Unstructured clinical documentation reflecting cognitive and behavioral dysfunction: toward an EHR-based phenotype for cognitive impairment. (27th June 2018)

• Record Type: Journal Article
• Authors: Gilmore-Bykovskyi, Andrea L; Block, Laura M; Walljasper, Lily; Hill, Nikki; Gleason, Carey; Shah, Manish N
• Is Part Of: Journal of the American Medical Informatics Association. Volume 25:Number 9(2018)
• Page Start: 1206
• Page End: 1212
• Publication Date: 2018-06-27
• Subjects: text mining -- dementia -- cognitive impairment -- unstructured text -- narrative text; Medical informatics -- Periodicals; Information Services -- Periodicals; Medical Informatics -- Periodicals; Médecine -- Informatique -- Périodiques; Informatica; Geneeskunde; Informatique médicale; Computer network resources; Electronic journals; 610.285
• DOI: 10.1093/jamia/ocy070
• Languages: English
• ISSNs: 1067-5027

Abstract

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 (κ 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.