How Big Data Informs Us About Cataract Surgery: The LXXII Edward Jackson Memorial Lecture.

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.

[1]  U. Stenevi,et al.  Decreasing rate of capsule complications in cataract surgery: Eight‐year study of incidence, risk factors, and data validity by the Swedish National Cataract Register , 2011, Journal of cataract and refractive surgery.

[2]  Liping Li,et al.  Risk Factors for Acute Endophthalmitis following Cataract Surgery: A Systematic Review and Meta-Analysis , 2013, PloS one.

[3]  News from the NIH: leveraging big data in the behavioral sciences , 2014, Translational behavioral medicine.

[4]  J. Stein,et al.  Severe adverse events after cataract surgery among medicare beneficiaries. , 2011, Ophthalmology.

[5]  Kirsten E. Martin.  Ethical Issues in the Big Data Industry , 2015, MIS Q. Executive.

[6]  J C Javitt,et al.  National outcomes of cataract extraction. Endophthalmitis following inpatient surgery. , 1991, Archives of ophthalmology.

[7]  U. Stenevi,et al.  One million cataract surgeries: Swedish National Cataract Register 1992-2009. , 2011, Journal of cataract and refractive surgery.

[8]  L. Sweeney.  Simple Demographics Often Identify People Uniquely , 2000 .

[9]  Paul Ohm.  Broken Promises of Privacy: Responding to the Surprising Failure of Anonymization , 2009 .

[10]  J. Sparrow,et al.  The Cataract National Dataset electronic multi-centre audit of 55 567 operations: updating benchmark standards of care in the United Kingdom and internationally , 2009, Eye.

[11]  E. Jackson.  The History of Cataract Operations , 1933 .

[12]  L. Herrinton,et al.  Decreased postoperative endophthalmitis rate after institution of intracameral antibiotics in a Northern California eye department , 2013, Journal of cataract and refractive surgery.

[13]  U. Stenevi,et al.  The changing pattern of cataract surgery indications: a 5-year study of 2 cataract surgery databases. , 2015, Ophthalmology.

[14]  U. Stenevi,et al.  Six‐year incidence of endophthalmitis after cataract surgery: Swedish national study , 2013, Journal of cataract and refractive surgery.

[15]  P. Friedmann,et al.  Prevalence and predictors of ocular complications associated with cataract surgery in United States veterans. , 2011, Ophthalmology.

[16]  P. Ackland.  The accomplishments of the global initiative VISION 2020: The Right to Sight and the focus for the next 8 years of the campaign , 2012, Indian journal of ophthalmology.

[17]  G. Gettinby,et al.  Prophylaxis of postoperative endophthalmitis following cataract surgery: Results of the ESCRS multicenter study and identification of risk factors , 2007, Journal of cataract and refractive surgery.

[18]  A. Coleman,et al.  Use of insurance claims databases to evaluate the outcomes of ophthalmic surgery. , 1997, Survey of ophthalmology.

[19]  Joshua D Stein,et al.  Use of health care claims data to study patients with ophthalmologic conditions. , 2014, Ophthalmology.

[20]  Paul Zikopoulos,et al.  Understanding Big Data: Analytics for Enterprise Class Hadoop and Streaming Data , 2011 .

[21]  Mats Lundström,et al.  Visual outcome of cataract surgery; Study from the European Registry of Quality Outcomes for Cataract and Refractive Surgery , 2013, Journal of cataract and refractive surgery.

[22]  P. Chandler.  Surgery of congenital cataract. , 1968, American journal of ophthalmology.

[23]  Melanie Swan,et al.  The Quantified Self: Fundamental Disruption in Big Data Science and Biological Discovery , 2013, Big Data.

[24]  A. Behrens,et al.  The incidence of endophthalmitis after cataract surgery among the U.S. Medicare population increased between 1994 and 2001. , 2005, Ophthalmology.

[25]  A. Coleman,et al.  Special Commentary: Food and Drug Administration and American Academy of Ophthalmology Sponsored: Developing Novel End Points for Premium Intraocular Lenses Workshop. , 2015, Ophthalmology.

[26]  Sean D Dessureault,et al.  Understanding big data , 2016 .

[27]  H. Flynn,et al.  Endophthalmitis prophylaxis for cataract surgery: are intracameral antibiotics necessary? , 2014, JAMA ophthalmology.

[28]  C. Bell,et al.  Risk factors for acute endophthalmitis after cataract surgery: a population-based study. , 2009, Ophthalmology.

[29]  S. Greenland,et al.  Glaucoma outcome studies using existing databases: opportunities and limitations. , 1995, Journal of glaucoma.

[30]  C. Blumberg.  Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction , 2016 .

[31]  K. Ahmad,et al.  Visual outcome of cataract surgery. , 2013, Journal of the College of Physicians and Surgeons--Pakistan : JCPSP.

[32]  M M Hansen,et al.  Big Data in Science and Healthcare: A Review of Recent Literature and Perspectives , 2014, Yearbook of Medical Informatics.

[33]  R. Forshee,et al.  Incidence of endophthalmitis after corneal transplant or cataract surgery in a medicare population. , 2014, Ophthalmology.

[34]  Joachim Roski,et al.  Creating value in health care through big data: opportunities and policy implications. , 2014, Health affairs.

[35]  A. Coleman,et al.  Risk of fractures following cataract surgery in Medicare beneficiaries. , 2012, JAMA.

[36]  Philippe Golle,et al.  Revisiting the uniqueness of simple demographics in the US population , 2006, WPES '06.

[37]  A. Bansal,et al.  Clinical validation of a genetic model to estimate the risk of developing choroidal neovascular age-related macular degeneration , 2011, Human Genomics.

[38]  U. Stenevi,et al.  Endophthalmitis after cataract surgery: a nationwide prospective study evaluating incidence in relation to incision type and location. , 2007, Ophthalmology.

[39]  Emily W. Gower,et al.  Postcataract surgery endophthalmitis in the United States: analysis of the complete 2003 to 2004 Medicare database of cataract surgeries. , 2012, Ophthalmology.

[40]  R. Tipperman.  Prophylaxis of postoperative endophthalmitis following cataract surgery: results of the ESCRS multicenter study and identification of risk factors , 2008 .