BackgroundVisual acuity is the primary measure used in ophthalmology to determine how well a patient can see. Visual acuity for a single eye may be recorded in multiple ways for a single patient visit (eg, Snellen vs. Jäger units vs. font print size), and be recorded for either distance or near vision. Capturing the best documented visual acuity (BDVA) of each eye in an individual patient visit is an important step for making electronic ophthalmology clinical notes useful in research.ObjectiveCurrently, there is limited methodology for capturing BDVA in an efficient and accurate manner from electronic health record (EHR) notes. We developed an algorithm to detect BDVA for right and left eyes from defined fields within electronic ophthalmology clinical notes.MethodsWe designed an algorithm to detect the BDVA from defined fields within 295,218 ophthalmology clinical notes with visual acuity data present. About 5668 unique responses were identified and an algorithm was developed to map all of the unique responses to a structured list of Snellen visual acuities.ResultsVisual acuity was captured from a total of 295,218 ophthalmology clinical notes during the study dates. The algorithm identified all visual acuities in the defined visual acuity section for each eye and returned a single BDVA for each eye. A clinician chart review of 100 random patient notes showed a 99% accuracy detecting BDVA from these records and 1% observed error.ConclusionsOur algorithm successfully captures best documented Snellen distance visual acuity from ophthalmology clinical notes and transforms a variety of inputs into a structured Snellen equivalent list. Our work, to the best of our knowledge, represents the first attempt at capturing visual acuity accurately from large numbers of electronic ophthalmology notes. Use of this algorithm can benefit research groups interested in assessing visual acuity for patient centered outcome. All codes used for this study are currently available, and will be made available online at https://phekb.org.
IntroductionThe Affordable Care Act (ACA) has expanded health coverage for thousands of Illinois residents. Expanded coverage, however, does not guarantee appropriate health care. Diabetes and its ocular complications serve as an example of how providers in underserved urban areas may not be able to keep up with new demand for labor- and technology-intensive health care unless changes in reimbursement policies are instituted.MethodsA retrospective cohort study was conducted using medical encounter information from the Chicago HealthLNK Data Repository (HDR), an assembly of non-duplicated and de-identified patient medical records. We used a method of estimating the geographic distribution of undiagnosed diabetic retinopathy in the city of Chicago to illustrate the magnitude of potentially preventable eye disease. All rates were calculated for all ZIP Codes within Chicago (Cook County), and statistical differences between observed and geographically adjusted expected rates (p < 0.10, p < 0.05, p < 0.01) were highlighted as underserved areas.ResultsThis analysis included 150,661 patients with diabetes identified from a total of nearly two million patients in Chicago. High rates of undetected diabetic retinopathy were found in low-income and minority areas. Within these areas, 37% of the identified diabetics were uninsured, with rates ranging widely from 20% to 68.6%. Among those with insurance, 32.8% were covered by Medicare and only 10% by Medicaid. Most patients with untreated diabetic retinopathy were found to reside in areas where primary health care is provided through Federally Qualified Health Centers.ConclusionsWith 150,661 diabetics identified in the city of Chicago, and this number continuing to rise each year, a manpower approach with ophthalmologist screening for diabetic retinopathy is not realistic. The ability to identify the growing number of diabetic patients with retinopathy in low-income areas will likely require the adoption of cost-effective screening technologies that are currently not funded by Medicare and Medicaid.
Publicly available molecular datasets can be used for independent verification or investigative repurposing, but depends on the presence, consistency and quality of descriptive annotations. Annotation and indexing of molecular datasets using well-defined controlled vocabularies or ontologies enables accurate and systematic data discovery, yet the majority of molecular datasets available through public data repositories lack such annotations. A number of automated annotation methods have been developed; however few systematic evaluations of the quality of annotations supplied by application of these methods have been performed using annotations from standing public data repositories. Here, we compared manually-assigned Medical Subject Heading (MeSH) annotations associated with experiments by data submitters in the PRoteomics IDEntification (PRIDE) proteomics data repository to automated MeSH annotations derived through the National Center for Biomedical Ontology Annotator and National Library of Medicine MetaMap programs. These programs were applied to free-text annotations for experiments in PRIDE. As many submitted datasets were referenced in publications, we used the manually curated MeSH annotations of those linked publications in MEDLINE as “gold standard”. Annotator and MetaMap exhibited recall performance 3-fold greater than that of the manual annotations. We connected PRIDE experiments in a network topology according to shared MeSH annotations and found 373 distinct clusters, many of which were found to be biologically coherent by network analysis. The results of this study suggest that both Annotator and MetaMap are capable of annotating public molecular datasets with a quality comparable, and often exceeding, that of the actual data submitters, highlighting a continuous need to improve and apply automated methods to molecular datasets in public data repositories to maximize their value and utility.
We report a case of an unsuspected ganglioneuroma of the choroid in a patient with neurofibromatosis type 1. A 5-year-old girl presented from an outside institution with right proptosis and glaucoma since birth. Magnetic resonance imaging was obtained and showed a cavernous sinus mass extending into the right orbit and multiple orbital lesions. Additionally, increased signal in the posterior globe of the right eye was noted, but its etiology was unclear at the time. She was lost to follow-up for 3 years and later returned with a blind painful eye. Enucleation was performed, and histopathology was significant for diffuse choroidal ganglioneuroma and advanced glaucoma. We report the atypical history, examination findings, and histopathology to support the diagnosis.
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