Pathology testing plays an important role in the management of complex patients. Pathology laboratories continue to improve the speed and accuracy of result reporting, however the clinical management of pathology remains challenging: there is well documented variation in ordering practices, slow take-up of order sets, and up to 40% of pathology tests are not reviewed by clinical staff. The inefficient clinical utilization of pathology is a significant cost, both directly and through increased patient length of stay in hospital. One of the established ways to improve health care delivery is integrated clinical decision support (CDS). Malcolm discusses how the effective implementation of CDS for pathology results can improve clinical productivity and patient safety. Looking further ahead the challenge for health care is to develop models of care that better tailor decisions to the needs of individual patients, and technology is required to achieve this goal. He gives a high level overview of recent advances in technology, such as big data analytics and deep learning, that will be vital in creating a sustainable health care system.

1. PATHOLOGY IN CLINICAL DECISION
SUPPORT AND THE ROLE
OF DEEP LEARNING
MALCOLM PRADHAN
CMO

2. ▶ Alcidion is a Health IT
company based in Adelaide
▶ Innovative products based
on the Miya Platform
▶ Miya ED
▶ Miya Patient Flow
▶ Miya Clinic
▶ Miya Clinicals
BACKGROUND

3. ▶ My background
▶ MBBS University of Adelaide
▶ PhD Stanford University
▶ Founding Fellow of ACHI
▶ Adjunct Professor, University of South Australia
▶ Previously Assoc. Dean IT at University of Adelaide, Nehta Clinical Lead
BACKGROUND
Alcidion’s view is that Health IT systems should play a more
significant role in assisting clinicians delivery high quality health care.

4. ▶ Aging population, by 2050
▶ Can healthcare scale?
WHAT’S ON THE HORIZON?
Actuaries Institute Green Paper
December 2014
75 y.o. +
2x
per 100 working
85 y.o. +
2.3x
per 100 working
Population > 65 y.o.
40%
Hospital costs
2.5x
by 2024

5. ▶ Australia has a very high performing health care system relative to
others in the OECD, however…
THE CURRENT STATE
Adverse Events
15%
multi-day episodes
Clinicians spend
30%
of time with patients
Up to
40%
lab results not seen
Non compliance
30%
to guidelines

6. ▶ Community based care
▶ Chronic disease management using precision medicine
▶ Focus on hospital avoidance
▶ But how?
▶ 2-3 year waiting list for outpatients
▶ ED blocks
▶ Significant % of healthcare dollars spent in last months of life
▶ And in the community
▶ Significant logistics to organise care
HEALTHCARE HORIZONS
Logistics,
planning,
prioritisation

7. ▶ Community based care
▶ Lower cost (skilled) healthcare workers supported with CDS
▶ Structured data capture + low cost tests + patient self data collection monitored by
CDS systems that identify and escalate risk to more expensive interventions
▶ Chronic Disease Management
▶ Monitoring of therapy, comorbidities
▶ Integrating current patient state with plans compatible with patient preference
▶ Hospital avoidance and management
▶ More objective criteria for admission and continued hospital stay
▶ Maximisation of utility across population
▶ Clinical expertise for exception based decisions
▶ Physician override based on patient condition and preferences
SUSTAINING HEALTHCARE

8. ▶ Effective use of pathology is essential
▶ Driver of clinical decisions
▶ Resource utilisation
▶ Early detection of chronic disease, deterioration
▶ Therapy monitoring
▶ Comorbidity detection
▶ Integration with other patient data
▶ Genomic
▶ Observations
▶ Structured data
▶ Preferences
PATHOLOGY’S KEY ROLE

9. ▶ Significant evidence that
shows clinical benefits
with increased use of
CDS (not guaranteed)
▶ Reduction in variation
▶ Reduction in error
▶ Improved compliance to
best practice
▶ Decreased LOS
▶ HIMSS EMRAM
▶ Meaningful Use
EVIDENCE FOR CDS

10. ▶ Saving time by results management in ED
▶ Reducing time to results by 1 hour +
▶ Increasing peer review of ordering in ED
▶ Full audit of witnessing results
▶ Missed results tracking
▶ Abnormal results, radiology, micro, histopathology returned post-discharge
▶ Full tracking of actions on follow up pathology
▶ Governance rules to ensure the right clinicians are notified
EXAMPLES OF PATHOLOGY CDS

11. MIYA ED FOR LAB RESULTS

12. MIYA ID

13. MISSED RESULTS TRACKING

14. ▶ Clinical context requires structured clinical data
▶ Without context alert spam is a problem
▶ Despite large investments in health IT most data are captured as free
text
▶ Structured data captures data so that the computer can understand
the concepts
▶ Mapped to standard terminology e.g. LOINC, Snomed-CT, ICD, etc.
▶ Used as part of a structure (ontology) for CDS
▶ Coded outcome data is particularly important, yet not routine
▶ In a $150b+ industry!
STRUCTURED DATA

15. SMARTFORMS

18. PREREQUISITES FOR IMPROVEMENT
Clinical Engagement
System Drivers
Technology
Workflow improvement
Leadership
?
?
Safety focus
Reimbursement
Flexible IT
CDS
✗
✗
?
✔

