Evaluationandrefinementofahandheldhealthinformationtechnologytooltosupportthetimelyupdateofbedsidevisualcuestopreventfallsinhospitals.pdf

ORIGINAL RESEARCH

Evaluation and refinement of a handheld healthinformation technology tool to support the timelyupdate of bedside visual cues to prevent fallsin hospitals

Ruth C.-A. Teh FRACP, MBBS, B Pharm (Hons),1,2 Renuka Visvanathan PhD, FRACP, FANZSGM, G.Cert.Ed (H.Ed.),

MBBS, ATCL,1,2 Damith Ranasinghe PhD, BOE3 and Anne Wilson PhD, MN, BN, RN, FACN2,4,5

1Aged and Extended Care Services, The Queen Elizabeth Hospital, 2Adelaide Geriatrics Training and with Aged Care (GTRAC) Centre, Adelaide

Medical School, The University of Adelaide, Adelaide, South Australia, 3School of Computer Science, The University of Adelaide, Adelaide, South

Australia, Australia, 4College of Medicine and Public Health, Flinders University of South Australia, and 5Prince of Wales Medical School, University of

New South Wales, Sydney, New South Wales, Australia

A B S T R A C T

Aim: To evaluate clinicians’ perspectives, before and after clinical implementation (i.e. trial) of a handheld healthinformation technology (HIT) tool, incorporating an iPad device and automatically generated visual cues for bedsidedisplay, for falls risk assessment and prevention in hospital.

Methods: This pilot study utilized mixed-methods research with focus group discussions and Likert-scale surveys toelicit clinicians’ attitudes. The study was conducted across three phases within two medical wards of the QueenElizabeth Hospital. Phase 1 (pretrial) involved focus group discussion (five staff) and surveys (48 staff) to elicitpreliminary perspectives on tool use, benefits and barriers to use and recommendations for improvement. Phase 2(tool trial) involved HIT tool implementation on two hospital wards over consecutive 12-week periods. Phase 3 (post-trial) involved focus group discussion (five staff) and surveys (29 staff) following tool implementation, with similarthemes as in Phase 1. Qualitative data were evaluated using content analysis, and quantitative data using descriptivestatistics and logistic regression analysis, with subgroup analyses on user status (P�0.05).Results: Four findings emerged on clinicians’ experience, positive perceptions, negative perceptions and recom-mendations for improvement of the tool. Pretrial, clinicians were familiar with using visual cues in hospital fallsprevention. They identified potential benefits of the HIT tool in obtaining timely, useful falls risk assessment toimprove patient care. During the trial, the wards differed in methods of tool implementation, resulting in lower uptakeby clinicians on the subacute ward. Post-trial, clinicians remained supportive for incorporating the tool into clinicalpractice; however, there were issues with usability and lack of time for tool use. Staff who had not used the tool hadless appreciation for it improving their understanding of patients’ falls risk factors (odds ratio 0.12), or effectivelypreventing hospital falls (odds ratio 0.12). Clinicians’ recommendations resulted in subsequent technologicalrefinement of the tool, and provision of an additional iPad device for more efficient use.

Conclusion: This study adds to the limited pool of knowledge about clinicians’ attitudes toward health technologyuse in falls avoidance. Clinicians were willing to use the HIT tool, and their concerns about its usability were addressedin ongoing tool improvement. Including end-users in the development and refinement processes, as well as havinghigh staff uptake of new technologies, is important in improving their acceptance and usage, and in maximizingbeneficial feedback to further inform tool development.

Key words: falls prevention, health information technology, mixed-methods, perspectives

Int J Evid Based Healthc 2018; 16:90–100.

Correspondence: Ruth C.-A. Teh, FRACP, MBBS, B Pharm (Hons),

Sunbury Hospital, 7 Macedon Road, Sunbury, Victoria, 3429,

Australia. E-mail: [email protected]

DOI: 10.1097/XEB.0000000000000129

90 International Journal of Evidence-Based

iversity of Adelaide, Joanna Briggs Institute. U

Background

F alls are the seventh most common cause of hospi-tal-acquired injury1 and are more prevalent amongolder persons.2,3 Despite the introduction of mandatory

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ORIGINAL RESEARCH

hospital falls risk assessment and prevention strategies

as a healthcare priority, the incidence of inpatient

falls continues to rise by 2% each year.3–5 Overall, the

reported incidence of falls in hospital varies widely from

2–3 (acute setting) to 46% (rehabilitation setting).6,7 Falls

are more prevalent in medical compared with surgical

wards,8 in public compared with private hospitals (4.2 vs.

1.6 per 1000 hospitalizations), and among patients living

in major cities compared with remote areas (3.4 vs.

