PaJR Presentation at Health Informatics Society of Ireland - Oct 2011

1. Patient Journey Record (PaJR)
Online Prediction System
Jing Su, Lucy Hederman, Atieh Zarabzadeh, Dee Grady, Carmel Martin,
Kevin Smith, Carl Vogel, Enda Madden, Brendan Madden
Trinity College Dublin, UCC, PHC Research, GroupNos
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2. Avoidable hospitalisations
• PaJR is a telephone service that targets avoidable
hospitalisations
• Most hospital admissions
• are in older, sicker people with multiple diseases
and conditions
• are unpredictable in the short term with current
systems
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3. $/day
$5,000
$1,000
$100
Early Alertsof
deterioration helps
prevent the patient
$10 entering expensive
treatment
$1
Self care Complicated Complex Hospital
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4. The PaJR Service
Michael Anon
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5. The PaJR Survey
Michael Anon
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6. The PaJR approach
• Use lay callers
• Ask the questions that predict
hospitalisation
• Not disease specific
• Intervene early
• Alert accurately …
• ~ 50% reduction in hospital
admissions in pilot sites
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7. PaJR Prediction Service
Michael Anon
• Predicts patient
deterioration based on
record of call.
– Self-rated health, taking
meds,…
– Brief text entries
• Uses a predictive
model learned from
examples of calls
leading to unplanned
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8. Machine Learning
ML: automatically induce from the
examples a model that accurately
predicts new cases.
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9. Simple predictive model:
a decision tree
A - decision node
A
yes no - leaf node
K=y (K means
B B
UnplannedEvent)
<n ≥n <p ≥p
C A sample decision tree
K=y K=n K=y on UnplannedEvent
yes no
Eg: A = “her sister”;
B = AvgWordLength; K=n K=y
C = takingMeds
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10. Machine learning
• ML Tools such as Weka or Timbl provide
algorithms to produce prediction models
from examples (“training data”).
• The examples must be presented to ML
tool as a collection of features.
• Expertise and skill is needed to identify /
derive / represent features of examples
that might predict the outcome.
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11. PaJR‟s Current ML
• Predicts unplanned events, urgent
unplanned events, self rated health.
• Uses decision trees.
• Weights false negatives 500 more costly
than false positives
– A missed deterioration is bad.
– An inappropraite alert to a carer to call a
patient is OK.
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12. Accuracy
• Predicting urgent
unplanned events True False
(UUE)
Negative 1091 4
• Training data (OK)
– 1621 phone calls
Positive 23 453
– 27 urgent unplanned
(UUE)
events
• False Negatives cost
500 times FPs • Fewer than 1/3 of the calls are
incorrectly prioritised
• Under 1/6 of the calls that should
be prioritised are not.
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13. Error Analysis
• False positive rate is worth further investigation:
– ML predicts an urgent unplanned event.
– No urgent unplanned event occurred.
– But is that because the PaJR caller intervened (with advice,
referral, comfort, …) and averted the event ML predicted?
• Analysing FP cases, we found evidence of some
intervention in a small number of cases.
– Further work needed.
• More significantly, UUEs were rarely (6/27) „anticipated‟
by lay callers (they didn‟t intervene), whereas ML
predicted 23 of them.
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14. Challenges
• Data
– ML requires lots of examples of each outcome.
Thanks to PaJR the number of unplanned events
among the users is declining.
• Features
– We have lots of data for each case but it takes time
and skill to identify features predictive of deterioration.
• Prediction Engine Pipeline
– The management of multiple cases, multiple models,
etc.
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15. Benefits of Machine Learning
• Compared with static rule-based alerts
– ML allows identification of features that emerge as predictive of
deterioration.
– ML uses evidence from data on real patients.
– ML can be easily transferred to new settings and new services
– ML adapts over time
• Compared with experienced callers without ML
– ML allows high accuracy, high volume at low cost.
– ML will identify features across callers, across time, etc.
– ML has perfect memory – callers go on leave, move on.
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16. Thank you
Any questions?
Lucy Hederman
hederman@tcd.ie
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