ArticleJMIR human factors2025
Knowledge Sharing Maturity Model for Medical Imaging Departments: Development Study.
Article in JMIR human factors, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundKnowledge sharing in medical imaging departments is driven by the need to improve health care services, develop health care professionals' skills, and reduce repetitive mistakes. It is considered an important step in the implementation of knowledge management solutions. By following a maturity model of knowledge sharing, knowledge-sharing practices can be improved.
objectiveThis study aimed to develop a maturity model for knowledge sharing in the medical imaging department to help managers to assess the level of maturity of knowledge-sharing practices. In modern health care institutions, improvements in health care professionals' skills and health care services are often driven through practicing knowledge-sharing behaviors. Managers can follow the indicators of maturity model of knowledge sharing and its measurements to identify the current level and move to the next level.
methodsThis study was conducted in 4 stages: an overview stage that highlighted the factors that affect knowledge-sharing practices in medical imaging departments; an analysis factor stage that was designed to assess the factors that affect knowledge sharing using a concurrent mixed methods approach (questionnaires and semistructured interviews) in 2 medical imaging departments; a structuring maturity model knowledge sharing stage, where a maturity model of knowledge sharing was developed based on the findings of the first and second stages; and finally, an assessment of reliability and validity stage, where a modified Delphi method was used to obtain consensus among experts on model components to be ready for implantation.
resultsThe model presented in this study includes 17 indicators, divided into 11 components. Those components were derived from the findings of the questionnaires and semistructured interviews that were applied in the medical imaging departments. It consisted of 5 maturity levels: initial, aware, defined, managed, and optimized. In each level, measurements were included to help managers assess the current level by answering the questions. On the basis of reliability, the experts reached a consensus agreement on the model's components in 2 rounds with SD <1.
conclusionsThis maturity model of knowledge sharing in medical imaging departments allows managers and policy makers to measure the maturity level of knowledge sharing in those departments. Although the model has been applied to medical imaging departments, it could easily be modified for application in other institutions.
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.