Citation

Engstrom CJ, Adelaine S, Liao F, Jacobsohn GC, Patterson BW. Front. Digit. Health 2022; 4: e958663.

Copyright

(Copyright © 2022, Frontiers Media)

DOI

10.3389/fdgth.2022.958663

PMID

36405416

PMCID

PMC9671211

Abstract

Predictive models are increasingly being developed and implemented to improve patient care across a variety of clinical scenarios. While a body of literature exists on the development of models using existing data, less focus has been placed on practical operationalization of these models for deployment in real-time production environments. This case-study describes challenges and barriers identified and overcome in such an operationalization for a model aimed at predicting risk of outpatient falls after Emergency Department (ED) visits among older adults. Based on our experience, we provide general principles for translating an EHR-based predictive model from research and reporting environments into real-time operation.

Language: en

Keywords

machine learning; AI; EHR; falls prevention; precision medicine; risk stratification