Integrating Remote Symptom Monitoring, Person-Centered Analytics, and Artificial Intelligence to Advance Precision Health Symptom Science in Oncology.

Objectives: To summarize the relevance of remote symptom monitoring, person-centered statistical analyses, and artificial intelligence to precision health symptom science in oncology; and propose ways in which these three approaches can be integrated to further advance the field.

Methods: The following commentary was adapted from a talk delivered at the Symptom Science Experts Meeting in October 2023 at the University of Lausanne, Switzerland. The commentary and talk were informed by an informal review of recent literature in precision health oncology symptom science.

Results: Several remote symptom monitoring interventions have demonstrated potential to reduce disease- and treatment-related symptom burden and improve health outcomes in patients with cancer. Data collected passively by wearable and sensor technologies are also being used to characterize patients' health status. Person-centered statistical analyses have identified interindividual variability in the symptom experiences of patients with cancer. Together with artificial intelligence-based approaches, these analyses have identified factors associated with relatively adverse symptom experiences. Future directions for the field include integrating these approaches to optimize clinical resource allocation, tailor symptom management in real-time, and advance scientific knowledge of the symptom experience.

Conclusions: Integrating remote symptom monitoring, person-centered statistical analyses and artificial intelligence may provide deeper insights into how patients with cancer experience symptoms.

Implications for nursing practice: Findings from research that uses remote symptom monitoring, person-centered statistical analyses, and artificial intelligence may enhance clinicians' ability to deliver personalized symptom management interventions.