ArticleJournal of general internal medicine2010
Use of health information technology to advance evidence-based care: lessons from the VA QUERI program.
Article in Journal of general internal medicine, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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.
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.
Who cites it
6 citing papers in PubMed.
- Bibliometric Analysis of Health Technology Research: 1990~2020.International journal of environmental research and public health · 2022Article
- Diffusion, implementation, and use of Research Electronic Data Capture (REDCap) in the Veterans Health Administration (VA).JAMIA open · 2019Article
- Importance of a Travel History in Evaluation of Respiratory Infections.Current emergency and hospital medicine reports · 2016Review
- Defining core issues in utilizing information technology to improve access: evaluation and research agenda.Journal of general internal medicine · 2011Article
- Connecting research and patient care: lessons from the VA's Quality Enhancement Research Initiative.Journal of general internal medicine · 2010Article
- What can the VA teach us about implementing proven advances into routine clinical practice?Journal of general internal medicine · 2010Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAs the Department of Veterans Affairs (VA) Health Services Research and Development Service's Quality Enhancement Research Initiative (QUERI) has progressed, health information technology (HIT) has occupied a crucial role in implementation research projects.
objectivesWe evaluated the role of HIT in VA QUERI implementation research, including HIT use and development, the contributions implementation research has made to HIT development, and HIT-related barriers and facilitators to implementation research.
participantsKey informants from nine disease-specific QUERI Centers. APPROACH: Documentation analysis of 86 implementation project abstracts followed up by semi-structured interviews with key informants from each of the nine QUERI centers. We used qualitative and descriptive analyses.
resultsWe found: (1) HIT provided data and information to facilitate implementation research, (2) implementation research helped to further HIT development in a variety of uses including the development of clinical decision support systems (23 of 86 implementation research projects), and (3) common HIT barriers to implementation research existed but could be overcome by collaborations with clinical and administrative leadership.
conclusionsOur review of the implementation research progress in the VA revealed interdependency on an HIT infrastructure and research-based development. Collaboration with multiple stakeholders is a key factor in successful use and development of HIT in implementation research efforts and in advancing evidence-based practice.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.