CC BY 4.0 · ACI Open 2020; 04(02): e108-e113
DOI: 10.1055/s-0040-1716748
Case Report

Barriers to Implementing an Artificial Intelligence Model for Unplanned Readmissions

Sally L. Baxter
1  Health Department of Biomedical Informatics, University of California San Diego, La Jolla, California, United States
2  Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California San Diego, La Jolla, California, United States
,
Jeremy S. Bass
1  Health Department of Biomedical Informatics, University of California San Diego, La Jolla, California, United States
3  Department of Psychiatry, University of California San Diego, La Jolla, California, United States
,
Amy M. Sitapati
1  Health Department of Biomedical Informatics, University of California San Diego, La Jolla, California, United States
4  Department of Medicine, University of California San Diego, La Jolla, California, United States
› Author Affiliations

Abstract

Background Electronic health record (EHR) vendors now offer “off-the-shelf” artificial intelligence (AI) models to client organizations. Our health system faced difficulties in promoting end-user utilization of a new AI model for predicting readmissions embedded in the EHR.

Objectives The aim is to conduct a case study centered on identifying barriers to uptake/utilization.

Methods A qualitative study was conducted using interviews with stakeholders. The interviews were used to identify relevant stakeholders, understand current workflows, identify implementation barriers, and formulate future strategies.

Results We discovered substantial variation in existing workflows around readmissions. Some stakeholders did not perform any formal readmissions risk assessment. Others accustomed to using existing risk scores such as LACE+ had concerns about transitioning to a new model. Some stakeholders had existing workflows in place that could accommodate the new model, but they were not previously aware that the new model was in production. Concerns expressed by end-users included: whether the model's predictors were relevant to their work, need for adoption of additional workflow processes, need for training and change management, and potential for unintended consequences (e.g., increased health care resource utilization due to potentially over-referring discharged patients to home health services).

Conclusion AI models for risk stratification, even if “off-the-shelf” by design, are unlikely to be “plug-and-play” in health care settings. Seeking out key stakeholders and defining clear use cases early in the implementation process can better facilitate utilization of these models.

Protection of Human and Animal Subjects

The study was performed in compliance with the Declaration of Helsinki and was reviewed by the UCSD Institutional Review Board, which declared the study as a quality improvement protocol and certified that the study did not qualify as human subjects research according to the Code of Federal Regulations, Title 45, part 46 and UCSD Standard Operating Policies and Procedures.


Funding

This study was supported by the National Institutes of Health/National Library of Medicine (grant T15LM011271). The funding organization had no role in the design or conduct of the study.


Authors' Contributions

S.L.B., J.B., and A.M.S. conceived and designed the study. S.L.B. and J.B. conducted interviews and data collection. S.L.B., J.B., and A.M.S. participated in data analysis and interpretation. S.L.B. and J.B. drafted the manuscript. All authors provided critical review of the manuscript for important intellectual content and approved the final version of the manuscript.




Publication History

Received: 14 April 2020

Accepted: 30 June 2020

Publication Date:
19 September 2020 (online)

© 2020. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution License, permitting unrestricted use, distribution, and reproduction so long as the original work is properly cited. (https://creativecommons.org/licenses/by/4.0/).

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