Evidence map›Paper›PMID 30165846›Full record

ArticleBMC health services research2018

Knowledge translation and health technology reassessment: identifying synergy.

Rosmin Esmail, Heather Hanson, Jayna Holroyd-Leduc, Daniel J Niven, Fiona Clement

Open access · goldAbstract read
In one paragraph

Article in BMC health services research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
7.5field-weighted citation impact, top 3% 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

15 citing papers in PubMed, 28 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Deliberative process of health technology reassessment by health technology assessment agency in Korea.International journal of technology assessment in health care · 2024
    Article
  6. Article
  7. Review
  8. Article
  9. Article
  10. Article
  11. Developing a How-to-Guide for Health Technology Reassessment: "The HTR Playbook".International journal of health policy and management · 2022
    Article
  12. Article
  13. Article
  14. Article
  15. 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

5 authors at 2 institutions in 2 countries.

Rosmin EsmailDepartment of Community Health Sciences, Cumming School of Medicine, University of Calgary, 3D14A Teaching and Wellness Building, 3280 Hospital Drive NW, Calgary, Alberta, T2N 4N1, Canada.
Heather HansonDepartment of Community Health Sciences, Cumming School of Medicine, University of Calgary, 3D14A Teaching and Wellness Building, 3280 Hospital Drive NW, Calgary, Alberta, T2N 4N1, Canada.
Jayna Holroyd-LeducDepartment of Community Health Sciences, Cumming School of Medicine, University of Calgary, 3D14A Teaching and Wellness Building, 3280 Hospital Drive NW, Calgary, Alberta, T2N 4N1, Canada.
Daniel J NivenDepartment of Community Health Sciences, Cumming School of Medicine, University of Calgary, 3D14A Teaching and Wellness Building, 3280 Hospital Drive NW, Calgary, Alberta, T2N 4N1, Canada.
Fiona ClementDepartment of Community Health Sciences, Cumming School of Medicine, University of Calgary, 3D14A Teaching and Wellness Building, 3280 Hospital Drive NW, Calgary, Alberta, T2N 4N1, Canada. fclement@ucalgary.ca.
Alberta Health Services · CAUniversity of Calgary · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundHealth Technology Reassessment (HTR) is an emerging field that shifts the focus from traditional methods of technology adoption to managing technology throughout its lifecycle. HTR is a mechanism to improve patient care and system efficiency through a reallocation of resources away from low-value care towards interventions and technologies that are high value. To achieve this, the outputs of HTR and its recommendations must be translated into practice. The evolving field of knowledge translation (KT) can provide guidance to improve the uptake of evidence-informed policies and recommendations resulting from the process of HTR. This paper argues how the theories, models and frameworks from KT could advance the HTR process. DISCUSSION: First, common KT theories, models and frameworks are presented. Second, facilitators and barriers to KT within the context of HTR are summarized from the literature. Facilitators and barriers to KT include ensuring a solid research evidence-base for the technology under reassessment, assessing the climate and context, understanding the social an political context, initiating linkage and exchange, having a structured HTR Process, adequate resources, and understanding the roles of researchers, knowledge users, and stakeholders can enhance knowledge translation of HTR outputs. Third, three case examples at the individual (micro), organizational (meso), and policy (macro) levels are used to illustrate to describe how a KT theory, model or framework could be applied to a HTR project. These case studies show how selecting and applying KT theories, models and frameworks can facilitate the implementation of HTR recommendations.

conclusionHTR and KT are synergistic processes that can be used to optimize technology use throughout its lifecycle. We argue that the application of KT theories, models and frameworks, and the assessment of barriers and facilitators to KT can facilitate translation of HTR recommendations into practice.

Indexed as

Biomedical TechnologyHealth Knowledge, Attitudes, PracticeHealth PolicyHealth ResourcesHumansResearch PersonnelTechnology Assessment, BiomedicalTranslational Research, BiomedicalDisinvestmentHealth technology reassessmentImplementationKnowledge translation

Identifiers

PMID30165846
PMCPMC6117899
OpenAlexW2889489343

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.