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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...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2023
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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. |
format | Online Article Text |
id | pubmed-10004557 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
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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