Evaluation of Sensor Technology to Detect Fall Risk and Prevent Falls in Acute Care

Potter, Patricia; Allen, Kelly; Costantinou, Eileen; Klinkenberg, W. Dean; Malen, Jill; Norris, Traci; O’Connor, Elizabeth; Roney, Wilhemina; Tymkew, Heidi Hahn; Wolf, Laurie

doi:10.1016/j.jcjq.2017.05.003

Cited by 12 publications

(17 citation statements)

References 13 publications

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“…Given the noted reluctance, more recent technology-based (e.g. sensors, cameras) interventions have been used in a myriad of ways for older adults including injury prevention 19,20 and fall detection. 21,22 Older adults seeking care in the ED after a fall offer healthcare providers a critical 'sentinel moment' to assess fall risk, discuss prevention techniques and perceived barriers, and set expectations regarding the possibility of future fear of falling.…”

Section: Discussionmentioning

confidence: 99%

Care Transition Decisions After a Fall‐related Emergency Department Visit: A Qualitative Study of Patients’ and Caregivers’ Experiences

Gettel

Hayes

Shield

et al. 2020

Academic Emergency Medicine

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Objective Falls are a leading cause of injury‐related emergency department (ED) visits and may serve as a sentinel event for older adults, leading to physical and psychological injury. Our primary objective was to characterize patient‐ and caregiver‐specific perspectives about care transitions after a fall. Methods Using a semistructured interview guide, we conducted in‐depth, qualitative interviews using grounded theory methodology. We included patients enrolled in the Geriatric Acute and Post‐acute Fall Prevention Intervention (GAPcare) trial aged 65 years and older who had an ED visit for a fall and their caregivers. Patients with cognitive impairment (CI) were interviewed in patient–caregiver dyads. Domains assessed included the postfall recovery period, the skilled nursing facility (SNF) placement decision‐making process, and the ease of obtaining outpatient follow‐up. Interviews were audio‐recorded, transcribed verbatim, and coded and analyzed for a priori and emergent themes. Results A total of 22 interviews were completed with 10 patients, eight caregivers, and four patient–caregiver dyads within the 6‐month period after initial ED visits. Patients were on average 83 years old, nine of 14 were female, and two of 14 had CI. Six of 12 caregivers were interviewed in reference to a patient with CI. We identified four overarching themes: 1) the fall as a trigger for psychological and physiological change, 2) SNF placement decision‐making process, 3) direct effect of fall on caregivers, and 4) barriers to receipt of recommended follow‐up. Conclusions Older adults presenting to the ED after a fall report physical limitations and a prominent fear of falling after their injury. Caregivers play a vital role in securing the home environment; the SNF placement decision‐making process; and navigating the transition of care between the ED, SNF, and outpatient visits after a fall. Clinicians should anticipate and address feelings of isolation, changes in mobility, and fear of falling in older adults seeking ED care after a fall.

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Section: Discussionmentioning

confidence: 99%

Care Transition Decisions After a Fall‐related Emergency Department Visit: A Qualitative Study of Patients’ and Caregivers’ Experiences

Gettel

Hayes

Shield

et al. 2020

Academic Emergency Medicine

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“…There is growing interest in collecting such data in institutional settings both continuously and passively using “intelligent” systems made up of multiple interconnected devices and artificial intelligence ( Stark, Tietz, Gattinger, Hantikainen, & Ott, 2017 ). A key assumption underpinning this interest is that technological monitoring is less intrusive than traditional methods (e.g., direct observation) and that it can enable “just-in-time” intervention with more accurate and reliable collection and processing of data ( Laplante et al, 2018 ; Potter et al, 2017 ). Such technologies are referred to as “passive” because they collect and transmit data without the individuals who are being monitored having to take any action to enable collection of data or to request assistance when an adverse event is detected.…”

