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Extracting patient-level data from the electronic health record: Expanding opportunities for health system research

BACKGROUND: Epidemiological studies of interstitial lung disease (ILD) are limited by small numbers and tertiary care bias. Investigators have leveraged the widespread use of electronic health records (EHRs) to overcome these limitations, but struggle to extract patient-level, longitudinal clinical...

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Autores principales: Farrand, Erica, Collard, Harold R., Guarnieri, Michael, Minowada, George, Block, Lawrence, Lee, Mei, Iribarren, Carlos
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10004557/
https://www.ncbi.nlm.nih.gov/pubmed/36897886
http://dx.doi.org/10.1371/journal.pone.0280342
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author Farrand, Erica
Collard, Harold R.
Guarnieri, Michael
Minowada, George
Block, Lawrence
Lee, Mei
Iribarren, Carlos
author_facet Farrand, Erica
Collard, Harold R.
Guarnieri, Michael
Minowada, George
Block, Lawrence
Lee, Mei
Iribarren, Carlos
author_sort Farrand, Erica
collection PubMed
description BACKGROUND: Epidemiological studies of interstitial lung disease (ILD) are limited by small numbers and tertiary care bias. Investigators have leveraged the widespread use of electronic health records (EHRs) to overcome these limitations, but struggle to extract patient-level, longitudinal clinical data needed to address many important research questions. We hypothesized that we could automate longitudinal ILD cohort development using the EHR of a large, community-based healthcare system. STUDY DESIGN AND METHODS: We applied a previously validated algorithm to the EHR of a community-based healthcare system to identify ILD cases between 2012–2020. We then extracted disease-specific characteristics and outcomes using fully automated data-extraction algorithms and natural language processing of selected free-text. RESULTS: We identified a community cohort of 5,399 ILD patients (prevalence = 118 per 100,000). Pulmonary function tests (71%) and serologies (54%) were commonly used in the diagnostic evaluation, whereas lung biopsy was rare (5%). IPF was the most common ILD diagnosis (n = 972, 18%). Prednisone was the most commonly prescribed medication (911, 17%). Nintedanib and pirfenidone were rarely prescribed (n = 305, 5%). ILD patients were high-utilizers of inpatient (40%/year hospitalized) and outpatient care (80%/year with pulmonary visit), with sustained utilization throughout the post-diagnosis study period. DISCUSSION: We demonstrated the feasibility of robustly characterizing a variety of patient-level utilization and health services outcomes in a community-based EHR cohort. This represents a substantial methodological improvement by alleviating traditional constraints on the accuracy and clinical resolution of such ILD cohorts; we believe this approach will make community-based ILD research more efficient, effective, and scalable.
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spelling pubmed-100045572023-03-11 Extracting patient-level data from the electronic health record: Expanding opportunities for health system research Farrand, Erica Collard, Harold R. Guarnieri, Michael Minowada, George Block, Lawrence Lee, Mei Iribarren, Carlos PLoS One Research Article BACKGROUND: Epidemiological studies of interstitial lung disease (ILD) are limited by small numbers and tertiary care bias. Investigators have leveraged the widespread use of electronic health records (EHRs) to overcome these limitations, but struggle to extract patient-level, longitudinal clinical data needed to address many important research questions. We hypothesized that we could automate longitudinal ILD cohort development using the EHR of a large, community-based healthcare system. STUDY DESIGN AND METHODS: We applied a previously validated algorithm to the EHR of a community-based healthcare system to identify ILD cases between 2012–2020. We then extracted disease-specific characteristics and outcomes using fully automated data-extraction algorithms and natural language processing of selected free-text. RESULTS: We identified a community cohort of 5,399 ILD patients (prevalence = 118 per 100,000). Pulmonary function tests (71%) and serologies (54%) were commonly used in the diagnostic evaluation, whereas lung biopsy was rare (5%). IPF was the most common ILD diagnosis (n = 972, 18%). Prednisone was the most commonly prescribed medication (911, 17%). Nintedanib and pirfenidone were rarely prescribed (n = 305, 5%). ILD patients were high-utilizers of inpatient (40%/year hospitalized) and outpatient care (80%/year with pulmonary visit), with sustained utilization throughout the post-diagnosis study period. DISCUSSION: We demonstrated the feasibility of robustly characterizing a variety of patient-level utilization and health services outcomes in a community-based EHR cohort. This represents a substantial methodological improvement by alleviating traditional constraints on the accuracy and clinical resolution of such ILD cohorts; we believe this approach will make community-based ILD research more efficient, effective, and scalable. Public Library of Science 2023-03-10 /pmc/articles/PMC10004557/ /pubmed/36897886 http://dx.doi.org/10.1371/journal.pone.0280342 Text en © 2023 Farrand et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Farrand, Erica
Collard, Harold R.
Guarnieri, Michael
Minowada, George
Block, Lawrence
Lee, Mei
Iribarren, Carlos
Extracting patient-level data from the electronic health record: Expanding opportunities for health system research
title Extracting patient-level data from the electronic health record: Expanding opportunities for health system research
title_full Extracting patient-level data from the electronic health record: Expanding opportunities for health system research
title_fullStr Extracting patient-level data from the electronic health record: Expanding opportunities for health system research
title_full_unstemmed Extracting patient-level data from the electronic health record: Expanding opportunities for health system research
title_short Extracting patient-level data from the electronic health record: Expanding opportunities for health system research
title_sort extracting patient-level data from the electronic health record: expanding opportunities for health system research
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10004557/
https://www.ncbi.nlm.nih.gov/pubmed/36897886
http://dx.doi.org/10.1371/journal.pone.0280342
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