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

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.

The use of robots and other automated devices in nurses' work: An integrative review

“…Lipsitz et al () found that over a 6‐month study period, the falls device that was used recorded 19% of the 89 falls that were manually recorded by the nurses. In addition, a study by Coahran et al () reported that during the follow‐up period, a fall detection system missed one of the number of documented falls but detected four falls that were not documented. Otero, Fernandez, Apalkov, and Armada () looked at a laboratory test that automatically measured the minute‐by‐minute flow and content of urine and found that after 4 hours, the error rate was 0.83% (SD = 0.60) of urine volume, compared with 26% when the nursing staff based their measurements on visual inspections.…”

Towards Responsible Implementation of Monitoring Technologies in Institutional Care

“…One type of data collected by such technologies is visual or image-based, generally using vision sensors (optical and infrared) embedded in cameras that wirelessly record and transmit video feeds of an environment in real-time (e.g., room, hallway) to another location. With advances in computer vision and other analytics, visual data can now be interpreted in real-time by intelligent monitoring systems that can independently take actions when an anomaly is detected (e.g., triggering an alarm to notify providers) ( Coahran et al, 2018 ; Khan, Ye, Taati, & Mihailidis, 2018 ).…”

“…Another claim that is inconsistently supported by evidence is that monitoring technologies in institutional settings decrease the workload of providers; existing research suggests that they do not, and there is evidence they in fact can increase it. The introduction of new technologies requires that care providers learn how to use them and assume new data management responsibilities ( Coahran et al, 2018 ; Fisher & Monahan, 2008 ). Providers report that monitoring technologies disrupt their usual workflow and practices (e.g., structured care routines, infection control) and that responding to triggered alarms interferes with other (parallel) activities ( Coahran et al, 2018 ; Potter et al, 2017 ; Timmons et al, 2019 ).…”

“…The introduction of new technologies requires that care providers learn how to use them and assume new data management responsibilities ( Coahran et al, 2018 ; Fisher & Monahan, 2008 ). Providers report that monitoring technologies disrupt their usual workflow and practices (e.g., structured care routines, infection control) and that responding to triggered alarms interferes with other (parallel) activities ( Coahran et al, 2018 ; Potter et al, 2017 ; Timmons et al, 2019 ). High rates of “false alarms” also lead to desensitization and inspire lack of trust in these technologies, both of which decrease providers’ response time and their continued engagement in traditional monitoring (e.g., nightly rounds) ( Coahran et al, 2018 ; Niemeijer et al, 2014 ; Potter et al, 2017 ; Stark et al, 2017 ; Timmons et al, 2019 ).…”

Identificando riscos a segurança do paciente idoso com demência: revisão integrativa

“…Nesta pesquisa, identificamos 48 % dos estudos que trazem as quedas como um risco para o paciente idoso com Demência. Foram relatados riscos em ambiente hospitalar (Sinvani,2019Dudevich 2018, Luxford 2015, em instituições (Evans 2018Taylor 2016, na comunidade e contexto domiciliar ( Häikiö,2019 ;Green,2018 ;Rahja 2018;Tudor Car 2016;Levy-Storms 2016;North C 2016), o uso da tecnologia para a prevenção de quedas ( Coahran 2018;Mao 2015) e os benefícios do exercício físico para prevenção de quedas ( Research, Society and Development, v. 9, n. 9, e612997877, 2020 (CC BY 4.0) | ISSN 2525-3409 | DOI: http://dx.doi.org/10.33448/rsd-v9i9.7877 Karuncharernpanit, 2015). Um ponto importante abordado é a avaliação do indivíduo para os riscos de quedas assim como a avaliação dos riscos ambientais (Green 2018).…”