Utilizing Natural Language Processing to Improve Abdominal Aortic Aneurysm Surveillance.

Background: Screening and surveillance are essential to prevent aneurysm rupture. We employed natural language processing (NLP) software to evaluate efficacy of aneurysm surveillance.

Methods: NLP software was employed to review 7 years of imaging reports at a single institution to identify patients with an abdominal aortic aneurysm. After identification of a patient cohort, review of the electronic medical record was completed to collect patient demographics and information about a compliance with nationally recommended aneurysm surveillance protocols.

Results: NLP identified a cohort of 1424 patients with a AAA, 1105 (77.6%) were male and the mean age was 74 years (±10). 76% of patients were white, 6% were black, 7% were Asian, 1% was Native American or Pacific Islander, and 11% identified as another race. At the time of data collection, 552 patients (39%) were under active surveillance, 346 patients (24%) had previously been under surveillance and subsequently lost to follow-up, and 523 patients (37%) had incidental findings without initiation of surveillance. In total, more than half of the cohort (869 patients, 61%) were not participating in recommended aneurysm surveillance. Findings did not differ significantly by gender, race, or ethnicity.

Conclusion: In the cohort, 39% of patients were compliant with recommended surveillance. Incorporating NLP into traditional screening and surveillance practices can allow providers to identify patients outside of recommended surveillance bridging gaps in care.