The field of healthcare is shifting towards a more patient-centered approach, with a growing emphasis on personalized health and data-driven insights. Recent studies have highlighted the importance of transparency, data privacy, and patient preferences in the management of electronic health records and biospecimens consent for research. The integration of patient-generated health data (PGHD) into clinical workflows is also becoming increasingly important, particularly in the context of physical activity planning and cardiac rehabilitation. Furthermore, the use of artificial intelligence (AI) and machine learning (ML) is being explored to enhance data sensemaking and analysis capabilities, as well as to identify metabolic subphenotypes and inform precision lifestyle changes. Noteworthy papers in this area include: Supporting Patients in Managing Electronic Health Records and Biospecimens Consent for Research, which developed a patient-centered electronic consent management portal, and Use of Continuous Glucose Monitoring with Machine Learning to Identify Metabolic Subphenotypes and Inform Precision Lifestyle Changes, which demonstrated the potential of machine learning models to predict gold-standard measures of muscle insulin resistance and beta-cell function. Overall, these advancements have the potential to revolutionize the field of healthcare, enabling more effective and personalized treatment approaches.