fall risk assessment: a prospective investigation of nurses’ clinical judgement and risk...
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International Journal of Nursing Practice
2003;
9
: 158–165
Blackwell Science, LtdOxford, UKIJNInternational Journal of Nursing Practice1322-71142003 Blackwell Science Asia Pty Ltd
93June 2003409
Fall risk assessmentH. Myers
10.1046/j.1322-7114.2003.00409.xOriginal Article158165BEES SGML
Correspondence: Helen Myers, Centre for Nursing Research, SirCharles Gairdner Hospital, Perth, Western Australia 6009, Australia.Email: [email protected]
✠
R E S E A R C H P A P E R
✠
Fall risk assessment: A prospective investigation of nurses’ clinical judgement and risk assessment
tools in predicting patient falls
Helen Myers RN BSc MN
Clinical Nurse (Research), Centre for Nursing Research, Sir Charles Gairdner Hospital, Perth, Western Australia, Australia, and Adjunct Senior Lecturer, School of Nursing and Public Health, Edith Cowan University, Perth, Western Australia, Australia
Sue Nikoletti RN PhD
Director, Aged Care Nursing Research Program, Centre for Nursing Research, Sir Charles Gairdner Hospital, Perth,Western Australia, Australia, and Senior Lecturer, School of Nursing and Public Health, Edith Cowan University, Perth,
Western Australia, Australia
Accepted for publication October 2002
Myers H, Nikoletti S.
International Journal of Nursing Practice
2003;
9
: 158–165
Fall risk assessment: A prospective investigation of nurses’ clinical judgement and risk assessment tools in predicting patient falls
A prospective cohort study was used to determine the reliability and validity of two fall risk assessment tools and nurses’clinical judgement in predicting patient falls. The study wards comprised two aged care and rehabilitation wards within a570 bed acute care tertiary teaching hospital in Western Australia. Instrument testing included test-retest reliability andcalculations of sensitivity, specificity, positive predictive value, negative predictive value and accuracy. The test retest reli-ability of all methods was good. In this setting, the three methods of assessing fall risk showed good sensitivity but poorspecificity. Also, all methods had limited accuracy, and overall, exhibited an inability to adequately discriminate betweenpatient populations at risk of falling and those not at risk of falling. Consequently, neither nurses’ clinical judgement northe fall risk assessment tools could be recommended for assessing fall risk in this clinical setting.
Key words:
clinical judgement, fall risk assessment, nursing research, older persons, screening tests.
BACKGROUND AND REVIEWOF LITERATURE
Fall prevention has been recognised as an important areafor research and intervention. A major strategy of manyfall prevention programmes has been the development oruse of a risk assessment tool to identify patients who are at
high risk of falling. Identification of high-risk patientsallows clinical staff to target interventions at those most inneed in order to use resources effectively. Commondomains of risk assessment tools include history of falling,altered mental state, altered gait/mobility and medica-tions, while less common domains include elimination,sensory deficits, diagnosis, continence, age and mood.
1
A comprehensive review of the literature on fall riskassessment tools was conducted utilizing electronic data-bases and reference list searching. The focus of the reviewwas on fall risk assessment tools administered by nurses
Fall risk assessment 159
and developed or used for adult populations in acute carehospital settings. This search strategy revealed a total of 47articles in which fall risk assessment tools had been devel-oped, tested or used; however, few of these were based ona rigorous research design.
Many articles did not describe the method used todevelop the fall risk assessment tool.
2–4
The developmentof some of the tools was based only on a literature reviewor expert opinion.
5,6
The majority of tools were developedon the basis of incident reviews.
7,8
This is of concern asincident reviews of patients who fall do not allow a com-parison of risk factors with a non-faller population whichmay lead to biased estimates of the importance or lack ofimportance of risk factors. In addition, most tools, oncedeveloped, were not tested and had no reported sensitiv-ity or specificity, making it difficult to evaluate the accu-racy of such tools.
