Cargando…

Temporal relationship of computed and structured diagnoses in electronic health record data

BACKGROUND: The electronic health record (EHR) holds the prospect of providing more complete and timely access to clinical information for biomedical research, quality assessments, and quality improvement compared to other data sources, such as administrative claims. In this study, we sought to asse...

Descripción completa

Detalles Bibliográficos
Autores principales: Schulz, Wade L., Young, H. Patrick, Coppi, Andreas, Mortazavi, Bobak J., Lin, Zhenqiu, Jean, Raymond A., Krumholz, Harlan M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7890604/
https://www.ncbi.nlm.nih.gov/pubmed/33596898
http://dx.doi.org/10.1186/s12911-021-01416-x
_version_ 1783652536621727744
author Schulz, Wade L.
Young, H. Patrick
Coppi, Andreas
Mortazavi, Bobak J.
Lin, Zhenqiu
Jean, Raymond A.
Krumholz, Harlan M.
author_facet Schulz, Wade L.
Young, H. Patrick
Coppi, Andreas
Mortazavi, Bobak J.
Lin, Zhenqiu
Jean, Raymond A.
Krumholz, Harlan M.
author_sort Schulz, Wade L.
collection PubMed
description BACKGROUND: The electronic health record (EHR) holds the prospect of providing more complete and timely access to clinical information for biomedical research, quality assessments, and quality improvement compared to other data sources, such as administrative claims. In this study, we sought to assess the completeness and timeliness of structured diagnoses in the EHR compared to computed diagnoses for hypertension (HTN), hyperlipidemia (HLD), and diabetes mellitus (DM). METHODS: We determined the amount of time for a structured diagnosis to be recorded in the EHR from when an equivalent diagnosis could be computed from other structured data elements, such as vital signs and laboratory results. We used EHR data for encounters from January 1, 2012 through February 10, 2019 from an academic health system. Diagnoses for HTN, HLD, and DM were computed for patients with at least two observations above threshold separated by at least 30 days, where the thresholds were outpatient blood pressure of ≥ 140/90 mmHg, any low-density lipoprotein ≥ 130 mg/dl, or any hemoglobin A1c ≥ 6.5%, respectively. The primary measure was the length of time between the computed diagnosis and the time at which a structured diagnosis could be identified within the EHR history or problem list. RESULTS: We found that 39.8% of those with HTN, 21.6% with HLD, and 5.2% with DM did not receive a corresponding structured diagnosis recorded in the EHR. For those who received a structured diagnosis, a mean of 389, 198, and 166 days elapsed before the patient had the corresponding diagnosis of HTN, HLD, or DM, respectively, recorded in the EHR. CONCLUSIONS: We found a marked temporal delay between when a diagnosis can be computed or inferred and when an equivalent structured diagnosis is recorded within the EHR. These findings demonstrate the continued need for additional study of the EHR to avoid bias when using observational data and reinforce the need for computational approaches to identify clinical phenotypes.
format Online
Article
Text
id pubmed-7890604
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-78906042021-02-22 Temporal relationship of computed and structured diagnoses in electronic health record data Schulz, Wade L. Young, H. Patrick Coppi, Andreas Mortazavi, Bobak J. Lin, Zhenqiu Jean, Raymond A. Krumholz, Harlan M. BMC Med Inform Decis Mak Research Article BACKGROUND: The electronic health record (EHR) holds the prospect of providing more complete and timely access to clinical information for biomedical research, quality assessments, and quality improvement compared to other data sources, such as administrative claims. In this study, we sought to assess the completeness and timeliness of structured diagnoses in the EHR compared to computed diagnoses for hypertension (HTN), hyperlipidemia (HLD), and diabetes mellitus (DM). METHODS: We determined the amount of time for a structured diagnosis to be recorded in the EHR from when an equivalent diagnosis could be computed from other structured data elements, such as vital signs and laboratory results. We used EHR data for encounters from January 1, 2012 through February 10, 2019 from an academic health system. Diagnoses for HTN, HLD, and DM were computed for patients with at least two observations above threshold separated by at least 30 days, where the thresholds were outpatient blood pressure of ≥ 140/90 mmHg, any low-density lipoprotein ≥ 130 mg/dl, or any hemoglobin A1c ≥ 6.5%, respectively. The primary measure was the length of time between the computed diagnosis and the time at which a structured diagnosis could be identified within the EHR history or problem list. RESULTS: We found that 39.8% of those with HTN, 21.6% with HLD, and 5.2% with DM did not receive a corresponding structured diagnosis recorded in the EHR. For those who received a structured diagnosis, a mean of 389, 198, and 166 days elapsed before the patient had the corresponding diagnosis of HTN, HLD, or DM, respectively, recorded in the EHR. CONCLUSIONS: We found a marked temporal delay between when a diagnosis can be computed or inferred and when an equivalent structured diagnosis is recorded within the EHR. These findings demonstrate the continued need for additional study of the EHR to avoid bias when using observational data and reinforce the need for computational approaches to identify clinical phenotypes. BioMed Central 2021-02-17 /pmc/articles/PMC7890604/ /pubmed/33596898 http://dx.doi.org/10.1186/s12911-021-01416-x Text en © The Author(s) 2021 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research Article
Schulz, Wade L.
Young, H. Patrick
Coppi, Andreas
Mortazavi, Bobak J.
Lin, Zhenqiu
Jean, Raymond A.
Krumholz, Harlan M.
Temporal relationship of computed and structured diagnoses in electronic health record data
title Temporal relationship of computed and structured diagnoses in electronic health record data
title_full Temporal relationship of computed and structured diagnoses in electronic health record data
title_fullStr Temporal relationship of computed and structured diagnoses in electronic health record data
title_full_unstemmed Temporal relationship of computed and structured diagnoses in electronic health record data
title_short Temporal relationship of computed and structured diagnoses in electronic health record data
title_sort temporal relationship of computed and structured diagnoses in electronic health record data
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7890604/
https://www.ncbi.nlm.nih.gov/pubmed/33596898
http://dx.doi.org/10.1186/s12911-021-01416-x
work_keys_str_mv AT schulzwadel temporalrelationshipofcomputedandstructureddiagnosesinelectronichealthrecorddata
AT younghpatrick temporalrelationshipofcomputedandstructureddiagnosesinelectronichealthrecorddata
AT coppiandreas temporalrelationshipofcomputedandstructureddiagnosesinelectronichealthrecorddata
AT mortazavibobakj temporalrelationshipofcomputedandstructureddiagnosesinelectronichealthrecorddata
AT linzhenqiu temporalrelationshipofcomputedandstructureddiagnosesinelectronichealthrecorddata
AT jeanraymonda temporalrelationshipofcomputedandstructureddiagnosesinelectronichealthrecorddata
AT krumholzharlanm temporalrelationshipofcomputedandstructureddiagnosesinelectronichealthrecorddata