Identifying individuals with physician-diagnosed chronic obstructive pulmonary disease in primary care electronic medical records: a retrospective chart abstraction study
Chronic lung disease: Novel algorithm search technique Researchers develop an algorithm that can accurately search through electronic health records to find patients with chronic lung disease. Mining population-wide data for information on patients diagnosed and treated with chronic obstructive pulm...
Main Authors: | , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
Nature Publishing Group
2017-05-01
|
Series: | npj Primary Care Respiratory Medicine |
Online Access: | https://doi.org/10.1038/s41533-017-0035-9 |
id |
doaj-35467c188fcc4b4fba7c69d718d6e586 |
---|---|
record_format |
Article |
spelling |
doaj-35467c188fcc4b4fba7c69d718d6e5862020-12-07T23:54:15ZengNature Publishing Groupnpj Primary Care Respiratory Medicine2055-10102017-05-012711610.1038/s41533-017-0035-9Identifying individuals with physician-diagnosed chronic obstructive pulmonary disease in primary care electronic medical records: a retrospective chart abstraction studyTheresa M. Lee0Karen Tu1Laura L. Wing2Andrea S. Gershon3Institute of Health Policy, Management and Evaluation, University of Toronto, Dalla Lana School of Public HealthInstitute of Health Policy, Management and Evaluation, University of Toronto, Dalla Lana School of Public HealthInstitute for Clinical Evaluative SciencesInstitute of Health Policy, Management and Evaluation, University of Toronto, Dalla Lana School of Public HealthChronic lung disease: Novel algorithm search technique Researchers develop an algorithm that can accurately search through electronic health records to find patients with chronic lung disease. Mining population-wide data for information on patients diagnosed and treated with chronic obstructive pulmonary disease (COPD) in primary care could help inform future healthcare and spending practices. Theresa Lee at the University of Toronto, Canada, and colleagues used an algorithm to search electronic medical records and identify patients with COPD from doctors’ notes, prescriptions and symptom histories. They carefully adjusted the algorithm to improve sensitivity and predictive value by adding details such as specific medications, physician codes related to COPD, and different combinations of terminology in doctors’ notes. The team accurately identified 364 patients with COPD in a randomly-selected cohort of 5889 people. Their results suggest opportunities for broader, informative studies of COPD in wider populations.https://doi.org/10.1038/s41533-017-0035-9 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Theresa M. Lee Karen Tu Laura L. Wing Andrea S. Gershon |
spellingShingle |
Theresa M. Lee Karen Tu Laura L. Wing Andrea S. Gershon Identifying individuals with physician-diagnosed chronic obstructive pulmonary disease in primary care electronic medical records: a retrospective chart abstraction study npj Primary Care Respiratory Medicine |
author_facet |
Theresa M. Lee Karen Tu Laura L. Wing Andrea S. Gershon |
author_sort |
Theresa M. Lee |
title |
Identifying individuals with physician-diagnosed chronic obstructive pulmonary disease in primary care electronic medical records: a retrospective chart abstraction study |
title_short |
Identifying individuals with physician-diagnosed chronic obstructive pulmonary disease in primary care electronic medical records: a retrospective chart abstraction study |
title_full |
Identifying individuals with physician-diagnosed chronic obstructive pulmonary disease in primary care electronic medical records: a retrospective chart abstraction study |
title_fullStr |
Identifying individuals with physician-diagnosed chronic obstructive pulmonary disease in primary care electronic medical records: a retrospective chart abstraction study |
title_full_unstemmed |
Identifying individuals with physician-diagnosed chronic obstructive pulmonary disease in primary care electronic medical records: a retrospective chart abstraction study |
title_sort |
identifying individuals with physician-diagnosed chronic obstructive pulmonary disease in primary care electronic medical records: a retrospective chart abstraction study |
publisher |
Nature Publishing Group |
series |
npj Primary Care Respiratory Medicine |
issn |
2055-1010 |
publishDate |
2017-05-01 |
description |
Chronic lung disease: Novel algorithm search technique Researchers develop an algorithm that can accurately search through electronic health records to find patients with chronic lung disease. Mining population-wide data for information on patients diagnosed and treated with chronic obstructive pulmonary disease (COPD) in primary care could help inform future healthcare and spending practices. Theresa Lee at the University of Toronto, Canada, and colleagues used an algorithm to search electronic medical records and identify patients with COPD from doctors’ notes, prescriptions and symptom histories. They carefully adjusted the algorithm to improve sensitivity and predictive value by adding details such as specific medications, physician codes related to COPD, and different combinations of terminology in doctors’ notes. The team accurately identified 364 patients with COPD in a randomly-selected cohort of 5889 people. Their results suggest opportunities for broader, informative studies of COPD in wider populations. |
url |
https://doi.org/10.1038/s41533-017-0035-9 |
work_keys_str_mv |
AT theresamlee identifyingindividualswithphysiciandiagnosedchronicobstructivepulmonarydiseaseinprimarycareelectronicmedicalrecordsaretrospectivechartabstractionstudy AT karentu identifyingindividualswithphysiciandiagnosedchronicobstructivepulmonarydiseaseinprimarycareelectronicmedicalrecordsaretrospectivechartabstractionstudy AT lauralwing identifyingindividualswithphysiciandiagnosedchronicobstructivepulmonarydiseaseinprimarycareelectronicmedicalrecordsaretrospectivechartabstractionstudy AT andreasgershon identifyingindividualswithphysiciandiagnosedchronicobstructivepulmonarydiseaseinprimarycareelectronicmedicalrecordsaretrospectivechartabstractionstudy |
_version_ |
1724397017030983680 |