Tracking health disparities through natural language processing

Background: Closing the gap of racial and ethnic health disparities is a national priority. Disparities and solutions are heterogeneous within and among racial and ethnic groups, yet it is difficult to assess the need for (and impact of) interventions in subset groups at scale because existing healthcare infrastructure lacks the granularity to reflect important sociocultural distinctions. For example, specific immigrant populations (e.g., Somali) are frequently lumped into categories (e.g., African American) that preclude meaningful assessment and monitoring of community or practice-based interventions. Therefore, we applied Natural Language Processing (NLP), a discipline that allows computers to process and understand human languages, to Electronic Medical Records (EMRs) as a tool to potentially bypass these limitations.

Methods: The study was conducted at a large academic medical center in the Midwestern US with a large regional Somali population. We utilized a rule-based NLP software tool that searches clinical text in EMRs to produce customized cohorts to develop an algorithm that will identify Somali patients. Manual chart review was used as the “gold standard” to identify Somali patients, and comparison of the NLP algorithm with this standard was achieved through calculations of sensitivity and specificity.

Results: On a set of 5,782 patients who were seen by primary care physicians within a 15-day interval, our algorithm detected 122 Somali patients. A manual check of the full set of patients showed that the algorithm had excellent sensitivity (92.2%), specificity (99.9%), positive predictive value (97.5%), and negative predictive value (99.8%) for identification of Somali patients.

Conclusions: In this single-center demonstration project, an NLP algorithm showed accuracy and precision in identifying patients from a subset immigrant group. This technology holds promise to identify and track immigrants and refugees in the United States at a local healthcare level, paving the way for improved patient care and reduction of health disparities.