Health organizations across the United States have jumped onto the population health management bandwagon striving to provide efficient care management services while lowering plan members’ health risks and costs. The desire to identify members with modifiable risks — while aligning care management services to fit the population’s needs — is pushing healthcare organizations toward advanced data management platforms, migrating to data-supported care management programs. Deerwalk’s integrated[...]
My last two posts addressed a general overview of the healthcare analytics continuum, and the importance of foundational data integrity to healthcare analytics. For today's post I'd like to dig a little deeper into the difference between descriptive, predictive and prescriptive analytics, with a focus on prescriptive analytics, as I've found it has different meanings for different users.
Ever tried to create a portrait image of another person using push pins? Neither have I, but it looks hard. Few have mastered the art.
My last post addressed a general overview of the entire healthcare analytics continuum - from foundational data integrity to true prescriptive analytics. At the end of that post I promised to dive into each layer with a bit more detail. So today's post will deal with the most important layer in the continuum - foundational data integrity. After all, without high quality, fully integrated and enriched healthcare data, any analytics exercise will be flawed at best.
Recent polls by GE and Accenture show that big data analytics is a priority for almost 90% of organizations within healthcare. Data is an essential tool for market participants that want to increase share while improving the quality of customer deliverables. And, in the not too distant future, healthcare providers and stakeholders that implement an analytics strategy will excel in clinical quality and prevention of operational bottlenecks.