Early detection of COVID-19 outbreaks using textual analysis of electronic medical records

https://doi.org/10.1016/j.jcv.2022.105251Get rights and content

Highlights

  • COVID-19 virus rapid spread mandates new technologies to detect early outbreaks.

  • Free medical text entered into electronic medical records contains vast information.

  • Our algorithm detected COVID-19 symptoms from free text equivalently to physicians.

  • Targeted testing guided by the algorithm significantly improved positive test rate.

  • Similar methods can be employed in early stages of future outbreaks of new pathogens.

Abstract

Purpose

Our objective was to develop a tool promoting early detection of COVID-19 cases by focusing epidemiological investigations and PCR examinations during a period of limited testing capabilities.

Methods

We developed an algorithm for analyzing medical records recorded by healthcare providers in the Israeli Defense Forces. The algorithm utilized textual analysis to detect patients presenting with suspicious symptoms and was tested among 92 randomly selected units. Detection of a potential cluster of patients in a unit prompted a focused epidemiological investigation aided by data provided by the algorithm.

Results

During a month of follow up, the algorithm has flagged 17 of the units for investigation. The subsequent epidemiological investigations led to the testing of 78 persons and the detection of eight cases in four clusters that were previously gone unnoticed. The resulting positive test rate of 10.25% was five time higher than the IDF average at the time of the study. No cases of COVID-19 in the examined units were missed by the algorithm.

Conclusions

This study depicts the successful development and large scale deployment of a textual analysis based algorithm for early detection of COVID-19 cases, demonstrating the potential of natural language processing of medical text as a tool for promoting public health.

Keywords

COVID-19
Outbreak detection
Natural Language Processing

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