ADVANCING PERSONALIZED MEDICINE: A STRATEGIC FRAMEWORK FOR INTEGRATING NON-TRADITIONAL DATA SOURCES

Abstract

This paper presents a strategic framework for advancing personalized medicine through the integration of traditional and non-traditional data sources. Recognizing the limitations of relying solely on clinical records, genomic data, and clinical trials, a comprehensive approach is proposed that includes data from online social networks and discussion forums, wearable devices, and patient-generated health data. The importance of these diverse sources is emphasized in capturing real-time insights into patient behaviors and responses, which are often missed in standard clinical settings. This approach employs Large Language Models (LLMs) to efficiently integrate and analyze the heterogeneous and unstructured nature of these data streams. This integration facilitates a more holistic view of health management and outcomes, enhancing the precision of personalized medicine. The complexities of combining structured clinical information with unstructured patient narratives and digital interactions are discussed in the paper, highlighting the role of LLM in extracting relevant health information. The paper also delves into the significance of alternative data sources like social media, wearable technology, and sensor data from smart devices in refining predictive analytics and improving patient care. Additionally, challenges and opportunities in data integration are discussed, emphasizing the need for a robust data strategy that encompasses data quality, stewardship, exchange, and analytics. Proposed methodology contributes to the field by offering a data-driven approach for health risk assessment and management, ultimately aiming to improve patient outcomes and healthcare practices in the domain of personalized medicine.