1.9 per 1000 hospitalizations).9 Actual fall rates are likely

to even be higher as there is no universal definition for a

fall, and falls incidents tend to be under-reported.10

Hospital falls tend to cause serious complications,

with 44–60% resulting in harm,11,12 especially among

older persons.13 The 6-PACK trial (2011–2013) in six

Australian hospitals demonstrated that hospital falls

increased length of stay (LOS) by 8 days [95% confidence

interval (CI) 5.8–10.4, P<0.001], and hospital costs by

AU$6669 (95% CI $3888–9450, P<0.001), even after

adjusting for age, sex, cognitive impairment, comorbid-

ities and admission type.14 Older persons who sustain

hip fractures in hospital have poorer outcomes com-

pared with their peers who sustain hip fractures in the

community,15 including longer LOS,16 reduced return

to preadmission ambulation and functional status,

increased rates of discharge to permanent residential

care15 and higher mortality rates.16 Indeed, falls may lead

to chronic pain, reduced quality of life, functional

impairment, permanent disability and higher rates of

inpatient mortality.13,17,18

Health technology has the potential to influence this

outcome but has been limited by the lack of rigorous

evidence for effective single-technology interventions,

including sensors and electronic medical records.19

Moreover, clinicians’ perspectives toward the use of

health technology in falls prevention are not well-known,

despite systematic review evidence that staff attitudes

are crucial to successfully integrating any falls preventive

strategy.19,20

Nursing staff are familiar with using visual cues to

communicate falls risk and preventive strategies.21 Visual

cues, as part of a Falls Prevention Tool Kit, have been

shown in a single randomized controlled trial to be

effective in lowering hospital falls rate (3.15 vs. 4.18

per 1000 patient-days; P¼0.04), especially among thoseaged 65 years and over (rate difference 2.08 vs. 1.03 per

1000 patient-days; P¼0.03).22 However, further researchwas needed into whether such findings could be repli-

cated in different settings. Within the Geriatric and

Evaluation (GEM) unit at the Queen Elizabeth Hospital

(TQEH), a preliminary audit found 20% staff compliance

with existing patient bedside posters for falls prevention

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iversity of Adelaide, Joanna Briggs Institute. Un

(Fig. 1; Visvanathan R, Ranasinghe D, Hoskins S, Wood J,

Mahajan N, unpublished data). Nursing staff reported

these paper-based posters were time-consuming and

hence not completed, as they involved placing adhesive

colored dots on eight different locations of the poster

to indicate falls risk (i.e. green for low risk, yellow for

medium risk, red for high risk), before displaying the

poster by the patient’s bedside (Visvanathan R, Rana-

singhe D, Hoskins S, Wood J, Mahajan N, unpublished

data). Due to poor uptake and negative feedback of the

existing posters, and mindful of the pending electronic

health record (EHR) system due to roll out across public

hospitals statewide in South Australia, the opportunity

was seized to develop a health information technology

(HIT) tool in collaboration with ward clinicians. This HIT

tool incorporated an iPad 2 device (model number

A1315; Apple, Cupertino, California, USA) for direct clini-

cians’ entry of up to 13 common falls risk activities23

(Fig. 2), with automatic generation of visual cues for

bedside display (Fig. 3).

Our pilot study aimed to evaluate clinicians’ attitudes

toward this HIT tool, in particular, their experiences,

positive and negative perspectives and recommenda-

tions for improvement, both preclinical and postclinical

implementation (i.e. trial), to inform ongoing tool refine-

ment, ultimately as part of a novel movement-detection

sensor technology system for hospital falls prevention.

MethodsEthics approvalThe study protocol was approved by the Human

Research Ethics Committee of the Basil Hetzel Institute,

South Australia (HREC/13/TQEHLMH/66), and conformed

to the World Medical Association Declaration of Hel-

sinki.24 Each participant provided written, informed con-

sent prior to research involvement, and participant

information was deidentified.

Research methodologyMixed methods design was applied to allow for greater

robustness and richness of information gathered,25,26

with focus group research used to obtain qualitative

data simultaneously from multiple individuals on differ-

ent ideas and perspectives.27

Study protocolThe current pilot study was divided into three phases.

Phase 1 (pretrial) evaluated clinicians’ perspectives on

the HIT tool (i.e. study aims) prior to implementation,

using focus group discussion and surveys. Phase 2 (tool

trial) involved tool implementation on hospital wards.

Phase 3 (post-trial) examined clinicians’ perspectives on

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ST MargaretõsRehabilitation

Hospital

Stepping forward programš falls risk chart

Showeringš once seated Toiletingš once seated

Wet area

Wet area transfer Wet area mobility/AMB

StickerSticker

Sticker Sticker

StickerSticker

Patient sticker

Dry area

Dry area mobility/AMB Night mobility

Red dot needs hands on assistance

Yellow dot needs supervision and/or standby

Green dot independent

Bed mobility Dry area transfer

Figure 1. Example of a paper-based bedside poster using colored stick-on dots to indicate patient’s falls risk.

RC-A Teh et al.

the tool after trial completion, using focus group discus-

sion and surveys with similar themes as in Phase 1.

Focus group sessions were led by the chief researcher,

who was employed by TQEH as a medical doctor, but not

working on the wards at the time of the study. The chief

researcher defined focus group goals (i.e. study aims)

at each session and facilitated discussion for an hour or

until data saturation was reached (i.e. when information

occurred so repeatedly that additional data collection had

no additional worth).28 Textual data were transcribed

verbatim by the chief researcher from Dictaphone (Philips

PocketMemo voice recorder DPM8000; Atlanta, Georgia,

USA) recordings and written notes. Transcripts were not

returned to participants for comment.

