For HCOs contemplating the launch of new population health programs, data analysis is often challenging due to the heterogeneous nature of patient-related data. Accessing and analyzing unstructured data is difficult without advanced technologies such as NLP – at least without resorting to expensive and time-consuming manual chart reviews.

However, the ability to automatically extract precise data from unstructured text is invaluable for HCOs that are transitioning to value-based contracting arrangements. By employing NLP, HCOs can look at structured and unstructured data for a complete, 360-degree-view of their patient populations. They can then identify and extract specific details to assess risk or improve population health. Additionally, NLP can enable clinicians to assess critical lifestyle details and specific behaviors from individual patients, such as smoking and alcohol consumption, and gain insights into living arrangements, access to care and mobility status.

Using NLP to improve diabetes population health
Consider how NLP can help an accountable care organization (ACO) better understand its patient population’s risk for diabetes — a condition that, along with prediabetes, affects more than 100 million Americans and exacts a high cost on the U.S. health system: The cost of care for people with diabetes averages about $16,752 a year and accounts for approximately 25% of healthcare dollars spent in the U.S., according to a study in Diabetes Care.

Diabetes risk is closely associated with social and economic factors, and is more common among non-white populations, with black, Hispanic, and Native American populations experiencing the disease at much higher rates than whites. An analysis of structured data can detail risk factors tied to weight, race and age, but is likely to miss additional risk factors that could be noted in free text within physicians’ notes.

By leveraging NLP to analyze patient records, however, the ACO could identify a host of additional risk factors impacting its patient population, such as limited access to proper medications and healthy foods, barriers to physical activity, high stress levels and social isolation.

Additionally, many signs of early diabetes symptoms appear in unstructured data, such as mentions of excessive thirst or hunger, frequent urination, fatigue, and blurred vision. Further, information pertaining to laboratory values such as hemoglobin A1c and blood glucose levels are also markers that may appear in free text lab reports and be missed when relying solely on structured data.

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