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Published in: BMC Medical Informatics and Decision Making 1/2021

01-12-2021 | Hypertension | Research article

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

Authors: Wade L. Schulz, H. Patrick Young, Andreas Coppi, Bobak J. Mortazavi, Zhenqiu Lin, Raymond A. Jean, Harlan M. Krumholz

Published in: BMC Medical Informatics and Decision Making | Issue 1/2021

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Abstract

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.
Literature
8.
11.
go back to reference Mitchell JB, Bubolz T, Paul JE, et al. Using Medicare claims for outcomes research. Med. Care 1994;32(7 Suppl):JS38–51. Mitchell JB, Bubolz T, Paul JE, et al. Using Medicare claims for outcomes research. Med. Care 1994;32(7 Suppl):JS38–51.
23.
go back to reference Singer A, Kroeker AL, Yakubovich S, Duarte R, Dufault B, Katz A. Data quality in electronic medical records in Manitoba: Do problem lists reflect chronic disease as defined by prescriptions? Can Fam Physician. 2017;63(5):382–9.PubMedPubMedCentral Singer A, Kroeker AL, Yakubovich S, Duarte R, Dufault B, Katz A. Data quality in electronic medical records in Manitoba: Do problem lists reflect chronic disease as defined by prescriptions? Can Fam Physician. 2017;63(5):382–9.PubMedPubMedCentral
25.
go back to reference Szeto HC, Coleman RK, Gholami P, Hoffman BB, Goldstein MK. Accuracy of computerized outpatient diagnoses in a Veterans Affairs general medicine clinic. Am J Manag Care. 2002;8(1):37–43.PubMed Szeto HC, Coleman RK, Gholami P, Hoffman BB, Goldstein MK. Accuracy of computerized outpatient diagnoses in a Veterans Affairs general medicine clinic. Am J Manag Care. 2002;8(1):37–43.PubMed
40.
go back to reference Weiner MG, Embi PJ. Toward reuse of clinical data for research and quality improvement: the end of the beginning? Ann Intern Med. 2009;151(5):359–60.CrossRefPubMed Weiner MG, Embi PJ. Toward reuse of clinical data for research and quality improvement: the end of the beginning? Ann Intern Med. 2009;151(5):359–60.CrossRefPubMed
42.
go back to reference Golladay KK, Collins AB, Ashcraft A, et al. Adverse Events in Hospitals: Methods for Identifying Events. Department of Health and Human Services; 2010:60. Golladay KK, Collins AB, Ashcraft A, et al. Adverse Events in Hospitals: Methods for Identifying Events. Department of Health and Human Services; 2010:60.
45.
go back to reference Hripcsak G, Duke JD, Shah NH, et al. Observational health data sciences and informatics (OHDSI): opportunities for observational researchers. Stud Health Technol Inform. 2015;216:574–8.PubMedPubMedCentral Hripcsak G, Duke JD, Shah NH, et al. Observational health data sciences and informatics (OHDSI): opportunities for observational researchers. Stud Health Technol Inform. 2015;216:574–8.PubMedPubMedCentral
Metadata
Title
Temporal relationship of computed and structured diagnoses in electronic health record data
Authors
Wade L. Schulz
H. Patrick Young
Andreas Coppi
Bobak J. Mortazavi
Zhenqiu Lin
Raymond A. Jean
Harlan M. Krumholz
Publication date
01-12-2021
Publisher
BioMed Central
Published in
BMC Medical Informatics and Decision Making / Issue 1/2021
Electronic ISSN: 1472-6947
DOI
https://doi.org/10.1186/s12911-021-01416-x

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