1
Only five of the fall risk assessment tools were devel-oped using a case control
9–12
or cohort
13
study and in-cluded details about the accuracy of the tool. Evaluation ofthe validity of these tools had usually occurred in one ortwo settings, usually by the development authors with thesame population in which the tool was developed. Onlyone of these tools
10
had been tested by other authors indifferent clinical settings to the development population.
The sensitivity of all five of these tools was generallystrong, ranging from 77% to 70% when tested by theauthors, and appeared to remain stable, ranging from 72%to 91% for the fall risk assessment tool tested by otherresearchers in different settings.
9–13
The specificity of thesetools was weaker, particularly when testing had been con-ducted by researchers other than those who developed therisk assessment tool. Specificity ranged from 38% to 88%when measured by the primary development authors andfrom 29% to 54% for the fall risk assessment tool testedby other researchers.
9–13
The moderate specificity of theserisk assessment tools is of concern when evaluating theclinical utility of such tools as too many patients who donot fall are identified as high risk. This has implications forthe implementation of fall prevention interventions thatare targeted at those at high risk.
14
In conclusion, although many fall risk assessment toolshave been developed, few have been tested for accuracy.In studies where the accuracy of tools had been tested, thishad usually been done by the developers of the tool in thesame population in which the tool was developed, limitinggeneralization of the findings. The one tool that had beentested by other researchers in different clinical settings
showed a decrease in specificity when tested outside thedevelopment population. This indicates the importance ofevaluating fall risk assessment tools for clinical utility out-side of the original population.
In addition to studies on fall risk assessment tools, a fewstudies have investigated nurses’ clinical judgement inrelation to fall risk assessment. For example, one studycompared nurses’ clinical judgement with two risk assess-ment methods and found that the two assessment methodswere no better in predicting patient falls than the nurses’clinical judgements.
15
Turkoski
et al
. suggest that nurses’clinical judgements about patients’ fall risk may aid thedevelopment of fall prevention protocols. Furtherresearch is warranted to build on limited knowledge inthis area.
16
There is an urgent need to test existing risk assessmenttools for validity in a variety of settings and to ascertainwhether these tools can outperform nurses’ clinicaljudgement in predicting fall risk. This view is supportedby the Joanna Briggs Institute for Evidence Based Nursingand Midwifery, which conducted a systematic review offalls in hospitals and found no evidence for the efficacy ofcurrent fall risk assessment tools.
17
RESEARCH OBJECTIVES
1.
To determine the reliability and validity (sensitivity,specificity, positive predictive value, negative predictivevalue) of selected fall risk assessment tools and nurses’clinical judgement.
2.
To compare the ability of selected fall risk assessmenttools and nurses’ clinical judgement to predict patientswho fall.
INSTRUMENTS
Two instruments were chosen for this study based on a lit-erature review of fall risk assessment tools. These instru-ments were chosen for further testing because thedomains assessed in the tool were consistent with the lit-erature, they had undergone preliminary validity testingand the categories were formulated in a clear and measur-able way.
Fall risk assessment tool 1
This instrument was originally developed by Berrryman
et al
. through a retrospective audit of patient falls(
n
=
1087) in a 480 bed acute care hospital in America.
18
The tool was then altered for use by MacAvoy
et al
. basedon a literature review.
19
The instrument was tested in an
160 H Myers and S Nikoletti
acute care hospital (
n
=
44 falls) and found to have a sen-sitivity of 43% and a specificity of 70%. The instrumentcontains nine items. The lowest possible score is zero andthe highest possible score is 26. A score of 10 or moreidentifies the patient as being at high risk for falls. Thedomains of this tool include age, mental status, elimina-tion, history of falling, sensory impairment, activity andmedications.
Fall risk assessment tool 2
This instrument was developed by Schmid through theuse of a case control study comparing fallers (
n
=
102) tonon-fallers (
n
=
102) in a 700 bed acute care hospital inAmerica.
