After months of speculation by players, fans and pundits, the long-awaited NFL draft has arrived. Questions like which ill-fated quarterback will go to the Browns are intriguing, but even more fascinating is the ‘Moneyball’ approach that team analysts are using to evaluate players. The best picks may no longer be decided in the film room, but rather on a laptop using a data-driven approach.
Professional football teams are certainly not unique in their use of data, and statistical analysis has long been a part of player selection decisions in all sports. The difference is in the speed and precision involved in effectively taking an Everest-sized volume of data and distilling it into a useful form that will allow those on the front lines of the draft to react. This type of information in the hands of some of the greatest talent evaluators, pro football coaches and scouts, is a weapon more powerful than any NFL Scouting Combine.
Data analytics isn’t just leaving its mark on football. From electronic strike zones to online shopping, use of predictive modeling based on consumer patterns is improving operational efficiencies across a spectrum of industries, and healthcare is no exception. Although the healthcare industry has long claimed exceptionalism based on regulatory constraints, an aging population and limited access to affordable care has opened the door for medical technology innovation and accelerators.
Enter data analytics. Similar to professional scouts, most physicians use their experience, knowledge and ingenuity to diagnose and treat – all within the span of an office visit. According to a 2013 survey by the American Academy of Family Physicians, the average family physician spends about 22 minutes with a patient. That’s 22 minutes to take in, digest, and analyze a lifetime of patient data. What’s worse is the average primary care physician would need to spend almost 22 hours a day to provide all the recommended acute, chronic and preventative care for a recommended panel (the total under his or her care) of 2,500 patients.
As reimbursements shift from fee to value-based models, chronic disease is on the rise while resources continue to be reduced. Healthcare providers are tasked with delivering higher quality care and better outcomes in an environment not equipped to handle an explosion of new research, approved pharmaceuticals, and medical devices. To meet these challenges, providers require immediate, easy access to the data and insights that matter most to their patient. Imagine a world where, within a few clicks, a physician has the ability to correlate and pattern match patient data with other cohort patient groups. What if these programs went a step further and incorporated information sources, such as proteomic and genomic data to enhance diagnostic and treatment decisions?
Data Analytics Slow Implementation in Healthcare
Some will argue that the use of big data analytics in healthcare isn’t anything new, which is true to a point. However, its implementation in the industry has been slow and tedious, plagued with red tape, misplaced intentions and architectures designed to verify past decisions versus predictive intelligence. Authors of a 2017 New England Journal of Medicine titled “Machine Learning and Prediction in Medicine,” articulated the issue best when they argued the amazing potential of big data is burdened by its own hype. Current big data analytics application often reaches conclusions that are already well known to patients. Additionally, real-time patient data is often inaccessible, making predictive analytics difficult.
The Next Wave of Disruptive Healthcare Innovation
However, high-performance computing, cloud-based healthcare data analytics platform, such as IBM Cognitive Healthcare Solutions, Apixio’s Iris and my company’s recently FDA-approved platform CereMetrix SilverTM, are beginning to emerge. Our next wave of disruptive innovation is well down a path toward enabling real Artificial Intelligence capabilities, CereMetrix Gold™*. This assisted intelligence platform will distill valuable data physicians collect – from imaging, clinical history, lab tests and more — into actionable data-driven insights. The software suite will enable healthcare providers to use individual patient data and insights from a similar patient population to enhance clinical decision-making. The goal of CereMetrix Gold™ is to quickly and easily provide medical practitioners with the data and insights that matter most to their patient.
While advanced healthcare data analytic platforms can offer more patient insights, physicians are still going to play a crucial role in making recommendations and choices based on the best input from all sources of information. The result should be to provide the most timely and improved guidance to patients. Rather than being a threat, data analytic applications offer a net gain for healthcare providers who use smart systems wisely. Whether you’re a physician or responsible for drafting the best players, machine intelligence has the potential to provide precision access to the information needed to make better decisions. To me, the future is bright. When our best medical professional have access to the best data and the best systems to tease out intelligent-based comparison analytics, the goal of achieving quality life decisions can be realized.
*CereMetrix Gold™ is not currently cleared or approved by the U.S. FDA or any other global regulator for commercial availability.