human cognition & artificial intelligence€¦ · •data presentation –show patterns,...

Human Cognition & Artificial IntelligenceRecognition of and response to the deteriorating patient

Raj Behal, MD MPH

[email protected]

Twitter @safetydoc

NSW Medical Leadership Forum24 November 2018

© Raj Behal, MD, MPH

mailto:[email protected]

Mimicking higher human cognition

An AI algorithm – generative adversarial network (GAN) – created the “painting” that some human paid over $400,000 to buy

Is this just a gimmick?

Or a potential breakthrough?


Can a computer “see”?

AlexNet (a deep convolutional neural network for computer vision)


Few examples of AI from healthcare

Pneumonia Cardiac Event Risk Skin Cancer Arrhythmia

Caveat: Only a few AI algorithms are street-ready and still require human-in-the-loop


About human cognition

• Human cognition is highly evolved and efficient

• Human cognition is subject to heuristics, biases, saturation

• Human cognition defaults to detecting marked, rapid changes

(rather than small, subtle changes)

• We can better detect phenomenon for which we have a

mental model and a goal


About deterioration

Which of these deteriorations is easier to recognize?

Sepsis, or

Cardiac arrest


About deterioration

• Delta: Subtle vs. marked change

• Time course: Gradual vs. rapid change

• Pattern: Linear vs. non-linear

Sepsis – subtle (initially), gradual, non-linear

Cardiac arrest – marked, rapid, linear

Retroperitoneal hemorrhage - ?

Respiratory insufficiency - ?


Is recognition of patient deterioration a real problem?


Major factors contributing to in-hospital mortality: Changed in the last decade?

Behal and Finn. Academic Medicine. Dec 2009.

We conducted these assessments in 16 US teaching hospitals a decade ago: These same factors still account for most of the morbidity and mortality

Events during off-hours (evenings, nights, weekends, holidays) are more likely to be complex cases, more likely to have issues with recognition of situation and rescue from complication


Behal (unpublished data based on DECS tool)

What must happen before we can recognize and act on signals?

1

Test results

Clinical monitors

Physical exam

Patient history

Cues

Team

Alarms

2

“I can put it all together to

understand what is happening”

3

“I can anticipatewhat might happen next and I know

what I should do”

Model of Situational Awareness in Patient Safety (SAPS): Go through 3 Levels of SA

Decisions and Actions

Feedback

“I have all the pertinent information”

Adapted from Endsley (1995)


1

Not interpreting data correctlyNot recognizing how fast situation is changingNot recognizing what will happen next

2 3

Model of Situational Awareness in Patient Safety (SAPS)

Decisions and Actions

Feedback

Not knowing what’s important to collect – blind spotsNormalization (of signals, e.g. alarms)Biases

Common cognitive errors

Cultural barriersReluctance to get help


SAPS Disruptors: What disrupts SA in real-life patient care?

Inexperience, stress, fatigue, workload, distractions, hand-over, incomplete or conflicting data, equipment malfunction, complex or rapidly evolving situation, rare condition or uncommon presentation, cognitive biases, over-confidence, no monitoring or follow up, …

What are the common clinical scenarios where deterioration is missed?

Is the deterioration marked/rapid/etc.?

What commonly goes wrong at each level of SA in these cases?

How will you design care workflows that mitigate many of these disruptors?

How can you prevent deterioration in the first place?

How can technology augment our ability to detect or predict deterioration?

Augmenting human cognition with EHR

• Electronic health record augmenting situational awareness• Level 1 SA

• Data presentation – show patterns, trends, interactions

• Alerts (must be used very selectively)

• Level 2 SA• Risk scores, classification (avoiding black boxes)

• Level 3 SA• Predictions, Order sets, protocols

• Action• Must plan human response to alerts and risk scores


LACTATE

>=2<2

>=90

<90

SBP

Conversion of an algorithm for sepsis to a simple 2x2 decision matrix (triggered in EHR)

Crisis Nurse (“MET”)

Human-in-the-loop system for sepsis


Gradual deterioration: AI-based prediction of kidney failure while waiting for liver transplant

Behal et al. Prediction of renal dysfunction among patients waitlisted for liver transplant using deep learning neural network (pre-publication)

Demographics & clinical data (features) available at time of listing for transplant

Binary Classifier

Modeled complex interactions using deep neural network

Measures of performance

AUC 0.9

Precision (PPV) 74%

Recall (Sensitivity) 66%

Multi-modality automation in ICU

Detection of pain or delirium from facial expression Detection of hand hygiene


Summing up

• Human cognition has limits, especially under stressors

• Slow and subtle changes in condition are more difficult to recognize

• Situational awareness is often disrupted

• Once lost, SA is hard to regain in real-time: FOCUS ON PREVENTION

• Design workflows with safeguards for disruptors of SA

• EHR and AI algorithms can augment human cognition

• Predictions are easy, implementation is hard -- but feasible



QUESTIONS


APPENDIX

SAPS Disruptor-Countermeasure Matrix: Tag each level with disruptors


Disruptor of SA L1Data

L2Sensemaking

L3Decision

Countermeasure

Inexperience Protocol, supervision

Stress

Fatigue

Distractions

Workload

Missing data

Monitoring / follow-up

Hand-over

Complex condition / scenario

Rapidly evolving situation Drills

Rare condition or uncommon presentation

Cognitive biases

Hubris, over-confidence

Other:

Cognitive biases important sources of error

Anchoring bias: Locking on to salient features in a patient's initial presentation too early in the diagnostic process and failing to adjust in light of later information.

Availability bias: Judging things as being more likely if they readily come to mind; for example, a recent experience with a disease may increase the likelihood of it being diagnosed.

Confirmation bias: Looking for evidence to support a diagnosis rather than looking for evidence that might rebut it.

Diagnosis momentum: Allowing a diagnosis label that has been attached to a patient, even if only as a possibility, to gather steam so that other possibilities are wrongly excluded.

Overconfidence bias: Believing we know more than we do, and acting on incomplete information, intuitions and hunches.

Premature closure: Accepting a diagnosis before it has been fully verified.

Search-satisfying bias: Calling off a search once something is found

25

Academic Medicine 2003

An analytic and action framework based in science of safety

Review of several thousand adverse events, with deeper dives into certain event types with serious harm –including death – and facilitation of improvements in ambulatory and hospital settings

Actual cases reported by agencies and in the literature

About SafetySteps

https://itunes.apple.com/us/book/safetysteps/id521567746?mt=11

https://itunes.apple.com/us/book/safetysteps/id521567746?mt=11

Two components of SafetySteps

Humanfactors

Hazards

System&process

design

Management

controls

Patientfactors

Learning&

culture

SafetySteps2.0100-PointTreatmentPlan

Removethehazard SubstitutelesserhazardConstrainexposureto

hazard

User-centereddesignof

processes,toolsSupportdecisionmaking

Managesituational

awareness

Simplify,standardize,

support

Managecriticalcontrol

points,decouplesteps

Create&testrescue

process

Privileges,oversight,

supervision

Provideeducation&skill

training

Alignpolicy,procedure,

resourceswithrequirements

Feedback,double-loop

learningManagesocialnorms Managebehaviors

PatientselectionRisk-stratification&

mitigationShareddecisionmaking

25

Treatments

20

20

15

10

10

ContributoryFactors

©RajBehalMD

Analysis Treatment (Action) Plan

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