Likert-scale surveys were derived following focus

group discussion and utilized similar themes. These were

distributed to ward staff over 2 week periods, before and

after the tool trial, by the chief researcher and two ward

clinical nurse consultants (CNCs), who were considered

nursing leaders and experts in clinical care.29 Completed

nonidentifiable questionnaires were returned to the

92 International Journal of Evidence-Based

iversity of Adelaide, Joanna Briggs Institute. U

researcher personally or via a designated tray on the

wards.

The HIT tool was implemented on the GEM unit (June

to August 2014), followed by the Acute Medical Unit

(AMU) (September to November 2014), over two conse-

cutive 12-week periods. Ward clinicians had up to

6 weeks of researcher training and reminders on tool

use (3-h-long sessions each week) and were indepen-

dent for the remaining 6 weeks. GEM staff utilized the full

period of researcher-led support, whereas AMU staff

declined researcher input after 1 day, citing staff confi-

dence with tool use.

The HIT tool took less than 5 min to use for each

patient. There was no automatic trigger for staff to use

the tool, other than reminders from the researcher in the

first 6 weeks. The iPad device was carried by the clinician

responsible for using the tool. This person directly

entered patient’s details (age, bed location, mobility

aid) and their own clinical judgment (yes/no responses)

about the patient’s day and nighttime falls risk for

13 different movement and location types (Fig. 2).

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Walking

Sitting/standing

Toilet

Corridor

Shower

In/out of bed

Yes

At-risk

No risk

At-risk

No risk

At-risk

No risk

Yes

Movements requiring supervision?

State additional locations where supervision required?

Figure 2. Example of a screenshot of direct clinician entry of patient’s falls risk assessment using the health informationtechnology tool.

ORIGINAL RESEARCH

Black-and-white A4-sized visual cues were automatically

printed at assessment completion (Fig. 3), and the same

clinician was responsible for displaying these paper-

based visual cues by the patient’s bedside. Ward staff

subsequently targeted falls preventive interventions

according to clinical judgment.

Both wards were given freedom on how to imple-

ment the HIT tool. AMU staff used the tool daily on all

ward patients. All registered nurses on AMU were rotated

to use the tool, which was usually completed by the

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registered nurse allocated to nonpatient-related duties

(e.g. ward medication management), to allow for timely

use of the HIT tool, unencumbered by other duties. GEM

staff used the tool on new admissions and in which falls

risk altered (e.g. posthospital fall), reasoning this as

appropriate for a subacute setting, in which patients’

falls risk changed less often compared with an acute

ward. The CNC and two registered nurses from GEM used

the HIT tool, due to limited confidence by the rest of the

staff in using the device.

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TQEHWard: GEMU

0700–2000Day

Movements requiring

supervision:

Walking Corridor Walking Corridor

Sitting/standing Sitting/standingShower

In/out of bed In/out of bedToilet

Toilet

Issue date: 28/01/2013

Movements requiring

supervision:

Additional locations where

supervision required:

Additional locations where

supervision required:

2001–0659NightYes

Requires walking aid?UR: 100001

Name: Alice AlicemanBed No.: 7.1

Figure 3. Example of an automatically generated visual cue from the health information technology tool.

RC-A Teh et al.

Setting and participantsThe study was conducted on two ground-floor medical

wards at TQEH, a tertiary teaching hospital in metropoli-

tan Adelaide, South Australia. The 16-bed AMU managed

patients in the acute phase of illness, whereas the 20-bed

GEM unit provided rehabilitative care aimed at restoring

patients’ function and independence after an acute

illness, usually with the goal of returning back home.30

Ward clinicians consisted of nursing [38.68 FTE (full-

time equivalent) GEM, 32 FTE AMU], junior medical (four

FTE GEM, five FTE AMU), and allied health staff, meaning

occupational and physical therapists (2.5 FTE GEM, two

FTE AMU). No pharmacists, speech therapists, dieticians,

social workers or senior medical staff were approached

to be part of this study.

Focus group participants were identified by ward

CNCs as clinicians having an expertise in falls prevention,

94 International Journal of Evidence-Based

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with greater than 5 years of clinical experience, and

working within GEM, AMU or the Central Adelaide Local

Health Network (CALHN) Falls Prevention group at the

time of the study. Five clinicians were involved in each

pretrial and post-trial focus group discussion, with one

participant involved on both occasions. All five post-trial

focus group participants were HIT tool users from AMU,

with six clinicians from GEM and CALHN declining to

participate as they had not used the tool or were unable

to attend the focus group session.

Survey participants consisted of clinicians working

within GEM or AMU at the time of the study, and

consecutively approached by the chief researcher in

the 2-week periods, before and after the tool trial. There

were 49 pretrial (29 GEM, 20 AMU) and 28 post-trial (20

GEM, eight AMU) participants. It was not recorded which

participants were involved both pretrial and post-trial.

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ORIGINAL RESEARCH

Post-trial, both those who had used the HIT tool (i.e. tool

users, n¼11) and those who had not (i.e. nonusers,n¼17), were included to reflect tool uptake. Post-trial,54 clinicians (65.9%) declined to participate as they had

no experience with or recommendations for improving

the HIT tool. Participation was voluntary with the option

to withdraw at any point.

