Evidence map›Paper›PMID 21765829›Full record

ArticleJournal of cancer epidemiology2011

Evaluation of the Reliability of Electronic Medical Record Data in Identifying Comorbid Conditions among Patients with Advanced Non-Small Cell Lung Cancer.

Catherine E Muehlenbein, J Russell Hoverman, Stephen K Gruschkus, Michael Forsyth, Clara Chen, William Lopez, Anthony Lawson, Heather J Hartnett, Gerhardt Pohl

Open access · hybridAbstract read
In one paragraph

Article in Journal of cancer epidemiology, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
1.0field-weighted citation impact, top 23% of its field
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

2 citing papers in PubMed, 10 citations in OpenAlex.

  1. Article
  2. 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

9 authors at 3 institutions in 1 country.

Catherine E MuehlenbeinLilly Corporate Center, Eli Lilly and Company, Indianapolis, IN 46285, USA.
J Russell Hoverman
Stephen K Gruschkus
Michael Forsyth
Clara Chen
William Lopez
Anthony Lawson
Heather J Hartnett
Gerhardt Pohl
The US Oncology Network · USEli Lilly (United States) · USTexas Oncology · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background. Traditional methods for identifying comorbidity data in EMRs have relied primarily on costly and time-consuming manual chart review. The purpose of this study was to validate a strategy of electronically searching EMR data to identify comorbidities among cancer patients. Methods. Advanced stage NSCLC patients (N = 2,513) who received chemotherapy from 7/1/2006 to 6/30/2008 were identified using iKnowMed, US Oncology's proprietary oncology-specific EMR system. EMR data were searched for documentation of comorbidities common to advanced stage cancer patients. The search was conducted by a series of programmatic queries on standardized information including concomitant illnesses, patient history, review of systems, and diagnoses other than cancer. The validity of the comorbidity information that we derived from the EMR search was compared to the chart review gold standard in a random sample of 450 patients for whom the EMR search yielded no indication of comorbidities. Negative predictive values were calculated. Results. The overall prevalence of comorbidities of 22%. Overall negative predictive value was 0.92 in the 450 patients randomly sampled patients (36 of 450 were found to have evidence of comorbidities on chart review). Conclusion. Results of this study suggest that efficient queries/text searches of EMR data may provide reliable data on comorbid conditions among cancer patients.

Identifiers

PMID21765829
PMCPMC3134088
OpenAlexW2077368855

What OpenQuestion holds

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Registered trials

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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.