ORIGINAL RESEARCH Mitigating errors caused interventions ... · Received 11 September 2013 Revised 20 May 2014 Accepted 22 May 2014 ... iveness of interventions as a prerequisite

Mitigating errors causedby interruptions during medicationverification and administration:interventions in a simulatedambulatory chemotherapy setting

Varuna Prakash,1,2 Christine Koczmara,3 Pamela Savage,4 Katherine Trip,5

Janice Stewart,6 Tara McCurdie,2 Joseph A Cafazzo,1,2 Patricia Trbovich1,7

▸ Additional material ispublished online only. To viewplease visit the journal online(http://dx.doi.org/10.1136/bmjqs-2013-002484).

For numbered affiliations seeend of article.

Correspondence toVaruna Prakash, HealthcareHuman Factors, Techna Institute,University Health Network, 190Elizabeth Street RFE 4th Floor,Toronto General Hospital,Toronto, Ontario, Canada,M5G 2C4;[email protected]

Received 11 September 2013Revised 20 May 2014Accepted 22 May 2014Published Online First6 June 2014

▸ http://dx.doi.org/10.1136/bmjqs-2014-003372

To cite: Prakash V,Koczmara C, Savage P, et al.BMJ Qual Saf 2014;23:884–892.

ABSTRACTBackground Nurses are frequently interruptedduring medication verification andadministration; however, few interventions existto mitigate resulting errors, and the impact ofthese interventions on medication safety is poorlyunderstood.Objective The study objectives were to (A)assess the effects of interruptions on medicationverification and administration errors, and (B)design and test the effectiveness of targetedinterventions at reducing these errors.Methods The study focused on medicationverification and administration in an ambulatorychemotherapy setting. A simulation laboratoryexperiment was conducted to determineinterruption-related error rates during specificmedication verification and administration tasks.Interventions to reduce these errors weredeveloped through a participatory designprocess, and their error reduction effectivenesswas assessed through a postinterventionexperiment.Results Significantly more nurses committedmedication errors when interrupted than whenuninterrupted. With use of interventions wheninterrupted, significantly fewer nurses madeerrors in verifying medication volumes containedin syringes (16/18; 89% preintervention errorrate vs 11/19; 58% postintervention error rate;p=0.038; Fisher’s exact test) and programmed inambulatory pumps (17/18; 94% preinterventionvs 11/19; 58% postintervention; p=0.012). Therate of error commission significantly decreasedwith use of interventions when interruptedduring intravenous push (16/18; 89%preintervention vs 6/19; 32% postintervention;p=0.017) and pump programming (7/18; 39%preintervention vs 1/19; 5% postintervention;

p=0.017). No statistically significant differenceswere observed for other medication verificationtasks.Conclusions Interruptions can lead tomedication verification and administration errors.Interventions were highly effective at reducingunanticipated errors of commission in medicationadministration tasks, but showed mixedeffectiveness at reducing predictable errors ofdetection in medication verification tasks. Thesefindings can be generalised and adapted tomitigate interruption-related errors in othersettings where medication verification andadministration are required.

INTRODUCTIONSeveral reports, including the Institute ofMedicine’s To Err is Human1 and theAgency for Healthcare Research andQuality’s The Effect of Health CareWorking Conditions on Patient Safety2

have identified interruptions and distrac-tions as factors contributing to medicalerrors. Distractions were cited as causalfactors in nearly half of all medicationerror reports submitted to the UnitedStates national error-reporting database,and were the most frequently reportedfactor contributing to patient harm.3

Although interruptions may occur atany stage of the medication process, themedication administration stage is of par-ticular interest because it represents thelast opportunity for an error to be inter-cepted before reaching the patient.4

Nurses have cited interruptions and dis-tractions as a top cause of errors duringmedication administration,5 and such

