Investigative Ophthalmology & Visual Science Cover Image for Volume 65, Issue 7
June 2024
Volume 65, Issue 7
Open Access
ARVO Annual Meeting Abstract  |   June 2024
Comparing Electronic Health Record Adherence Metrics to Self-Reported Adherence among Glaucoma Patients
Manreet Brar; Sonali Bhanvadia; Jo-Hsuan Wu; Kiana Tavakoli; Bharanidharan Radha Saseendrakumar; Ariadne Nichol; Niki Rangin; Daniela Vital; Camille Nebeker; Cynthia Schairer; Robert N. Weinreb; Sally L. Baxter
Author Affiliations & Notes
  • Manreet Brar
    Division of Ophthalmology Informatics and Data Science and Hamilton Glaucoma Center, Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California San Diego, La Jolla, California, United States
    UCSD Health Department of Biomedical Informatics, University of California San Diego, La Jolla, California, United States
  • Sonali Bhanvadia
    Division of Ophthalmology Informatics and Data Science and Hamilton Glaucoma Center, Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California San Diego, La Jolla, California, United States
    UCSD Health Department of Biomedical Informatics, University of California San Diego, La Jolla, California, United States
  • Jo-Hsuan Wu
    Division of Ophthalmology Informatics and Data Science and Hamilton Glaucoma Center, Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California San Diego, La Jolla, California, United States
    UCSD Health Department of Biomedical Informatics, University of California San Diego, La Jolla, California, United States
  • Kiana Tavakoli
    Division of Ophthalmology Informatics and Data Science and Hamilton Glaucoma Center, Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California San Diego, La Jolla, California, United States
    UCSD Health Department of Biomedical Informatics, University of California San Diego, La Jolla, California, United States
  • Bharanidharan Radha Saseendrakumar
    Division of Ophthalmology Informatics and Data Science and Hamilton Glaucoma Center, Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California San Diego, La Jolla, California, United States
    UCSD Health Department of Biomedical Informatics, University of California San Diego, La Jolla, California, United States
  • Ariadne Nichol
    Division of Ophthalmology Informatics and Data Science and Hamilton Glaucoma Center, Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California San Diego, La Jolla, California, United States
    UCSD Health Department of Biomedical Informatics, University of California San Diego, La Jolla, California, United States
  • Niki Rangin
    Division of Ophthalmology Informatics and Data Science and Hamilton Glaucoma Center, Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California San Diego, La Jolla, California, United States
    UCSD Health Department of Biomedical Informatics, University of California San Diego, La Jolla, California, United States
  • Daniela Vital
    Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, California, United States
  • Camille Nebeker
    Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, California, United States
  • Cynthia Schairer
    Herbert Wertheim School of Public Health and Human Longevity Science, University of California San Diego, La Jolla, California, United States
  • Robert N. Weinreb
    Division of Ophthalmology Informatics and Data Science and Hamilton Glaucoma Center, Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California San Diego, La Jolla, California, United States
  • Sally L. Baxter
    Division of Ophthalmology Informatics and Data Science and Hamilton Glaucoma Center, Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California San Diego, La Jolla, California, United States
    UCSD Health Department of Biomedical Informatics, University of California San Diego, La Jolla, California, United States
  • Footnotes
    Commercial Relationships   Manreet Brar None; Sonali Bhanvadia None; Jo-Hsuan Wu None; Kiana Tavakoli None; Bharanidharan Radha Saseendrakumar None; Ariadne Nichol None; Niki Rangin None; Daniela Vital None; Camille Nebeker None; Cynthia Schairer None; Robert N. Weinreb AbbVie, Aerie Pharmaceuticals, Alcon, Allergan, Amydis, Equinox, Eyenovia, iSTAR Medical, Nicox, and Topcon, Code C (Consultant/Contractor), National Eye Institute, National Institute of Minority Health and Health Disparities, Research to Prevent Blindness (New York, NY), Heidelberg Engineering, Carl Zeiss Meditec, Centervue, Topcon, and Zilia , Code F (Financial Support), Amydis, Eyenovia, and Iantrek, Code I (Personal Financial Interest), Toromedes and Carl Zeiss Meditec, Code P (Patent), Alcon, Code R (Recipient), Amydis, Eyenovia, Iantrek, and Implandata, Code S (non-remunerative); Sally L. Baxter Topcon and Optomed, Code F (Financial Support)
  • Footnotes
    Support  NIH Grants DP5OD029610, P30EY022589, R01MD014850; unrestricted departmental grant from Research to Prevent Blindness
Investigative Ophthalmology & Visual Science June 2024, Vol.65, 1887. doi:
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      Manreet Brar, Sonali Bhanvadia, Jo-Hsuan Wu, Kiana Tavakoli, Bharanidharan Radha Saseendrakumar, Ariadne Nichol, Niki Rangin, Daniela Vital, Camille Nebeker, Cynthia Schairer, Robert N. Weinreb, Sally L. Baxter; Comparing Electronic Health Record Adherence Metrics to Self-Reported Adherence among Glaucoma Patients. Invest. Ophthalmol. Vis. Sci. 2024;65(7):1887.

