Unstructured clinical documentation reflecting cognitive and behavioral dysfunction: Toward an EHR-based phenotype for cognitive impairment

Andrea L. Gilmore-Bykovskyi, Laura M. Block, Lily Walljasper, Nikki Hill, Carey Gleason, Manish N. Shah

Research output: Contribution to journalArticlepeer-review

Abstract

Despite increased risk for negative outcomes, cognitive impairment (CI) is greatly under-detected during hospitalization. While automated EHR-based phenotypes have potential to improve recognition of CI, they are hindered by widespread under-diagnosis of underlying etiologies such as dementia-limiting the utility of more precise structured data elements. This study examined unstructured data on symptoms of CI in the acute-care EHRs of hip and stroke fracture patients with dementia from two hospitals. Clinician reviewers identified and classified unstructured EHR data using standardized criteria. Relevant narrative text was descriptively characterized and evaluated for key terminology. Most patient EHRs (90%) had narrative text reflecting cognitive and/or behavioral dysfunction common in CI that were reliably classified (j 0.82). The majority of statements reflected vague descriptions of cognitive/behavioral dysfunction as opposed to diagnostic terminology. Findings from this preliminary derivation study suggest that clinicians use specific terminology in unstructured EHR fields to describe common symptoms of CI. This terminology can inform the design of EHR-based phenotypes for CI and merits further investigation in more diverse, robustly characterized samples.

Original languageEnglish (US)
Pages (from-to)1206-1212
Number of pages7
JournalJournal of the American Medical Informatics Association
Volume25
Issue number9
DOIs
StatePublished - Sep 1 2018

All Science Journal Classification (ASJC) codes

  • Health Informatics

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