the green button project - himss365 · 2019. 2. 8. · the green button project physician symposium...
TRANSCRIPT
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The Green Button ProjectPhysician Symposium Session #3, February 11 2019
Alison Callahan MISt PhD, Research Scientist, Stanford University
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Alison Callahan, PhD, has no real or apparent conflicts of interest to report.
Conflict of interest
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• The Green Button origin story
• Anatomy of the consult service
• Methods and challenges
• Learning from the first 100 consults
• Deploying the service at a new site
• Ongoing efforts
Agenda
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• Define use cases for an informatics consult service
• Describe requirements for setting up an informatics consult service
• Plan deployment of an informatics consult service at a new site
Learning objectives
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Informatics Consult team
Acknowledgements
Funding: NLM, NIGMS, Stanford School of Medicine, Department of Medicine, Department of
Biomedical Data Science, Center for Population Health Sciences, an anonymous donor
Stanford Health Care partners
David Entwistle Tip Kim
Christopher SharpNigam Shah
Saurabh Gombar
Robert Harrington
Alison Callahan Vladimir Polony
Rob Tibshirani
Ken Jung
Trevor Hastie
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A teenager with systemic lupus erythematosus
• proteinuria
• antiphospholipid antibodies
• pancreatitis
Meet Laura
Source: Mayo Foundation for Medical Education and Research
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Managing Laura’s care
Source: Mayo Foundation for Medical Education and Research
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The Origin of the Green Button
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Finding patients with
“X”
2-3 weeks to
generate a cohort
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Given a specific case, provide a report with a descriptive summaryof similar patients in Stanford’s clinical data warehouse, thecommon treatment choices made, and the observed outcomes afterspecific treatment choices.
An institutional review board approved study (IRB # 39709) overone year.
The Informatics Consult Service
http://greenbutton.stanford.edu
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An example report
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The process
Requesting
physician
Informatics
physician
EHR data
specialist
Data
scientist
Request
consultRefine
clinical
question
Create
definitions
for
exposures
and
outcomesBuild
patient
cohorts
Perform
statistical
analysis
Write
consult
report
Review
results
Apply
evidence
to clinical
decision
24 to 72 hours
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• Building patient cohorts accurately and quickly
• Asking the right question
• Controlling for confounding
• Ensuring quick turnaround
Methods and challenges
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1 0 -1
1 0 -1
1 0 -1
Building patient cohorts
1. How will you handle time?2. What features will you use?3. How will you state your phenotype definition?
From timelines to data frames
Phenoty
pin
g
P
ers
on
s
Features
1 0 -1
ProceduresDevicesDiseasesDrugs
1 • cost
• utility
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The search engine
• Diagnosis and procedure
codes
• Clinical notes
• Lab results
• Vital signs
• Inpatient and outpatient visits
www.tinyurl.com/search-ehr
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Asking the right question
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• Identify subsets of patient cohorts that are “similar”
– Matching on age, gender, record length, year
– Using propensity score matching
Controlling for confounding
Pj
Pi
Pk
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• Use negative controls for empirical calibration
• E-values to quantify the degree of confounding that can produce the observed effect
• Ask the question using multiple datasets
• Schedule an in-person debrief
What we do to not be wrong
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• Search engine API available in CRAN
• R library for data pre-processing
• Semi-automated pipeline for survival and causal analyses, report generation
Ensuring quick turnaround
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Guideline
available?
Use level A
guideline
Yes
No
Use
Green
Button
Large cohort of patients
present?
Yes
Use
professional
judgementNo
Analysis + Report
• The question as posed
• How we asked the question
• Our interpretation
• Research walkthrough
List of clinical
situations
Candidates for further
study
Point of care randomization/
large simple trial
Useful
byproduct
High
priority
Increment
priority
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Learning from the first 100 consults
• How many? 55%
• Which treatment? 30%
• How long? 15%
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Learning from the first 100 consults
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“likely to recommend” was 100%
Learning from the first 100 consults
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• Institutional support
• Data science expertise
• Marketing
• A process to sanity-check the data and consult findings
Deploying the service at your site
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We’re not the first to provide an on-demand informatics consult service
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Stanford: 3 million
Optum: 55 million
Truven: 126
million
Now versus then
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• What is really useful?
– Description of what happened
– Estimation: Population or Individual level
– Patient level prediction
• Financial viability – who can pay for this “test”?
• Informatics research
– Phenotyping (how do I know the patient had X)
– Representation learning
– Matching, and population level inference
– Personalized effect estimates
• Deploying as a hospital-side service at Stanford Health Care
Open questions and ongoing efforts