12
The instrument was then tested in the samesetting with reported sensitivity of 95% and specificity of66%. The instrument contains five items. The lowest pos-sible score is zero and the highest possible score is six. Ascore of three or more identifies the patient as being athigh risk for falls. The domains of this tool include mobil-ity, mental status, elimination, history of falling and med-ications.
METHOD
A prospective cohort study was used to evaluate theselected fall risk assessment tools and nurses’ clinicaljudgement in predicting patient falls. The study wardscomprised two aged care and rehabilitation wards within a570 bed acute care tertiary teaching hospital facility inWestern Australia. Fall risk data collection was completedon all consecutive admissions to the study wards over aperiod of 14 weeks. Readmissions during the study periodwere not included. Data were collected at least one dayafter admission to allow time for clinical assessment datato be collected and entered in the patient records and fornurses to become familiar with the patients.
Each new patient was assessed for fall risk by the clin-ical judgement of the nurse caring for the patient and bythe researcher using a data collection form containing thetwo fall risk assessment tools. The information used tocomplete the risk assessment tools was gained from thepatients’ records, using the most up-to-date entries. If aspecific piece of information was not contained in therecords, or if conflicting information was present, theresearcher asked the nurse who was caring for the patientto provide this information.
Nurses were asked to state whether the patient was afall risk and also to rate the patients’ fall risk on a scale ofzero to 10, with zero being no risk and 10 being the high-
est risk. Nurses were not informed of the information onthe data collection form prior to making a clinical judge-ment about the fall risk of patients. All study patients werefollowed until the time of the first fall, discharge or death.Patient fall data was collected via the hospital accident/incident forms. Information from the ward clinical nursespecialists and hospital Quality Improvement Coordinatorsuggested that accident/incident forms were the mostreliable method of collecting data on patient falls availablein the hospital, although the likelihood of falls beingunder-reported was a limitation of this data collectionmethod.
As the risk assessment tools are used to predict a laterevent (fall) there is the potential for confounding due to‘treatment paradox’.
20
In other words, a fall may be pre-vented due to the fall prevention measures in place on theward. For this reason, data on fall prevention strategiesimplemented for patients in the study were collected froma review of the patient’s records. A checklist was compiledfor this purpose.
Additionally, a test-retest reliability study of the fallrisk assessment tools and nurses’ clinical judgement wasconducted with a separate sample of 20 patients. Patients’fall risk was assessed on two consecutive days by theresearcher (a time period of 24 hours). Nurses caring forthese patients were asked to provide a risk assessment andrisk rating at the beginning and the end of a shift (a timeperiod of five to six hours). It was impracticable to con-duct the nurse test-retest over a longer period due to shiftchanges and variations in patient allocation.
RESULTS
Data were analysed with
SPSS
for Windows version 10(SPSS Inc., Chicago, USA) using
t
-tests, chi square testsand descriptive statistics.
Demographics
During the study period, 226 patients were assessed forfall risk. Of these, 34 patients fell, giving a period prev-alence of fallers of 15%. Data were collected on the num-ber of patients who fell rather than on number of falls, soalthough some patients fell more than once, only the firstfall for each patient was included in the data collection andanalysis. The mean age of patients was 84.91 (SD 8.53)with a minimum of 41 years and a maximum of 98 years.The majority of the sample were female (71.7%,
n
=
162), with most of the sample either widowed(57.5%,
n
=
130) or married (31.0%,
n
=
70). The mean
Fall risk assessment 161
length of stay of patients was 29.13 days (SD 31.12) witha minimum length of stay of one day and a maximumlength of stay of 218 days.
There were no significant differences between themean age of patients who fell (85.50 years, SD 7.84) andpatients who did not fall (84.80 years, SD 8.66) (
t
=-
0.439, d.f.
=
224,
P
=
0.661), or in the gender distribu-tion of patients who fell and patients who did not fall(
x
2
=
0.321, d.f.
