Data-driven analytics for

decision support

Stein Olav Skrøvseth

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A rapid learning health care service

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Clinicalpractice

Knowledge

Hypotheses

Clinicaltrials

Reviews

17 Years*

Data

Synthesizedknowledge

Immediate

Reuse of data from clinical practice will enable continuous learning and translation of research results back into practice!

*Morris et al., J R Soc Med (2011)

© 2014 International Business Machines Corporation© 2014 International Business Machines Corporation 4

Natural Language Processing

Question & Answer Technology

MachineLearning

High PerformanceComputing

UnstructuredInformationManagement

KnowledgeRepresentation

& Reasoning

5Skrøvseth et al, Diabetes Techn. Ther. (2012)Årsand et al., J Diabetes Sci Technol (2012)

http://snow.telemed.noSkrøvseth et al., PLOS ONE (2012)

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Readmissions after index surgery

Augestad, Skrøvseth, et al, Am. Coll. Surgeons (2014)

Gastrointestinal surgery

Challenge: data analysis

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Days since first entry

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Words / day

Unique words

Entropy

Large p, small N

“Big” data may be big in only one direction

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p

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It is very easy to fit this model perfectly!

Causality?

Correlation is not causality, but it can be a very good hint.

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1999 2001 2003 2005 2007 2009

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Fatal collisions betweencars and trains (US)

Oil exports Norway to US

Bradford Hill criteria*

Temporality, strength, consistency, specificity, gradient, plausibility, coherence, analogy, experiment.

Pearl causality†

Directed acyclic graphs (DAGs) and causal calculus.

*Lucas & McMichael, Bull WHO (2005)†Pearl, Causality (2009)

Analytics solutions

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Statistical learning

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ClassificationClusteringRegressionDimensionality reductionCross-validation

Hastie et al., The Elements of Statistical Learning (2012)

Anastomosis leakage

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At surgery Surgery + 4 days

Sensitivity 94% 100%

Specificity 66% 77%

Soguero-Ruiz et al., IEEE J Biomed Health Inform, Oct 2014

Test utility

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Lots of tests are taken in healthcare.Many are unnecessary, or taken at wrong time.

Quantify the expected information content of a test at a given time in the patient’s trajectory.

Tests have different costs.Utility = information content/cost

Skrøvseth et al., AMIA Annual Symposium 2014

17Skrøvseth et al., Visual analytics in healthcare, AMIA (2014)

Challenge: Access to data

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Snow – Health Research Infrastructure

• Anonymized, aggregated data (N > 5)

• Access to identifiable datasets after legal and organizational clearance• Ethics committee• Data inspectorate / privacy ombudsman

• System owner committee• Patient consent

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Primary Care Hospitals

Snow

Patients

Researchers

Goals

Improve patient treatment and safety through secondary use of patient data.

New clinical knowledge is possible through use of analytics solutions.

Immediate transfer of knowledge back to clinical practice possible through decision support. 20

Challenges

Access to data

Random correlations

Variable and unknown data quality

Sparse data

Overfitting models

Unknown confounders

Dynamic systems

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Thank you for your attention

Stein Olav Skrøvsethsteins@telemed.no

www.telemed.no/sos

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