Hess, Erik P.

AbstractObjectivesOvercapacity issues plague emergency departments (EDs). Studies suggest that triage liaison providers (TLPs) may shorten patient length of stay (LOS) and reduce the proportion of patients who leave without being seen (LWBS), but these results are not universal. Previous studies used physicians as TLPs. We evaluated whether a physician assistant (PA), acting as a TLP, would shorten LOS and decrease LWBS rates.MethodsThe authors used an observational cohort controlled before‐and‐after study design with predefined outcome measures, comparing 8 pilot days to 8 control days. The TLP evaluated all Emergency Severity Index (ESI) level 3, 4, and 5 patients, excluding pediatric and behavioral health patients.ResultsA total of 353 patients were included on pilot days and 371 on control days. LOS was shorter on pilot days than control days (median [interquartile range {IQR}] = 229 [168 to 303] minutes vs. 270 [187 to 372] minutes, p < 0.001). Waiting room times were similar between pilot and control days (median [IQR] = 69 [20 to 119] minutes vs. 70 [19 to 137] minutes, p = 0.408), but treatment room times were shorter (median [IQR] = 151 [92 to 223] minutes vs. 187 [110 to 254] minutes, p < 0.001). Finally, a lower proportion of patients LWBS on pilot days (1.4% vs. 9.7%, p < 0.001).ConclusionsThe addition of a PA as a TLP was associated with a 41‐minute decrease in median total LOS and a lower proportion of patients who LWBS. The decrease in total LOS is likely attributable to the addition of the TLP, with patients having shorter duration in treatment rooms on pilot days compared to control days.

Amiodarone therapy in adults with congenital heart disease (CHD) is associated with a significant risk of amiodarone-induced thyrotoxicosis (AIT). We developed a risk index to identify those patients being considered for amiodarone treatment who are at high risk for AIT. We reviewed the health records of adults with CHD and assessed the association between potential clinical predictors and AIT. Significant predictors were included in multivariate analyses. The parameter estimates from multivariate analysis were subsequently used to develop a risk index. 169 adults met eligibility criteria and 23 developed AIT. The final model included age, cyanotic heart disease and BMI. The risk index developed identified 3 categories of risk. Their AIT likelihood ratios were: 0.37 for low risk (95% CI 0.15–0.92); 1.12 for medium risk (95% CI 0.65–1.91); and 3.47 for high risk (95% CI 1.7–7.11). The AIT predicted risk in our population was 5% for the low risk group, 15% for the medium risk group and 47% for the high risk group. Conclusions . We derived the first model to quantify the risk for developing AIT among adults with CHD. Before using it clinically to help selecting among alternative antiarrhythmic options, it needs validation in an independent population.