Steven Horng

This paper tests if a visual aid reduces unintentional duplicate orders in an EHR by highlighting previously ordered items, finding a 49% decrease in lab orders and 40% in radiology orders.

The paper develops a standardized ontology for emergency department presenting problems using real-world data and expert consensus, achieving high validation accuracy.

The paper evaluates the Angus ICD9-CM sepsis abstraction criteria, finding high specificity but moderate sensitivity in identifying suspected infections in emergency department patients.

This paper demonstrates a contextual autocomplete system that improves structured data capture and quality in emergency department documentation using machine learning.

The paper develops and validates a machine learning algorithm to link EMS electronic patient care reports to emergency department records, achieving high sensitivity and positive predictive value.