=
1,
P
=
0.571). However, there was asignificant difference in the mean length of stay betweenpatients who fell (56.03 days, SD 34.19) and patients whodid not fall (24.37 days, SD 28.06) (
t
= -
5.859,d.f.
=
224,
P
=
0.000).
Fall prevention interventions
Standard procedure on the study wards was that allpatients were assessed for fall risk using a tool derivedfrom unknown origins and incorporated into a care plan.If a patient was deemed to be at high risk for falls theywere placed on a fall risk care plan by ward staff. Of the226 patients admitted to the study, 202 (89.4%) had a riskassessment completed on admission by ward staff and 199(98.5%) of these were placed on a fall risk care plan.There were no significant differences between the numberof fallers and non-fallers who had a risk assessment com-pleted on admission (
x
2
=
0.136, d.f.
=
1,
P
=
0.712) orwere placed on a fall risk care plan (
x
2
=
0.371, d.f.
=
1,
P
=
0.542).
Reliability of the riskassessment methods
The test-retest reliability of the two risk assessmenttools and nurses’ clinical ratings was determined by cal-culating the intraclass correlation coefficient (two waymixed effect model single measure) for each method.
All three methods showed good test-retest reliability(see Table 1), being above the minimum acceptable levelof 0.7.
21
Validity of the risk assessment tools
The ability of the fall risk assessment methods to discrim-inate between patients with a high probability of fallingand patients with a low probability of falling was deter-mined by calculating the sensitivity, specificity, positivepredictive value and negative predictive value of eachmethod. The reference criterion used for these calcula-tions was whether or not the patient fell within the hos-pitalization period in which they were admitted to thestudy. In addition, the accuracy of each method was deter-mined by calculating the number of times the risk assess-ment method classified the patient into the correct fall riskcategory, expressed as a percentage. The same referencecriterion was used for this calculation.
The risk assessment tools showed good sensitivity;however, both tools had poor specificity and positive pre-dictive value (see Table 2). This meant that both riskassessment tools classified too many patients who did notfall as high risk for falls. Only 35 percent (
n
=
79) ofpatients were classified into the correct fall risk categoryby Fall Risk Assessment Tool 1 and only 36 percent(
n
=
82) of patients were classified into the correct riskcategory by Fall Risk Assessment Tool 2. There was, how-ever, a significant association between risk category andpatient fall status for both Fall Risk Assessment Tool 1(
x
2
=
4.326, d.f.
=
1,
P
=
0.038) and Fall Risk AssessmentTool 2 (
x
2
=
4.998, d.f.
=
1,
P
=
0.025).Receiver operating characteristic (ROC) curves were
constructed for each of the fall risk assessment tools (seeFigs 1 and 2) to illustrate the relationship between the sen-sitivity and the specificity of the methods. The curve close
Table 1
Reliability of risk assessment methods
Method Mean test score
(
n
=
20)
Mean retest score
(
n
=
20)
SD test
(
n
=
20)
SD retest
(
n
=
20)
ICC
(
n
=
20)
FRAT1 11.75 12.15 3.68 3.60 0.85
FRAT2 3.80 3.90 1.36 1.37 0.80
CR 6.05 5.80 2.26 2.02 0.90
ICC, intraclass correlation coefficient; SD, standard deviation; FRAT1, fall risk assessment tool 1 (possible score was zero to 26 with 10
or more identifying a patient as at high risk for falls); FRAT2, fall risk assessment tool 2 (possible score was zero to six with three or more
identifying a patient as at high risk for falls); CR, clinical rating.
162 H Myers and S Nikoletti
to the diagonal and the area under the curve close to 0.5indicate the lack of accuracy of both fall risk assessmenttools. A tool approaching 100% accuracy would expect tohave a curve running close to the upper left hand cornerand an area under the curve close to 1.
