Literature DB >> 21689215

Electronic health record identification of prediabetes and an assessment of unmet counselling needs.

Laura J Zimmermann1, Jason A Thompson, Stephen D Persell.   

Abstract

RATIONALE, AIMS AND
OBJECTIVES: Large clinical trials demonstrate that lifestyle modification can prevent or delay the onset of diabetes in those with prediabetes. However, recent National Health and Nutrition Survey data suggest that prediabetes often goes unrecognized, and the majority of prediabetic individuals do not report having received lifestyle advice from physicians. We explored whether electronic health record (EHR) query of glucose measurements can identify prediabetic patients, and we estimated rates of prediabetic lifestyle counselling in a large, urban, primary care practice.
METHODS: Electronic search identified patients with plasma glucose levels of 100 to 199 mg dL(-1) between 1 June 2007 and 1 June 2009, excluding those with diabetes or diabetic medications/supplies. From these 5366 patients, 100 randomly selected patients underwent classification into provisional categories based on available EHR data: likely prediabetes, likely diabetes, glucose abnormality in the setting of acute illness, or normal glucose metabolism. In those likely to have prediabetes, we assessed lifestyle modification counselling.
RESULTS: Fifty-eight per cent (95% CI 48% to 68%) of patients sampled were likely to have prediabetes. Fourteen per cent of those sampled were likely to have diabetes. Thirty-one per cent of prediabetics (95% CI 22% to 42%) had documented lifestyle counselling. Counselled patients had a significantly higher baseline mean body mass index compared to those not counselled (34.1 versus 29.9, P = 0.037).
CONCLUSIONS: EHR query using glucose measurements can identify prediabetic patients and those requiring further glucose metabolism evaluation, including those with undiagnosed diabetes. Future research should investigate EHR-based, population-level interventions to facilitate prediabetes recognition and counselling.
© 2011 Blackwell Publishing Ltd.

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Year:  2011        PMID: 21689215     DOI: 10.1111/j.1365-2753.2011.01703.x

Source DB:  PubMed          Journal:  J Eval Clin Pract        ISSN: 1356-1294            Impact factor:   2.431


  4 in total

1.  Automating assessment of lifestyle counseling in electronic health records.

Authors:  Brian L Hazlehurst; Jean M Lawrence; William T Donahoo; Nancy E Sherwood; Stephen E Kurtz; Stan Xu; John F Steiner
Journal:  Am J Prev Med       Date:  2014-05       Impact factor: 5.043

2.  Did the 2015 USPSTF Abnormal Blood Glucose Recommendations Change Clinician Attitudes or Behaviors? A Mixed-Method Assessment.

Authors:  Tainayah W Thomas; Carol E Golin; Alan C Kinlaw; M Sue Kirkman; Shelley D Golden; Alexandra F Lightfoot; Carmen D Samuel-Hodge
Journal:  J Gen Intern Med       Date:  2021-04-07       Impact factor: 5.128

3.  Electronic Health Record Data Versus the National Health and Nutrition Examination Survey (NHANES): A Comparison of Overweight and Obesity Rates.

Authors:  Luke M Funk; Ying Shan; Corrine I Voils; John Kloke; Lawrence P Hanrahan
Journal:  Med Care       Date:  2017-06       Impact factor: 3.178

4.  Race and gender differences in abnormal blood glucose screening and clinician response to prediabetes: A mixed-methods assessment.

Authors:  Tainayah W Thomas; Carol Golin; Carmen D Samuel-Hodge; M Sue Kirkman; Shelley D Golden; Alexandra F Lightfoot
Journal:  Prev Med       Date:  2021-04-27       Impact factor: 4.637

  4 in total

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