One of the core functions in population health management (PHM) is finding care gaps so they can be addressed to drive healthier outcomes. Yet this process itself often has its own type of gap that can cause PHM efforts to be less effective than they could – or should – be.
These “knowledge” gaps result from the types of data gathered, and how those data are used across population health programs. If the health care organization relies solely on data from its own electronic health records (EHR), it only sees the care that has been provided within its organization – or sometimes even just within a particular practice or institution. They lack the “big picture” of all the care the patient has received or the full health status, which means care gaps may have been filled elsewhere, risk profiles might be skewed, and overall current health outcomes could look different when a complete longitudinal view is seen.
Claims data from payers offers a broader view of care across the continuum. But even if the health care organization can obtain it, there is typically a three-month lag between the time the care is delivered and when claims data become available. It’s possible some of those care gaps may have been filled in the interim, again leading to wasted time, resources, and money that could have been used to help another patient. Claims data also lack the biometrics that deliver deeper, more specific insights into the patient’s health status, which can have a significant bearing on the treatment of chronic conditions in particular.
Clearly, each type of data has its merits – and its drawbacks. By combining the two, health care organizations can take advantage of the strengths of each while overcoming their negatives. The result is a more timely and accurate picture of the patient’s current health and care gaps, which helps health care organizations prioritize those who have the highest risk so they can assign their limited care management resources to the patients where they can do the most good. Add in socioeconomic, psychographic, and other data that show more about how that patient lives and what motivates him/her, and the conclusions become even more powerful.
Obtaining all the data is only part of the challenge, however. The larger issue is being able to integrate all this disparate data in a way that helps the health care organization create actionable insights. This is where next-generation analytics drive a strong data strategy to the next level.
