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Methods of Documentation VI: Case Management Model01:15

Methods of Documentation VI: Case Management Model

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The case management model is a multidisciplinary approach that involves healthcare professionals from diverse disciplines, such as physicians, nurses, therapists, social workers, and pharmacists, working collaboratively to address the various needs of patients. Each healthcare professional brings unique expertise and perspectives, contributing to a more comprehensive understanding of the patient's condition and tailoring treatment plans accordingly.
For example, a patient with a chronic...
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Cocreating the Visualization of Digital Mobility Outcomes: Delphi-Type Process With Patients.

Jack Lumsdon1, Cameron Wilson2, Lisa Alcock3,4

  • 1Population Health Sciences Institute, Faculty of Medical Sciences, Newcastle University, Newcastle Upon Tyne, United Kingdom.

JMIR Formative Research
|May 9, 2025
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Summary
This summary is machine-generated.

Patient input is crucial for developing effective digital mobility data visualizations. Co-creating visual displays ensures they are understandable and meaningful for individuals managing long-term health conditions.

Keywords:
cocreationdata visualizationdigital mobility outcomesmobilitywearable devices

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Area of Science:

  • Digital Health
  • Wearable Technology
  • Data Visualization

Background:

  • Wearable devices offer new ways to measure real-world mobility.
  • The Mobilise-D project validated digital mobility outcomes for Parkinson disease, multiple sclerosis, COPD, and hip fracture.
  • Optimal visualization of patient mobility data is underexplored.

Purpose of the Study:

  • To identify meaningful mobility outcomes for specific long-term health conditions.
  • To determine the best methods for visualizing patient mobility data from an end-user perspective.

Main Methods:

  • A Delphi-type protocol with patients as experts was used over three questionnaire rounds.
  • Round 1 gathered qualitative feedback on mobility aspects influenced by health conditions.
  • Subsequent rounds involved co-creating and refining visualizations based on patient and expert feedback, rating usefulness and clarity.

Main Results:

  • Key outcomes like walking speed and step count were identified for different conditions.
  • Patient feedback guided the development of visualizations for mobility data.
  • While consensus on specific visualizations wasn't reached, participants generally found them understandable.

Conclusions:

  • Recommendations for future mobility data visualizations were developed.
  • Visualizations should prioritize readability, accessibility (e.g., for color blindness), and incorporate patient-specific factors.
  • Close patient collaboration is essential for creating meaningful and effective data representations.