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Documentation in Long-Term and Home Healthcare Setting

Documentation in long-term care facilities and home healthcare settings is crucial for ensuring continuous, coordinated, and comprehensive care for patients. Each setting has its specific documentation processes and tools:
Long-Term Care Facilities

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A Novel Privacy Preservation and Quantification Methodology for Implementing Home-Care-Oriented Movement Analysis

Pablo Aqueveque1, Britam Gómez1, Patricia A H Williams2

  • 1Electrical Engineering Department, Universidad de Concepción, Concepción 4070409, Chile.

Sensors (Basel, Switzerland)
|July 9, 2022
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Summary

This study introduces a novel privacy-preserving method for analyzing human movement data, inspired by memory

Keywords:
gait analysisinertial movement unitsmovement analysispatient-centric healthcareprivacy preservationprivacy quantificationprivacy risksrisk of falls

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Area of Science:

  • Biomedical Engineering
  • Health Informatics
  • Human-Computer Interaction

Background:

  • Traditional human movement analysis for motor control assessment faces privacy challenges in remote or home settings.
  • Existing technological systems for motion analysis raise privacy concerns, limiting their application in patient-centric healthcare.
  • The need for secure and effective methods for monitoring patient movement outside clinical environments is critical.

Purpose of the Study:

  • To present a novel privacy preservation and quantification methodology for patient-centric healthcare.
  • To develop a distributed and disposable (DnD) data analytic approach to minimize patient health data disclosure.
  • To create a risk-driven privacy quantification framework and a home-care movement analysis system for validation.

Main Methods:

  • Implemented a privacy preservation methodology mimicking human memory's forgetting process.
  • Developed a distributed and disposable (DnD) data analytic framework.
  • Created a home-care movement analysis system with an inertial measurement sensor and mobile application.
  • Established a risk-driven privacy quantification framework.
  • Conducted a technological appreciation survey with 16 health professionals.

Main Results:

  • The proposed methodology effectively minimizes the disclosure of patients' health data through a DnD approach.
  • The developed privacy quantification framework aids in assessing the efficacy of DnD-based privacy preservation.
  • The home-care movement analysis system successfully acquires movement data and fall/gait risk indexes.
  • Health professionals provided positive feedback on the system and methodology for home-care suitability.

Conclusions:

  • The novel privacy preservation and quantification methodology is effective for patient-centric healthcare.
  • The distributed and disposable (DnD) approach offers a viable solution to privacy concerns in human movement analysis.
  • The home-care movement analysis system and methodology are perceived as suitable for real-world application in home-care settings.