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interruptions are significantly associated with a varietyof medication administration errors (eg, administeringwrong medication, dose, infusion rate).6 Thus, there isa strong need to develop interventions that can reduceinterruption-related errors during medication adminis-tration. To date, a variety of interventions have beenproposed, including: prohibition of non-essential con-versation, phone calls and pages7 8; use of ‘Do NotDisturb’ vests and signage9 10; use of a medicationadministration checklist9 10; and use of a clearlydemarcated ‘No Interruption Zone’11 or physicalbarrier12 in medication preparation areas. Notably,most of the above interventions were designed toreduce the number of interruptions occurring duringmedication administration, with limited evaluation ofthe resulting impact on medication administrationerror rates. Indeed, a recent review suggests that thereis only weak evidence regarding the effectiveness ofsuch interventions in reducing interruptions andresulting medication errors.13 Thus, there is a need todevelop effective interventions for interruption-relatederrors, and to assess the impact of these interventionson medication error rates.In a previous ethnographic study14 in an ambula-

tory chemotherapy unit at a large cancer centre inToronto, we identified two broad categories ofsafety-critical tasks prone to interruptions (ie, medi-cation verification tasks and medication administra-tion tasks) that could lead to errors. Medicationverification tasks consisted of checking the five rightsof medication administration (ie, right patient, rightmedication, right dose, right route, right time), andwere found to be primarily susceptible to errors ofdetection (eg, failing to notice a discrepancy betweenthe medication order and medication label). In con-trast, medication administration tasks such as admin-istering medication via infusion pumps orintravenous push were found to be susceptible toerrors of commission (eg, setting the wrong infusionrate). In the current study we aimed to (A) investigatethe association, if any, between interruptions andmedication verification and administration errors, (B)design interventions to reduce such errors in thepresence of interruptions, and (C) assess the effect-iveness of these interventions in reducing the identi-fied medication verification and administrationerrors arising from interruptions. We conducted asimulation laboratory experiment to assess the effect-iveness of interventions as a prerequisite to live clin-ical implementation.

MethodologyThe current work was conducted in three phases overa time period of 6 months. An overview of the threephases is shown in figure 1. Details of each phase aredescribed in the following sections.

Phases A and C: preintervention and postinterventionexperimentsStudy settingExperiments conducted in phases A and C took placein a high-fidelity simulation laboratory, where nurseswere asked to carry out medication verification andadministration tasks within a highly realistic but con-trolled setting. This experimental design was chosenas it allows test administrators to make detailed obser-vations of the impact of interruptions and interven-tions in a manner that would be impractical andunduly disruptive in a live clinical environment.The simulation laboratory was equipped with

theatre-style rooms, one-way glass and cameras (seeonline supplementary figures A1 and A2 in appendix1) that allowed realistic simulation of an ambulatorychemotherapy unit, including patient beds, chairs,computerised physician order entry (CPOE) system,intravenous infusion equipment and paperwork.Manikins were used instead of patients. All medica-tion bags, syringes, intravenous tubing sets, papermedication orders, medication labels and compu-terised medication order screens were identical tothose used in the institution’s regular practice.Coloured water or saline was used in place of realmedications. An audio recording of a busy hospitalunit was played throughout the experiment to providerealistic ambient noise. An actor-facilitator playing therole of a charge nurse guided participants througheach scenario. To further recreate the busy,interruption-filled environment, actors played theroles of patients, family members and fellow nurses.Four actors participated in this study, playing the rolesof a charge nurse, a family member and two patients.A fifth person, whose primary role was to assist theinvestigator in the observation room, also played theinterjectory role of a physician. Additionally, threerealistic patient manikins were placed in beds andchairs, thereby bringing the total number of mockpatients to five. Thus, the simulated environmentmimicked the cognitive load experienced by nursesworking in the chemotherapy unit. Further detailsregarding the simulation setting are provided in onlinesupplementary appendix 1.

Study designAn initial preintervention experiment was conductedto understand whether or not interruptions were asso-ciated with medication errors. Nurses were asked toperform medication verification and administrationtasks under two conditions: uninterrupted (Condition1) and interrupted (Condition 2). Thus, the experi-ment was a 2 (interruption condition)×7 (task type)within-subjects (repeated measures) design. The orderof interruption and non-interruption tasks was coun-terbalanced to avoid carryover effects.Results emanating from the preintervention

