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Electronic Medical Records (EMRs) primarily center around electronically documenting patients' health information within a single healthcare organization or practice. They contain essential clinical data related to a patient's medical history, diagnoses, medications, treatment plans, lab results, and other pertinent information relevant to the specific encounter or episode of care. EMRs are designed to streamline documentation and workflow processes within individual healthcare...

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Identifying Pediatric Long COVID: Comparing an EHR Algorithm to Manual Review.

Morgan Botdorf¹, Kimberley Dickinson¹, Vitaly Lorman¹

¹Applied Clinical Research Center, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, United States.

Applied Clinical Informatics

|October 24, 2025

Summary

This summary is machine-generated.

A new computable phenotype (CP) identifies pediatric long COVID using electronic health records. While moderate agreement with chart review exists, accounting for pre-existing conditions improves accuracy, aiding research and definition development.

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Area of Science:

Pediatric Health
Infectious Diseases
Medical Informatics

Background:

Long COVID presents diagnostic challenges in children due to a lack of standardized definitions.
Existing adult-focused phenotypes are not suitable for pediatric populations.
Pediatric-specific phenotypes require validation against clinical data.

Purpose of the Study:

To develop and evaluate a pediatric-specific, rule-based computable phenotype (CP) for identifying long COVID.
To compare the CP's performance against manual chart review in a large pediatric cohort.
To analyze discrepancies between CP identification and clinician assessment.

Main Methods:

Applied a CP using diagnostic codes to over 339,000 pediatric patients with SARS-CoV-2 infection in the RECOVER PCORnet EHR database.
Conducted manual chart reviews on a subset of patients (n=651) across 16 hospital systems for performance assessment.
Qualitatively reviewed discordant cases to understand differences in identification criteria.

Main Results:

The CP identified 31,781 pediatric long COVID cases with moderate agreement (accuracy=0.62) compared to chart review.
Discrepancies often arose from clinicians attributing symptoms to pre-existing conditions or using broader criteria.
Improved CP performance (accuracy=0.71) when accounting for pre-existing conditions.

Conclusions:

A pediatric-specific CP for long COVID shows moderate but improvable agreement with clinical review.
Addressing pre-existing conditions in CP development is crucial for accurate pediatric long COVID identification.
This study supports the creation of scalable tools for pediatric long COVID research and definition consensus.