22,23
Validity of nurses’ clinical judgements
Clinical judgements about patients’ fall risk were given101 times by registered nurses (44.7%), 69 times byenrolled nurses (30.5%), 36 times by first year graduateregistered nurses (15.9%) and 20 times by clinical nurses(8.8%). In two cases, nurses were unsure about the fallrisk status of a patient and, therefore, these cases wereexcluded from the analysis (giving a sample size of 224patients). The mean number of years that participants hadbeen nursing was 12.08 years (SD 10.80) with a range of39.92 years, from a minimum of one month to a maxi-mum of 40 years. It should be noted that in many casesnurses gave a clinical judgement about more than onepatient; therefore, the above figures contain multiple casesand do not refer to one clinical judgement per nurse.
As with the fall risk assessment tools, nurses’ clinicaljudgements also exhibited good sensitivity but poor spec-ificity and positive predictive value (see Table 2). In con-trast to the fall risk assessment tools there was nosignificant association between nurses’ clinical judgementand patient fall status (
x
2
=
3.141, d.f.
=
1,
P
=
0.076).Nurses were also asked to rate the patients’ fall risk on
a scale of zero to 10. The ROC curve for these ratings isillustrated in Figure 3 and, consistent with the fall riskassessment tools, shows a curve close to the diagonal andan area under the curve close to 0.5, indicating poor dis-criminating ability.
Data indicated that nurses gave a correct clinical judge-ment in 35.3% of cases (
n
=
79). The accuracy of the clin-ical judgements varied across levels of nurses, withenrolled nurses having the highest level of accuracy andfirst year graduate registered nurses having the lowestlevel of accuracy (see Fig. 4). In particular there was alarge difference in accuracy between enrolled nurses intheir first year of clinical practice (44.4%) and graduateregistered nurses in their first year of clinical practice(8.6%).
Table 2
Validity of the fall risk assessment methods
Instrument Sensitivity % Specificity % PPV % NPV %
Fall risk assessment tool 1 91 25 18 94
Fall risk assessment tool 2 91 27 18 94
Clinical judgement 88 26 18 92
PPV, positive predictive value; NPV, negative predictive value.
Figure 1.
Receiver operating characteristic (ROC) curve forfall risk assessment tool 1. Area under the ROC curve
=
0.646.
Specificity
1.000.750.500.250.00
Sen
sitiv
ity
1.00
0.75
0.50
0.25
0.00
Figure 2.
Receiver operating characteristic (ROC) curve forfall risk assessment tool 2. Area under the ROC curve
=
0.622.
Specificity
1.000.750.500.250.00
Sen
sitiv
ity
1.00
0.75
0.50
0.25
0.00
Fall risk assessment 163
In addition, the accuracy of nurses’ clinical judgementwas influenced by the number of years they had been nurs-ing (see Fig. 5). Accuracy improved as the number of yearsof nursing increased.
DISCUSSION
Accuracy of risk assessment methods
In this setting, the methods of assessing fall risk that weretested were reliable but were not accurate. This indicatedthat all three methods were unable to adequately discrim-inate between patient populations at risk of falling andthose not at risk of falling. All of the methods overesti-mated the population at risk.
One explanation for the low specificity of the riskassessment methods is that patients who were assessed tobe at high risk for falls were, in fact, at high risk, butbecause of the fall prevention interventions in place on thestudy wards these ‘potential’ falls were prevented. It is dif-ficult to assess the likelihood of this statement as it was notpossible to determine the extent to which fall preventioninterventions were applied on the study wards. Althoughthe documentation collected for this study provides anindication of the intentions of nurses in relation to fall pre-vention, it is not known how these intentions translatedinto practice. There were, however, no significant differ-ences between the number of fallers and-non-fallers whohad a risk assessment completed on admission or whowere placed on a fall risk care plan. This suggests that fall-ers and non-fallers were treated similarly.
