Bridging the gap

Tussen kunst en kunde

Ronald Petru, kinderarts-intensivist

CMIO

Radboudumc, Nijmegen

Gates’ Way: The Bill & Melinda Gates

Foundation is the force behind a global

mechanism of drug supply.

Medical Practice versus IT: different worlds

Information- -Data

It's far more important to

know what person the

disease has than what

disease the person has.

Hippocrates

A computer would deserve to

be called intelligent if it could

deceive a human into

believing that it was human.

Alan Turing

Building bridges between cultures

Human errors

• Endogenous versus exogenous

• Situation assessment versus response planning

• Problem detection • Diagnosis • Planning and execution

• Level of analysis • Perceptual • Cognitive

• Communication • Organizational

Risks of human interpretation

• Routine, overseeing of details, lack of focus

• Tunnel vision

• Confirmation bias (wishful thinking, ‘Hineininterpretierung’)

• Emotion

• Human memory

Lessons from the Aviation industry

•Efficiency

•Legibility / readability

•Safety culture

•Crew Resource Management

•And lots more….

Information

• Should be presented “ ready to digest” = ready to decide

• Additional information should always be readily available “on demand”

• Routine is handled by the “autopilot” (= clinical pathways)

Focus on the most relevant items (data filtering)

Information-related disasters

•Too little: missing

•Too much: overwhelmed

Loss of

“situational awareness”

From paper to better....

Capture data from very different sources

Gather information -> create data

Data (Big)

Observations

Labs Imaging

Functional tests

Vitals, fluids,

meds

Physical exam.

Measurements

Knowledge, training

Reference

Consults, (MD)Team

Compassion

Subjective ‘Art’, creativity

Diagnosis, prognosis

Treatment plan

Organize, reconcile Experience

Information

Traditional information management Modern

Data validity

• Source

• Validation steps?

• Who • How • When • …

• Gather data from the normal, intuitive workflow

• Caution with making entries mandatory and using defaults

Data interpretation • Small amounts: charts

• Larger amounts: graphical display (curves, trends)

• Huge amounts

• Complex graphical presentation • Show relevant relationships • Use multiple dimensions, visual and others

• Trend analysis?

“Everything that can be counted

does not necessarily count;

everything that counts cannot

necessarily be counted.”

Albert Einstein.

Data + Context = Information

www.worldmapper.org

EEG

Seeing the invisible: circulation

Seeing the invisible: breathing

Seeing the invisible: eye movements

Predict and prevent (adverse) events rather than to act on them

Individual differences in sub-enzymes their effects

Word of caution on using “Big Data”

• Distribution with 95% confidence interval

• A priori probability -> predictive value

• Medical diagnose ≠ direct marketing

Correlation validity <-> “p-hacking”

• “Traditional scientific evidence”:

• Hypothesis: H0 versus H1

• P < 0,05 Chance of coincidence is 1:20

• What if we test multiple hypotheses at the same time…..

• (do NOT ‘torture the data until they obey….’)

Conclusion: (Big) Data ≠ Information

ronald.petru@ .nl

• Data can lead to information

• Big Data can lead to more information

• Information must be translated into data • In order to support the human provider (brain)

• (Big) Data can, do, and will help to improve care

• In the end: it’s the information that counts!

• As a part of patient centred care

• With the ‘Art of Medicine’ and compassion

• Be cautious with traditional ‘scientific evidence’

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