Anne Louise Coleman1. 1. Department of Ophthalmology, UCLA Stein Eye Institute, David Geffen School of Medicine at UCLA, Los Angeles, California. Electronic address: coleman@jsei.ucla.edu.
Abstract
PURPOSE: To characterize the role of Big Data in evaluating quality of care in ophthalmology, to highlight opportunities for studying quality improvement using data available in the American Academy of Ophthalmology Intelligent Research in Sight (IRIS) Registry, and to show how Big Data informs us about rare events such as endophthalmitis after cataract surgery. DESIGN: Review of published studies, analysis of public-use Medicare claims files from 2010 to 2013, and analysis of IRIS Registry from 2013 to 2014. METHODS: Statistical analysis of observational data. RESULTS: The overall rate of endophthalmitis after cataract surgery was 0.14% in 216 703 individuals in the Medicare database. In the IRIS Registry the endophthalmitis rate after cataract surgery was 0.08% among 511 182 individuals. Endophthalmitis rates tended to be higher in eyes with combined cataract surgery and anterior vitrectomy (P = .051), although only 0.08% of eyes had this combined procedure. Visual acuity (VA) in the IRIS Registry in eyes with and without postoperative endophthalmitis measured 1-7 days postoperatively were logMAR 0.58 (standard deviation [SD]: 0.84) (approximately Snellen acuity of 20/80) and logMAR 0.31 (SD: 0.34) (approximately Snellen acuity of 20/40), respectively. In 33 547 eyes with postoperative VA after cataract surgery, 18.3% had 1-month-postoperative VA worse than 20/40. CONCLUSIONS: Big Data drawing on Medicare claims and IRIS Registry records can help identify additional areas for quality improvement, such as in the 18.3% of eyes in the IRIS Registry having 1-month-postoperative VA worse than 20/40. The ability to track patient outcomes in Big Data sets provides opportunities for further research on rare complications such as postoperative endophthalmitis and outcomes from uncommon procedures such as cataract surgery combined with anterior vitrectomy. But privacy and data-security concerns associated with Big Data should not be taken lightly.
PURPOSE: To characterize the role of Big Data in evaluating quality of care in ophthalmology, to highlight opportunities for studying quality improvement using data available in the American Academy of Ophthalmology Intelligent Research in Sight (IRIS) Registry, and to show how Big Data informs us about rare events such as endophthalmitis after cataract surgery. DESIGN: Review of published studies, analysis of public-use Medicare claims files from 2010 to 2013, and analysis of IRIS Registry from 2013 to 2014. METHODS: Statistical analysis of observational data. RESULTS: The overall rate of endophthalmitis after cataract surgery was 0.14% in 216 703 individuals in the Medicare database. In the IRIS Registry the endophthalmitis rate after cataract surgery was 0.08% among 511 182 individuals. Endophthalmitis rates tended to be higher in eyes with combined cataract surgery and anterior vitrectomy (P = .051), although only 0.08% of eyes had this combined procedure. Visual acuity (VA) in the IRIS Registry in eyes with and without postoperative endophthalmitis measured 1-7 days postoperatively were logMAR 0.58 (standard deviation [SD]: 0.84) (approximately Snellen acuity of 20/80) and logMAR 0.31 (SD: 0.34) (approximately Snellen acuity of 20/40), respectively. In 33 547 eyes with postoperative VA after cataract surgery, 18.3% had 1-month-postoperative VA worse than 20/40. CONCLUSIONS: Big Data drawing on Medicare claims and IRIS Registry records can help identify additional areas for quality improvement, such as in the 18.3% of eyes in the IRIS Registry having 1-month-postoperative VA worse than 20/40. The ability to track patient outcomes in Big Data sets provides opportunities for further research on rare complications such as postoperative endophthalmitis and outcomes from uncommon procedures such as cataract surgery combined with anterior vitrectomy. But privacy and data-security concerns associated with Big Data should not be taken lightly.
Authors: Joshua S Hardin; Donald W Gauldin; Mohamed K Soliman; Colin J Chu; Yit C Yang; Ahmed B Sallam Journal: JAMA Ophthalmol Date: 2018-02-01 Impact factor: 7.389
Authors: Cecilia S Lee; Julia P Owen; Ryan T Yanagihara; Alice Lorch; Suzann Pershing; Leslie Hyman; Joan W Miller; Julia A Haller; Michael F Chiang; Flora Lum; Aaron Y Lee Journal: Ophthalmol Glaucoma Date: 2020-03-31