It is difficult to overcome this limitation as it would beunethical to discourage fall prevention interventions in theclinical setting in order to test risk assessment tools. Atthis stage, there is no other measure to use as the goldstandard for determining the validity of fall risk assess-ment methods besides an actual patient fall as there are nocurrent reliable and valid tests of fall risk. For this reason,‘treatment paradox’
20
remains a likely explanation for thestudy findings. Another explanation for the study findingsis that the domains of the fall risk assessment tools andthe constructs in nurses’ clinical judgements do not ade-quately capture the factors that place an inpatient atincreased risk for falls, resulting in inaccurate risk assess-ments, and in particular, low specificity.
Nurses’ clinical judgements
In this study, enrolled nurses had the highest level of accu-racy in determining a patient’s fall risk. Of note was thelarge difference between the accuracy of first year enrolledand graduate registered nurses in assessing patient fall risk.First year enrolled nurses achieved an accuracy level of44.4% (
n
= 9) while first year graduate registered nursesachieved an accuracy level of only 8.6% (n = 35). This find-ing is of concern as enrolled nurses undertake an 18-
Figure 3. Receiver operating characteristic (ROC) curve fornurses’ clinical ratings. Area under the ROC curve = 0.646.
Specificity
1.000.750.500.250.00
Sen
sitiv
ity
1.00
0.75
0.50
0.25
0.00
Figure 4. Accuracy of clinical judgement based on level of nurse.�, enrolled nurse; , graduate nurse; , registered nurse; ,clinical nurse.
0
20
40
60
80
100
Clin
ical
judg
emen
tw
as c
orre
ct (
%)
Figure 5. Accuracy of clinical judgement based on years ofnursing. �, 0–1 year; , 1.5–5 years; , 6–18 years; , 20–40 years.
0
20
40
60
80
100
Clin
ical
judg
emen
tw
as
corr
ect (
%)
164 H Myers and S Nikoletti
month education course at a TAFE (Technical and FurtherEducation) college and are required to work under thesupervision of a registered nurse, while registered nursesundertake a minimum three year degree course at univer-sity level and work independently. These results are poten-tially biased, as measuring differences in accuracy betweentypes of nurses was not a main focus of this study and, inmany cases, the same nurse gave multiple judgementsabout patients’ fall risk. The results, however, provide anindication that further study is warranted using a specifi-cally designed methodology to explore this issue.
CONCLUSION AND RECOMMENDATIONS
The results indicated that the methods of assessing fall risktested in this study were not accurate and were unable toadequately discriminate between patient populations atrisk of falling and those not at risk of falling. All threemethods had low specificity and identified too manypatients as at high risk for falls. None of the methodstested in this study can be recommended for assessing fallrisk in the clinical setting. Based on the results, there is nobenefit in using either of the fall risk assessment tools inpreference to nurses’ clinical judgements about a patient’sfall risk.
There are two possible explanations for this finding.The first is that patients assessed to be at high risk forfalls were, in fact, at high risk, but because of the fallprevention interventions in place on the study wards,these ‘potential’ falls were prevented. However, datacollected on fall prevention interventions implementedon the ward showed that fallers and-non-fallers weretreated similarly. This suggests that ‘treatment paradox’cannot provide a full explanation for the research find-ings. The second explanation is that the domainsincluded in the fall risk assessment tools and the compo-nents of nurses’ clinical judgements are not capable ofadequately distinguishing between fallers and-non-fallers in acute care settings.
Recommendations arising from the results of this studyare that further studies should be undertaken to test fallrisk assessment tools in acute care settings. Such studiesrequire careful attention to research design in order tominimize or control for ‘treatment paradox’. Additionally,further investigation into the clinical judgement of regis-tered and enrolled nurses in the early years of clinicalpractice should be undertaken and results reported toappropriate educational institutions.
ACKNOWLEDGEMENTSThe researchers would like to thank the nursing staff onthe aged care and rehabilitation wards at Sir CharlesGairdner Hospital.
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