مقاله انگلیسی رایگان در مورد یک مورد کسب و کار برای ابزارهای هوش مصنوعی – الزویر ۲۰۲۰

Introduction

Fee for Service

Value-Based Payment Models

MIPS

Alternative Payment Models

Take-Home Points

References

Abstract

For data science tools to mature and become integrated into routine clinical practice, they must add value to patient care by improving quality without increasing cost, by reducing cost without changing quality, or by both reducing cost and improving quality. Artificial intelligence (AI) algorithms have potential to augment data-driven quality improvement for radiologists. If AI tools are adopted with population health goals in mind, the structure of value-based payment models will serve as a framework for reimbursement of AI that does not exist in the fee-for-service system.

Introduction

Two of the most anticipated disruptors in health care in recent history are value-based care transformation and the emergence of artificial intelligence (AI) tools. Certainly, neither has sweepingly transformed health care to date. Yet both innovations will undoubtedly continue to shape the future of health care, and the intersection of the two has implications for radiologists both in terms of payment policy and data-driven quality improvement. For data science tools to mature and become integrated into routine clinical practice, they must add value to patient care by improving quality without increasing cost, by reducing cost without changing quality, or by both reducing cost and improving quality. Increasing the efficiency of radiologists is one example of value added by AI tools; however, for the purposes of this article, we will focus on the potential role of these tools specifically in the value-based payment structure of the Quality Payment Program (QPP).