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Identifying Long COVID Patients Using General Practice Data: Challenges, Classification and Long COVID Patterns.

Mirela Prgomet¹, Abbish Kamalakkannan¹, Judith Thomas¹

¹Australian Institute of Health Innovation, Macquarie University, Sydney, Australia.

Studies in Health Technology and Informatics

|May 17, 2025

Summary

This summary is machine-generated.

Identifying long COVID patients in electronic health records presents challenges due to varied documentation. This study classified long COVID cases, finding most affected females aged 40-49, highlighting data needs for public health research.

Keywords:

data analytics electronic health records general practice long COVID

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Published on: September 19, 2025

Area of Science:

Public Health
Health Informatics
Epidemiology

Background:

Electronic medical records (EMRs) from general practice offer valuable public health insights.
Utilizing EMRs for research, especially for conditions like long COVID, faces challenges in data consistency and classification.
Accurate identification of patient cohorts within EMRs is crucial for epidemiological studies.

Purpose of the Study:

To develop and validate a data classification method for identifying long COVID patients within a large general practice EMR dataset.
To analyze initial demographic and clinical patterns of the identified long COVID cohort.
To provide insights into the challenges and best practices for using general practice data in long COVID research.

Main Methods:

Data classification techniques were applied to a large general practice EMR dataset to identify individuals with long COVID.
The classification model's performance was validated.
Descriptive statistics were used to analyze the demographic and clinical characteristics of the identified long COVID cohort.

Main Results:

Significant variability was observed in how general practitioners document long COVID symptoms and diagnoses.
Fewer than half of the identified long COVID patients had a recorded acute COVID-19 infection.
The primary demographic of long COVID patients identified were females aged 40-49 years.

Conclusions:

General practice data requires careful handling and classification for reliable long COVID research.
Inconsistent documentation of acute COVID-19 and long COVID presentations impacts cohort identification.
Collaboration among researchers, clinicians, and data managers is essential for robust EMR-based public health knowledge generation.