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Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
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Redevelopment of the Predict: Breast Cancer website and recommendations for developing interfaces to support

George D Farmer1,2, Mike Pearson1, William J Skylark3

  • 1Winton Centre for Risk and Evidence Communication, University of Cambridge, Cambridge, UK.

Cancer Medicine
|June 21, 2021
PubMed
Summary

This study enhanced the Predict: Breast Cancer tool with a user-centered design, improving communication of prognostic information for breast cancer patients and clinicians. The new interface aids treatment decisions by providing clearer, more comprehensive results.

Keywords:
breast cancercancer managementprognosisrisk assessmenttranslational research

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Area of Science:

  • Oncology
  • Medical Informatics
  • Human-Computer Interaction

Background:

  • The Predict: Breast Cancer tool is widely used for prognostic assessment.
  • Effective communication of prognostic model outputs is crucial for patient and clinician decision-making.
  • Existing interfaces may not fully meet the needs of diverse users.

Purpose of the Study:

  • To develop an updated, user-centered interface for the Predict: Breast Cancer tool.
  • To improve the facilitation of treatment decisions for post-surgery breast cancer patients.
  • To establish recommendations for communicating prognostic model results.

Main Methods:

  • A user-centered design process involving background research.
  • Iterative prototype testing with breast cancer patients and clinicians.
  • Utilized surveys, focus groups, and usability testing.

Main Results:

  • The redesigned interface features enhanced visualizations, real-time results, and expanded predictors/outputs.
  • Six key recommendations for communicating prognostic model outputs were identified, emphasizing contextualization and user engagement.
  • Future research directions include quantifying adverse effects of adjuvant treatments.

Conclusions:

  • Well-designed interfaces are essential for prognostic algorithms to support clinical decision-making.
  • User-centered design prioritizes patient and clinician needs, maximizing the benefits of prognostic models.
  • Effective communication strategies are vital for translating complex data into actionable insights.