By leveraging emerging data analytic capabilities like Artificial Intelligence Natural Language Processing (AINLP), organizations can identify high-need patients beyond those identified through traditional means. This approach can lead to earlier interventions, effectively addressing gaps in care and improving patient outcomes, and early identification of patient needs can reduce patient and insurer exposure to higher costs.
Coupling AINLP with advanced Machine Learning Predictive Modeling can identify members who may become high-cost and high-need early on, lessening the burden on patients, providers and insurers while providing further opportunities for early intervention.
Implementing NLP and Predictive Modeling into health and well-being data analytics provides a personalized and more relevant experience. Understanding how to utilize this approach can benefit not only individual patients, but employers and insurers, as well as health care systems and providers.
Through the use of multiple employer case studies and extensive data analysis, this session will provide attendees with an understanding of the capabilities of NLP and Predictive Modeling when applied to population health, and will illustrate how to effectively put this information into action to improve clinical outcomes across the workforce and reduce medical costs.