Evidence map›Paper›PMID 28507288›Full record

ArticleNPJ primary care respiratory medicine2017

Identifying individuals with physician-diagnosed chronic obstructive pulmonary disease in primary care electronic medical records: a retrospective chart abstraction study.

Theresa M Lee, Karen Tu, Laura L Wing, Andrea S Gershon

Abstract readValidation Study
In one paragraph

Article in NPJ primary care respiratory medicine, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

11 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Observational
  11. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Theresa M LeeInstitute of Health Policy, Management and Evaluation, University of Toronto, Dalla Lana School of Public Health, 155 College Street, Suite 425, Toronto, ON, M5T 3M6, Canada. tmh.lee@mail.utoronto.ca.ORCID http://orcid.org/0000-0003-0201-8127
Karen TuInstitute of Health Policy, Management and Evaluation, University of Toronto, Dalla Lana School of Public Health, 155 College Street, Suite 425, Toronto, ON, M5T 3M6, Canada.
Laura L WingInstitute for Clinical Evaluative Sciences, 2075 Bayview Avenue, G1 06, Toronto, ON, M4N 3M5, Canada.
Andrea S GershonInstitute of Health Policy, Management and Evaluation, University of Toronto, Dalla Lana School of Public Health, 155 College Street, Suite 425, Toronto, ON, M5T 3M6, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Little is known about using electronic medical records to identify patients with chronic obstructive pulmonary disease to improve quality of care. Our objective was to develop electronic medical record algorithms that can accurately identify patients with obstructive pulmonary disease. A retrospective chart abstraction study was conducted on data from the Electronic Medical Record Administrative data Linked Database (EMRALD 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.

Indexed as

AlgorithmsElectronic Health RecordsPrimary Health CareAdultAgedCanadaDatabases, FactualFemaleHumansMaleMiddle AgedPulmonary Disease, Chronic ObstructiveRetrospective StudiesSensitivity and Specificity

Identifiers

PMID28507288
PMCPMC5435091

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.