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Knowledge Sharing Maturity Model for Medical Imaging Departments: Development Study.

Maryam Almashmoum1,2, James Cunningham1, John Ainsworth1

  • 1Division of Informatics Imaging and Data Sciences, School of Health Sciences Faculty of Biology, Medicine, and Health, University of Manchester, Manchester, United Kingdom.

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|May 6, 2025
PubMed
Summary
This summary is machine-generated.

This study developed a maturity model to assess knowledge sharing in medical imaging departments, enabling managers to improve practices and healthcare services. The model offers 5 levels to guide progress toward optimized knowledge sharing.

Keywords:
KCCCKuwait Cancer Control Centercancer centersfactorsindicatorsknowledge managementknowledge sharingmaturity modelmeasurementmedical imaging departmentsthe Christie

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Area of Science:

  • Healthcare Management
  • Knowledge Management Systems
  • Medical Imaging Technology

Background:

  • Knowledge sharing is crucial for enhancing medical imaging services, professional development, and error reduction.
  • Implementing knowledge management solutions relies on effective knowledge sharing practices.
  • A maturity model can systematically improve knowledge-sharing practices within medical imaging departments.

Purpose of the Study:

  • To develop a maturity model for assessing knowledge sharing in medical imaging departments.
  • To provide managers with tools to evaluate current knowledge-sharing levels and identify areas for improvement.
  • To support the enhancement of healthcare professionals' skills and services through structured knowledge sharing.

Main Methods:

  • A four-stage study involving an overview of influencing factors, a mixed-methods analysis (questionnaires, interviews) of these factors in two departments.
  • Development of a knowledge sharing maturity model based on empirical findings.
  • Reliability and validity assessment using a modified Delphi method with expert consensus.

Main Results:

  • A 5-level maturity model (initial, aware, defined, managed, optimized) with 17 indicators across 11 components was developed.
  • The model components were derived from questionnaire and interview data from medical imaging departments.
  • Expert consensus was achieved through a modified Delphi method, confirming the model's reliability (SD <1).

Conclusions:

  • The developed maturity model enables managers and policymakers to measure and enhance knowledge sharing in medical imaging.
  • The model is adaptable and can be applied to other healthcare institutions beyond medical imaging departments.