Operationalizing a real-time scoring model to predict fall risk among older adults in the emergency department.
Front Digit Health
; 4: 958663, 2022.
Article
de En
| MEDLINE
| ID: mdl-36405416
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.
Texte intégral:
1
Collection:
01-internacional
Base de données:
MEDLINE
Type d'étude:
Etiology_studies
/
Prognostic_studies
/
Risk_factors_studies
Langue:
En
Journal:
Front Digit Health
Année:
2022
Type de document:
Article
Pays d'affiliation:
États-Unis d'Amérique