1 the impact of video monitoring on patient falls and cost ...€¦ · 5 picot question in...
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1
April
The Impact of Video Monitoring onPatient Falls and Cost of Care
Ellen Barrington DNP, RN-BC, NEA-BCMarie Foley-Danecker DNP, RN, CCRN, NE-BC
Cira Fraser PhD., RN, ACNS-BCJanet Mahoney, PhD., RN, APN-C, NEA-BC
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Presentation Outline2
11
44
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33
55
66
77
88
99
Introduction to Falls and Video Monitoring
Critique and Review of the Literature
Methods
Data Collection and Analysis
Results
Discussion
Implications
Limitations
Conclusions
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The purpose of this study wasto examine the impact ofvideo monitoring on patientfalls and cost of care.
3
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Background and Significance4
Cost of inpatient falls $54.9
billion by 2020 (CDC, 2013)
Video monitoring -
newest technology
to prevent patient
falls
Hundreds of thousands of
patients fall,
30-50% result in injury
annually (The Joint
Commission, 2015)
Prevention Strategies -
low-cost personal alarms,
costly 1:1 direct
observation (Rausch,
Bjorkland, 2010)
Since 2009, 465 falls
with injury, 63%
resulted in death (The
Joint Commission,
2015)
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PICOT Question5
In hospitalized adults (P), how does the use of a patient video monitoringsystem (I) versus the usual care (C), impact patient falls and affect the cost
of care (O) over a six-month period in two hospitals (T)?
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Literature Search6
Linkage
RevisedSearch
Keywords
Databases
Review
CINHAL, PubMed, EBSCO
Key words: video monitoring,
falls and return on investment
Linked terms: falls and video monitoring, and
falls with return on investment
Linked terms: fall prevention in hospitals and
cost of falls in hospitals
CINHAL, Google Scholar
Abstract review completed
Search criteria: English and peer reviewed
Key words: falls prevention in hospital since
2012 and cost of falls in hospitals since 2012
Search criteria: English and peer reviewed
71 abstracts reviewed
929 articles reviewed independently
for specificity to video monitoring.
Final yield 9 articles.
Researcher 2Researcher 1
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Donabedian Patient Safety Model-Theoretical Framework7
Organizational Structure -
how care is organized and
applied by nursing services
Organizational
Process – how care
is provided; actions
measured to reduce
negative outcome
1
23
Evaluates howstructure &
process influenceoutcomes
(Donabedian,1980)
Berenholtz, S.M., Pustavoitau, A., Schwartz, S.J., & Pronovost, P.J. (2007). How safe is my intensive care unit? Methods for monitoring and measurement. CurrentOpinion in Critical Care 13, 703-708.
Donabedian, A. (1980). The definition of quality and approaches to assessment. Ann Arbor: MI, Health Administration Press.Reed, K.D. (2008). The American association of critical care nurse’s Beacon award: a framework for quality. Critical Care Nurse, 20, 383-391. doi:
10.1016/j.ccell.2008.08.002.