AnalysisQualitative data from focus group sessions were manu-

ally analyzed using content analysis to systematically

code data and identify themes, to gain new knowledge

and initiate action.31,32 Descriptive statistics and logistic

regression were performed on quantitative survey data,

to describe and evaluate differences between clinicians’

perspectives pretrial and post-trial (P<0.05), with sub-

group analysis on users and nonusers using SPSS Statis-

tics for Windows, Version 22.0 (IBM Corp., Armonk, New

York, USA). Responses indicating ‘strongly agree’ or

‘agree’ were classified as positive, whereas those indicat-

ing ‘strongly disagree’, ‘disagree’ or ‘uncertain’ were

classified as negative responses to the item statement.

ResultsThe qualitative and quantitative data were integrated

into four main findings, and presented from Phase 1

(pretrial), followed by Phase 3 (post-trial), regarding

clinicians’ experience, positive perceptions, negative per-

ceptions and barriers to use, and recommendations for

refinement of the HIT tool.

Phase 1 (pretrial): Qualitative results fromfocus group sessionClinicians’ experiencePretrial, no participant had used the HIT tool. All partic-

ipants were familiar with using visual cues in falls pre-

vention, with four participants expressing negative views

about the existing posters using colored stick-on dots to

indicate falls risk. These were seen as a bit complicated,

tedious to complete, ineffective and therefore, underu-

tilized, due to time constraints with high patient turnover

and competing clinical duties.

Positive perceptionsIncorporating technology into falls risk assessment was

identified by three participants as beneficial in providing

staff with a fun, quick means of risk assessment. One

participant stated the HIT tool would serve as a stress

reduction tool for staff, in providing an immediate visual

of each patient’s falls risk factors. Four participants cited

benefits to patients and their families in increasing

knowledge on falls risk and preventive strategies, both

in hospital and on discharge.

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iversity of Adelaide, Joanna Briggs Institute. Un

Negative perceptions and barriers to useClinicians perceived the main barrier to tool implemen-

tation to be shifting a workplace culture that resisted

change and did not view hospital falls as a problem. The

HIT tool was seen as increasing work for clinicians, with

time pressures on staff thought to compromise accuracy

of falls risk assessment and placement of visual cues

at the correct patient’s bedside. Three participants

expressed apprehension about clinicians using new

health technology, with one participant especially con-

cerned about older workers and technology use.

Recommendations for refinementThree participants requested tool technology be simple

to use, and eventually incorporated into the upcoming

EHR system. They recommended providing staff with

tool education, with training attendance linked to points

for continuous professional development (CPD). CPD

referred to the number of hours stipulated by national

registration standards for clinicians to engage in ongoing

professional education per annum.33 Four participants

suggested involving patients and families in the tool

process, to improve adherence to falls preventive mea-

sures in hospital and at home. One participant advo-

cated senior leadership endorsement to drive tool

integration into hospital programs.

Phase 1 (pretrial): Quantitative results fromsurvey participantsThe majority of survey participants were women (81.6%),

nursing staff (73.4%), aged between 18 and 39 years old

(63.3%) and had 10 years or less of experience in clinical

care (57.1%).

Clinicians’ experienceNo participants had used the HIT tool pretrial.

Positive perceptionsThe majority perceived the HIT tool as an easy,

accurate and timely means of assessing patients’ falls

risk (items 1, 2 and 3, Table 1). Over 70% thought it

facilitated safer, better quality patient care, improved

staff’s understanding of patients’ falls risk factors, effec-

tively prevented falls, and were willing to use the tool if

made available (items 4, 5, 6, 8 and 9). Half the partic-

ipants cited that it would effectively prevent inpatient

falls (item 7).

Negative perceptions and barriers to useLess than half the participants considered potential

barriers to tool use as being duplication of written work

(44.9%), lack of time to use the tool (38.8%) and lack of

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Table

1.Comparisonbetw

eenpretrialandpost-trialresultsofclinicians’

perspectivesofthehealthinform

ationtech

nologytool,with

subgroupanalysesforuserstatus Pretrial,

n¼49(%

)

Post-trial

Total,

n¼28(%

)Users,

n¼11(%

)Nonusers,

n¼17(%

)Preusers

vs.

postusers

Prenonusers

vs.

postnonusers

Pre

vs.

post

(usersþnonusers)

Postusers

vs.

postnonusers

Benefits

ofHIT

tooluse

Easy

touse

duringbedto

bed

handover

39(75%)

13(46.4%)

6(54.5%)

7(41.2%)

0.22�

0.13�

0.16�

0.58

accurate

updatingfallsrisk

inform

ationcf.currentmethod

37(75.5%)

17(60.7%)

7(63.6%)

10(58.8%)

0.57

0.54

0.55

0.95

Updatesfallsrisk

inform

ationin

atimely

manner

36(73.5%)

17(60.7%)

8(72.7%)

9(52.9%)

0.89

0.43

0.62

0.48

Providessafercare

forpatients

risk

offalls

39(79.6%)

19(67.9%)

9(81.8%)

10(58.8%)

1.15

0.43

0.70

0.37

Improvesquality

ofpatientcare

43(87.8%)

19(67.9%)

9(81.8%)

10(58.8%)

0.63

0.23�

0.38

0.37

Improvesstaff’sunderstandingof

patients’fallsrisk

factors

35(71.4%)

