Note: The below article includes excerpts from the September 2018 Health Affairs article “Implementing and Scaling Artificial Intelligence Solutions: Considerations for Policy Makers and Decision Makers.” The full article can be accessed through the Health Affairs Journal, and was developed in partnership with Eric Just, MS, from Health Catalyst; and Bruce L. Gillingham, MD, CPE, FAOA RADM, MC, USN, Rear Admiral in the US Navy.
The New England Journal of Medicine, Harvard Business Review, and other publications, noted that Artificial Intelligence (AI) and machine learning can achieve breakthroughs in improving patient safety, health, and reducing waste. AI infers patterns, relationships, and rules directly from large volumes of data in ways that can exceed human cognitive capabilities. Today, opportunities for greater deployment of AI in health are made possible by increased electronic data availability from mobile devices, sensors, cameras, and electronic health records; faster data processing capabilities; and newly developed computing techniques. AI solutions are still only used sparingly in health/healthcare organizations. However, those organizations able to increase their AI capabilities will likely be better-positioned for the transition to value-based care and achieving desired results with greater efficiency.
While there are complex technical and methodological details that underlie specific AI applications, there are three basic types of AI solutions including: simple task automation (Type 1), pattern recognition (Type 2), and contextual reasoning (Type 3). All AI solution types can offer significant improvements across multiple use cases in health including clinical support, population/public health, patient/consumer experience, and administrative/business processes. Health organizations will likely benefit from critical, data-driven improvements across all use-cases to achieve desired results effectively and efficiently.
Decision makers in health organizations need to decide if and how to adopt AI technologies for their organizations. They need to take a wide array of considerations into account including AI solution types, requirements of different user groups or use cases, varying technical/analytic complexities, the ability to scale and sustain solutions across their enterprise, and return on investment.