ArticleBMC health services research2018
Knowledge translation and health technology reassessment: identifying synergy.
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.
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
15 citing papers in PubMed, 28 citations in OpenAlex.
- From product to knowledge: the life cycle of health technologies 2.0 as a framework for health system improvement.Frontiers in pharmacology · 2026Article
- Building capacity in horizon scanning, early awareness, and disinvestment: a framework for education and training.International journal of technology assessment in health care · 2025Article
- Editorial: Translation of health technology reassessment and its outputs of disinvestment, de-implementation and de-adoption of drugs and medical devices in practice-what have we learned?Frontiers in pharmacology · 2025Article
- Evidence is not enough: health technology reassessment to de-implement low-value care.Health research policy and systems · 2024Article
- Deliberative process of health technology reassessment by health technology assessment agency in Korea.International journal of technology assessment in health care · 2024Article
- Cost-effectiveness of intraoperative radiation therapy versus intensity-modulated radiation therapy for the treatment of early breast cancer: a disinvestment analysis.BMC health services research · 2024Article
- From bench to policy: a critical analysis of models for evidence-informed policymaking in healthcare.Frontiers in public health · 2024Review
- How health technology reassessment can support disinvestment in China's national drug reimbursement list.BMJ (Clinical research ed.) · 2023Article
- The added value of applying a disinvestment approach to the process of health technology assessment in Italy.International journal of technology assessment in health care · 2023Article
- How Least Developed to Lower-Middle Income Countries Use Health Technology Assessment: A Scoping Review.Pathogens and global health · 2023Article
- Developing a How-to-Guide for Health Technology Reassessment: "The HTR Playbook".International journal of health policy and management · 2022Article
- Article
- Characteristics of knowledge translation theories, models and frameworks for health technology reassessment: expert perspectives through a qualitative exploration.BMC health services research · 2021Article
- Community Stakeholders' Perspectives on Introducing Human Papillomavirus Vaccination and Biobanking Evidence-Based Programs Within Medically Underserved Communities: A Community-Engaged Approach.International quarterly of community health education · 2021Article
- A scoping review of full-spectrum knowledge translation theories, models, and frameworks.Implementation science : IS · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
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.
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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.