Clinical outcomes -
are the results
achieved
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Target Audience and Participants8
Inclusion CriteriaAll adult admitted patients for a three month periodbefore and a six month period post implementation ofvideo monitoring
Study SampleInpatient adults, male and female, 18-110 years,hospitalized, April 1, 2015 through November 30, 2017Two sets of participants from each of the two sites
Sets ofParticipants
Sample Size
Patients who fell pre and post implementation of videomonitoringSet 1Systematic sampling of every third video monitoredpatient as listed chronologically on reports generated inthe video monitor software
Set 2
N=595, VM n=397, Falls n=197 in Non VM and n=1 in VMExclusion criteria - contraindicated for video monitoring (patientsnot able to be redirected verbally, suicidal or homicidal ideation),pregnant
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Setting for Data Collection9
Acute care hospitals inthe same network
Both hospitals hadthe same number of
cameras, policiesand operating models
Hospital Two
315 bedcommunity hospitalrange of specialized
inpatient andoutpatient services
Magnet award fornursing excellence
Hospital One
224 bedcommunity hospitalrange of specialized
inpatient andoutpatient services
Magnet award fornursing excellence
Same procedure andform for data collection
used at both sites
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Data Collection – How the VM System Works10
Data – no video footage is recorded, hence no videofootage was reviewed
Digital system – retains alarm event and demographicinformation (admission, transfer, discharge information,census)
Demographic information – entered by the videomonitor technicianAlarm events – verbal, automated or alarm in room
Data - saved in Excel format - available to download
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Fall Data Collection11
OneLink – documentationsystem used by RN’s to recordfalls
Fall data - pre and postimplementation wascollected
Data Collection Form - usedto collect data from allsources (OneLink reports,medical records and VMreports)
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Cost Data Collection12
Calculated total salary expense of patient observation - based on theknown median patient observer and patient care technician salary
Calculated total hours used for VM program (VM Tech and Rounder)
Calculated hours per month spent on non VM patient observation (1:1)
Calculated hours per month Patient Care Technicians performed patientobservation (time they could have been on the unit)
Human Resources provided salary informationNurse Information Resource Center (NIRC) provided worked hours
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Cost of Falls to the Organization
Estimated cost of afall with injury
Financial Risk to theorganization
$30,000 X 41 =Financial risk
41
Falls with injury
In 9-month totalsample period
$1,230,000$30,000
Burns, E. R., Stevens, J. A., & Lee, R. (2016). The direct costs of fatal and non-fatal falls among older adults – United States. Journal of Safety Research, Sep (58), 99-103. doi:10.1016/j.jsr.2016.05.001.
Centers for Disease Control & Prevention. (2016, August 19). Costs of falls among older adults. Retrieved from https://www.cdc.gov/homeandrecreationalsafety/falls/fallcost.html
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Descriptive Data Pre and Post Implementation
Hospital 1Monthly Mean
Hospital 2Monthly Mean
Pre Post Pre Post
Patient Days Total 10814 11014 19549 19454
1:1 Observation Hrs 5132.7 3243.93 4117.03 3802.93
VM Hours n/a 1500.1 n/a 1460.17
Morse Score 50.83 50.19 53.05 53.65
Falls 8.33 4.0 14.33 16.67
Falls/1,000 PatientDays 2.36 1.06 2.2 2.55
Fall Injury Rate 0.46 0.36 0.20 0.51
PCA Hrs asObserver 3656.42 2112.17 1916.17 2231.67
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Analysis of Pre and Post Implementation
Hospital 2Hospital 1
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Results by Gender
50.1% 50.5% 51.3%
SampleVideo
MonitoredFalls
49.1% 49.5% 48.7%
Women
Men
16
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Characteristics of Patient Monitoring Group17
68.97 Mean Age
197Patients
Female 101Male 96
n/aMean DaysMonitored
55.05 Admission Morse
59.43Morse Prior to VM
or Fall66.33Morse Prior to VM
or Fall
60Admission Morse
03Mean DaysMonitored
398Patients
Female 197Male 201
77.96Mean Age
Video Monitoring Non Video Monitoring
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Analysis of Patient Monitoring Group18
Change in MorseThe change in Morse score for the patients who fell in the non VM group (n=197)from admission to prior to fall was significant (t=3.279, p=0.001).
Relationship between falls and Morse scoreA Pearson's Correlation revealed a strong, positive relationship between falls bymonth and average Morse scores by month in 2016 (r=0.847, p=.000), whichexplained 72% of the variance. In 2017, 73% of the variance between falls bymonth and average Morse was explained with the Pearson’s Correlation(r=0.854, p=.000).
GenderSlightly more men were video monitored, and marginally more women fell
Relationship between volume and fallsIn 2016, Pearson’s Correlations between patient days by month and falls bymonth showed a strong positive correlation (r=0.762, p= 0.001). In 2017,Pearson’s Correlation between patient days by month and falls by monthshowed a strong positive correlation (r=.904, p=0.000).
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Fall Severity Pre and Post Video Monitoring19
41 fallswith
injury
Fall injuryscale 1-3:1= mild2=moderate3=severe
Pre-videomonitored fall
severityaverage was
1.36.
