However, this paper's innovation stems from the authors' acknowledgment of the need to include caregivers, in addition to patients, in the design of DMHI to ensure a client-centered approach with a human focus rather than a data-driven ethos. They found that digital treatments must be adaptable and flexible, and that patients must be able to optimize information in their own EMR to focus on beneficial patterns. Enhancing patient-centered care can be accomplished by incorporating caregivers into platform design, particularly when their participation improves digital platform interfaces. The data collected through digital mental health interventions must be used to develop the digital therapeutic alliance, which promotes shared accountability between health providers and patients. This study emphasizes the significance of evaluating several data sources and data users to appropriately serve the mental health setting. It broadens the definition of participatory healthcare to include not only the patient, but also their significant others. [PERSON] et al. [17] use the central repository of EMR data to add knowledge and wisdom through research, demonstrating how data taken from a primary-care network allowed them to study an intervention that compared two distinct groups of patients with type 2 diabetes. They demonstrated how a data-driven strategy might bring valuable insights in this area of substantial challenge for health systems. This study intended to create an information-driven approach to peer-to-peer help. The authors relied on two approaches: segmentation, which divides patients into groups based on their needs (in this case, medication-taking behavior), and positive deviance, which applies learning principles. The article demonstrated how data from EMR may be utilized to segment patients into peer-to-peer virtual workshops, potentially leading to treatments that are more specifically targeted to patient needs. Overall, this collection of articles provides a new look at some of the challenges and opportunities offered by EMR. They show how the use of EMR data may be used to help support some of the most significant health issues of our time such as diabetes and mental health. The two articles in these areas [16; 17] indicate the benefit of future work on how EMR can help promote more equitable health outcomes with a greater focus on prevention and wellness. [PERSON] et al. [14] suggest further research into how EMR can promote patient-doctor face-to-face communication in a less hierarchical setting. This is especially crucial in a data-driven medical landscape where patients are expected to provide extra medical information, collected for example via wearable devices, so that it can be incorporated into EMR data. Finally, [PERSON] et al. 's [15] article on smartwatch sensing data provides considerable impetus for future research, as the topic of IoT becomes increasingly relevant. Interest in the data from such devices and how they might be used will undoubtedly increase as more users adopt these technologies, resulting in enormous untapped data repositories. The development of smartwatches from individual tools to part of a wider EMR hub will raise several ethical concerns that must be investigated and addressed in order to protect its tremendous potential advantages. Multimodal medical data fusion, achievable with EMR, has been developed with the aim of revolutionizing preventive, personalized, and participatory techniques to enhance health outcomes, identify risk factors, offer customized interventions, and empower patients throughout their healthcare experiences [1]. However, there remain numerous challenges that require exploration from various disciplinary viewpoints.