Condition 2 were used as a baseline (control) for the

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postintervention experiment. In other words, thepostintervention experiment compared Condition 2(where nurses were interrupted, with no interven-tions) to Condition 3 (interrupted, with interventions)using a between-subjects design. To permit compar-ability across the three conditions, equivalent scen-arios, planted errors, and type/timing of interruptions(where applicable) were used in all conditions, aslisted in table 1. The postintervention experimenttook place approximately 2 months following the pre-intervention experiment, as the time in between wasused to develop interventions (ie, Phase B).Table 1 describes all tasks (some of which contained

planted errors), interruptions and performancemetrics pertinent to the simulation experiment. Thetasks, planted errors and interruptions were designedbased on extensive ethnographic observations gath-ered during a prior study in this care area.14

Specifically, interruptions were selected based on thefrequency with which they occurred during each task,as observed during the ethnographic study. To furtherensure that the experiment accurately reflected partici-pants’ real-world practice, the tasks were presented toparticipants in realistic scenarios. Participants encoun-tered each planted error only once per experiment,even if they performed that task in multiple scenarios.For example, a participant may have been asked toverify medication names in five scenarios inCondition 1, but only one of the five scenarios con-tained a planted error in the medication name. Eachscenario contained a maximum of one planted error.Further details regarding the scenarios are presentedin online supplementary appendix 1.

ParticipantsNurses from the ambulatory chemotherapy unit wererecruited via a sign-up sheet located in the unit, andwere eligible to participate if they worked in the unitand routinely administered chemotherapy at the timeof the study. In accordance with institutional ethics

protocols, nurses provided informed consent andwere remunerated for their participation with anamount commensurate with their hourly wages.Participant characteristics are summarised in table 2. Aχ2 test of homogeneity revealed no significant demo-graphic differences between the two participantcohorts.

Experimental procedureAt the start of the study, the investigator introducedthe participant to the lab environment and brieflydescribed the process of simulation testing. In the pre-intervention condition, participants were asked tostart carrying out the medication verification andadministration tasks. In the postintervention condi-tion, the participant received 30 min of training onthe interventions prior to carrying out the medicationtasks. Specifically, in the training session, the investiga-tor explained each applicable intervention and how touse it. The participant was then asked to practiceusing each intervention and resolve any doubts beforestarting the experiment. Actors playing the roles offamily members and patients also assisted in the train-ing process by providing interruptions during the par-ticipant’s practice with interventions. The trainingprocess was concluded once the participant haddemonstrated his/her ability to correctly use eachintervention by successfully completing each practicetask and using each intervention when applicable. Theactor playing the role of the charge nurse then pro-ceeded to start the experiment by directing the partici-pant towards the first scenario.

Data collectionTwo trained observers collected live data from anobservation room located behind one-way glass whilethe experiment was in session. Specifically, observersdocumented errors (ie, Pass, Fail) on an Excel work-sheet containing a list of all tasks. If there was anintervention for which compliance was dependent on

Figure 1 An overview of the three phases, Phase A: Preintervention Experiment, Phase B: Intervention Design, Phase C:Postintervention Experiment.

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Table1

Descriptionof

tasks,interruptions,p

lanted

errors,performance

metricsandapplicableinterventions

insim

ulationexperim

ents

Task

Num

beran

dtim

ingof

interrup

tions*

Plan

tederror

Performan

cemetric

†

App

licab

leinterven

tion(s)‡

Medicationverificationtasks(assessm

ento

ferrord

etection)

1.Verifyin

gmedicationname

Particip

antw

asrequiredto

verifymedication

nameon

labelagainstelectronicandpaper

medicationorders

Duringmedicationverification,

particip

antw

asinterrupted

by:

2requestsfromnursingcolleague;1

question

frompatient

Medicationnameon

labeldidnot

match

nameon

medicationorders.

Sound-alike,look-alikemedications

werechosen

(eg,

Carboplatin

vsCisplatin)

Task

was

codedas

‘fail’ifparticip

antd

idnot

detecttheplantederror

Verificationbooth,

standardisedworkflow

2.Verifyin

gmedicationdosage

Particip

antw

asrequiredto

verifymedication

dosage

onlabelagainstelectronicandpaper

medicationorders


particip

antw

asinterrupted

by:

1questionfrompatient;1

work-relatedcall;1

requestfromphysician;1

background

infusion

pumpalarm

Dosage

onthemedicationlabeldid

notm

atch

thatinthemedication

order

‘Fail’ifparticip

antd

idnotd

etectthe

plantederror

Verificationbooth,


3.Verifyin

gmedicationvolumeinsyringe

Particip

antw

asrequiredto

verifymedication

volumeon

syringe

labelagainstelectronicand

paperm

edicationorders


particip

antw

asinterrupted

by:

1requestfrompatient’sfamily

Thesyringe

containedan

incorrect

volumeof

medication(underfilled

by5mL—

aclinically

significant

amount)


antd

idnotd

etectthe

plantederror

Verificationbooth,


4.Verifyin

gmedicationvolumeinam

bulatory

infusionpump(AIP)

Particip

antw

asrequiredto

verifymedication

volumeprogrammed

inAIPagainstm

edication

orderand

medicationlabel


particip

antw

asinterrupted

by:1

questionfromnursing

colleague,1

questionfrompatient’sfamily

Themedicationvolume

programmed

intheAIPdidnot

match

thaton

themedicationorder


antd

idnotd

etectthe

plantederror

Verificationbooth,


5.Verifyin

gpatient

identification(ID

)Particip

antw

asrequiredto

verifypatient

name

onmedicationlabelagainstthepatient’s

armband

Duringpatient

armband

verification,

particip

antw

asinterrupted

by:1

question

frompatient,1

requestfromnursingcolleague

Thenameon

themedicationlabel

didnotm

atch

thaton

thepatient’s

armband.Sound-alike,look-alike

names

werechosen

(eg,Pamela

Chan

vsPatricia

Chan)


antd

idnotd

etectthe

plantederror

Speaking

aloud

Medicationadministrationtasks(assessm

ento

ferrorcom

mission)

6.Intravenous

push

Particip

antw

asrequiredto

administera

chem

otherapeuticagenttoapatient

viamanual

intravenous

push

overthepharmacy-prescribed

timelineof

6–10

min

Duringtheintravenous

push,the

particip

ant

was

interrupted

by:conversations

frompatient

andfamily,1

requestfromnursingcolleague,

1questionfrompatient,repeatedbackground

infusionpumpalarms

Noerrorw

asplantedinthistask


antd

idnota

dministermedication

withinpharmacy-prescribed

timeline(eg,

6–10

min

forvinorelbine)

Visualtim

ers

7.Pumpprogrammingandinfusioninitiation§

Particip

antw

asrequiredto

administermedication

bycorrectlyhangingthemedicationbag,clo

sing

theclampof

previouslyhangingmedication

tubing

set,openingtheclampform

edicationto

bedelivered,and

programmingan

infusionpump

with

theprescribed

administrationrateand

volumeof

medication

Duringpumpprogrammingandinfusion

initiation,

theparticip

antw

asinterrupted

by:

requestsfromnursingcolleague,requestsfrom

patient,conversations

with

patient’sfamily,

andbackground

infusionpumpalarms

Noerrorw

asplantedinthistask


antp

rogram

med

pumpwith

incorrectrateor

volume,forgot

toopen/close

appropriateclamps,hungmedicationbags

atincorrectheightssuch

thatthewrong

medication

was

beinginfused,or

forgot

tostartthe

infusion

entirely

Nointerruptionzoneswith

motion-activated

indicators,

speaking

aloud,reminder

signage

*Applicableto

Conditions2and3only.

Thenumberand

timingof

interruptions

was

kept

consistentb

etweenthetwoconditionsto

permitcomparability.

†Particip

antswereinstructed

toreportdetected

errorsto

thecharge

nurse(playedby

anactor-facilitator).

‡Ap

plicableto

postinterventioncondition

(ie,C

ondition3)

only.

§Asdescribed

inonlinesupplementaryappendix1,

thepumpprogrammingtask

occurredinfour

ofthefivescenarios.Therefore,Pass/Failperformance

was

determined

usingcollective

criterion;thatis,

particip

antshadto

correctlyprogramthepumpinallfourscenariosto

receive

a‘Pass’.

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the participant (eg, speaking aloud), observers add-itionally documented whether or not the interventionwas used at each instance where an opportunity foruse was present. Observers compared notes after eachsession to ensure consensus. Any discrepanciesbetween observer notes were resolved by consultingvideo recordings of the session.

