making process. One of the promises of eHealth solutions is to ease the burden on healthcare professionals, offering to complement their efforts if integrated effectively into their workflows, potentially reducing their workload (Howard et al., 2013). Yet, the investment in research, development, implementation, training, and maintenance must be justified by long-term benefits. Developing a sound business and implementation strategy alongside the intervention can help manage these costs (van Limburg et al., 2011). Considering these factors, a blended approach emerges as the most practical route for developing personalized equitable eHealth interventions in the foreseeable future. 7.4.4. Implications for design for eHealth equity Reflecting on the discussed implications, it is important to underscore the key themes that played a vital role in this dissertation. Future research should explore the applicability of these themes in various contexts, which could pave the way for identifying universally relevant and validated core principles for designing equitable eHealth interventions. To take the first step, I will summarize the key themes emerging from this thesis.
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