Simplifying population health management and the identification of social determinants with natural language processing
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Simplifying population health management and the identification of social determinants with natural language processing

June 20, 2019
Health IT
By Elizabeth Marshall

The healthcare industry has come a long way in its appreciation of non-clinical factors impacting a patient’s overall well-being, such as social determinants of health (SDoH).

However, the industry has made less progress when it comes to gathering information on individual patients’ social determinants, analyzing the details and — most importantly — translating the findings into actionable information that healthcare organizations (HCOs) can use to improve population health management.

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One reason for this lag is that SDoH information frequently isn’t readily available to payer and provider organizations when important treatment decisions are being made. Too often, clinicians are unaware of key SDoH information until after a patient’s health has been negatively affected.

Frustratingly, HCOs often already have SDoH data in their patient records, but struggle to analyze it and leverage it for quality reporting because it is essentially trapped as unstructured data (e.g., free text within clinical notes). Indeed, it’s estimated that 80% of clinical data is unstructured and difficult to analyze. Data such as clinician narratives, nurse notes, radiology reports, discharge summaries and patient-reported information have the ability to contribute a wealth of useful clinical information, but the details are rarely easy to access.

To unlock value from this unstructured data, more HCOs are looking to Natural Language Processing (NLP) technology, which makes unstructured data usable by automating the identification and extraction of key concepts from large volumes of clinical documentation. HCOs can then transform this information into structured data to guide more informed treatment decisions.

Why SDoH data is key for developing population health strategies
Before designing effective population health management programs, HCOs must first understand the health risks that their patient populations face. Once HCOs have stratified their population based on level of risk, they can then initiate evidence-based care plans and safety-net programs to improve outcomes for at-risk patients and install preventive programs for all patients.

However, unless HCOs account for SDoH factors, the accuracy of risk assessments and patient stratification is compromised. That’s because, according to the Centers for Disease Control and Prevention, only 10 percent of factors affecting premature death are related to clinical care, and 30 percent of factors relate to genetics. This means that 60 percent of factors impacting premature death are based on a combination of social/environmental factors (20 percent) and behavior (40 percent).

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