Data analysisData emanating from the experiment were codedaccording the criteria described in table 1(‘Performance Metrics’ column). McNemar’s χ2 testwas used to assess differences in error rates betweenConditions 1 and 2 in the preintervention experiment.Fisher’s exact test was used to assess differences inerror rates between Conditions 2 and 3 following thepostintervention experiment. These comparisons werejustified because all tasks, interruptions and scenarioswere kept equivalent between the two experiments.An α of 0.05 was used for all statistical tests. All datawere analysed using SPSS V.18.0 for Mac.

Phase B: intervention developmentTo ensure a participatory design approach (ie, anapproach where key stakeholders and end-users areinvolved in intervention design), nine nurses from thechemotherapy unit who had participated in previousphases of the study were recruited to take part infocus groups, where they brainstormed potential error

mitigation strategies and iterated upon the design ofinterventions. When appropriate, designs for interven-tions were sketched on paper. Qualitative inputregarding nurses’ impressions of the potential effect-iveness, uptake and feasibility of implementation ofeach solution was gathered during each discussion.Focus group data therefore served as a form ofrequirements gathering (supplemented by prior obser-vational studies) to inform intervention design.The resulting interventions are described below.

With the exception of the patient ID verification task,all other tasks employed multiple applicable interven-tions at a time (ie, interventions were employed as asystem, as shown in table 1).

Interventions for medication verification tasks (errors ofdetection)1. Verification Booth: Results of previous ethnography

revealed that nurses were interrupted 57% of the timewhile verifying medication label information against theCPOE system.14 With this in mind, a ‘Verification Booth’(figure 2A) was developed to provide nurses with a phys-ically distinct quiet space to conduct verifications at com-puter stations. The booth was a transparent enclosurefitting around computer stations that allowed nurses tomonitor and access their patients in case of medicalemergency.15 Strategic signage was placed on the boothto remind passers-by of the criticality of tasks takingplace within.

2. Standardised Workflow: During preceding phases of thestudy,14 it was observed that nurses rarely followed astandardised workflow for verifying medications prior toreaching the patient. When interrupted, nurses oftenomitted verification of medications against the CPOE,paper order or patient’s armband. The dual paper/elec-tronic order system used in the unit exacerbated thepotential for such omissions.

To mitigate errors resulting from these omissions,nurses’ workflow was standardised through training,Information Technology (IT) cues, and making use ofphysical space. Nurses were requested to pick up medica-tions from the pharmacy area, and proceed directly to theVerification Booth rather than approaching the patientfirst. Nurses would then check each medication labelagainst the electronic order, followed by the paper order,and would document on screen and paper that the medi-cations had been checked. A redesigned prototype of theCPOE software interface was created16 that accommo-dated a forced verification check process, and displayedvisual indicators of the status of verification of each medi-cation. Any discrepancies would therefore be resolvedbefore the medications reached the point of care and hadthe potential to cause harm.

3. Speaking Aloud: Nurses were asked to use a ‘SpeakAloud’ protocol when verifying medication labels againstthe patient’s armband.15 This required the nurse to ver-balise identifying information (eg, patient’s name, dateof birth and medical record number) during verification.

Table 2 Characteristics of participants in preintervention andpostintervention experiments

Characteristic

Participants inPhase A:Preinterventionexperiment (n=18)

Participants inPhase C:Postinterventionexperiment (n=19)

Age

18–29 years 5 3

30–39 years 8 7

40–49 years 3 5

50–65 years 1 3

>65 years 1 1

Sex

Male 3 2

Female 15 17

Years of nursing experience

<1 year 0 0

1–10 years 11 7

11–20 years 6 8

21–30 years 0 1

>31 years 1 3

Frequency of administering chemotherapy via infusion pumps

<Once a week 1 5

1–5 times per week 12 7

2–3 times per day 0 0

>3 times per day 5 7

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It was hypothesised that this action of speaking aloudwould alert patients and coworkers of the critical task athand, and help increase nurses’ focus on the numericalmatching task. An analogous scenario would be a bankteller counting money out loud before customers; in themedication administration environment, the action ofspeaking aloud cues patients and coworkers to wait untilthe critical task is complete before asking questions orotherwise engaging the nurses’ attention.