12(42.9%)

8(72.7%)

4(23.5%)

1.07

0.12�

0.36

0.12�

Effectively

prevents

falls

26(53.1%)

7(25%)

5(45.5%)

2(11.8%)

0.74

0.12�

0.29�

0.16

Allowsmore

timeforstaffto

attend

tootherduties

7(14.3%)

3(10.7%)

2(18.2%)

1(5.9%)

1.33

0.38

0.71

0.28

Iwilluse

thistoolifitismade

available

44(89.8%)

21(75%)

10(90.9%)

11(64.7%)

1.14

0.25

0.53

0.22

Barriers

toim

plementingHIT

tool

Lack

oftime

19(38.8%)

11(39.3%)

6(54.5%)

5(29.4%)

1.90

0.66

1.11

0.35

Lack

offamiliarity

withtechnology

14(28.6%)

5(17.9%)

3(27.3%)

2(11.8%)

0.94

0.33

0.56

0.36

Duplicateswrittenwork

22(44.9%)

4(14.3%)

3(27.3%)

1(5.9%)

0.46

0.08�

0.19�

0.17

Lack

ofusability

0(0%)

6(21.4%)

1(9.1%)

5(29.4%)

>100

Undefined

4.17

Suggestedtoolim

provements

Providingtoolfeedback

tostaff

31(63.3%)

4(14.3%)

4(36.4%)

0(0%)

0.33

0.00

<0.01

Providingeducationalpresentations

ontoolto

staff

19(38.8%)

9(32.1%)

8(72.3%)

1(5.9%)

4.21

0.10�

0.65

0.02�

AwardingCPDpoints

tostafffor

attendingtooleducation

15(30.6%)

3(10.7%)

3(27.3%)

0(0%)

0.85

0.00

<0.01

cf.,comparedwith;CPD,continuousprofessionaldevelopment;HIT,healthinform

ationtechnology;OR,oddsratio.

� P�0.05,i.e.significant.

RC-A Teh et al.

96 International Journal of Evidence-Based Healthcare � 2018 University of Adelaide, Joanna Briggs Institute

ORIGINAL RESEARCH

familiarity with tool technology (28.6%) (items 10, 11 and

12). No participants perceived the HIT tool as lacking

usability (item 13).

Recommendations for refinementOver 60% recommended providing regular feedback to

clinicians to improve tool uptake (item 14, Table 1). A

third felt regular staff education on tool use and award-

ing of CPD points for training attendance would help

foster HIT tool use (items 14, 15 and 16)

Phase 3 (post-trial): Qualitative findings fromfocus group sessionClinicians’ experiencePost-trial, all focus group participants had used the HIT

tool. Participants A (tool use >10 times) and B (tool use

1–2 times) were the most verbal during discussion.

Positive perceptionsAll participants were positive about the tool’s benefits

and wanted to continue using it after trial completion. It

was perceived as beneficial to staff in being a visually

appealing and useful snapshot of patients’ falls risks.

Participants A and B cited its benefit to patients and

families as a teaching tool for falls risk and preventive

strategies.

Negative perceptions and barriers to useCompeting clinical duties and time pressures on a busy

ward were seen as barriers to tool use. One participant

outlined these barriers extended to challenges ensuring

visual cues were physically moved when patients were

swapped into another bed. Participants A and B reported

difficulties with technical aspects of the iPad application,

including difficulties managing these bed swaps and

surplus patient numbers, and re-entering the same

medical record number and demographic details for

returned patients.

Recommendations for refinementParticipants debated and decided against displaying

extra falls risk information on visual cues, preferring to

keep these uncluttered for simplicity and visual appeal.

Having A4-sized black-and-white visual cues, as opposed

to larger colored posters, was seen as appropriate given

already cluttered bedside walls and ongoing printing

costs. Participant B recommended coding high falls risk

status as a red dot on visual cues, with an automatic

trigger for staff to provide patients with printed infor-

mation on falls prevention. Participants A and B

requested an extra iPad device for more efficient and

timely tool use.

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Phase 3 (post-trial): Quantitative findings fromsurvey participantsPost-trial, survey participants were mainly women

(85.7%), nurses (92.9%), and had 10 years or less of

clinical experience (67.8%). Half were aged between

18 and 39 years old (50%). More than half (n¼54,65.9%) of ward clinicians declined to participate, citing

lack of use of, or recommendations for improving, the

HIT tool.

Clinicians’ experienceOf the 28 participants surveyed, 11 [eight (100%) AMU,

three (15%) GEM] had used the HIT tool on researcher

questioning. Most survey participants (60.7%) had not

used the tool, mainly due to low uptake on GEM unit.

Positive perceptionsThe majority of participants advocated ongoing use of

the HIT tool in clinical practice (75%) and were positive

about its accuracy, timeliness and facilitation of safer

patient care (items 2, 3, 4 and 9, Table 1). Compared with

pretrial, there were significantly lower numbers of non-

users who thought the tool was easy to use [odds ratio

(OR) 0.13], improved quality of patient care (OR 0.23) or

informed staff’s understanding of patients’ falls risk

factors (OR 0.12) post-trial (items 1, 5 and 6, Table 1).

