1. Decaying Relevance of Clinical Data when Predicting Future Decisions
Presenter: Muthu Alagappan, MD
Jonathan Chen, MD, PhD
Mary K Goldstein, MD, MS
Steven M Asch MD, MPH
Russ B Altman MD, PhD
March 30th, 2017
Joint Summits on Translational Science
General Medicine Disciplines, Department of Medicine, Stanford University

2. Disclosures
I do not have any relevant financial relationships or conflicts of interest

3. Objectives To develop a clinical order recommender system
To determine how varying historical training data affects prediction
To estimate the “decay rate” (i.e. relevance) of clinical data

6. Randomized Clinical Trials (RCTs)
Knowledge
Scientific inquiry
Randomized Clinical Trials (RCTs)
Clinical Orders
Patient Care
Expert Opinion
Anecdotal Experience

7. Clinical Decision Support - Order Sets

8. Clinical Decision Support - A bottom-up approach
Collaborative filter
Doctor
Collective
Medical Record

9. Data Description

10. Patient Timeline Model

11. Example: Chest Pain (ICD9: 786.5)
Top orders following an admission diagnosis of “Chest Pain”
Co-occurrences within 24 hours, ranked by Odds Ratio (OR)

12. Internal Validation Use initial data as query
Predict subsequent orders
Compare against next actual 24 hour orders
Identify real order set use

13. Prediction Accuracy

14. Add more data?
“…invariably, simple models and a lot of data trump more elaborate models based on less data.”
“…use available large-scale data rather than hoping for annotated data that isn’t available.”

15. Is More Data Better? Prediction is difficult
Clinical knowledge evolves quickly
Recent data > More data

16. Decaying Relevance of Clinical Data

17. Objectives To develop a clinical order recommender system
To determine how varying historical training data affects prediction
To estimate the “decay rate” (i.e. relevance) of clinical date

18. Acknowledgements
Mentor
Jonathan Chen, MD, PhD