Interventions for medication administration tasks (errorsof commission)The following interventions were proposed for medi-cation administration tasks15:1. Visual timers for intravenous pushes: Results of a preced-

ing phase revealed that nurses lost track of time whenthey were interrupted during administration of intraven-ous push medications. This resulted in medications beingadministered too quickly or too slowly, both of whichcan have severe physiological consequences forpatients.17 To mitigate such errors, it was proposed thata visual timer (figure 2C) be attached to each intravenouspole with the infusion pump. Rather than a numericalstopwatch-like function, the timer counted down by pro-portionally reducing the visual coloured indicator, withno audible alarms or distractions. Nurses would start thetimer prior to commencing manual intravenous pushes.

2. No interruption zones with motion-activated indicators:The immediate area surrounding infusion pump poleswas visually demarcated as a ‘No Interruption Zone’(figure 2B). A motion-activated ‘busy’ indicator wasmounted on top of the intravenous pole, and would

light up when nurses stepped in front of an intravenouspole to hang bags, adjust tubing or program infusionpumps. This served as an automatic indicator topassers-by that the nurse was conducting a critical taskand should not be interrupted.

3. Speaking aloud: For the reasons listed previously (seepoint 3 under Interventions for Medication Verification),nurses were also asked to speak aloud when program-ming infusion pumps. For instance, a nurse would say,‘I’m programming a volume of 250 mL at a rate of500 mL/h.’

4. Reminder signage: To aid nurses in recovering from inter-ruptions during pump programming, and to assist themin programming infusion parameters correctly even afterbeing interrupted, strategic signage was placed on andnear infusion pumps (figure 2D). The signage remindednurses to check infusion parameters, clamps and tubingconnections. The prominent presence of this signage dir-ectly on the intravenous pole served as a visual cue,reminding nurses to double-check infusion parametersprior to administration.

RESULTSIntervention utilisationThe use of some interventions (such as theVerification Booth, No Interruption Zone,Standardised Workflow and CPOE enhancements)was forced upon the participant according to thedesign of the physical environment. For interventionsthat required active use by participants, the rate ofutilisation was as follows: Visual Timers: 100% util-isation; Speaking Aloud during Pump Programming:

Figure 2 Photographs depicting, (A) Verification Booth, (B) No Interruption Zones with Motion-activated Indicator, (C) Visual Timers,(D) Reminder Signage.

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53%; Speaking Aloud during Patient IdentificationVerification: 74%.

Error rates in medication verification and administrationError rates for medication verification and administra-tion tasks under all three experimental conditions areshown in table 3. The results show that interruptionswere associated with a significant increase in errorrates for the following four tasks: verifying volume ina syringe, verifying volume in an ambulatory infusionpump, intravenous push and infusion pump program-ming. The number of nurses committing errors inthese four tasks significantly decreased in the postin-tervention condition. However, use of interventionsdid not significantly decrease error rates for othermedication verification tasks.

DISCUSSIONTo our knowledge, this is the first study to make useof controlled high-fidelity simulation to explicitlyexamine the relationship between interruptions, errorrates and the effect of interventions on medicationerror rates. We identified that nurses committed sig-nificantly more errors in infusion pump programmingand intravenous push delivery, and failed to detecterrors in several critical parameters of medicationverification when interrupted. These findings provideimportant insight into understanding the contributionof work interruptions to medication errors. More sig-nificantly, we identified characteristics of interventionsthat were effective at mitigating these error types.Intravenous push delivery errors were significantly

reduced through use of a simple visual timer thatallowed nurses to temporally monitor the pushwithout requiring them to perform mental

calculations of elapsed time or remember numericalstarting time values. Nurses commented that the timerdisplay provided an easy visual reference withoutdetracting from their ability to teach, monitor andcare for patients throughout the duration of the push.Nurses were extremely eager to use the timers in theirown care environments, which is an encouragingfinding given the simple implementation and low-costnature of this intervention.Similarly, pump programming errors were signifi-