Negative perceptions and barriers to useParticipants identified the main barriers to tool use as

lack of time to complete the tool (39.3%) and lack of tool

usability (21.4%) (items 10 and 13, Table 1). Significantly,

fewer participants thought duplication of written work

was a barrier, post-trial vs. pretrial (OR 0.19, item 12).

Recommendations for refinementThe main recommendation for improvement was for

staff education on the HIT tool (32.1%); however, this

was less so among nonusers compared with users (OR

0.02, item 15, Table 1).

DiscussionThe majority of clinicians advocated incorporating the

HIT tool in clinical practice, both pretrial and post-trial,

due to the benefits for staff and patients in hospital falls

risk assessment and prevention. Pretrial, clinicians were

positive about using a tool that incorporated visual cues

and health technology, both well accepted methods of

evaluating risk and preventing falls within literature.20,34

Post-trial, most clinicians continued to view the HIT tool

as useful to staff as an accurate, quick and timely means

of assessing patients’ falls risk. Indeed ease of workflow

has been identified by clinicians as an advantage of

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RC-A Teh et al.

incorporating EHR into clinical routine.35 Clinicians

within this study cited benefits to patients in facilitating

safer, better quality care and increasing their knowledge

of and participation in falls preventive strategies. This

echoes previous research espousing the advantages of

technology in promoting patient and family education

and engagement in health care.35

Pretrial, clinicians were concerned about potential

barriers to tool use being duplication of existing paper-

work, lack of time for tool use, difficulties navigating new

technology and workplace resistance to change. Paper-

work duplication and time constraints are well docu-

mented barriers to clinicians using EHRs.36,37 Systematic

review evidence has shown technical concerns and

opposition to change are frequently cited barriers to

EHR adoption.38 Addressing nihilistic staff attitudes

and workplace resistance to change have proved impor-

tant in the success of many hospital falls prevention

programs.20,39,40

Post-trial, clinicians criticized the HIT tool in terms of

lack of usability, lack of time to use it amidst competing

clinical duties and lack of clinical effectiveness in pre-

venting inpatient falls. Usability has been shown to be a

key factor in determining user acceptance of health

technology.41,42 Software difficulties are known barriers

to using technology in falls prevention programs,20,43

with users often requesting increasingly sophisticated

software function over time.44 Similar to our findings, a

previous qualitative study found clinicians viewed EHRs

negatively as one more thing to do in an already over-

burdened healthcare system, felt time constraints lim-

ited their use and wanted technology to accommodate

heavy patient volumes and busy clinical workloads.45

The perceived barriers of lack of usability and time to

use the tool were reflected in clinicians’ recommenda-

tions for technological refinement of the iPad application

and provision of another iPad device for more efficient

tool completion. User engagement and feedback have

been used to refine the HIT tool as part of action research

methodology,46–48 by improving technology, color cod-

ing falls risk, having an automated patient education

trigger and providing an additional iPad device. Other

recommendations for improving tool uptake included

providing staff education, a key component of many

effective hospital falls prevention programs,20 and ensur-

ing leadership endorsement, an important factor in

sustaining best nursing practice.49

Strengths and limitationsDespite user attitudes being a major factor in interven-

tion uptake,20 there remains a gap in knowledge on staff

perspectives of health technology use in falls assessment

98 International Journal of Evidence-Based

iversity of Adelaide, Joanna Briggs Institute. U

and prevention.19 This article adds to the depth and

richness of understanding of this area, through the

employment of mixed-methods design.50 Research

limitations included small sample size, single hospital

setting, poor response rate, lack of consistency in partic-

ipant follow-up and incomplete data on which partic-

ipants took the survey on both occasions and how many

times they had used the tool. Sample sizes and with-

drawal rates within this pilot study, were influenced by

the pragmatics of recruitment and the need to assess

study feasibility.51 In addition, items developed for sur-

vey data collection (based on interviews with five focus

group participants) may not have been representative of

all relevant issues. These survey biases may limit gener-

alizability of outcomes and comparison of pretrial and

post-trial results. Additional biases may have been intro-

duced by focus group participants’ reluctance to provide

their opinions, due to researcher presence and concerns

about workplace implications, and researcher bias in

interpreting textual responses to match preconceived

notions.

Future research directionsThe refined HIT tool will be retrialed on the wards, with

future research directed at evaluating clinicians’ use and

perspectives, and clinical effectiveness in falls avoidance,

of this improved HIT tool. The HIT tool could be imple-

mented in healthcare facilities with high prevalence of

falls, and among those patients who are at high falls risk,

such as older persons and those in residential care.

Ensuring the same clinicians participate in pretrial and

post-trial focus group discussions and surveys would

enhance the robustness of data gathered. In addition,

greater depth of information may be elicited by includ-

ing patients and caregivers in discussion, conducting

personal interviews and discussing one topic per focus

group session.

ConclusionThe findings from this study contributed to the limited

pool of evidence on clinicians’ perspectives toward

health technology use in falls prevention. Clinicians were

willing to use the HIT tool, identifying benefits to them-

selves and patients. Their concerns about usability and

time constraints were addressed in ongoing tool refine-

ment, with technological improvement and provision of

an additional iPad device for more efficient use. Includ-

ing end-users in development processes, as well as

having high staff uptake, are important in improving

the acceptance and usage of new technologies, and in

maximizing beneficial feedback to further inform tool

development. Further research directions may include

Healthcare � 2018 University of Adelaide, Joanna Briggs Institute

nauthorized reproduction of this article is prohibited.

