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Patient Safety Tip
of the Week
September
13, 2022
Smart
Socks and Robots for Fall Prevention?
Fall prevention in both the hospital setting and the
community is an important patient safety endeavor. Many have felt that
technology would provide the solutions we are desperately looking for. But, to
date, technology has failed to make much of an impact. Bed/chair pressure
sensors, designed to alarm when a patient attempts to get out of bed or out of
a chair, were widely touted as a fall prevention intervenion. But randomized,
controlled trials of bed/chair pressure sensors (Shorr
2012, Sahota
2014)
failed to demonstrate a reduction in patient falls. In fact, a systematic
review and meta-analysis of clinical trials of in-hospital use of sensors for
prevention of falls (Cortes
2021) actually
found an increase of 19% in falls among elderly patients who are users of
sensors located in their bed, bed-chair, or chair.
Bed
pressure sensors have even had some unintended consequences. In our June 19,
2007 Patient Safety Tip of the Week Unintended Consequences of Technological
Solutions we gave an example where a hospital purchased
a new bed pressure alarm system intended to alert staff when a patient attempted
to get out of bed. It turned out that on some units there were not enough
electrical outlets for both the new bed alarms and the nurse call buttons. So,
a decision was made in some cases to swap out these two devices. You can guess
what happened: nursing staff responded to the out-of-bed alarm only to find
the patient lying on the floor with an injury because he tried to get out of
bed after no one responded when he pushed the nurse call button!
Enter
the newest technologies
Smart socks and robots!
Moore
et al. (Moore 2022) published results of a prospective study of
smart socks to prevent falls in hospitalized patients. The smart socks contain
pressure sensors which detect when a patient is trying to stand up. The system
also uses a wireless connection to a monitoring device at the nurses station,
and Smart Badge notification devices worn by the nurses. When the Smart Socks
detect an attempt to stand up, the 3 closest nurses to the alarming room
receive an alert through their badge. Once a nurse with a badge then enters the
patients room, the alert is automatically deactivated. If none of the 3 nurses
enter the room within the first 60 seconds, then it will escalate to the next 3
closest. At a total of 90 seconds, the system proceeds to an all call and
alerts all Smart Badges logged on to the alarming unit. If staff are getting a patient
out of bed for therapy, or a bathroom visit, or any other activity, the alarms
can be suspended via an in-room tablet before getting the patient out of bed.
We
really like that alarm concept. In our many columns on alarm fatigue and alarm
management, we have stressed the importance of alerting only those with a need
to know but having an escalation capability in case no one heeds the alarm
promptly. And, clearly, this alarm is a good alarm in that it is actionable,
unlike so many other alarms that do not lead to any actions, and has a very low
false alarm rate.
The
study took place in neurological and neurosurgical based units at a major
academic health center. Patients 18 years or older who were determined to be at
risk for falls were eligible. During 13 months of data collection on 569
enrolled patients (mean age of 59.5 years), zero falls happened. That
calculated to a fall rate of 0 falls per 1000 patient-days compared to a historical
rate of 4 falls per 1000 patient-days at the study site that was observed in
the general patient population that consisted of both patients with and without
fall risk.
They
also monitored nurse response times. During the study period, 5010 alarms were
associated with the Smart Socks system. Only 11 of these were reported to be
false alarms, so 99.8% of the alarms were true patient standing events. Median
nurse response time to each alarm was 24 seconds, with a range of 1 second
to nearly 10 minutes.
Despite
the impressive results of this study, we still consider the conclusions to be
preliminary. This was not a randomized, controlled trial. Comparison with
historical controls is always subject to bias from unrecognized confounding
factors. (Note also that the historical fall rate was on a population that also
included some patients not at risk of falls.) Also, when the study began, a sample
size of 2500 patients was estimated provide at least 70% power to detect a 25% reduction
in the fall rate. But, largely because of the COVID-19 pandemic, they enrolled
only 569 patients. Also, the fact that patients in the study were hospitalized for
a median of 2 days suggests to us that few would have been a very high risk for
falls.
In prior
work with the smart socks system in patients at high risk for falls, Baker et
al. (Baker 2021) reported on 567 patients in a single-arm clinical
trial and 949 patients in an observational study in med-surg units at two
hospitals. In the clinical trial, fall rate was reduced from 4 to 0 per 1,000
patient-days (p < 0.01). In the observational study, fall rate was reduced
from 4 to 1.3 per 1,000 patient-days (p < 0.05).
Obviously,
the next step before smart socks technology gets widely adopted would be for a
true randomized, controlled trial. Remember, the bed/chair pressure alarms were
also heralded early on as the next best thing in fall prevention. But they
fizzled out when randomized, controlled clinical trials were done. But the
smart socks system, with its unique alarm distribution pattern, certainly
sounds exciting.
