Machine learning (ML) has the power to transform care delivery and help achieve the holy grail of providing better care at lower costs. ML algorithms uncover patterns in data that identify patient-specific risk factors that can be proactively managed to keep patients from developing costly and harmful conditions. It also has a litany of uses in driving more efficient healthcare operations. Despite these benefits, we hear of organizations that are so daunted or confused by this technology that they delay or neglect bringing this technology into their organizations. This presentation will bring a practical understanding of ML, which is a game-changer, and show both non-technical and technical leaders:
- How to disambiguate the buzzwords associated with ML: predictive analytics, artificial intelligence (AI), and deep learning.
- Use cases and benefits of real-world examples associated with these technologies.
- Personnel and technology requirements for using ML (hint: it may be less than you think!).
- How to break out of the “black box” and provide transparency to promote understanding and adoption, rather than fear and loathing.
- Questions to ask when purchasing ML models or technology.