Dataset Information

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

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 (? 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.

SUBMITTER: Gilmore-Bykovskyi AL

PROVIDER: S-EPMC6118865 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature

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

Gilmore-Bykovskyi Andrea L AL Block Laura M LM Walljasper Lily L Hill Nikki N Gleason Carey C Shah Manish N MN

Journal of the American Medical Informatics Association : JAMIA 20180901 9

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 hospi ...[more]

PMID: 29947805

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Project description:IntroductionMild cognitive impairment (MCI) is a significant public health concern and a potential precursor to Alzheimer's disease (AD). This study leverages electronic health record (EHR) data to explore rural-urban differences in MCI incidence, risk factors, and healthcare navigation in West Michigan.MethodsAnalysis was conducted on 1,528,464 patients from Corewell Health West, using face-to-face encounters between 1/1/2015 and 7/31/2022. MCI cases were identified using International Classification of Diseases (ICD) codes, focusing on patients aged 45+ without prior MCI, dementia, or AD diagnoses. Incidence rates, cumulative incidences, primary care physicians (PCPs), and neuropsychology referral outcomes were examined across rural and urban areas. Risk factors were evaluated through univariate and multivariate Cox regression analyses. The geographic distribution of patient counts, hospital locations, and neurology department referrals were examined.ResultsAmong 423,592 patients, a higher MCI incidence rate was observed in urban settings compared to rural settings (3.83 vs. 3.22 per 1,000 person-years). However, sensitivity analysis revealed higher incidence rates in rural areas when including patients who progressed directly to dementia. Urban patients demonstrated higher rates of referrals to and completion of neurological services. While the risk factors for MCI were largely similar across urban and rural populations, urban-specific factors for incident MCI are hearing loss, inflammatory bowel disease, obstructive sleep apnea, insomnia, being African American, and being underweight. Common risk factors include diabetes, intracranial injury, cerebrovascular disease, coronary artery disease, stroke, Parkinson's disease, epilepsy, chronic obstructive pulmonary disease, depression, and increased age. Lower risk was associated with being female, having a higher body mass index, and having a higher diastolic blood pressure.DiscussionThis study highlights rural-urban differences in MCI incidence and access to care, suggesting potential underdiagnosis in rural areas likely due to reduced access to specialists. Future research should explore socioeconomic, environmental, and lifestyle determinants of MCI to refine prevention and management strategies across geographic settings.HighlightsLeveraged EHRs to explore rural-urban differences in MCI in West Michigan.Revealed a significant underdiagnosis of MCI, especially in rural areas.Observed lower rates of neurological referrals and completions for rural patients.Identified risk factors specific to rural and urban populations.

Project description:To evaluate whether race influences agreement between screening results and documentation of cognitive impairment and delirium.Secondary data analysis.An urban, public hospital and healthcare system.Hospitalized older adults aged 65 and older admitted to general inpatient medical services evaluated for cognitive impairment (n = 851) and evaluated for delirium (n = 424).Cognitive impairment and delirium were measured in each participant using the Short Portable Mental Status Questionnaire (SPMSQ) and the Confusion Assessment Method (CAM), respectively, as the reference identification method. Clinical documentation of cognitive impairment and delirium was defined according to the presence of International Classification of Diseases, Ninth Revision (ICD-9), codes from within 1 year before hospitalization through discharge for cognitive impairment or from hospital admission through discharge for delirium.Two hundred ninety-four participants (34%) had cognitive impairment based on SPMSQ performance, and 163 (38%) had delirium based on CAM results. One hundred seventy-one (20%) of those with cognitive impairment had an ICD-9 code for cognitive impairment, whereas 92 (22%) of those with delirium had an ICD-9 code for delirium. After considering age, sex, education, socioeconomic status, chronic comorbidity, and severity of acute illness, of those who screened positive on the SPMSQ, African Americans had a higher adjusted odds ratio (AOR) than non-African Americans for clinical documentation of cognitive impairment (AOR = 1.66, 95% confidence interval (CI) = 0.95-2.89), and of those who screened negative on the SPMSQ, African Americans had higher odds of clinical documentation of cognitive impairment (AOR = 2.10, 95% CI = 1.17-3.78) than non-African Americans. There were no differences in clinical documentation rates of delirium between African Americans and non-African Americans.Racial differences in coding for cognitive impairment may exist, resulting in higher documentation of cognitive impairment in African Americans screening positive or negative for cognitive impairment.

Dataset Information

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

Publications

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

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