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CC BY-NC-ND 4.0 · Appl Clin Inform 2021; 12(04): 888-896
DOI: 10.1055/s-0041-1735183
Case Report

Principles for Real-World Implementation of Bedside Predictive Analytics Monitoring

Liza Prudente Moorman
1   Clinical Implementation Specialist, Advanced Medical Predictive Devices, Diagnostics, and Displays (AMP3D), Charlottesville, Virginia, United States
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Abstract

A new development in the practice of medicine is Artificial Intelligence-based predictive analytics that forewarn clinicians of future deterioration of their patients. This proactive opportunity, though, is different from the reactive stance that clinicians traditionally take. Implementing these tools requires new ideas about how to educate clinician users to facilitate trust and adoption and to promote sustained use. Our real-world hospital experience implementing a predictive analytics monitoring system that uses electronic health record and continuous monitoring data has taught us principles that we believe to be applicable to the implementation of other such analytics systems within the health care environment. These principles are mentioned below:

• To promote trust, the science must be understandable.

• To enhance uptake, the workflow should not be impacted greatly.

• To maximize buy-in, engagement at all levels is important.

• To ensure relevance, the education must be tailored to the clinical role and hospital culture.

• To lead to clinical action, the information must integrate into clinical care.

• To promote sustainability, there should be periodic support interactions after formal implementation.

Keywords

artificial intelligence - clinical decision support - implementation science

Protection of Human and Animal Subjects

No human subjects were involved in the project.




Publication History

Received: 10 April 2021

Accepted: 12 July 2021

Article published online:
22 September 2021

© 2021. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/)

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