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Related Experiment Videos

Automated Fall Detection Technology in Inpatient Geriatric Psychiatry: Nurses' Perceptions and Lessons Learned.

Marge Coahran1, Loretta M Hillier2, Lisa Van Bussel2

  • 1Toronto Rehabilitation Institute,Toronto.

Canadian Journal on Aging = La Revue Canadienne Du Vieillissement
|July 4, 2018
PubMed
Summary

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This summary is machine-generated.

The HELPER system, a fall detection technology for hospitalized older adults, showed high sensitivity but a low positive predictive value due to false alarms. Nurse feedback highlighted practical insights for improving health technologies.

Area of Science:

  • Gerontology
  • Health Informatics
  • Medical Technology

Background:

  • Hospitalized older adults face a significant risk of falls, necessitating effective monitoring solutions.
  • Existing fall detection methods may have limitations in real-world hospital settings.

Purpose of the Study:

  • To evaluate the performance of the HELPER system, a ceiling-mounted fall detection device.
  • To assess the accuracy of the HELPER system against documented hospital fall records.
  • To gather qualitative feedback from nurses on the system's usability and effectiveness.

Main Methods:

  • Pilot testing of the HELPER system in a geriatric mental health hospital.
  • Quantitative analysis of fall detection accuracy (sensitivity, positive predictive value).
Keywords:
agingdétection des chutesfall detectiongeriatric psychiatryinpatient carenursingpsychiatrie gériatriquesoins hospitalierssoins infirmierstechnologietechnologyvieillissement

Related Experiment Videos

  • Qualitative interviews with nurses to understand their perceptions and experiences with the technology.

    • Main Results:

    • The HELPER system demonstrated high sensitivity (.80) in detecting falls.
    • The system missed one documented fall but detected four undocumented falls.
    • A high number of false alarms resulted in a low positive predictive value (.01).

    Conclusions:

    • The HELPER system shows promise for fall detection in older adults, evidenced by its high sensitivity.
    • Frequent false alarms require significant improvement for practical clinical implementation.
    • Nurse feedback is crucial for refining health and social care technologies for real-world use.