19. ▶ Multiple copies of labs are sent, even if the data doesn’t change
▶ Some variation in HL7 message structures
▶ Lab naming consistency
▶ Multiple lab result variations
▶ Micro results are not always atomic
▶ Inconsistent use of LOINC
▶ Sequencing problems
▶ Invalid state transitions e.g. Final back to pending
▶ Lab sent with different URN without delete messages on first URN
▶ Assay units changed without notification
PATHOLOGY AND CDS

20. ▶ Vision, image recognition, speech, natural language, self-driving cars
▶ Large networks
▶ 24m nodes, 15 billion connections
▶ Recently a 160b paramater network
▶ In 2012 Google improved speech recognition
by 30%, training < 5 days on 800 machines
▶ Require a lot of ‘labelled’ data (coded outcomes)
DEEP LEARNING

21. ▶ Highly paramaterised, non-linear
▶ Traditional models apply to linearly separable problems
▶ Neural networks (and belief networks) synthesise new values as they learn
▶ Can handle correlated data, not just strongest parameters
▶ Neural networks with 3, 6, and 20 neurons
WHAT’S SPECIAL ABOUT NNS?
Figure2-10. A visualization of neural networkswith 3, 6, and 20neurons(in that order)
in their hidden layer.
Buduma, N. Fundamentals of Deep Learning

22. ADAPATIVE MACHINES

23. VISUAL QUESTION ANSWERING

24. PICTURE TO DESCRIPTION
A close up of a child holding a stuffed animal.

25. ▶ Trained on letters of War and Peace
▶ Generated random sentences from training
▶ 500 iterations
▶ 2000 iterations
GENERATING TOLSTOY
we counter. He stutn co des. His stanted out one ofler that concossions and
was to gearang reay Jotrets and with fre colt otf paitt thin wall. Which das
stimn
"Why do what that day," replied Natasha, and wishing to himself the fact the
princess, Princess Mary was easier, fed in had oftened him.
Pierre aking his soul came to the packs and drove up his father-in-law
women.
http://karpathy.github.io/2015/05/21/rnn-effectiveness/

26. ▶ Very similar algorithms to 1990’s neural
networks
▶ Networks are deeper
▶ Many filter layers for images (Convolutional
networks)
▶ Many steps over time for language and sequences
(Recurrent networks)
▶ Lots of labelled data
▶ Stanford has created a 14m + database of labelled
images for training
▶ Compute power is the main difference
▶ Thanks to video games!
SO WHAT’S CHANGED?

27. PROGRESS IN COMPUTE 1996-2013

28. COMPUTE 2015

29. ▶ 600 images, 1157 mitosis
▶ Used pixels close to mitoses
▶ 66K positive training samples (all pixels closer than 10 px to a mitosis)
▶ 2M negative training samples
▶ GPU Compute
▶ 5 months training time
on a CPU
▶ 3 days on a GPU
▶ Expert performance
MITOSES DETECTION
3
Fig.1. Top left: one image (4 MPixels) corresponding to one of the 50 high power fields repre-
Ciresan, D. Mitosis Detection in Breast Cancer Histology Images with Deep Neural Networks

30. STILL PROGRESS TO BE MADE…

31. ▶ Many neural networks do not output probabilities
▶ Can’t be integrated with preferences (utilities) for decision making
▶ Overconfident
▶ Can’t be used to determine what’s next best test to do
▶ Can’t update probability of missing outputs and therefore can’t be used to calculate
value of information
▶ Note easily used in new settings
▶ Prior probabilities and likelihoods are combined in NN
▶ Not easy to identify areas of the network responsible for solving particular parts of
the problem
▶ Parameter settings is a bit of a black art
LIMITATIONS

32. ▶ Patient flow
▶ Recognizing impending problems
▶ Identifying complex and well patients
▶ Planning flow strategies to mitigate block
▶ Chronic disease management, aged care
▶ Detection of changes in health trajectory
▶ Changes in behaviour, affect, engagement
▶ Integrating genomics with clinical data
▶ Image analysis
POTENTIAL IN HEALTH CARE

33. ▶ Pathology has numeric and image data so is a strong candidate for
machine learning solutions
▶ Early days
▶ Significant care required for production use
▶ Integrating innovations into the clinical workflows is a big challenge
▶ High quality data with outcomes is required
▶ Innovation in this area is essential for the sustainability of health care
▶ Video games are pretty cool these days…
SUMMARY

35. SMARTFORMS EXAMPLE

36. ▶ Computers are good, and getting better at
▶ Monitoring thousands of data points to identify patterns — lab results and
increasingly images
▶ Learning from experience
▶ Temporal patterns
▶ Planning and monitoring complex logistics
▶ People are good at
▶ Complex diagnoses
▶ Managing and understanding patients
▶ Matching therapy to patient goals
COMPUTERS IN HEALTHCARE

37. ▶ Meaningful Use program, resulting from the HITECH act
▶ About US$30b of incentives to incorporate IT systems since 2010
▶ In the last 12 months there have been numerous US Senate hearings
about the program
LESSONS FROM THE US
Low user
satisfaction
No evidence of
cost savings
“Information
blocking”
“Gag orders” in
contracts