How Big Data & Predictive Analytics are Diagnosing Eye Disease Faster
Bryan M. Rogoff, OD, MBA, CPHM, FAAO
Today, it is a digital world. We all have learned to operate as professionals and consumers where data has been the key driver for business to lower economies of scale and create more efficiencies. The terms “Big Data” and “Data Analytics” are thrown loosely around, but what do they mean and how does it play a role in eye care? Big data is driving most, if not all, modern industries and has become a game-changer regarding how we interact with businesses and each other. In eye care, like many other industries, the funding and return on investment on big data and analytics can be challenging for short term practical uses. However, more companies are looking to implement big data projects to enhance customer experience, reduce costs, target marketing improvements, and improve overall efficiencies.1
The definition of big data seems intuitive, but it is differentiated from basic data due to certain characteristics. In 2001, Gartner, a world-leading research and advisory company regarding information technology, described big data having 3 V’s: volume, velocity and variety, and stated that it will be the “new” normal.2 Furthermore, they described big data as, “high volume, high velocity and/or high variety information assets that demand cost-effective, innovative format of information processing that enable enhanced insight, decision making, and process automation.”3 Generally, volume refers to the amount of data that can be tens of terabytes; velocity is the rate of how data is received where it may need to evaluate in real, or near real-time for action; and variety is the type of data, either structured, unstructured, and semi-structured.4 Since then, more V’s have been added to describe big data like veracity, which refers to the inconsistencies and quality of data, and value, that refers to the importance of converting data into something useful.5
Predictive analytics uses big data to forecast future events, outcomes or trends with acceptable level reliability to lower risk.6 It uses historical data, along with statistical algorithms and supervised machine learning as the methodology for converting big data into useful information to give entities a competitive advantage. Predictive analytics has been already been applied in various industries of finance, healthcare, pharmaceuticals, aerospace, governments, manufacturing, and social media to improve marketing, improve safety and compliance, increase production, improve efficiencies, and improve health outcomes and personalize care for each patient. As seen in Figure 1, predictive analytics demonstrates the plateau of productivity for industries.
Knowing that big data and predictive analytics can statistically forecast outcomes and personalize care for patients, the National Institute of Health (NIH) has announced a collaboration of data to advance the research for dry age-related macular degeneration. Since ARMD affects 11 million people in the United States, and lacks preventative strategies and treatment options, utilizing integrated, big data and predictive analytics could provide better insights of risk factors to effectively personalize the disease management.7 Also, hubs have been created to house large datasets, such as INSIGHT, that focus to provide real-world data for clinical trials of drugs and devices, especially in eye care. INSIGHT allows users to utilize their datasets to discover new ways to detect, diagnose, treat eye disease and allow individualized care.8 This data can be used to build predictive models such as ones in diabetic retinopathy and glaucoma. Additionally, the American Academy of Ophthalmology created the IRIS (Intelligent Research in Sight) registry and was launched in 2014 to create datasets to reveal the causes of rare and common eye disease. The data spans over 60 million patients and has become the world's largest medical specialty clinical database.9 Eyecare, like many health professions, is gathering massive data to better predict outcomes, lower costs and detect the progression of many sight-threatening diseases. Startups that are using predictive analytics to build robust AI algorithms are growing bigger every day. How will you utilize this technology in your practice?
Bryan M. Rogoff, OD, MBA, CPHM has a unique background in areas of holistic eye care, business management and healthcare reform. He specializes in LEAN clinical management and operations, technology implementation, healthcare strategy, and strategic partnerships. Currently, he serves as a consultant for for the FDA, Immediate Past-President & Education Chairperson for the Maryland Optometric Association, Federal Keyperson and Meetings Committee Member for the American Optometric Association, reviewer for the Council on Optometric Practitioner Education and is the Founder of Eye-Exec Consulting, LLC. To contact Bryan, visit www.eye-exec.com or email email@example.com. He can also be found on LinkedIn, Facebook, Twitter and Instagram.