Extracting Cognitive Impairment Assessment Information From Unstructured Notes in Electronic Health Records Using Natural Language Processing Tools: Validation with Clinical Assessment Data.

PURPOSE

We aimed to develop a Natural Language Processing (NLP) algorithm to extract cognitive scores from electronic health records (EHR) data and compare them with cognitive function recorded by Centers for Medicare & Medicaid Services (CMS)-mandated clinical assessments in nursing homes and home health visits.

PATIENTS AND METHODS

We identified a cohort of Medicare beneficiaries who had either the Minimum Data Set (MDS) or Outcome and Assessment Information Set (OASIS) linked to EHR data from the Research Patient Data Registry (Mass General Brigham, Boston, MA) from 2010 to 2019. We applied an NLP approach to identify the Montreal Cognitive Assessment (MoCA) and the Mini-Mental State Examination (MMSE) scores from unstructured clinician notes in EHR. Using the NLP-extracted MoCA or MMSE scores from EHR, we compared mean differences of extracted MoCA or MMSE by cognition status determined by MDS (impaired vs intact cognition) and OASIS (severe impairment vs intact cognition) data, respectively.

RESULTS

Our study cohort had 7419 patients who had MDS (19.7%) or OASIS (80.3%) assessments, with a mean age of 80 (SD=7) years and 60% female. In EHR, the NLP algorithm extracted cognitive test scores with 97% accuracy (95% CI: 92-99%) for MoCA and 100% accuracy (95% CI: 84-100%) for MMSE. In MDS, the mean difference in extracted MoCA was -5.6 (95% CI: -8.7, -2.4, p=0.0008), and the mean difference in extracted MMSE was -7.9 (95% CI: -12.4, -3.5, p=0.0012). In OASIS, the mean difference in extracted MoCA and extracted MMSE was -4.8 (95% CI: -9.1, -0.6, p=0.0006) and -4.5 (95% CI: -9.5, -0.5, p=0.0182), respectively.

CONCLUSION

We developed an NLP algorithm to accurately extract cognitive scores from unstructured EHR, and these extracted cognitive scores were well correlated with cognition function recorded in CMS-mandated clinical assessments. This could help researchers identify patients with various degrees of cognitive impairment in EHR-based research.