Using Natural Language Processing to Unlock SDOH in Unstructured EHR Data

Medical care is estimated to account for only 10-20% of healthcare outcomes. As a result, healthcare executives who wish to deliver high-quality care have to consider other elements that impact patient health, including income, access to healthcare, racial discrimination, adequate medication, and dietary intake.

These are social determinants of health. They offer a wealth of information about non-clinical factors that have an impact on a patient's wellbeing. But identifying a patient's SDOH can be challenging because details aren't always easily accessible, especially at the time when clinicians make key treatment decisions.

SDOH data often resides in EHRs but are essentially trapped as unstructured text within clinical notes, patient-reported data, secure e-mail exchanges, patient portal messages, and other places.

Using NLP, providers can identify patients at risk of poorer outcomes due to SDOH issues. With this insight, healthcare providers can then take proactive measures to connect patients with additional resources.