Section: Examples Of Monitoring Technologies and The Data They Collecmentioning

confidence: 99%

“…Such technologies are referred to as “passive” because they collect and transmit data without the individuals who are being monitored having to take any action to enable collection of data or to request assistance when an adverse event is detected. One example of this type of technology involves the use of sensors that are embedded in the clinical environment (e.g., mattress, bed, ceiling) and that automatically capture motion and visual data of patients and generate alerts regarding bed exits and falls ( Potter et al, 2017 ). While wearable monitoring technologies may engender more awareness of monitoring, current research suggests that older adults (and family members) do not fully understand how such technologies function, what data they collect and for what purpose, nor how they can impact their future care ( Hall, Wilson, Stanmore, & Todd, 2017 ; Niemeijer et al, 2015 ).…”

Section: Examples Of Monitoring Technologies and The Data They Collecmentioning

confidence: 99%

“…For example, a randomized control trial (RCT) ( Sahota et al, 2014 ) found that a monitoring technology did not reduce falls or the time it took for nurses to respond to the fall of an older patient in an acute care setting. However, an observational study ( Potter et al, 2017 ) in acute care concluded that a reported reduction in the rate of falls was the direct result of the implementation of a monitoring technology and greater provider awareness of fall risk.…”

Section: Impact Of Monitoring Technologies On Older Adults Providersmentioning

confidence: 99%

“…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 ).…”

Section: Impact Of Monitoring Technologies On Older Adults Providersmentioning

confidence: 99%

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Towards Responsible Implementation of Monitoring Technologies in Institutional Care

Grigorovich

Kontos

2020

The Gerontologist

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Increasing awareness of errors and harms in institutional care settings, combined with rapid advancements in artificial intelligence, have resulted in a widespread push for implementing monitoring technologies in institutional settings. There has been limited critical reflection in gerontology regarding the ethical, social, and policy implications of using these technologies. We critically review current scholarship regarding use of monitoring technology in institutional care, and identify key gaps in knowledge and important avenues for future research and development.

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Unit‐level variation in bed alarm use in US hospitals

Staggs

Turner

Potter

et al. 2020

Research in Nursing & Health

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Bed and chair alarms are widely used in hospitals, despite lack of effectiveness and unintended negative consequences. In this cross‐sectional, observational study, we examined alarm prevalence and contributions of patient‐ and unit‐level factors to alarm use on 59 acute care nursing units in 57 US hospitals participating in the National Database of Nursing Quality Indicators®. Nursing unit staff reported data on patient‐level fall risk and fall prevention measures for 1,489 patients. Patient‐level propensity scores for alarm use were estimated using logistic regression. Expected alarm use on each unit, defined as the mean patient propensity‐for‐alarm score, was compared with the observed rate of alarm use. Over one‐third of patients assessed had an alarm in the “on” position. Patient characteristics associated with higher odds of alarm use included recent fall, need for ambulation assistance, poor mobility judgment, and altered mental status. Observed rates of unit alarm use ranged from 0% to 100% (median 33%, 10th percentile 5%, 90th percentile 67%). Expected alarm use varied less (median 31%, 10th percentile 27%, and 90th percentile 45%). Only 29% of variability in observed alarm use was accounted for by expected alarm use. Unit assignment was a stronger predictor of alarm use than patient‐level fall risk variables. Alarm use is common, varies widely across hospitals, and cannot be fully explained by patient fall risk factors; alarm use is driven largely by unit practices. Alarms are used too frequently and too indiscriminately, and guidance is needed for optimizing alarm use to reduce noise and encourage mobility in appropriate patients.

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Evaluation of Sensor Technology to Detect Fall Risk and Prevent Falls in Acute Care

Cited by 12 publications

References 13 publications

Care Transition Decisions After a Fall‐related Emergency Department Visit: A Qualitative Study of Patients’ and Caregivers’ Experiences

Care Transition Decisions After a Fall‐related Emergency Department Visit: A Qualitative Study of Patients’ and Caregivers’ Experiences

Towards Responsible Implementation of Monitoring Technologies in Institutional Care

Unit‐level variation in bed alarm use in US hospitals

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