From imaging to frailty assessments, innovations in machine learning hold great promise for improving healthcare. Yet some myths remain that hold organizational leaders, practitioners, caregivers, and others from embracing this technology. Understand these myths and the realities behind them:
- Myth 1: Machine learning can do much of what doctors (and other practitioners) do. Applications are designed to supplement, improve, enhance, or support the work of practitioners. For instance, machine learning algorithms have proven useful in making difficult diagnoses and identifying disease risks. Machine learning will never replace practitioners.
- Myth 2: Big data and brilliant data scientists are always a recipe for success. In fact, more data isn’t necessarily better. The key is having the right data and team members who can analyze this information and apply it effectively to specific patients and care settings.
- Myth 3: Successful algorithms will be adopted and utilized. Even good processes and protocols aren’t likely to be used if they aren’t integrated into the workflow of potential users.
