Review of Artificial Intelligence Business Cases to Advance Towards Learning Healthcare Systems.
Authors
Affiliations (3)
Affiliations (3)
- Harvard Medical School, Boston MA, Department of Radiology, Beth Israel Medical Center, Boston, MA.
- Department of Radiology, University of Washington and Harborview Medical Center, Seattle WA.
- Department of Radiology, University of Washington and Harborview Medical Center, Seattle WA. Electronic address: [email protected].
Abstract
Multiple barriers have been identified to developing a learning healthcare systems (LHS) including organizational culture, data systems and interoperability, funding and workforce limitations and regulatory challenges. Artificial Intelligence (AI) is being explored both inside and outside of healthcare, with varying degrees of scientific rigor in the testing of AI applications. LHS and AI face similar implementation challenges, which presents an opportunity for synergy. By reviewing AI use cases from the lens of how it can be used to reduce previously identified barriers to progressing towards a LHS, opportunities for facilitating this journey can be identified. AI tools can impact both clinical and non-clinical business processes. The process of testing and implementing AI tools based on high-quality evidence or signal should pre-specify thresholds and expectations of incremental effectiveness (marginal risk-benefit) improvement compared to current standards of care, as is standard in health services research, quality improvement, process improvement, and best-practices of comparative healthcare research. Business process examples to improve workflow using AI tools may adhere to less-rigorous evidentiary standards compared to tools guiding patient-centered clinical decision scenarios, such as with AI-based diagnostic applications. This review indicates that AI tools provide tremendous opportunities for radiology-and healthcare to to improve healthcare systems, workflow processes, and patients' health outcomes.