Businesses analyze all of the data they collect in order to make predictions about what you will do next or what will motivate you to make a future purchase. For example, the grocery store prints out coupons for products based on what the cashier just scanned. Called predictive analytics, it’s the practice of extracting information from existing data sets in order to determine patterns and predict future outcomes and trends. While predictive analytics doesn’t tell you for certainty what will happen, it does provide a forecast of sorts, with an acceptable level of reliability. It also addresses opportunities and risks for the company.
The healthcare industry lags behind other sectors that have successfully used predictive analytics. But the electronic medical record (EMR) now gives healthcare institutions access to large volumes of patient data – information about their customer base that can be analyzed to make projections and change actions accordingly.
Data from EMRs, billing systems, electronic prescribing systems, health information exchanges and other similar sources can provide the healthcare industry with actionable insights into population health, high-risk patient management, clinical best practices and more. This insight will become increasingly important (and necessary) as healthcare continues to move toward value-based reimbursement.
Don’t expect data analytics to become an overnight sensation. Other industries have spent decades investing in infrastructure, IT staff and other resources needed to collect, maintain, analyze and act on big data. And, while 80% of hospital CIOs and other executives believe that data analytics are important to their organizations’ strategic goals, only 45% of them have a big data management plan and just 17 percent have the staff to execute that plan (according to a recent survey conducted by the eHealth Initiative and the College of Healthcare Information Management Executives).
Recognizing a need for guidance, HIMSS Analytics set out to develop a predictive analytics roadmap for healthcare organizations. Looking at the way other industries successfully manage information for effective business use, HIMSS Analytics worked with The International Institute for Analytics to develop the DELTA Poweredtm Analytics Maturity Model, specifically tailored for healthcare use. The model focuses on five areas:
HIMSS Analytics estimates that most healthcare organizations are at about the second of the model's five levels, where data is being collected and used at a localized level.
The ultimate goal for healthcare organizations using predictive analytics is to improve patient care, control costs and avoid financial penalties. One way to achieve that goal is by using analytics to predict patients who are at risk for hospital readmissions and then implementing successful interventions for those patients. Other applications for data analytics could include predicting heart failure or predicting specific events within pediatric populations. How do you think predictive analytics can be used to help control costs and improve patient care? Share your ideas in the comment section below.