ORIGINAL RESEARCH

evaluating clinicians’ and patients’ perspectives of the

refined HIT tool, and evaluating its clinical effectiveness

in hospital falls prevention.

AcknowledgementsThe Hospital Research Foundation provided a research

grant to Dr Damith Ranasingghe. Dr Ruth Teh thanks

Stephen Hoskins & Sharon Berry of the GEM & AMU

wards at the Queen Elizabeth Hospital, in Adelaide, for

their assistance with the research.

Conflicts of interestThe authors report no conflicts of interest.

References1. Australian Institute of Health and Welfare. Australian hos-

pital statistics 2012–13. Health services series no. 54. Cat.

no. HSE 145. Canberra: AIHW; 2014.

2. Lord S, Sherrington C, Menz H. Falls in older people: risk

factors and strategies for prevention Sydney: Cambridge

University Press; 2001.

3. Bradley C. Hospitalisations due to falls by older people,

Australia 2009–10. Injury research and statistics series.

Canberra: AIHW; 2013.

4. Moller J. Patterns of fall injury in an ageing population in

South Australia: a challenge for prevention and care Flag-

staff Hill: New Directions in Health and Safety; 2003.

5. Rutledge D, Donaldson N, Pravikoff D. Update 2003: fall risk

assessment and prevention in hospitalized patients. J Clin

Innovat 2003; 6: 1–55.

6. Department of Health and Ageing. An analysis of research

on preventing falls and falls injury in older people: com-

munity, residential care and hospital settings Canberra:

Australian Government; 2004.

7. Halm M, Quigley P. Reducing falls and fall-related injuries in

acutely and critically ill patients. Am J Crit Care 2011; 20:

480–4.

8. Staggs V, Dunton N. Associations between rates of unas-

sisted inpatient falls and levels of registered and non-

registered nurse staffing. Int J Qual Health Care 2014; 26:

87–92.

9. Australian Institute of Health and Welfare. Admitted patient

care 2014–15: Australian hospital statistics Canberra: AIHW;

2016.

10. Shorr RI, Mion LC, Chandler AM, et al. Improving the capture

of fall events in hospitals: combining a service for evaluat-

ing inpatient falls with an incident report system. J Am

Geriatr Soc 2008; 56: 701–4.

11. Cumming R, Sherrington C, Lord S, et al. Cluster randomised

trial of a targeted multifactorial intervention to prevent falls

among older people in hospital. BMJ 2008; 336: 758–60.

12. Barker A, Kamar J, Morton A, Berlowitz D. Bridging the gap

between research and practice: review of a targeted hos-

pital inpatient fall prevention programme. Qual Saf Health

Care 2008; 18: 467–72.

International Journal of Evidence-Based Healthcare � 2018 University

iversity of Adelaide, Joanna Briggs Institute. Un

13. Zhao Y, Kim H. Older adult inpatient falls in acute care

hospitals: intrinsic, extrinsic, and environmental factors.

J Gerontol Nurs 2015; 41: 29–43.

14. Morello R, Barker A, Watts J, et al. The extra resource

burden of in-hospital falls: a cost of falls study. Med J Aust

2015; 203: 367.

15. Murray G, Cameron I, Cumming R. The consequences of

falls in acute and subacute hospitals in Australia that cause

proximal femoral fractures. J Am Geriatr Soc 2007; 55:

577–82.

16. BrandC,SundararajanV.A10-yearcohortstudyoftheburden

and risk of in-hospital falls and fractures using routinely

collected hospital data. Qual Saf Health Care 2010; 19: e51.

17. Kannus P, Khan K, Lord S. Preventing falls among elderly

people in the hospital environment. Med J Aust 2006; 184:

372–3.

18. Kannus P, Sievanen H, Palvanen M, et al. Prevention of falls

and consequent injuries in elderly people. Lancet 2005; 366:

1885–93.

19. Teh R, Mahajan N, Visvanathan R, Wilson A. Clinical effec-

tiveness of and attitudes and beliefs of health professionals

towards the use of health technology in falls prevention

among olderadults. IntJEvidBasedHealthc2015;13:213–23.

20. Miake-Lye I, Hempel S, Ganz D, Shekelle P. Inpatient fall

prevention programs as a patient safety strategy: a system-

atic review. Ann Intern Med 2013; 158 (5 Pt 2): 390–6.

21. Dykes PC, Carroll DL, Hurley AC, et al. Why do patients in

acute care hospitals fall? Can falls be prevented? J Nurs Adm

2009; 39: 299–304.

22. Dykes PC, Carroll DL, Hurley A, et al. Fall prevention in

acute care hospitals: a randomized trial. JAMA 2010; 304:

1912–8.

23. Hitcho E, Krauss M, Birge S, et al. Characteristics and

circumstances of falls in a hospital setting: a prospective

analysis. J Gen Intern Med 2004; 19: 732–9.

24. World Medical Association. World Medical Association Dec-

laration of Helsinki – ethical principles for medical research

involving human subjects. 1964.

