Predicting Low-Resource-Intensity Emergency Department Visits in Children
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Updated: Feb 15, 2026

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Published on: June 30, 2023
Margaret Samuels-Kalow1, Alon Peltz2, Jonathan Rodean3
1Department of Emergency Medicine, Massachusetts General Hospital, Boston, Mass.
Insights
Predicting frequent low-resource emergency department (ED) visits in children is crucial. Prior ED use and chronic conditions help identify high-risk children for targeted interventions.
Area of Science:
- Health Services Research
- Pediatric Health
- Healthcare Analytics
Background:
- Frequent emergency department (ED) use by children presents a challenge for healthcare resource allocation.
- Predicting high-frequency, low-resource-intensity (HF-LRI) ED visits is difficult, limiting targeted interventions.
Purpose of the Study:
- To develop a population-based model to predict Medicaid-insured children at risk for HF-LRI ED visits.
- To identify key predictors of frequent low-value ED utilization in pediatric populations.
Main Methods:
- Retrospective cohort analysis of Medicaid-insured children (1-18 years) from the MarketScan database (2013).
- Defined LRI visits as ED encounters without hospitalization, procedures, or advanced diagnostics.
- Developed and validated a generalized linear regression model using split-sample validation.
Main Results:
- 5% of children accounted for 21% of all LRI ED visits.
- Prior ED visits (≥2) and ≥3 chronic conditions were strong predictors of future HF-LRI ED use.
- The predictive model achieved an area under the curve of 0.74.
Conclusions:
- Demographic factors and prior ED utilization patterns can identify children at risk for future HF-LRI ED visits.
- Interventions targeting high-risk children can help reduce low-value ED utilization.
Objectives:
Interventions to reduce frequent emergency department (ED) use in children are often limited by the inability to predict future risk. We sought to develop a population-based model for predicting Medicaid-insured children at risk for high frequency (HF) of low-resource-intensity (LRI) ED visits.
Methods:
We conducted a retrospective cohort analysis of Medicaid-insured children (aged 1-18 years) included in the MarketScan Medicaid database with ≥1 ED visit in 2013. LRI visits were defined as ED encounters with no laboratory testing, imaging, procedures, or hospitalization; and HF as ≥3 LRI ED visits within 365 days of the initial encounter. A generalized linear regression model was derived and validated using a split-sample approach. Validity testing was conducted examining model performance using 3 alternative definitions of LRI.
Results:
Among 743,016 children with ≥1 ED visit in 2013, 5% experienced high-frequency LRI ED use, accounting for 21% of all LRI visits. Prior LRI ED use (2 visits: adjusted odds ratio = 3.5; 95% confidence interval, 3.3, 3.7; and ≥3 visits: adjusted odds ratio = 7.7; 95% confidence interval, 7.3, 8.1) and presence of ≥3 chronic conditions (adjusted odds ratio = 1.7; 95% confidence interval, 1.6, 1.8) were strongly associated with future HF-LRI ED use. A model incorporating patient characteristics and prior ED use predicted future HF-LRI ED utilization with an area under the curve of 0.74.
Conclusions:
Demographic characteristics and patterns of prior ED use can predict future risk of HF-LRI ED use in the following year. Interventions for reducing low-value ED use in these high-risk children should be considered.
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Prediction Intervals
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.

