health informatics society of ireland - patient journey record
DESCRIPTION
Health Informatics Society of Ireland - Patient Journey Record Presentation of the research behind the patient journey record readmissions management platform.TRANSCRIPT
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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
HISI 2011
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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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$5,000
$1
$10
$100
$1,000
Early Alerts of deterioration helps prevent the patient entering expensive treatment
Self care Complicated Complex Hospital
$/day
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The PaJR ServiceMichael Anon
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The PaJR SurveyMichael Anon
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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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PaJR Prediction Service• 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 events.
Michael Anon
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Machine Learning
ML: automatically induce from the examples a model that accurately
predicts new cases.
Ok A&E Hosp ??Ok A&E
...
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Simple predictive model: a decision tree
A
B B
A
CK=y K=n K=y
K=n K=y
K=y
- decision node
- leaf node (K means UnplannedEvent)
noyes
<n ≥n ≥p<p
yes no
A sample decision tree on UnplannedEvent
Eg: A = “her sister”; B = AvgWordLength; C = takingMeds
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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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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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Accuracy
• Predicting urgent unplanned events (UUE)
• Training data– 1621 phone calls– 27 urgent unplanned
events
• False Negatives cost 500 times FPs
True False
Negative(OK)
1091 4
Positive(UUE)
23 453
• 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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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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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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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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