Visual Acuity

Goldstein, Judith E.; Guo, Xinxing; Boland, Michael V.; Smith, Kerry

doi:10.1016/j.xops.2022.100215

Cited by 5 publications

(1 citation statement)

References 29 publications

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“…Goldstein et al 1 from Johns Hopkins have performed an elegant and important analysis of their data quality associated with ophthalmology’s most important variable, visual acuity (VA). They concluded that direct analysis of electronic medical record (EMR) data at their institution was less than ideal, and even reconstructed data had limited reliability.…”

mentioning

confidence: 99%

Re: Goldstein et al.: Visual acuity: assessment of data quality and usability in an electronic health record system (Ophthalmol Sci. 2023;3(1):100215)

Arnold¹

2023

Ophthalmology Science

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mentioning

confidence: 99%

Re: Goldstein et al.: Visual acuity: assessment of data quality and usability in an electronic health record system (Ophthalmol Sci. 2023;3(1):100215)

Arnold¹

2023

Ophthalmology Science

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Electronic health record data quality assessment and tools: a systematic review

Lewis

Weiskopf

Abrams

et al. 2023

Journal of the American Medical Informatics Association

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Objective We extended a 2013 literature review on electronic health record (EHR) data quality assessment approaches and tools to determine recent improvements or changes in EHR data quality assessment methodologies. Materials and Methods We completed a systematic review of PubMed articles from 2013 to April 2023 that discussed the quality assessment of EHR data. We screened and reviewed papers for the dimensions and methods defined in the original 2013 manuscript. We categorized papers as data quality outcomes of interest, tools, or opinion pieces. We abstracted and defined additional themes and methods though an iterative review process. Results We included 103 papers in the review, of which 73 were data quality outcomes of interest papers, 22 were tools, and 8 were opinion pieces. The most common dimension of data quality assessed was completeness, followed by correctness, concordance, plausibility, and currency. We abstracted conformance and bias as 2 additional dimensions of data quality and structural agreement as an additional methodology. Discussion There has been an increase in EHR data quality assessment publications since the original 2013 review. Consistent dimensions of EHR data quality continue to be assessed across applications. Despite consistent patterns of assessment, there still does not exist a standard approach for assessing EHR data quality. Conclusion Guidelines are needed for EHR data quality assessment to improve the efficiency, transparency, comparability, and interoperability of data quality assessment. These guidelines must be both scalable and flexible. Automation could be helpful in generalizing this process.

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Estimating Visual Acuity Without a Visual Acuity Chart

Wu,

Yu,

Goldstein

et al. 2024

Trans. Vis. Sci. Tech.

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Purpose This study explored whether visual acuity (VA) can be inferred from self-reported ability to recognize everyday objects using a set of yes/no questions. Methods Participants answered 100 yes/no questions designed to assess their ability to recognize familiar objects at typical viewing distances, such as distinguishing between a full moon and a half moon on a clear night. The questions demanded VA ranging from normal to severe vision impairment. Responses were analyzed using item response theory, and the results were compared with participants' VA values. Results We recruited 385 participants from 4 sites in the United States. Participants had a mean age of 56.7 years with VA ranging from −0.3 to 2.0 logarithm of the minimum angle of resolution (logMAR) (mean = 0.58). A strong relationship was observed between participants' estimated vision ability and their VA ( r = −0.72). The linear relationship can be used to predict each participant's VA based on their estimated vision ability. The average signed and unsigned prediction errors were 0 and 0.24 logMAR, respectively, with a coefficient of repeatability of 0.59 logMAR between the estimated VA and measured VA. The same linear function was used to determine the VA limit required for each question. For instance, the VA limit for the moon question was 1.0 logMAR. Conclusions Yes/no questions about everyday visual activities have the potential to estimate an individual's VA. Future refinements may enhance reliability. Translational Relevance The survey provides insights into the real-world visual capabilities of people with low vision, making it potentially useful for telehealth applications.

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Visual Acuity

Cited by 5 publications

References 29 publications

Re: Goldstein et al.: Visual acuity: assessment of data quality and usability in an electronic health record system (Ophthalmol Sci. 2023;3(1):100215)

Re: Goldstein et al.: Visual acuity: assessment of data quality and usability in an electronic health record system (Ophthalmol Sci. 2023;3(1):100215)

Electronic health record data quality assessment and tools: a systematic review

Estimating Visual Acuity Without a Visual Acuity Chart

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