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J Am Med Inform Assoc. 2010 Sep-Oct;17(5):588-94. doi: 10.1136/jamia.2009.001396.

Under-documentation of chronic kidney disease in the electronic health record in outpatients.

Author information

1
Department of Biomedical Informatics, Columbia University, New York, NY, USA. herbert.chase@dbmi.columbia.edu

Abstract

OBJECTIVE:

To ascertain if outpatients with moderate chronic kidney disease (CKD) had their condition documented in their notes in the electronic health record (EHR).

DESIGN:

Outpatients with CKD were selected based on a reduced estimated glomerular filtration rate and their notes extracted from the Columbia University data warehouse. Two lexical-based classification tools (classifier and word-counter) were developed to identify documentation of CKD in electronic notes.

MEASUREMENTS:

The tools categorized patients' individual notes on the basis of the presence of CKD-related terms. Patients were categorized as appropriately documented if their notes contained reference to CKD when CKD was present.

RESULTS:

The sensitivities of the classifier and word-count methods were 95.4% and 99.8%, respectively. The specificity of both was 99.8%. Categorization of individual patients as appropriately documented was 96.9% accurate. Of 107 patients with manually verified moderate CKD, 32 (22%) lacked appropriate documentation. Patients whose CKD had not been appropriately documented were significantly less likely to be on renin-angiotensin system inhibitors or have urine protein quantified, and had the illness for half as long (15.1 vs 30.7 months; p<0.01) compared to patients with documentation.

CONCLUSION:

Our studies show that lexical-based classification tools can accurately ascertain if appropriate documentation of CKD is present in a EHR. Using this method, we demonstrated under-documentation of patients with moderate CKD. Under-documented patients were less likely to receive CKD guideline recommended care. A tool that prompts providers to document CKD might shorten the time to implementing guideline-based recommendations.

PMID:
20819869
PMCID:
PMC2995666
DOI:
10.1136/jamia.2009.001396
[Indexed for MEDLINE]
Free PMC Article

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