TY  - JOUR
PY  - 2022//
TI  - Operationalizing a real-time scoring model to predict fall risk among older adults in the emergency department
JO  - Frontiers in digital health
A1  - Engstrom, Collin J.
A1  - Adelaine, Sabrina
A1  - Liao, Frank
A1  - Jacobsohn, Gwen Costa
A1  - Patterson, Brian W.
SP  - e958663
EP  - e958663
VL  - 4
IS  - 
N2  - 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.<p />  <p>Language: en</p>
LA  - en
SN  - 2673-253X
UR  - http://dx.doi.org/10.3389/fdgth.2022.958663
ID  - ref1
ER  -