INVESTIGATING GENDER DISPARITIES IN EMERGENCY DEPARTMENT TO HOSPITAL ADMISSION WAIT TIMES

Stephanie Torres BSN,M4; Gregory Stadter MPH; Siddhartha Singh MD Medical College Physicians, Medical College of Wisconsin, Milwaukee, WI

Background

o Froedtert was identified as a low performer on the Equity metric related to a longer ED length of stay for females on the 2015 University HealthSystem Consortium Quality and Accountability Performance scorecard (UHC scorecard)

oGender disparities have been described in the Emergency Department (ED) treatment of abdominal pain and sepsis, stroke care, and cardiac catheterization recommendations

oHypothesis: Systematic processes or unique characteristics lead to the disparity

o The aim of this project was to investigate the gender disparity in ED to hospital admission wait times among patients who contributed to the UHC scorecard

Methods

oRetrospective chart review completed on 399 patients randomly sampled from those that contributed to the UHC scorecard for Froedtert and the Medical College of Wisconsin (MCW)

oPrimary and secondary drivers assessed

oDrivers incorporated into a data entry form for completion of the chart review using EPIC electronic medical record

oComparisons made between females and males

oWilcoxon two-sample test utilized to evaluate statistical significance

Driver Diagram

Emergency Severity Index (ESI)

EMERGENCY SEVERITY INDEX Level 1 Requires life-saving interventions or

Apneic, pulseless, intubated, unresponsive or acute respiratory distress

Level 2 High risk situation Or confused/lethargic/disoriented or severe pain or distress

Level 3 Requires 2 or more resources Level 4 Requires 1 resource Level 5 No resources required Danger Zone Vitals Consider up-triage from ESI level 3 to ESI level 2

HR >100/RR >20/SaO2 < 92%

ESI Continued

LIFE-SAVING INTERVENTIONS RESOURCES Bag valve mask ventilation or intubation Surgical airway Emergent CPAP/BIPAP Defibrillation Emergent Cardioversion Chest needle decompression Pericardiocentesis Open thoracotomy Intraosseous access Significant fluid or blood administration Control of hemorrhage Naloxone D50 Dopamine Atropine Adenosine

Labs (blood, urine, cultures EKG X ray CT MRI Ultrasound Angiography Intravenous fluids Intravenous, Intramuscular or nebulized meds Specialty consults Eye irrigation Splinting or casting Simple procedures: laceration repair, foley, Incision & Drainage

Results

o54.8 % of patients were females (n=219)

oAverage ED length of stay (LOS) was 38.2 minutes longer in females (283.8 vs 245.6)

oLarger number of females triaged as being less acutely ill and given ESI level 3 scores (n=71 vs n=50, p=0.038)

oFemales categorized at ESI level 3 waited an average of 37 minutes longer (330 vs 293.1, p= 0.038)

Results Continued

oThere was a longer ED LOS in females in the presence and absence of radiological imaging

oFemales with <5 lab tests had a 56.5 minute longer wait time (294.6 vs 238, p=0.0396)

oUnique exams: 3 documented pelvic exams, 30 urine and serum human chorionic gonadotropin pregnancy tests, and 1 point of care ED pregnancy ultrasound performed. These factors displayed no statistical significance

oFemales experienced a longer ED LOS across all drivers

Results Continued

Discussion

oNo obvious explanation for the prolonged disposition

oHypotheses surrounding trauma, atypical presentation of symptoms, special testing unique to females, and necessity of chaperones for pelvic exams or trans-vaginal ultrasounds did not account for the difference

oImplicit bias should be considered

Discussion

o Implicit bias is defined as the “bias in judgement and/or behavior that results from subtle cognitive processes (e.g., implicit attitudes and implicit stereotypes) that often operate at a level below conscious awareness and without intentional control”

oNot much literature available on how implicit bias impacts female patients being admitted to the hospital from the ED

oRecommend creating a tool-kit with strategies to reduce implicit bias as part of competency training for staff

Quotation source: Casey PM, Warren RK, Cheesman FL, Elek JK. Helping courts address implicit bias. Williamsburg VA: National Center for State Courts; 2012

Strategies to Combat Implicit Bias

FIGURE 5 Strategies to Combat Implicit Bias Adapted from study published by Devine et al.

Limitations

oData analysis did not examine specific diagnoses although this information was obtained during the chart review

oMost factors assessed during this study were physician oriented

oData regarding transitions of care such as ED arrival to ED rooming, rooming to disposition, and disposition to admission were not assessed

Conclusion

oOverall delay in hospital admission times for females across all drivers

oFindings suggest that females are being triaged as less acutely ill leading to downstream delays in care and implicit bias may be contributing

oConsider implementing integrated training on techniques to reduce implicit bias

oFuture studies are needed to analyze how specific diagnoses, triage nurse gender and pattern of ESI scoring, and ED time flow stamps impact hospital admission time

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