Analyzing your data can reveal important insights about your patient population, including identifying those who are at highest risk for hospitalization.
In my last post, we discussed the importance of having “good” data. This means data that has been gleaned from as many sources as possible, and has been normalized and validated so that it is suitable for analyses. But generating and using good data is merely a first step. The next step requires investment in advanced analytical systems that provide accurate, timely and precise risk perspectives.
Analytic tools help organizations by identifying broad cohorts, and segmenting those cohorts into targeted risk populations. Specific gaps in the care of specific patients can then be identified and presented to physicians as a work list or within an EMR.
Baylor Quality Alliance, for example, uses a blend of Optum analytics and home-grown systems for population segmentation, predictive modeling and performance measures around quality and cost. The Optum system Baylor uses applies four components:
- Integration of clinical and claims data across the continuum of care to give providers a complete view of population health
- Better prediction of at-risk patients to reduce preventable costs via clinical analytics
- Improved performance via deep comparative clinical benchmarks
- Easy-to-use interfaces so non-technical people can interact without extensive training and support
Yet another provider group, Brown and Toland, uses a robust data analytics platform to enhance coordination of primary care. The platform applies population cohort analysis, risk stratification, predictive analytics and longitudinal outcomes tracking to help care teams identify individual patients at high risk. Brown and Toland’s patient-centered medical home care teams use the data to create a working registry used for outreach, and they have used this content to improve follow-up care for high-risk patients.
Quality data analytics can improve patient care in other ways as well. Using trusted data can be a powerful catalyst for changing clinical culture and physician behavior. Using Brown and Toland again as an example, they provide their physicians with a variety of information through scorecards, which help track and improve physician performance and quality measures.
Understanding where individual patients and providers stand in relation to one another is important information that can help organizations improve their clinical outcomes. In my next post, we’ll explore other ways in which data analytics play an important role in managing value-based contracts.
For more on using analytics to turn big data into good data, download “Getting from Big Data to Good Data: Creating a Foundation for Actionable Analytics.”
–Jeremy Orr, MD, MPH, Chief Medical Officer, Optum Analytics