Evidence map›Paper›PMID 25278645›Full record

ArticleThe Canadian journal of nursing research = Revue canadienne de recherche en sciences infirmieres2013

Practice-Based Knowledge Discovery for Comparative Effectiveness Research: An Organizing Framework.

Robert J Lucero, Suzanne Bakken

Abstract read
In one paragraph

Article in The Canadian journal of nursing research = Revue canadienne de recherche en sciences infirmieres, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–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

1 citing paper in PubMed.

  1. Observational
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

2 authors.

Robert J LuceroAssistant Professor of Nursing Center for Evidence-Based Practice in the Underserved, School of Nursing, Columbia University, New York, New York, United States.
Suzanne BakkenAlumni Professor of Nursing, Professor of Biomedical Informatics, and Director Center for Evidence-Based Practice in the Underserved, School of Nursing, Columbia University.

Funding

RESEARCH COREP60MD000206 · NIMHD · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI LUCHSINGER, JOSE ALEJANDRO · 2003 to 2012
$16.2M
Washington Heights Initiative Community-based Comparative Effectiveness ResearchR01HS019853 · AHRQ · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI BAKKEN, SUZANNE · 2010 to 2010
$8.9M
Reducing Health Disparities Through Informatics - Genomics SupplementT32NR007969 · NINR · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI SUZANNE BAKKEN, Rebecca Schnall · 2002 to 2026
$7.9M
AHRQ HHS R01 HS019853NIMHD NIH HHS P60 MD000206NINR NIH HHS T32 NR007969
6 · The paper itself

Abstract

Electronic health information systems can increase the ability of health-care organizations to investigate the effects of clinical interventions. The authors present an organizing framework that integrates outcomes and informatics research paradigms to guide knowledge discovery in electronic clinical databases. They illustrate its application using the example of hospital acquired pressure ulcers (HAPU). The Knowledge Discovery through Informatics for Comparative Effectiveness Research (KDI-CER) framework was conceived as a heuristic to conceptualize study designs and address potential methodological limitations imposed by using a single research perspective. Advances in informatics research can play a complementary role in advancing the field of outcomes research including CER. The KDI-CER framework can be used to facilitate knowledge discovery from routinely collected electronic clinical data.

Indexed as

comparative effectivenessdata mininghealth servicesinformaticsknowledge generationresearch

Identifiers

PMID25278645
PMCPMC4179869

What OpenQuestion holds

Textmetadata
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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.