Using Big Data to Improve Clinical Practice
Large studies using claims data have demonstrated considerable regional differences in practice and in the quality of eye care in the United States and worldwide. For example, Stein and colleagues showed a large difference in the use of laser trabeculoplasty between ophthalmologists and optometrists in Oklahoma. Were these high usage rates standards of care, or did they represent overusage, particularly when compared to other treatments?
An analysis of the Medicare database revealed that some ophthalmologists had large Medicare expenditures when compared with those of the average ophthalmologist. This big data analysis, among others, prompted investigators to discover fraud in our health care system. Big data can also be used to evaluate how individual ophthalmologists compare to their peers for an outcome of interest, such as the proportion of patients who need to return to the operating room after cataract surgery. If an ophthalmologist has a higher complication rate than that of his or her peers, this information may prompt that ophthalmologist to begin a quality improvement project to lower their patient complication rate via education and further training. Big data offer clinicians many opportunities to measure and improve eye care, particularly when accompanied by an organizing framework to understand the data and develop improvement activities (see the following sections).
Medicare Provider Utilization and Payment Data: Physician and Other Supplier Look-up Tool Database. Baltimore, MD: Centers for Medicare & Medicaid Services. https://data.cms.gov/utilization-and-payment-explorer. Accessed February 21, 2019.
Stein JD, Zhao PY, Andrews C, Skuta GL. Comparison of outcomes of laser trabeculoplasty performed by optometrists vs ophthalmologists in Oklahoma. JAMA Ophthalmol. 2016; 134(10):1095–1101.
Excerpted from BCSC 2020-2021 series: Section 1 - Update on General Medicine. For more information and to purchase the entire series, please visit https://www.aao.org/bcsc.