cantly reduced through a combination of NoInterruption Zones, motion-activated indicators,speak-aloud protocols and infusion pump signage.Because our study design tested these interventions asa system rather than individually, it is difficult to con-clusively identify the specific mechanisms that led tothis result. Speaking aloud may have helped improvenurses’ focus on pump programming parameters byincreasing the distinctiveness of the information beingverbalised,18 and the presence of the No InterruptionZones and associated signage may have acted as finalvisual cues for nurses, reminding them to conduct onelast check of pump parameters prior to administra-tion. Thus, a combination of environmental modifica-tions and simple speak-aloud interventions mayprovide a low-cost method of mitigating pump pro-gramming and infusion initiation errors caused byinterruptions.Interestingly, the speak-aloud intervention was not

effective when applied to patient identification verifi-cation tasks. We suggest that this differential effectmay be due to the very different nature of medicationverification vs medication administration. In contrastto the unpredictable and constantly evolving nature ofmedication administration, medication verification is a

Table 3 Error rates in medication verification and administration tasks, under all three conditions

Task

Number of nurses committing error (%)

Preintervention experiment Postintervention experiment

Condition 1:uninterrupted (n=18)

Condition 2:interrupted (n=18)

Significance(Condition 1 vs 2)*

Condition 3:interrupted (n=19)

Significance(Condition 2 vs 3)†

Medication verification tasks (assessment of error detection)

1. Verifying medicationname

3 (17%) 6 (33%) No (p=0.160) 4 (21%) No (p=0.319)

2. Verifying medicationdosage

4 (22%) 4 (22%) No (p=0.595) 1 (5%) No (p=0.153)

3. Verifying medicationvolume in syringe

9 (50%) 16 (89%) Yes (p=0.003) 11 (58%) Yes (p=0.038)

4. Verifying medicationvolume in AIP

10 (56%) 17 (94%) Yes (p=0.002) 11 (58%) Yes (p=0.012)

5. Verifying patient ID 7 (39%) 6 (33%) No (p=0.591) 6 (32%) No (p=0.593)

Medication administration tasks (assessment of error commission)

6. Intravenous push 8 (44%) 16 (89%) Yes (p=0.02) 6 (32%) Yes (p=0.001)

7. Pump programming andinfusion initiation

0 (0%) 7 (39%) Yes (p=0.03) 1 (5%) Yes (p=0.017)

*McNemar’s χ2 test (within-subjects analysis).†Fisher’s exact test (between-subjects analysis).

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highly mechanistic and predictable task19 that may bemore prone to habituation, confirmation bias andcomplacency effects. Thus, reliance on a ‘people-dependent’ intervention such as speaking aloud maybe less effective at reducing errors because it is ultim-ately reliant on human memory, vigilance and adher-ence to rules.20 21 After the experiment, some nursescommented that they may not remember to consist-ently speak out loud when interrupted in the realenvironment, suggesting that there is a ‘ceiling effect’to the effectiveness of this intervention. Studiessuggest that technological solutions that automatetasks (eg, bar code medication administrationsystems), force functions and relieve the memoryburden placed on humans may be more effective atreducing adverse events,20 21 and this automation maybe particularly well-suited to tasks that involve mech-anistic comparison or routine checking of informa-tion.19 22 The real value of the speak-aloudintervention might be in deterring people from inter-rupting nurses. However, we were not able to evaluatethis hypothesis because all interruptions were heldconstant in our experiments.For other tasks involving mechanistic verification of

information, interventions such as the VerificationBooth and standardised workflow with CPOEenhancements were effective at reducing wrongvolume errors in syringes and AIPs. We suggest thatour enhancements to the CPOE system (ie, forcedchecks of all medication parameters and clearly visibleverification status) acted as a cueing function thatencouraged task resumption by reminding nurses ofoutstanding verification items after being interrupted.This finding is in line with research suggesting thatuse of cueing functions on clinical IT systems canencourage task resumption by reminding the user ofthe task at hand.23–25 Interestingly, the same interven-tion was not effective at mitigating wrong medicationname and wrong dose errors. We attribute this findingto two reasons. First, the preintervention error ratefor these two tasks was already relatively low, indicat-ing that there was less room for improvement com-pared with the other verification tasks. This may be theresult of nurses being more vigilant in verifying medi-cation name and dosage compared with other medica-tion information. Second, the limited nature of theCPOE enhancements may have had an effect: whilethe prototype incorporated layout changes and visualcues, it did not incorporate interventions such asTALLman lettering (eg, CARBOplatin vs CISplatin)that specifically targeted ‘look alike, sound alike’ medi-cations. This further highlights the need for more spe-cificity in automated interventions to reduce nurses’reliance on vigilance and memory for error detection.

