June 2023
Volume 64, Issue 8
Open Access
ARVO Annual Meeting Abstract  |   June 2023
Predicting Urgency of Patient Complaints Presenting to the Massachusetts Eye & Ear Emergency Department
Author Affiliations & Notes
  • Bushra Rahman
    Vanderbilt University School of Medicine, Nashville, Tennessee, United States
  • Yan Zhao
    Department of Ophthalmology, Massachusetts Eye and Ear, Boston, Massachusetts, United States
  • Grayson Wilkes Armstrong
    Department of Ophthalmology, Harvard Medical School, Boston, Massachusetts, United States
    Department of Ophthalmology, Massachusetts Eye and Ear, Boston, Massachusetts, United States
  • Footnotes
    Commercial Relationships   Bushra Rahman None; Yan Zhao None; Grayson Armstrong Kriya Therapeutics, Ocular Technologies, Optomed Inc, Chart Biopsy, DynaMed, Xenon-VR, McKinsey & Company, Code C (Consultant/Contractor), Ocular Technologies, Code O (Owner)
  • Footnotes
    Support  None
Investigative Ophthalmology & Visual Science June 2023, Vol.64, 3055. doi:
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    • Get Citation

      Bushra Rahman, Yan Zhao, Grayson Wilkes Armstrong; Predicting Urgency of Patient Complaints Presenting to the Massachusetts Eye & Ear Emergency Department. Invest. Ophthalmol. Vis. Sci. 2023;64(8):3055.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Purpose : Emergency departments (EDs) are meant to see urgent conditions, but high volumes of low acuity pathology can lead to increased wait times, unnecessary costs, and patient dissatisfaction. Predicting which populations are more likely to present with low acuity complaints could allow targeted interventions to redirect these patients to non-ED care settings. We aimed to predict which Massachusetts Eye & Ear (MEE) ED patients are most likely to present with non-urgent ophthalmic issues.

Methods : Retrospective cohort study of a representative sample of MEE ED ophthalmic patients presenting from April to June 2022. Key outcomes included urgency of eye diagnoses and subsequent follow-up visit. Urgency was defined as an ICD-10 diagnosis deemed urgent based on prior literature or visual acuity ≥0.2 logMAR from baseline. Established patients were defined as seen within three years. Linear and multivariate regression, recursive partitioning and classification tree were performed using Microsoft Excel and R Studio.

Results : Our study included 513 patients. On multivariate regression, established MEE patients were less likely to present with urgent conditions (OR=0.42, CI 0.23-0.74, p=0.004), and increased odds of follow up was noted for established patients (OR=2.93, CI 1.76-4.95, p<0.001), urgent conditions (OR=2.47, CI 1.57-3.93, p<0.001), older age (OR=1.02, CI 1.01-1.03, p=0.003), those with Medicare (OR=2.83, CI 1.42-5.88, p=0.004), and those needing surgery (OR=4.25, CI 1.82-11.23, p=0.002). Decision tree predicts that uninsured patients are more likely to present with urgent conditions (accuracy 73.8%), and Medicaid or privately-insured patients who are <24 years of age, non-surgical, non-established, and non-urgent are less likely to follow-up.

Conclusions : Established patients are more likely to present to the MEE ED with non-urgent complaints, suggesting this may be an appropriate population to redirect toward ambulatory instead of emergency care. While many patients receive post-ED follow up for urgent conditions or surgery, others had follow-up despite non-urgent complaints (established patients), indicating that follow up status is not a good indicator of urgency. Future studies should evaluate interventions aimed at reducing ED visits among established patients with low acuity conditions.

This abstract was presented at the 2023 ARVO Annual Meeting, held in New Orleans, LA, April 23-27, 2023.

 

Figure 1. Decision tree predicting urgent cases

Figure 1. Decision tree predicting urgent cases

 

Figure 2. Decision tree predicting post-ED follow up

Figure 2. Decision tree predicting post-ED follow up

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