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Abstract

Purpose : To examine the differences between self-reported medication adherence scores and medication adherence scores automatically generated by electronic health record (EHR) systems among patients with glaucoma.

Methods : Adults with glaucoma who were prescribed intraocular pressure (IOP)-lowering medications at an academic eye center were asked to complete a survey including demographic characteristics and the Morisky Medication Adherence Scale (MMAS-8), a standardized tool for evaluating medication adherence (maximum adherence score of 8). Glaucoma severity based on diagnosis codes was extracted for each patient. Their EHR medication adherence scores were also extracted, reflecting the proportion of days covered (PDC) for each medication based on EHR and pharmacy data (maximum score 100). We analyzed survey responses, compared mean adherence scores across demographic characteristics through T-tests and ANOVA, and compared MMAS-8 scores to EHR generated adherence scores.

Results : Among 201 patients, the mean (standard deviation, SD) age was 73.2(11.5) years. About half (106, 52.7%) identified as female, 9(4.5%) as Black, 25(12.4%) as Asian, and 25 (12.4%) as Hispanic/Latino. The mean (SD) MMAS-8 score was 7.0(1.3), reflecting high self-reported adherence based on standardized scales. For 185 patients with EHR scores available, the mean (SD) EHR generated adherence score was 58(32), reflecting moderate adherence. There were no statistically significant differences in MMAS-8 or EHR generated adherence scores by gender, race, ethnicity, or glaucoma severity (Table 1). Self-reported adherence scores and EHR-generated adherence scores were weakly correlated (correlation coefficient 0.1; Fig. 1).

Conclusions : Patients tended to self-report better glaucoma medication adherence than reflected in their medication refill data. EHR-generated adherence scores may potentially assist clinicians to identify patients who need assistance with medication adherence, but who may not recognize their non-adherence or are unwilling to report it.

This abstract was presented at the 2024 ARVO Annual Meeting, held in Seattle, WA, May 5-9, 2024.

 

Table 1. Demographic characteristics and glaucoma medication adherence scores among a cohort of adults with glaucoma
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Table 1. Demographic characteristics and glaucoma medication adherence scores among a cohort of adults with glaucoma

Table 1. Demographic characteristics and glaucoma medication adherence scores among a cohort of adults with glaucoma

Table 1. Demographic characteristics and glaucoma medication adherence scores among a cohort of adults with glaucoma
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Figure 1. EHR generated glaucoma medication adherence scores vs self-reported MMAS-8 adherence scores among adults with glaucoma (correlation coefficient 0.1).
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Figure 1. EHR generated glaucoma medication adherence scores vs self-reported MMAS-8 adherence scores among adults with glaucoma (correlation coefficient 0.1).

Figure 1. EHR generated glaucoma medication adherence scores vs self-reported MMAS-8 adherence scores among adults with glaucoma (correlation coefficient 0.1).

Figure 1. EHR generated glaucoma medication adherence scores vs self-reported MMAS-8 adherence scores among adults with glaucoma (correlation coefficient 0.1).
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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
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Copyright © 2025 Association for Research in Vision and Ophthalmology.
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