Patient segmentation and prioritization… based on what data?

Population health management’s success is based on many factors that fundamentally rely on data. To start, you will need data to segment higher risk patients and rising risk patients from the others. Once segmented, you may also need additional data on those specific patients to continue to assess their risk levels or consider other attributes that contribute to those risks. This data typically originates from the EHR, laboratory, e-prescribing, specialist reports, and claims data.

so how complete is your data?

We can all agree that the more “complete” data that you have about a patient, the better chance you can use analytics to assess, manage and even prevent common population health issues.  

How can you actually get more complete data?

One existing and easily accessible source of “missing” data for any patient is clinical information that is stuck in paper or electronic documents. Although this data is “accessible” to providers as a scanned document, it does not play a role in supplying population health databases or in running analytics on the data to assess if a patient is at risk. If blood tests or consultative notes from specialists are not getting into the EHR as discrete clinical data, then your population health risk assessments cannot be accomplished easily and reliably.

Mining your existing paper and electronic documents and extracting all pertinent clinical data from them is the easiest, most cost-effective way to begin to fill the gap of missing data. You already have the data, you just need to get it into your systems!

In doing so, it is also important to create workflows that automate this process, not burden clinical staff with data entry, and ensure high accuracy and quality assurance of the data being extracted and entered. By essentially “converting” all document/paper-based information into discrete clinical data, you are on your way to better patient prioritization and better preventative care.


About the Author: Ellen Bzomowski

With 20 years of experience in data capture and voice recognition, Ellen’s experience has focused on achieving higher efficiency and automation in getting data where it will be most useful to an organization. At Extract Systems, she continues to focus on the same ideas and works to get the word out about how Extract Systems’ advanced data capture and redaction solutions make more data valuable and accessible, while securing anything that is private. She holds an MBA from Northeastern University and lives and works in Boston.