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. 2020 Aug 21:3:107.
doi: 10.1038/s41746-020-00318-y. eCollection 2020.

Developing a delivery science for artificial intelligence in healthcare

Affiliations

Developing a delivery science for artificial intelligence in healthcare

Ron C Li et al. NPJ Digit Med. .

Abstract

Artificial Intelligence (AI) has generated a large amount of excitement in healthcare, mostly driven by the emergence of increasingly accurate machine learning models. However, the promise of AI delivering scalable and sustained value for patient care in the real world setting has yet to be realized. In order to safely and effectively bring AI into use in healthcare, there needs to be a concerted effort around not just the creation, but also the delivery of AI. This AI "delivery science" will require a broader set of tools, such as design thinking, process improvement, and implementation science, as well as a broader definition of what AI will look like in practice, which includes not just machine learning models and their predictions, but also the new systems for care delivery that they enable. The careful design, implementation, and evaluation of these AI enabled systems will be important in the effort to understand how AI can improve healthcare.

Keywords: Health services; Translational research.

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Conflict of interest statement

Competing interestsThe authors declare no competing interests.

Figures

Fig. 1
Fig. 1. Multidisciplinary process for creating, implementing, and evaluating an AI enabled system for healthcare.
Methods from process improvement, design thinking, data science, information technology, and implementation science are combined into an iterative participatory process to build an AI enabled system for improving advance care planning. The expertize used across the different disciplines are as follows: (1) user experience design, (2) data science, (3) healthcare operations, (4) clinical informatics, (5) evaluation, and (6) ethical integrity assessment.

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