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Barriers to Implementing an Artificial Intelligence Model for Unplanned Readmissions.

Sally L Baxter^1,2, Jeremy S Bass^1,3, Amy M Sitapati^1,4

¹Health Department of Biomedical Informatics, University of California San Diego, La Jolla, California, United States.

|December 4, 2020

Summary

This summary is machine-generated.

Implementing artificial intelligence (AI) models in electronic health records (EHRs) requires careful planning. Identifying stakeholder needs and defining clear use cases are crucial for successful AI adoption in healthcare settings.

Keywords:

artificial intelligence case management clinical informatics electronic health records health system machine learning predictive analytics predictive models readmissions

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Area of Science:

Health Informatics
Artificial Intelligence in Healthcare
Clinical Decision Support

Background:

Electronic health record (EHR) vendors provide "off-the-shelf" artificial intelligence (AI) models.
A health system encountered challenges in promoting end-user adoption of a new AI model for predicting readmissions within the EHR.

Purpose of the Study:

To conduct a case study identifying barriers to the uptake and utilization of an AI model for readmission prediction.
To understand stakeholder perspectives on AI model implementation.

Main Methods:

A qualitative study utilizing interviews with key stakeholders.
Interviews focused on identifying stakeholders, mapping current workflows, pinpointing implementation barriers, and developing future strategies.

Main Results:

Significant variation exists in existing readmission workflows.
Stakeholders expressed concerns regarding model relevance, workflow integration, training needs, change management, and potential unintended consequences.
Some stakeholders were unaware of the new AI model's availability despite having compatible workflows.

Conclusions:

"Off-the-shelf" AI models are not "plug-and-play" in healthcare and require tailored implementation.
Early engagement with stakeholders and clear definition of use cases are essential for successful AI model utilization.