Creation of an accurate algorithm to detect snellen best documented visual acuity from ophthalmology electronic health record notes

Michael Mbagwu*, Dustin D. French, Manjot Gill, Christopher Mitchell, Kathryn Jackson, Abel Kho, Paul J. Bryar

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


Background: Visual acuity is the primary measure used in ophthalmology to determine how well a patient can see. Visual acuity for a single eye may be recorded in multiple ways for a single patient visit (eg, Snellen vs. Jäger units vs. font print size), and be recorded for either distance or near vision. Capturing the best documented visual acuity (BDVA) of each eye in an individual patient visit is an important step for making electronic ophthalmology clinical notes useful in research. Objective: Currently, there is limited methodology for capturing BDVA in an efficient and accurate manner from electronic health record (EHR) notes. We developed an algorithm to detect BDVA for right and left eyes from defined fields within electronic ophthalmology clinical notes. Methods: We designed an algorithm to detect the BDVA from defined fields within 295,218 ophthalmology clinical notes with visual acuity data present. About 5668 unique responses were identified and an algorithm was developed to map all of the unique responses to a structured list of Snellen visual acuities. Results: Visual acuity was captured from a total of 295,218 ophthalmology clinical notes during the study dates. The algorithm identified all visual acuities in the defined visual acuity section for each eye and returned a single BDVA for each eye. A clinician chart review of 100 random patient notes showed a 99% accuracy detecting BDVA from these records and 1% observed error. Conclusions: Our algorithm successfully captures best documented Snellen distance visual acuity from ophthalmology clinical notes and transforms a variety of inputs into a structured Snellen equivalent list. Our work, to the best of our knowledge, represents the first attempt at capturing visual acuity accurately from large numbers of electronic ophthalmology notes. Use of this algorithm can benefit research groups interested in assessing visual acuity for patient centered outcome. All codes used for this study are currently available, and will be made available online at

Original languageEnglish (US)
Article numbere14
JournalJMIR Medical Informatics
Issue number2
StatePublished - Apr 2016


  • Best corrected visual acuity
  • Best documented visual acuity
  • Data mining
  • Electronic health record
  • Electronic medical record
  • Ophthalmology
  • Phenotyping
  • Visual acuity

ASJC Scopus subject areas

  • Health Informatics
  • Health Information Management


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