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Bias in Recording of Body Mass Index Data in the Electronic Health Record
The relationship between patient disease status and the presence or absence of body mass index (BMI) data in the electronic health record (EHR) has not been characterized. We conducted a descriptive study of the completeness of BMI data for three patient cohorts. Cross-sectional descriptions of BMI...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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American Medical Informatics Association
201
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3845755/ https://www.ncbi.nlm.nih.gov/pubmed/24303267 |
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author | Rea, Susan Bailey, Kent R. Pathak, Jyotishman Haug, Peter J. |
author_facet | Rea, Susan Bailey, Kent R. Pathak, Jyotishman Haug, Peter J. |
author_sort | Rea, Susan |
collection | PubMed |
description | The relationship between patient disease status and the presence or absence of body mass index (BMI) data in the electronic health record (EHR) has not been characterized. We conducted a descriptive study of the completeness of BMI data for three patient cohorts. Cross-sectional descriptions of BMI presence rates per patient were compared between a cohort having at least one ICD-9-CM code for diabetes mellitus (DM) versus a cohort with no diagnosis constraints. Conversely, frequencies of encounter diagnoses were compared among subgroups having BMI recorded or not in both cohorts described and a third cohort having DM codes from a separate organization’s EHR. The data demonstrate a correlation with presence of BMI and higher disease status. This effect may bias the cohort average BMIs, which appear higher than expected. When EHR BMI data are repurposed for research, biases in the selective recording of BMI may affect the results. |
format | Online Article Text |
id | pubmed-3845755 |
institution | National Center for Biotechnology Information |
language | English |
publishDate |
201 |
publisher |
American Medical Informatics Association
|
record_format | MEDLINE/PubMed |
spelling | pubmed-38457552013-12-03 Bias in Recording of Body Mass Index Data in the Electronic Health Record Rea, Susan Bailey, Kent R. Pathak, Jyotishman Haug, Peter J. AMIA Jt Summits Transl Sci Proc Articles The relationship between patient disease status and the presence or absence of body mass index (BMI) data in the electronic health record (EHR) has not been characterized. We conducted a descriptive study of the completeness of BMI data for three patient cohorts. Cross-sectional descriptions of BMI presence rates per patient were compared between a cohort having at least one ICD-9-CM code for diabetes mellitus (DM) versus a cohort with no diagnosis constraints. Conversely, frequencies of encounter diagnoses were compared among subgroups having BMI recorded or not in both cohorts described and a third cohort having DM codes from a separate organization’s EHR. The data demonstrate a correlation with presence of BMI and higher disease status. This effect may bias the cohort average BMIs, which appear higher than expected. When EHR BMI data are repurposed for research, biases in the selective recording of BMI may affect the results. American Medical Informatics Association 2013 -03- 18 /pmc/articles/PMC3845755/ /pubmed/24303267 Text en ©2013 AMIA - All rights reserved. |
spellingShingle | Articles Rea, Susan Bailey, Kent R. Pathak, Jyotishman Haug, Peter J. Bias in Recording of Body Mass Index Data in the Electronic Health Record |
title |
Bias in Recording of Body Mass Index Data in the Electronic Health Record
|
title_full |
Bias in Recording of Body Mass Index Data in the Electronic Health Record
|
title_fullStr |
Bias in Recording of Body Mass Index Data in the Electronic Health Record
|
title_full_unstemmed |
Bias in Recording of Body Mass Index Data in the Electronic Health Record
|
title_short |
Bias in Recording of Body Mass Index Data in the Electronic Health Record
|
title_sort | bias in recording of body mass index data in the electronic health record |
topic | Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3845755/ https://www.ncbi.nlm.nih.gov/pubmed/24303267 |
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