And,
after we had already begun writing todays column, there was a report about a
robot that can predict and catch seniors before they fall (Verma 2022). This robot, developed in Singapore, looks
like a motorized wheelchair, with guard rails that come up to a persons hip
and are outfitted with sensors to judge when a person begins to go off balance.
Users are strapped into a harness. When they are starting to tip, the robot
engages to keep them from falling.
The
robot has only been tested in small numbers of patients, who suffered from
strokes, traumatic brain injuries and spinal cord injuries. The developer is working
on 2 models, an at-home model (estimated cost $3000-4000) and a hospital
version with a camera and multiple sensors (estimated cost about $20,000). Obviously,
more testing is needed and then the robot would have to go through the
regulatory approval process, but developers are targeting potential availability
in two years. This device obviously is targeted at a different population than the
population for the smart socks.
Our
success at reducing falls seems to have plateaued in recent years. Its nice to
see some new potential interventions on the horizon.
Some of our prior columns related to falls:
- April 16, 2007 Falls With Injury
- July 17, 2007 Falls in Patients on Coumadin or Heparin
or Other Anticoagulants
- January 1, 2008 Fall Prevention
- October 7, 2008 Lessons from Falls....from Rehab
Medicine
- November 18, 2008 Ticket to Ride: Checklist, Form, or
Decision Scorecard?
- August 4, 2009 Faulty Fall Risk Assessments?
- September
22, 2009 Psychotropic
Drugs and Falls in the SNF
- December
22, 2009 Falls
on Toileting Activities
- January
2010 Falls
in the Radiology Suite
- June
2010 Seeing
Clearly a Common Sense Intervention
- May
29, 2012 Falls,
Fractures, and Fatalities
- June
5, 2012 Minor
Head Trauma in the Anticoagulated Patient.
- January
15, 2013 Falls
on Inpatient Psychiatry
- March
2013 Sedative/Hypnotics
and Falls
- December
3, 2013 Reducing
Harm from Falls on Inpatient Psychiatry
- June
2014 New
Glasses and Fall Risk
- July
8, 2014 Update:
Minor Head Trauma in the Anticoagulated Patient
- August
2014 Cataract
Surgery and Falls
- November
4, 2014 Progress
on Fall Prevention
- March
2015 Another
Paradox: Falls Due to Walking Aids
- June
9, 2015 Add
This to Your Fall Risk Assessment
- July
28, 2015 Not
All Falls Are the Same
- October
2015 Patient
Perception of Fall Risk
- October
27, 2015 Sentinel Event Alert on Falls and View
from Across the Pond
- February
16, 2016 Fall Prevention Failing?
- March
14, 2017 More on Falls on Inpatient Psychiatry
- July
2017 Mobility vs. Falls
- February
2018 Global Sensory Impairment and Patient
Safety
- February
20, 2018 Delirium and Falls
- March
2019 Newborn Falls
- July
2019 Increasing Mortality After Falls in
Elderly
- January
14, 2020 More on Newborn Falls
- June
16, 2020 Tracking Technologies
- October
2020 Pre-op Testing Before Cataract Surgery Leads to What?
- August
23, 2022 Yes, There is a Proper Way to Assess Orthostatic
Hypotension
References:
Shorr
RI, Chandler AM, Mion LC, et al. Effects of an intervention to increase bed
alarm use to prevent falls in hospitalized patients: a cluster randomized
trial. Ann Intern Med 2012; 157(10): 692-699
https://www.acpjournals.org/doi/10.7326/0003-4819-157-10-201211200-00005
Sahota
O, Drummond A, Kendrick D, et al. REFINE (Reducing Falls in In-patieNt Elderly)
using bed and bedside chair pressure sensors linked to radio-pagers in acute hospital.
Age Ageing 2014; 43(2): 247-253
https://academic.oup.com/ageing/article/43/2/247/10785
Cortes
OL, Pineros H, Aya PA, et al. Systematic review and meta-analysis of clinical
trials: In-hospital use of sensors for prevention of falls. Medicine (Baltimore)
2021; 100(41): e27467
Moore
T, Kline D, Palettas M, et al. Fall Prevention with the Smart Socks System
Reduces Hospital Fall Rates. Journal of Nursing Care Quality 2022; Published online August 19, 2022
Baker
PA, Roderick MW, Baker CJ. PUPฎ (Patient Is Up) Smart Sock technology prevents
falls among hospital patients with high fall risk in a clinical trial and observational
study. J Gerontol Nurs 2021; 47(10): 37-43
https://journals.healio.com/doi/10.3928/00989134-20210908-06
Verma
P. This robot catches grandma before she falls. Washington Post 2022; September
10, 2022
https://www.washingtonpost.com/technology/2022/09/10/fall-prevention-robot/
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