Alarms That Deserve AttentionMike Rayo3/29/2016

Tackling “Alarm Fatigue”

… With Sound Design

… With Policy

… By Sharpening

… With Multimodal Design

What I’ll becovering

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Alarm fatigue is …

Too many alarms

Startle effect of alarms

Alarms are too loud

Patients can’t sleep

Can’t hear alarms

False alarms

Non-actionable alarms

Nuisance alarms

Annoying alarms

Can’t remember what alarm is for

Can’t discriminate alarms

Can’t identify alarm

Worn out by alarms

Desensitized

OverwhelmedTired

Alarm fatigue – words matterResearchers looked for alarm-induced fatigueResearchers FOUNDalarm-reduced fatigueHealthcare Informatics Research journal published findings

BUTOnly used 7 of the 77 questionsUnclear whether results are due to fatigue mechanism or something else

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Cho et. al., 2016

So, what is the problem?Sensory discriminabilityCan I hear the alarms? Can I identify what events they are meant to signify?InformativenessHow often, historically, have the alarms (1) correctly identified a (2) hazardous eventAttentionDoes the clinician have sufficient attentional resources required to respond to the alarms?ResponseDoes the alarming system give the clinician sufficient information to respond appropriately to the event?

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Addressing the alarm problem by making phone-based alerts discriminable and

identifiable

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Cisco / connexall tones: current state

One tone associated with all eventsTelemetry low battery, Patient call, Asystole

Alerts often routed to multiple phones45% only go to primary phone35% go to a second phone20% go to second and third phone

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Questions for intervention

How many tones should we have?Fewer: easier to rememberMore: less ambiguous what tone is signifyingAnswer: few tone families, variations within family

What should they be?Same – already learned them (or have we?), and we already have themDifferent – ability to improve them, but have to design them!

How do we improve when and how alerts are routed from phone to phone?How will we get the answers to these questions?

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Quick-cycling for learning

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Discovery

Analysis

DesignDevelopment

Implementation

Quickening the learning cycle

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Large Implementation

Cycle length

Years

Months

Small Pilot

Weeks

“Research”Study

Heuristics

Days

Multiple tones project setup

Tone designResearch literature on memorability and encoding urgencyPast experience with emotional aspect of sounds and what they mean

Iterate!Learning experiment – 2 weeks, current against new setIterate!Pilot – comparing 2 units with new tones, escalation paths to “sister” units for 3 months (Ross 7 and James 18 new tones, Ross 5 and 7 stayed the same)Iterate!Implement! – Starting late March

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Iterations of Cardiac Crisis

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Initial Designs Sketches

Variations on Theme:CAR-DI-O-VAS-CU-LAR

After Learning Experiment

Iteration of Code Blue

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Initial Designs

After Pilot

Then roughly 10 more sounds combined with 10 different human and synthetic voices

Learning Experiment

Pilot

Resultant tones

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Cardiac CrisisAsystole, Vfib, Vtac)

Cardiac Other

Nurse Call

Technical High(Leads off, bed exit)

Technical Low(Low battery)

Code Blue Staff Assist

Listening experiment results - urgency

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Listening experiment results -identification

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Pilot results Intervention units:

Reduced response time8 seconds, 9% lower

Reduced escalation8% less to second phone13% less to third phone

Raised patient satisfaction scores

Received top rating for alarms up 19% more often

Varied with respect to nursing satisfaction with alarms

Addressing the alarm problem by decreasing false and non-

actionable alarms

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Cognitive Underpinnings of Alarm Problem

In high-tempo workplaces, many tasks and signals (e.g., alarms) compete for attention.Over time, clinicians determine (consciously and subconsciously) the informativeness of each of these signals.Informativeness is the likelihood that a signal is signifying what it is meant to signify (i.e., not false), and that what is signified is worthy of directing attention to (i.e., actionable).

Non-actionable Alarm: Correctly identified by the system, but it has no clinical significance and/or results in no change in plan of care—Asymptomatic Bradycardia)

False Alarm: A triggered event that is invalid or incorrect—Artifact; Asystole for a paced rhythm.

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Informativeness

Informativeness drops quickly when:There are a lot of false alarmsThere are a lot of non-actionable alarmsGroup alarms

1 signal meant to alarm for multiple different reasons1 signal meant to alarm for multiple different urgencies

Drops in signal informativeness result in:Proportional reduction in clinician response (i.e., 80% false alarms predicts 20% clinician response)Lack of response examples include ignoring, overriding, and disabling alarms (turn off, lower volume)

NOTE: these responses are not due to a lack of vigilance or effort – similar responses have been seen in animals, machines, and proven mathematically

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Performance with varying D’, resources

n=5 n=15

Addressing the alarm problem by decreasing false and non-

actionable alarms

… with Policy Changes

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Policy: Low-risk patients off monitoring

Findings: Less Monitoring, False Alarms

MeasureFalse Alarm Rate

Fewer False AlarmsBefore After p Value18.8% 9.6% <0.001

Findings: Freed up Resources

Addressing the alarm problem by decreasing false and non-

actionable alarms

… With Configuration Changes

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SpO2 Alarms: Pre & Post System Changes

33% actual reduction in Pulse Oximetry alerts in the Medical

Center

Another ~52,000 alarms triggered for SpO2 of 88%.

Future total reduction in Pulse Oximetry alerts in the Medical

Center will be ~61%!!!!

Reality check: Are we there yet?

SpO2 alarms default setting: less than 88% [2015]Results in heart hospital

3,400 alarms/day1.24M alarms/year

No, not there yet!

Addressing the alarm problem by decreasing false and non-

actionable alarms

… With Technology Design

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Next steps: directly targeting informativeness!

Using heuristics, experiments, and pilots to improve alarm algorithms

Less false and non-actionable alarmsIdentifying hazardous events betterRouting to additional clinicians only when needed

Designing visual displays when sensor algorithms cannot get to high enough positive predictive value

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Questions?

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