25. Morse J. Approaches to qualitative-quantitative methodo-

logical triangulation. Nurs Res 1991; 40: 120–3.

26. Johnson R, Onwuegbuzie A. Mixed methods research: a

research paradigm whose time has come. Educ Res 2004;

33: 14–26.

27. Krueger R, Casey M. Focus groups: a practical guide for

applied researchers. 3rd ed Thousand Oaks: SAGE; 2000.

28. Sandelowski M. Theoretical saturation. In: Given L, editor.

The SAGE encyclopedia of qualitative methods. 1st ed. Thou-

sand Oaks: SAGE, 2008;875–6.

29. Wilkes L, Luck L, O’Baugh J. The role of a clinical nurse

consultant in an Australian Health District: a quantitative

survey. BMC Nurs 2015; 14: 25.

30. South Australia Health. Terms of reference: SALHN (South

Adelaide Local Health Network) Adelaide: Falls Prevention

and Management Committee; 2014.

31. Krippendorff K. Content analysis: an introduction to its

methodology. 2nd ed Newbury Park: SAGE; 1980.

of Adelaide, Joanna Briggs Institute 99

authorized reproduction of this article is prohibited.

RC-A Teh et al.

32. Hsieh H, Shannon S. Three approaches to qualitative con-

tent analysis. Qual Health Res 2005; 15: 1277–88.

33. Australian Health Practitioner Regulation Agency. Continu-

ing professional development. Canberra: Australian Health

Practitioner Regulation Agency; 2016 ; Available from:

https://www.ahpra.gov.au/Education/Continuing-Profes-

sional-Development.aspx. [Updated 26 May 2014].

34. Dykes P, Carroll D, Hurley A, et al. Fall prevention in acute

care hospitals: a randomized trial. JAMA 2010; 304: 1912–8.

35. Bell S, Roche S, Johansson A, et al. Clinician perspectives on

an electronic portal to improve communication with

patients and families in the intensive care unit. Ann Am

Thorac Soc 2016; 13: 2197–206.

36. Fung C. Computerized condition-specific templates for

improving care of geriatric syndromes in a primary care

setting. J Gen Intern Med 2006; 21: 989–94.

37. Ammenwerth E, Mansmann U, Iller C, Eichstädter R. Factors

affecting and affected by user acceptance of computer-

based nursing documentation: results of a two-year study. J

Am Med Inform Assoc 2003; 10: 69–84.

38. Kruse C, Kristof C, Jones B, et al. Barriers to electronic health

record adoption: a systematic literature review. J Med Syst

2016; 40: 252.

39. Semin-Goossens A, van der Helm J, Bossuyt P. A failed

model-based attempt to implement an evidence-based

nursing guideline for fall prevention. J Nurs Care Qual

2003; 18: 217–25.

40. Dempsey J. Falls prevention revisited: a call for a new

approach. J Clin Nurs 2004; 13: 479–85.

41. Dillon T, McDowell D, Salimian F, Conklin D. Perceived ease

of use and usefulness of bedside-computer systems. Com-

put Nurs 1998; 16: 151–6.

100 International Journal of Evidence-Based

iversity of Adelaide, Joanna Briggs Institute. U

42. Abdekhoda M, Ahmadi M, Dehnad A, Hosseini A. Informa-

tion technology acceptance in health information manage-

ment. Methods Inf Med 2014; 53: 14–20.

43. El Mahalli A. Adoption and barriers to adoption of elec-

tronic health records by nurses in three governmental

hospitals in Eastern Province, Saudi Arabia. Perspect Health

Inf Manag 2015; 12: 1f.

44. Campbell E, Sittig D, Ash J, et al. Types of unintended

consequences related to computerized provider order

entry. J Am Med Inform Assoc 2006; 13: 547–56.

45. Nápoles A, Appelle N, Kalkhoran S, et al. Perceptions of

clinicians and staff about the use of digital technology in

primary care: qualitative interviews prior to implementa-

tion of a computer-facilitated 5As intervention. BMC Med

Inform Decis Mak 2016; 16: 44.

46. McNiff JWJ. All you need to know about action research.

Thousand Oaks: SAGE; 2006.

47. Dykes PC, Carroll DL, Hurley A, et al. Fall TIPS: strategies to

promote adoption and use of a fall prevention toolkit. AMIA

Annu Symp Proc 2009; 2009: 153–7.

48. Green L, Seifert C. Translation of research into practice: why

we can’t ‘just do it’. J Am Board Fam Pract 2005; 18: 541–5.

49. Fleiszer A, Semenic S, Ritchie J, et al. An organizational

perspective on the long-term sustainability of a nursing

best practice guidelines program: a case study. BMC Health

Serv Res 2015; 15: 535.

50. Creswell J, Plano-Clark V, Gutman M, Hanson W. Advanced

mixed methods research designs. In: Tashakkori A, Teddlie

C, editors. Handbook of mixed methods in social & behavioral

research. Thousand Oaks: SAGE, 2003;209–40.

51. LeonA,DavisL,KraemerH.Theroleandinterpretationofpilot

studies in clinical research. J Psychiatr Res 2011; 45: 626–9

Healthcare � 2018 University of Adelaide, Joanna Briggs Institute

nauthorized reproduction of this article is prohibited.