Evaluation of an Express Care Triage Model for Identifying Clinically Relevant Cases in an STD Clinic

Background:  Many STD clinics have incorporated “express visits” – testing-only visits without a clinician evaluation. We evaluated a computer assisted self-interview (CASI) triage algorithm to identify which patients can safely receive express care at the Public Health–Seattle & King County (PHSKC) STD Clinic. 

Methods:  We used CASI responses from all new problem visits to reconstruct a triage status using the current algorithm, which considers age, gender, symptoms, contact to STD/HIV, and health service needs. The outcome measure, need for a standard visit, included report of key symptoms, receipt of empiric treatment, or diagnosis with an infection that could have been diagnosed and treated at the visit. We estimated the sensitivity, specificity, and area under the receiver operating curve (AUC) of the algorithm, by gender, to appropriately triage patients.

Results:  Between 10/2010-06/2015, patients completed the CASI at 32,113 visits, including 7,639 women (23.8%) and 24,474 men (76.2%). The algorithm appropriately triaged 6,259 women (81.9%) and 21,337 men (87.2%). 120 women (1.6%) and 893 men (3.6%) would have been mistriaged to express care, and 1,260 women (16.5%) and 2,244 men (9.2%) would have been mistriaged to standard visits. The most common reason for mistriage to express care was patient report of symptoms to the clinician after reporting no symptoms to the CASI [31.7% of women and 51.5% of men mistriaged to express care]. Hispanic patients (p=0.005) and men (p<0.0005) were significantly more likely to be mistriaged to express care. For women, the triage algorithm had 97.9% sensitivity, 33.0% specificity, and AUC=0.65 (95%CI=0.64-0.67). For men, the triage algorithm had 94.6% sensitivity, 71.9% specificity, and AUC=0.83 (95%CI=0.83-0.84). 

Conclusions:  The sensitivity of the triage algorithm was acceptable for both men and women for screening purposes, but Hispanic patients may experience a language barrier. Additional optimization of the algorithm may improve its specificity.