Let’s face it, the healthcare industry will be going through another major transformation, and this one will be powered by data analytics. Not long ago, Booz Allen published a research paper that showed organizations that actively developed data analytics structure performed two times better than the rest. They also reported that the same organizations were three times more likely to execute on their decisions. Most Fortune 500 firms are implementing data analytics initiatives with renewed focus and energy, and the opportunities and benefits of data analytics in transforming a business or a healthcare organization are tremendous. Business journals are filled with success stories that inspire many leaders to make intentional investments in data analytics. But what strategic intents can analytics serve that will change healthcare in particular? There are countless opportunities, but I believe in the next few years we will see four specific changes.
1. Improved Quality
Data analytics enables many use cases, from better diagnosis to personalized medicine, that promise higher quality medications and better outcomes. It’s easy to get overwhelmed by the plethora of patient data, and physicians need a “data scope” to pick the relevant information from the noise. In other words, we need to—and we can—develop better clinical decision support systems for care providers. The big multiplier effect comes into play from more effective and shorter drug development cycles as a result of more accurate clinical trials and post-trial real world evidence data analysis. Watch this space as data analytics empowers personalized medicine to deliver drastically better outcomes.
2. Improved Financials
Data analytics helps providers and payers throughout billing and revenue cycle management to be more accurate, consistent, and to prevent fraud. The overall impact of analytics on reducing costs and improving revenue capture delivers amazing return on investment. Data analytics can show hospitals where they can better negotiate on prices with suppliers, and the optimal time to order supplies to minimize their inventories. Some hospitals have used data analytics to reduce re-admission and unnecessary testing. According to some data scientists, using these strategies could save more than $5M per hospital, improving cash flow and bottom line at the same time.
3. Prediction, Prevention, & Intervention
Data analytics can identify at-risk individuals, and predict how effective preventative care will be for them. Prediction and prevention can categorize patients based on their disease markers and predict their future health status. Knowing what may happen allows us to intervene and prevent adverse diseases or lessen their negative impact.
4. Population Health Management
Thanks to widespread use of electronic health record (EHR) systems, we now have the ability to gather data across large populations of patients and disease categories. With this information, we’re able to gain insight about shifts and trends in population health. Access to large population data sets enable us to implement more effective healthcare policies, practices, and plans across communities and segments of the population. One insurance company offers discounts if patients offer their Fitbit data for analysis, which another partners with grocery chain stores to track their patient population diet over time.
Ultimately, data analytics detects patterns and provides insights in data. These patterns have always existed, but now we have the tools to see them. The insight we gain provides better clues about our situation in healthcare, and it answers questions about why something happened, what is happening now, and what is likely to happen next.
Healthcare has gone through a lot of changes in the last decade, but new ones are on their way that will move providers and payers into new care and payment models. Data analytics will make these transformations feasible and the transitions smoother.
This article originally appeared in the 2017 Open Source & Big Data Market Report. Request your free copy for more insight like this.
Peter Ghavami, PhD
is the Head of IT Development and Data Analytics at CEB Global (Corporate Executive Board), a global best practices and technology company. Dr. Ghavami leads several data analytics programs including the CEB Data Innovation Center at the University of Illinois Research Park. He has published numerous books about data analytics and conducts research in machine learning, predictive modeling and Natural Language Processing (NLP) applications for clinical data. Prior to CEB, he was the Head of Data Analytics at Capital One Financial. He is co-founder of the Association of Data Scientists and Data Engineers in Washington, D.C.