Objective:

To define current diagnostic testing and resource utilization patterns for patients hospitalized with encephalitis. 

Background:

In the US, encephalitis results in >20,000 hospitalizations, over 1,000 deaths, and more than $2 billion in hospital charges yearly (Vora et al, 2014). Correctly and rapidly distinguishing between infectious and non-infectious causes of encephalitis is critical for early initiation of therapeutic interventions. A systematic, electronic medical record (EMR)-based diagnostic pathway may improve the accuracy and cost-effectiveness of diagnostic investigations in encephalitis. Here we present demographic, clinical and resource utilization data from patients hospitalized with encephalitis within a single, large hospital system over a twelve-month period. A proposed diagnostic pathway and prospective intervention study are additionally outlined. 

Design/Methods:

This is a retrospective study identifying cases of encephalitis using ICD-10 codes from University of Colorado Hospital (UCH) system EMR for chart review. Cases identified within a 12-month period (01 June 2021 to 31 May 2022), are under review for analysis of key clinical and outcome measures.

Results:

A diagnosis of encephalitis was identified in 140 patients hospitalized during a twelve-month period preceding diagnostic pathway implementation. Patient ages ranged from 20 to 90 years (mean 57). There was equal division between sexes. 78% of patients were white, 14% were Black or African American, and 14% the race was unknown. All patients had a lumbar puncture performed. Magnetic resonance imaging of the brain was obtained for 43% of patients. Additional clinical and resource utilization data will be extracted and analyzed.

Conclusions:

The UCH system has a high volume of encephalitis cases accompanied by a high utilization of diagnostic testing. Analysis of clinical, outcome and resource utilization metrics may identify areas for improvement, allowing for rational design, implementation, and subsequent prospective study of an EMR-based diagnostic pathway to facilitate the rapid and cost-effective determination of encephalitis etiologies.