June 2022
Volume 63, Issue 7
Open Access
ARVO Annual Meeting Abstract  |   June 2022
Can EHR Data Automatically Calculate Ophthalmic Quality Measures? The Impact of Baselines
Author Affiliations & Notes
  • Michelle Hribar
    DMICE, Oregon Health & Science University School of Medicine, Portland, Oregon, United States
    Ophthalmology, Oregon Health & Science University School of Medicine, Portland, Oregon, United States
  • Jimmy S Chen
    Ophthalmology, Oregon Health & Science University School of Medicine, Portland, Oregon, United States
  • Aiyin Chen
    Ophthalmology, Oregon Health & Science University School of Medicine, Portland, Oregon, United States
  • Michael F Chiang
    National Eye Institute, Bethesda, Maryland, United States
  • Footnotes
    Commercial Relationships   Michelle Hribar None; Jimmy Chen None; Aiyin Chen None; Michael Chiang Novartis, Code C (Consultant/Contractor), Genentech, Code F (Financial Support), InTeleretina, Code I (Personal Financial Interest)
  • Footnotes
    Support  Supported by grants R21LM013937, and P30EY10572 from the National Institutes of Health (Bethesda, MD) and by unrestricted departmental funding from Research to Prevent Blindness (New York, NY)
Investigative Ophthalmology & Visual Science June 2022, Vol.63, 3827. doi:
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    • Get Citation

      Michelle Hribar, Jimmy S Chen, Aiyin Chen, Michael F Chiang; Can EHR Data Automatically Calculate Ophthalmic Quality Measures? The Impact of Baselines. Invest. Ophthalmol. Vis. Sci. 2022;63(7):3827.

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

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Abstract

Purpose : The American Academy of Ophthalmology (AAO) ophthalmic clinical quality measures (QM) define successful clinical outcomes for ophthalmic care. Of the AAO’s 30 ophthalmic QMs in 2020, 17 of them use an improvement over baseline as the outcome. Nevertheless, the QM definitions do not provide details about what baseline value to use, which makes automatic calculation of the quality measure difficult and variable.

Methods : We evaluated two QMs using different baselines in two different patient populations: 1) Oregon Health & Science University (OHSU) clinical practices and 2) the IRIS Registry. We analyzed IRIS 39, Glaucoma – Intraocular Pressure Reduction Following Trabeculectomy or an Aqueous Shunt Procedure, and IRIS 59, Regaining Vision after Cataract Surgery using surgeries that occurred between January 1, 2019 and December 31, 2019. Neither measure defined the time frame or rules for selecting which values to use as baseline. We chose to calculate IRIS 39 using the best (lowest) and worst (highest) interocular pressure (IOP) values measured within 1 year prior to surgery as baselines, and chose the best (lowest ) and worst (highest) visual acuity (VA) values measured within 30 days prior to surgery for baselines in IRIS 59.The results for the different baselines were compared using Z tests.

Results : The percentage of patients who met each of the measures varied at both OHSU and in the IRIS Registry depending on baseline used (Figures 1 and 2). For both measures and populations, the worst baseline values resulted in higher percentage of patients meeting the measure; in all cases over 70% of patients met the measure (the benchmark for full reimbursement), but the best baseline values achieved this benchmark in only one case (IRIS 39 in IRIS Registry). The difference in the % of patients meeting the measure for different baselines were statistically significant (p < 0.01) for both measures in both populations.

Conclusions : This study shows that baseline values have an impact on the number of patients who meet QMs. Careful definition of baselines has important implications for healthcare quality and reimbursement; without these definitions, electronic health records cannot accurately and consistently calculate QMs. Moreover, explicit baseline definitions are crucial for demonstrating effectiveness of treatments in research and clinical trials.

This abstract was presented at the 2022 ARVO Annual Meeting, held in Denver, CO, May 1-4, 2022, and virtually.

 

 

Figure 2: IRIS 59.

Figure 2: IRIS 59.

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