Post videomonitored fall
severitydecreased to
1.24
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Percentage and Number of Falls by Diagnosis Category20
14.713.6
24.6
7.6
2
37.9
0
5
10
15
20
25
30
35
40
Neuro CV Resp Ortho SubstanceAbuse
Other
Perc
enta
ge
Diagnosis Category
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Percentage of Falls at Change of Shift21
60Morse
Admission
0.3% VMfall8.1%
83.5 fallsmid shift
Non Shift Change Falls
Shift Change Falls
8.1%Non VM pmshift change
Non VM amshift change
83.5%Non VM
falls
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Percentage and Number of Falls by Time Period22
0
2
4
6
8
10
12
14
16
18
0001-0300 0301-0600 0601-0900 0901-1200 1201-1500 1501-1800 1801-2100 2101-0000
29
2220
3032
17
21
27
Perc
enta
ge
Time
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Falls Weekend vs. Weekday23
0.3%
23.9%
76.1%
Non VM
Weekend Falls
VM Weekend Fall
Non VM
Weekday Falls
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Falls by Location24
8 (4.1%) falls occurred in other areas
152 (77.2%) falls
occurred in a patient
room
37 (18.8%) falls occurred in the
bathroom
1 (0.3%) patient fell in their room while on video monitor
77.2%
18.8%
4.1%0.3%
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Hospital 1 Fall Rate25
Video monitoring was implemented April 2016. The trend line for thedecrease in fall rate was noted through 2016 and 2017.
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Hospital 2 Fall Rate26
Video monitoring was implemented November 2016. A slight increase in thefall rate trend was noted from 2016 through 2017.
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Findings
Hospital 1
Reduced:
patient
falls/1,000
patient days
one to one
observation hrs
PCA as observer
hrs
1
Hospital 2
Reduced:
1:1 observation
hrs
Increased:
patient falls/1,000
patient days
PCA as observer
hrs
2
Significant
change:
Morse score
on admission
and Morse
score just
before fall
3
Reduced:
fall severity
from 1.36 to
1.24
4
Reduced:
1:1 observation
hrs 24%
cost per
observed hr
20%
5
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Return on Investment
Annual salary costof staffing VM at
both hospitals peryear
The current staffing model at both hospitals costs an estimated
$428,890 in salary per year. While increasing the capacity of
video monitoring will also incur an increase in salary expense,
this expense is reasonable in relation to patient safety, and the
high costs of patient falls with injury.
$428,890
Reduction in costper observed hour
Related to the decrease
in one-to-one observation
hours and shifting of
resources
20%
Reduction in 1:1observation hours
24%
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Discussion29
The study found a strongpositive correlation
between patient daysand number of patient
falls. Hospital 2 had 44%more patient days than
Hospital 1.
Both Hospital 1 and Hospital 2implemented the VM program with
the same amount of videomonitoring cameras. Not evidentin the research of the literature isthe number of video monitoringcameras based on hospital sizenecessary to impact patient falls.However, based on the difference
in size of the two hospitals, thecorrelation between patient daysand falls, and the impact of ten
cameras on the smaller hospital’sfall rate, further exploration is
warranted.
Hospital 2 used more PCAas observer hours in the
post implementation period.Removing frontline
caregivers to provide 1:1observation has been
shown to deplete staffingresources (Sand-Jecklin,Johnson, & Tylka, 2016)
and does not decrease falls(Boswell, Ramsey, Smith,
and Wagers, 2001;Harding, 2010; Tzeng &
Yin, 2007).
Consistent with thepublished research,
the increase in PCA asobserver hours in
Hospital 2 could be aconfounding factor to
explain why fallsincreased exclusive ofVM implementation.
Boswell, D. J., Ramsey, J., Smith, M. A., & Wagers, B. (2001). The cost-effectiveness of a patient sitter program in an acute care hospital: A test of the impact ofsitters on the incidence of falls and patient satisfaction. Quality Management in Healthcare, 10(1), 10-16.
