Extracting Medical Information from Paper COVID-19 Assessment Forms.

ABSTRACT:

Objective

This study examines the validity of optical mark recognition, a novel user interface, and crowdsourced data validation to rapidly digitize and extract data from paper COVID-19 assessment forms at a large medical center.

Methods

An optical mark recognition/optical character recognition (OMR/OCR) system was developed to identify fields that were selected on 2,814 paper assessment forms, each with 141 fields which were used to assess potential COVID-19 infections. A novel user interface (UI) displayed mirrored forms showing the scanned assessment forms with OMR results superimposed on the left and an editable web form on the right to improve ease of data validation. Crowdsourced participants validated the results of the OMR system. Overall error rate and time taken to

SUBMITTER: White-Dzuro CG 

PROVIDER: S-EPMC7946598 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature