June 2020
Volume 61, Issue 7
Open Access
ARVO Annual Meeting Abstract  |   June 2020
Identification of glaucoma patients with poor medication compliance from the electronic health record
Author Affiliations & Notes
  • Mohammed Sami Hamid
    Department of Ophthalmology and Visual Sciences, University of Michigan, Ann Arbor, Michigan, United States
  • Brianne Brenneman
    Department of Ophthalmology and Visual Sciences, University of Michigan, Ann Arbor, Michigan, United States
  • Leslie Niziol
    Department of Ophthalmology and Visual Sciences, University of Michigan, Ann Arbor, Michigan, United States
  • Joshua D Stein
    Department of Ophthalmology and Visual Sciences, University of Michigan, Ann Arbor, Michigan, United States
  • Paula Anne Newman-Casey
    Department of Ophthalmology and Visual Sciences, University of Michigan, Ann Arbor, Michigan, United States
  • Footnotes
    Commercial Relationships   Mohammed Hamid, None; Brianne Brenneman, None; Leslie Niziol, None; Joshua Stein, None; Paula Anne Newman-Casey, None
  • Footnotes
    Support  NEI Grant K23EY025320-01A1, Research to Prevent Blindness Career Development Award
Investigative Ophthalmology & Visual Science June 2020, Vol.61, 5115. doi:
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    • Get Citation

      Mohammed Sami Hamid, Brianne Brenneman, Leslie Niziol, Joshua D Stein, Paula Anne Newman-Casey; Identification of glaucoma patients with poor medication compliance from the electronic health record. Invest. Ophthalmol. Vis. Sci. 2020;61(7):5115.

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

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Abstract

Purpose : To assess the feasibility of using automated text parsing to screen physician notes in the electronic health record (EHR) to identify glaucoma patients with poor medication compliance.

Methods : For recruitment to a larger study assessing the impact of a glaucoma coaching program on medication adherence, we used an automated EHR pull to identify patients who received ophthalmic care at the University of Michigan, had a diagnosis of glaucoma, were ≥40 years old, and took ≥1 glaucoma medication. A manual chart review was performed to exclude those deceased, or with severe mental illness or cognitive impairment. A research associate called patients and, if interested, assessed their medication adherence with two validated instruments, the Chang Scale and the Morisky Medication Adherence Scale. In tandem, we used the Electronic Medical Record Search Engine (EMERSE), a text parsing tool that abstracts data from the text section of the EHR to search for the terms “noncompliant” and “noncompliance” in the physician note of eligible, interested patients. The proportion of patients with self-reported poor adherence were compared between chart review and text parsing identification with a Fisher’s exact test. Discordance of compliance status between adherence surveys and text parsing the EHR was tested with McNemar’s test.

Results : 738 glaucoma patients were eligible and interested in participating in the larger study. Of these, 148 patients (20%) self-reported poor medication adherence. Alternatively, text parsing identified 8 patients with documented medication noncompliance in the physician note, of which 4 (50%) went on to self-report poor medication adherence on the surveys (p=0.058). While compliance status agreed between survey results and text parsing the EHR in 80% of patients (590/738), 19.5% of patients (144/738) self-reported poor adherence on surveys but had no indication of noncompliance in the physician note and 0.5% of patients did not self-report poor adherence but had documentation of noncompliance in the note (p<0.0001).

Conclusions : Text parsing the physician note to identify patients noncompliant to their medications identified a larger proportion of patients who then self-reported poor medication adherence, but was limited by the small number of patients identified. Optimizing the way clinicians document medication adherence would maximize the utility of this automated approach.

This is a 2020 ARVO Annual Meeting abstract.

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