Limitations of the studyWe acknowledge that there are limitations to thisstudy. First, participants were aware that they were

being observed during the high-fidelity simulationexperiment. It is possible that their behaviour mayhave been altered as a consequence (ie, theHawthorne effect), though post-test debriefs suggestedthat this was not a significant problem given the highfidelity of the simulation. Second, the number oferrors planted in the simulation experiment was artifi-cially high compared with real life, and may havecaused participants to become more vigilant for errorsas the experiment progressed. However, the order ofpresentation of task types was counterbalanced tolimit this effect. Lastly, we were able to assess theeffectiveness of interventions when they were groupedtogether as a system, but our study design did notallow us to definitively assess the effectiveness of eachindividual intervention. We also did not assess the lon-gitudinal impact of interventions. Conducting theseadditional assessments is a goal of future research.

CONCLUSIONSThe present research identifies that interruptionsincrease the chances of nurses committing safety-critical errors when delivering high-risk medications.Our study adds to the literature by providing exam-ples of low-cost interventions (eg, visual timers) thatcan enhance patient safety by reducing medicationadministration errors. We found that our proposedinterventions were effective at reducing errors of com-mission in medication administration tasks, but lesseffective at reducing errors of detection in medicationverification tasks. We suggest that routine, predictableerrors of detection cannot be successfully mitigatedthrough ‘people-dependent’ interventions alone, butwould likely benefit from interventions that are moreautomated and less reliant on human memory andvigilance. Identifying and testing the effectiveness ofsuch interventions is a potential avenue of futurework. Because interruptions represent a highlycomplex sociotechnical phenomenon26 with poten-tially different effects on different task types, nosingle intervention is sufficient to achieve a reductionin error. Rather, mitigation efforts must be designedwith a thorough understanding of task and error typesto be effective.

Author affiliations1Faculty of Medicine, Institute for Biomaterials and BiomedicalEngineering, University of Toronto, Toronto, Ontario, Canada2Healthcare Human Factors, Techna Institute, University HealthNetwork, Toronto, Ontario, Canada3Institute for Safe Medication Practices Canada, Toronto,Ontario, Canada4Princess Margaret Cancer Centre, University Health Network,Toronto, Ontario, Canada5Lawrence S. Bloomberg Faculty of Nursing, University ofToronto, Toronto, Ontario, Canada6Odette Cancer Program, Sunnybrook Health Sciences Centre,Toronto, Ontario, Canada7HumanEra, Techna Institute, University Health Network,Toronto, Ontario, Canada

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Acknowledgements The authors are grateful to the oncologynurses who participated in all phases of this study. We alsothank Karin Ayanian, Michelle Dowling, Archana Gopal,Melissa Griffin, Diane Kostka and Ilia Makedonov for theirassistance in conducting the simulation experiment.

Contributors All authors contributed to this work. VP executedthe design and testing of interventions, analysed data, andprepared the manuscript. CK, PS, KT, JS, TM and JACparticipated in intervention development and analysis activities.PT conceived, designed and executed the overall research study.All authors contributed to and approved the final manuscript.

Funding This research was funded by the Canadian PatientSafety Institute. The opinions in the present paper are those ofthe authors and do not necessarily reflect the sponsor’s officialposition.

Competing interests None.

Ethics approval Research Ethics Board approval for this studywas obtained from the University Health Network (Reference:#08-0306-BE) and the University of Toronto (Reference:#24457).

Provenance and peer review Not commissioned; externallypeer reviewed.

Open Access This is an Open Access article distributed inaccordance with the terms of the Creative CommonsAttribution (CC BY 3.0) license, which permits others todistribute, remix, adapt and build upon this work, forcommercial use, provided the original work is properly cited.See: http://creativecommons.org/licenses/by/3.0/

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ORIGINAL RESEARCH Mitigating errors caused interventions ... · Received 11 September 2013 Revised 20 May 2014 Accepted 22 May 2014 ... iveness of interventions as a prerequisite