Literature DB >> 18998827

Identification of documented medication non-adherence in physician notes.

Alexander Turchin1, Holly I Wheeler, Matthew Labreche, Julia T Chu, Merri L Pendergrass, Jonathan S Einbinder, Jonathan Seth Einbinder.   

Abstract

Medication non-adherence is common and the physicians awareness of it may be an important factor in clinical decision making. Few sources of data on physician awareness of medication non-adherence are available. We have designed an algorithm to identify documentation of medication non-adherence in the text of physician notes. The algorithm recognizes eight semantic classes of documentation of medication non-adherence. We evaluated the algorithm against manual ratings of 200 randomly selected notes of hypertensive patients. The algorithm detected 89% of the notes with documented medication non-adherence with specificity of 84.7% and positive predictive value of 80.2%. In a larger dataset of 1,000 documents, notes that documented medication non-adherence were more likely to report significantly elevated systolic (15.3% vs. 9.0%; p = 0.002) and diastolic (4.1% vs. 1.9%; p = 0.03) blood pressure. This novel clinically validated tool expands the range of information on medication non-adherence available to researchers.

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Year:  2008        PMID: 18998827      PMCID: PMC2655985     

Source DB:  PubMed          Journal:  AMIA Annu Symp Proc        ISSN: 1559-4076


  17 in total

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8.  Physician-related barriers to the effective management of uncontrolled hypertension.

Authors:  Susan A Oliveria; Pablo Lapuerta; Bruce D McCarthy; Gilbert J L'Italien; Dan R Berlowitz; Steven M Asch
Journal:  Arch Intern Med       Date:  2002-02-25

9.  Variations in patients' adherence to medical recommendations: a quantitative review of 50 years of research.

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10.  Effect of board certification on antihypertensive treatment intensification in patients with diabetes mellitus.

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  5 in total

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4.  Improving the validity of determining medication adherence from electronic health record medications orders.

Authors:  Nikki M Carroll; Jennifer L Ellis; Capp F Luckett; Marsha A Raebel
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5.  Supervised embedding of textual predictors with applications in clinical diagnostics for pediatric cardiology.

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  5 in total

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