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Review: A Business Case for Artificial Intelligence Tools: The Currency of Improved Quality and Reduced Cost

Author(s)

Lauren Parks Golding, MD, Gregory N. Nicola, MD

Link(s)

https://www.sciencedirect.com/science/article/pii/S1546144019305733

Summary of Paper

In this paper, contrary to other articles, we want to focus on a practical example of the utilization of AI and reasons, rules, and obstacles.

This paper is concentrating on the methodologies and implications of real practice in healthcare systems and how the tools should keep the quality without increasing the prices and how the AI will be utilized for this matter. According to (Golding & Nicola, 2019), AI algorithms have the 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.

The research question is interesting and, at the same time, practical and essential.

Quality of the Research and The Research Method Item

The Value-based care transformation and AI tools have been introduced as the most anticipated disruptors in the Healthcare system. The reference and data feed for this conclusion is missing. Moreover, the significant impact of these factors on quality and cost in healthcare is emphasized. The concept of improving the quality without increasing the price and vise versa is established as one of the bases of the article.

In this article, the economic and social sectors of the integration of AI are noticeable, while the ethical issues and concerns are not appropriately highlighted. For example, what are/were the ethical problems before and after the integration of AI and the utilization of AI-based tools?

The paper is well structured [Introduction, Fee For Service, MIPS, Value-Based Payment Models, Alternative Payment Models, and Conclusion]. The methodology and approach toward comparison and pros & cons are valuable while the methods are adequately described. The procedure provided somehow enough details for the reader to understand and distinguish the techniques and be able to recreate the models. As one of the points here, we can talk about the real statistics and numbers which are missing from the content while the narrative explains the fact and understandings. The comparisons through MIPS were exciting, and the findings are reliable. Regarding the payment, Value-based models as the bases and typical setups are studied, and other AMPs have been introduced.

Quality of Presentation Item

The presentations of figures and key-points are helpful, but the percentages and numerical illustrations are missing for the readers to distinguish and recognize the differences.
Proper Citation and increasing the referencing throughout the body of the paper
A more visual Presentation can be effective
Provision of other examples and covering future possibilities are recommended
More in-depth studies of Risks and Risk analysis are recommended

Bibliography

Anon., n.d. RVS Update Committee (RUC) | American Medical Association. [Online]
Available at: https://www.ama-assn.org/about/rvs-update-committee-ruc/rvs-update-committee-ruc

Anon., n.d. The CPT® code process | American Medical Association. [Online]
Available at: https://www.ama-assn.org/about/cpt-editorial-panel/cpt-code-process

Golding, L. P. & Nicola, G. N., 2019. A Business Case for Artificial Intelligence Tools: The Currency of Improved Quality and Reduced Cost. Journal of the American College of Radiology, 1 9, 16(9), pp. 1357-1361.

Rubin, D. L. & Kahn, C. E., 2017. common Data elements in radiology 1. Radiology, Volume 283.