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

  • Marge Coahran (a1), Loretta M. Hillier (a2), Lisa Van Bussel (a2), Edward Black (a2), Rebekah Churchyard (a3), Iris Gutmanis (a4), Yani Ioannou (a5), Kathleen Michael (a2), Tom Ross (a2) and Alex Mihailidis (a6)...

Abstract

Hospitalized older adults are at high risk of falling. The HELPER system is a ceiling-mounted fall detection system that sends an alert to a smartphone when a fall is detected. This article describes the performance of the HELPER system, which was pilot tested in a geriatric mental health hospital. The system’s accuracy in detecting falls was measured against the hospital records documenting falls. Following the pilot test, nurses were interviewed regarding their perceptions of this technology. In this study, the HELPER system missed one documented fall but detected four falls that were not documented. Although sensitivity (.80) of the system was high, numerous false alarms brought down positive predictive value (.01). Interviews with nurses provided valuable insights based on the operation of the technology in a real environment; these and other lessons learned will be particularly valuable to engineers developing this and other health and social care technologies.

Les personnes âgées hospitalisées présentent un haut risque de chute. Le système HELPER est un système de détection des chutes fixé au plafond qui envoie une alerte à un téléphone intelligent lorsqu’une chute est détectée. Cet article décrit la performance du système HELPER, qui a été testé dans un projet pilote mené dans un centre de santé mentale gériatrique. La précision du système pour la détection des chutes a été comparée aux données de l’hôpital liées à la documentation des chutes. Au terme du projet pilote, le personnel infirmier a été interviewé afin de documenter comment cette technologie était perçue. Dans cette étude, le système HELPER n’a pas permis de détecter une chute qui a été documentée par le personnel, mais en a détecté 4 autres qui n’avaient pas été documentées. Bien que la sensibilité du système soit élevée (0.80), les fausses alarmes qu’il génère diminuent sa valeur prédictive (0.01). Les entrevues avec le personnel infirmier ont permis de recueillir plusieurs informations utiles liées au fonctionnement de cette technologie dans un environnement réel; ces données seront utiles aux ingénieurs travaillant sur de tels systèmes et sur des technologies associées aux soins de santé et aux services sociaux.

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Corresponding author

La correspondance et les demandes de tirés-à-part doivent être adressées à : / Correspondence and requests for offprints should be sent to: Marge Coahran, MSc Toronto Rehabilitation Institute 550 University Avenue, Room 12-019 Toronto, ON, M5G2A2 <mcoahran@dgp.toronto.edu>

Footnotes

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This work would not have been possible without the contributions of several research staff members including Amer Burhan, Colin Harry, Leander Pereira, Luli Pallaveshi, and Bing Ye. The authors are immeasurably indebted to the study participants, both nursing staff and patients, at the geriatric mental health hospital where the system was deployed. This work has been possible through support from the St. Joseph Healthcare London President’s Grants for Innovation, the Academic Medical Organization of Southwestern Ontario Opportunities Fund, and the Ontario Centres of Excellence Market Readiness Program. It has been strengthened by insightful comments from the reviewers.

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