Harding, A. D. (2010). Observation assistants: Sitter effectiveness and industry measures. Nursing Economics, 28(5), 330-336.Sand-Jecklin, K, Johnson, J.R., & Tylka, S. (2016). Protecting patient safety: Can video monitoring prevent falls in high-risk patient populations? Journal of Nursing
Care Quality, 31(2), 131-138.
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Discussion30
The study found astrong positive
correlation betweenpatient days andnumber of patientfalls. Hospital 2had 44% more
patient days thanHospital 1.
Both Hospital 1 and Hospital2 implemented the VMprogram with the same
amount of video monitoringcameras. Not evident in theresearch of the literature is
the number of videomonitoring cameras based
on hospital size necessary toimpact patient falls.
However, based on thedifference in size of the two
hospitals, the correlationbetween patient days andfalls, and the impact of ten
cameras on the smallerhospital’s fall rate, furtherexploration is warranted.
Hospital 2 used morePCA as observer hours
in the postimplementation period.
Removing frontlinecaregivers to provide1:1 observation has
been shown to depletestaffing resources
(Sand-Jecklin,Johnson, & Tylka,
2016) and does notdecrease falls (Boswell,
Ramsey, Smith, andWagers, 2001; Harding,
2010; Tzeng & Yin,2007).
Consistent withthe publishedresearch, the
increase in PCAas observer
hours in Hospital2 could be aconfounding
factor to explainwhy fallsincreased
exclusive of VMimplementation.
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Implications
Increasing the video monitoringcapacity will reduce falls. This ideais further supported by the smallerhospital realizing a reduction in fallswith ten cameras and the largerhospital not realizing an overallreduction in falls.
4
Findings of this study suggest that thenursing team is triaging use of videomonitoring to patients with the mostobvious risk of fall (average Morseprior to being put on VM 66.33). Thisstudy found benefit would occur forpatients with a Morse of 55 andabove.
2
The average admission Morse scorefor all patients at both hospitals for2016 and 2017 was 51.5. Theaverage Morse on admission forpatients who fell was 55.05,implicating that it is difficult todifferentiate on admission theaverage patient (Morse 51.5) from thepatients who will fall (Morse 55.05).
3
Results of this study indicate videomonitoring is effective in decreasingfalls in patients at risk for fall. Of 198falls reviewed only one occurredwhile the patient was videomonitored.
1
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Limitations32
Limited DataMorse Score
The Morse score was takenin total; no investigation
variables that calculate theMorse score was performed.
While the Morse score is the majorindication for initiation of VM,
another element is the subjectiveopinion of the nurse in assessing
the need for VM. This can bedriven by patient behaviors such asimpulsivity, disruption of treatment
and lack of safety awareness.
VM is not an appropriate strategyfor all patients – at the time ofthis study exceptions includepatients who cannot be re-
directed, suicidal and homicidalpatients, and restrained patients.These patients were included in
the fall sample.
The limited number of VMcameras available results intriaging the use of this finiteresource. Further research is
needed to establish an appropriatenumber of VM cameras to impactthe fall rate of a hospital based on
acuity and patient volume.
Limited data wascollected in the pre
video monitoring samplefor patients who did not
fall.
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Limitations and Diagnostic Categories33
This study used the
admitting
diagnosis which
could change for a
patient based on
clinical findings.
1
Co-morbidities
were not
considered, and
the clinical status
of the patient at
time of fall was not
assessed.
2
The diagnosis categories
were broad, and there is
room for overlap among
diagnoses. In this study,
the largest diagnostic
category for the patients
who fell was the “other”
category.
3
The reason for a
fall may not be
related to the
diagnosis.
4
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Conclusions34
Findings of thisstudy suggestthat a videomonitoringprogram iseffective in
reducing fallsand falls with
injury.
Further studyis needed toestablish aminimum
number of VMcameras to
impact the fallrate based onpatient acuityand volume.
Additionalstudy is
needed toestablish acut point toefficiently
initiate VM,to have themaximumimpact onfall rates.
To effectivelymaximize costsavings, further
study shouldfocus on the
maximum ratioof patients tovideo monitor
technician, andto assess theimpact of the
patient rounderon the fall rate.
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35
Any Questions ?
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Thank you
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