In a recent H&HN article, Janice Powers outlines the "6 Strategies to Get the Most out of Investing in Population Health." You can access the original here: http://www.hhnmag.com/articles/7063-strategies-for-population-health-investments. What she includes are six areas that require investment, understanding, and time to drive value from population health management (PHM) activities. Predictive analytics is one of these areas.
While Powers' analysis of predictive analytics is correct, it is narrow. She defines predictive programs as those that "use big data analytics to identify health trends and at-risk populations." This omits an entire predictive approach that uses the data that a hospital has on hand to drive predictive value. The machine learning and artificial intelligence capabilities embedded in these solutions can account for missing, messy, and inconsistent data. They are predicated on advanced data science techniques that combine "small" patient-level data with outside sources of clinical, socioeconomic, and consumer data to build clinically relevant patient pods or clusters. These complex pattern formations enable precise predictions that are more accurate than current statistical and-in some cases-medical tests.
There is a lot to be said about Big Data including its potential and its cost. But we also need to understand the role of predictive analytics in driving PHM goals beyond the consolidation and aggregation of massive data sets. The intelligent predictive solutions that are available today are using the clinical, contextual, and administrative data that already exists to save patient lives inside the hospital and outside in the community.
- By Jvion Health