June 2017
Volume 58, Issue 8
Open Access
ARVO Annual Meeting Abstract  |   June 2017
Validation of the TOtal Visual acuity extraction Algorithm (TOVA) for automated extraction of visual acuity and intraocular pressure data from free text clinical records
Author Affiliations & Notes
  • Doug Baughman
    Ophthalmology, University of Washington, Seattle, Washington, United States
  • Cecilia Lee
    Ophthalmology, University of Washington, Seattle, Washington, United States
  • Aaron Y Lee
    Ophthalmology, University of Washington, Seattle, Washington, United States
  • Footnotes
    Commercial Relationships   Doug Baughman, None; Cecilia Lee, None; Aaron Lee, None
  • Footnotes
    Support  NEI, Bethesda, MD, K23EY02392; Research to Prevent Blindness, Inc. New York, NY
Investigative Ophthalmology & Visual Science June 2017, Vol.58, 2206. doi:
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    • Get Citation

      Doug Baughman, Cecilia Lee, Aaron Y Lee; Validation of the TOtal Visual acuity extraction Algorithm (TOVA) for automated extraction of visual acuity and intraocular pressure data from free text clinical records. Invest. Ophthalmol. Vis. Sci. 2017;58(8):2206.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Purpose : With an ever-increasing volume of electronic health record data, algorithm-driven data extraction must replace manual extraction for large scale analyses. Visual acuity and intraocular pressure (IOP) are important outcome measures commonly extracted from clinical records in vision research. The TOtal Visual acuity extraction Algorithm (TOVA) is presented and validated for automated extraction of best corrected visual acuity and IOP from clinical notes.

Methods : Consecutive outpatient ophthalmology notes over a 10 day period from the University of Washington healthcare system in Seattle, WA were used for testing and validation of TOVA. The algorithm applied natural language processing (NLP) to recognize Snellen visual acuity targets in each line of free text and assigned laterality for each target. Visual acuity targets were divided into four discrete elements for analysis; numerator (e.g. the first number in 20/40), denominator (e.g. the second number in 20/40), adjustment sign (plus or minus for letters read or missed within a Snellen chart line), and adjustment letters (the number of such letters). The best corrected measurement was determined for each eye. Highest recorded IOP measurements for each eye were also extracted using NLP. The algorithm was validated against the full set of extracted notes.

Results : A total of 200 clinical records were obtained, giving 1560 data points. Interrater reliabilities by kappa for Snellen numerator, denominator, adjustment sign, and adjustment letters were 0.96, 0.93, 0.87, and 0.84, respectively. Pearson correlation coefficient for IOP was 0.93 (95% CI: 0.92 to 0.95, p < 2.2x10^-16).

Conclusions : TOVA is a validated tool for extraction of visual acuity and IOP data from free text clinical notes and provides an open source method of accurate, efficient data extraction.

This is an abstract that was submitted for the 2017 ARVO Annual Meeting, held in Baltimore, MD, May 7-11, 2017.

 

Fig 1. TOVA algorithm logic. A diagram outlining the rule-based algorithm for extracting visual acuities from clinical notes. Stepwise algorithm logic is given in the upper portion of the figure with examples (a-d) of free text applications for each step shown below.

Fig 1. TOVA algorithm logic. A diagram outlining the rule-based algorithm for extracting visual acuities from clinical notes. Stepwise algorithm logic is given in the upper portion of the figure with examples (a-d) of free text applications for each step shown below.

 

Fig 2. Tokenized Scoring System. Examples of application of the Tokenized Scoring System for assigning laterality. The line containing visual acuity is parsed into word and punctuation tokens which are scored, summed, and compared.

Fig 2. Tokenized Scoring System. Examples of application of the Tokenized Scoring System for assigning laterality. The line containing visual acuity is parsed into word and punctuation tokens which are scored, summed, and compared.

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