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Large scale analytics for electronic health records:
Lessons from Observational Health Data Science and
Informatics (OHDSI)
Patrick Ryan, PhD on behalf of OHDSI team
15 November 2016
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Odyssey (noun): \oh-d-si\ 1. A long journey full of adventures 2. A series of experiences that give
knowledge or understanding to someone
http://www.merriam-webster.com/dictionary/odyssey
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A caricature of the patient journey
Conditions
Drugs
Procedures
Measurements
Person time
Dise
ase
Trea
tmen
t
Out
com
e
0 Baseline time Follow-up time
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Each observational database is just an (incomplete) compilation of patient journeys
Person 1
Conditions
Drugs
Procedures
Measurements
Person time
Dise
ase
Trea
tmen
t
Out
com
e
0 Baseline time Follow-up time
Person 2
Conditions
Drugs
Procedures
Measurements
Person time
Dise
ase
Trea
tmen
t
Out
com
e
0 Baseline time Follow-up time
Person 3
Conditions
Drugs
Procedures
Measurements
Person time
Dise
ase
Trea
tmen
t
Out
com
e
0 Baseline time Follow-up time
Person N
Conditions
Drugs
Procedures
Measurements
Person time
Dise
ase
Trea
tmen
t
Out
com
e
Baseline time Follo p time
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Questions asked across the patient journey
Conditions
Drugs
Procedures
Measurements
Person time
Dise
ase
Trea
tmen
t
Out
com
e
0 Baseline time Follow-up time
Which treatment did patients choose after diagnosis?
Which patients chose which treatments?
How many patients experienced the outcome after treatment?
What is the probability I will experience the outcome?
Does treatment cause outcome?
Does one treatment cause the outcome more than an alternative?
What is the probability I will develop the disease?
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Classifying questions across the patient journey
• Clinical characterization: What happened to them? – What treatment did they choose after diagnosis? – Which patients chose which treatments? – How many patients experienced the outcome after treatment?
• Patient-level prediction: What will happen to me?
– What is the probability that I will develop the disease? – What is the probability that I will experience the outcome?
• Population-level effect estimation: What are the causal effects?
– Does treatment cause outcome? – Does one treatment cause the outcome more than an alternative?
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Complementary evidence to inform the patient journey
Clinical characterization:
What happened to them?
Patient-level prediction:
What will happen to me?
Population-level effect estimation:
What are the causal effects?
inference causal inference
observation
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Introducing OHDSI
• The Observational Health Data Sciences and Informatics (OHDSI) program is a multi-stakeholder, interdisciplinary collaborative to create open-source solutions that bring out the value of observational health data through large-scale analytics
• OHDSI has established an international network of researchers and observational health databases with a central coordinating center housed at Columbia University
http://ohdsi.org
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OHDSI’s mission
To improve health, by empowering a community to collaboratively generate the evidence that promotes better health decisions and better
care.
http://ohdsi.org
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What is OHDSI’s strategy to deliver reliable evidence?
• Methodological research – Develop new approaches to observational data analysis – Evaluate the performance of new and existing methods – Establish empirically-based scientific best practices
• Open-source analytics development – Design tools for data transformation and standardization – Implement statistical methods for large-scale analytics – Build interactive visualization for evidence exploration
• Clinical evidence generation – Identify clinically-relevant questions that require real-world evidence – Execute research studies by applying scientific best practices through
open-source tools across the OHDSI international data network – Promote open-science strategies for transparent study design and
evidence dissemination
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OHDSI community
OHDSI Collaborators: • >140 researchers in academia, industry, government, health systems • >20 countries • Multi-disciplinary expertise: epidemiology, statistics, medical
informatics, computer science, machine learning, clinical sciences Databases converted to OMOP CDM within OHDSI Community: • >50 databases • >660 million patients
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Drug safety surveillance Device safety surveillance Vaccine safety surveillance
Comparative effectiveness Health economics Quality of care Clinical research
One common data model to support multiple use cases
Concept
Concept_relationship
Concept_ancestor
Vocabulary
Source_to_concept_map
Relationship
Concept_synonym
Drug_strength
Cohort_definition
Standardized vocabularies
Attribute_definition
Domain
Concept_class
Cohort
Dose_era
Condition_era
Drug_era
Cohort_attribute
Standardized derived elem
ents
Stan
dard
ized
clin
ical
dat
a
Drug_exposure
Condition_occurrence
Procedure_occurrence
Visit_occurrence
Measurement
Observation_period
Payer_plan_period
Provider
Care_site Location
Death
Cost
Device_exposure
Observation
Note
Standardized health system data
Fact_relationship
Specimen CDM_source
Standardized meta-data
Standardized health econom
ics
Person
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Complementary evidence to inform the patient journey
Clinical characterization:
What happened to them?
Patient-level prediction:
What will happen to me?
Population-level effect estimation:
What are the causal effects?
inference causal inference
observation
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How should patients with major depressive disorder be treated?
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How are patients with major depressive disorder ACTUALLY treated?
Hripcsak et al, PNAS, 2016
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OHDSI participating data partners Code Name Description Size (M) AUSOM Ajou University School of Medicine South Korea; inpatient hospital
EHR 2
CCAE MarketScan Commercial Claims and Encounters
US private-payer claims 119
CPRD UK Clinical Practice Research Datalink UK; EHR from general practice 11
CUMC Columbia University Medical Center US; inpatient EHR 4
GE GE Centricity US; outpatient EHR 33
INPC Regenstrief Institute, Indiana Network for Patient Care
US; integrated health exchange 15
JMDC Japan Medical Data Center Japan; private-payer claims 3
MDCD MarketScan Medicaid Multi-State US; public-payer claims 17 MDCR MarketScan Medicare Supplemental and
Coordination of Benefits US; private and public-payer claims
9
OPTUM Optum ClinFormatics US; private-payer claims 40 STRIDE Stanford Translational Research Integrated
Database Environment US; inpatient EHR 2
HKU Hong Kong University Hong Kong; EHR 1
Hripcsak et al, PNAS, 2016
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Treatment pathway study design
Hripcsak et al, PNAS, 2016
• >250,000,000 patient records used across OHDSI network • >=4 years continuous observation • >=3 years continuous treatment from first treatment • N=264,841 qualifying patients with depression
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How are patients with major depressive disorder ACTUALLY treated?
• Substantial variation in treatment practice across data sources, health systems, geographies, and over time
• Consistent heterogeneity in treatment choice as no source showed one preferred first-line treatment
• 11% of depressed patients followed a treatment pathway that was shared with no one else in any of the databases
Hripcsak et al, PNAS, 2016
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Type 2 Diabetes Mellitus Hypertension Depression
OPTUM
GE
MDCD CUMC
INPC
MDCR
CPRD
JMDC
CCAE
One standardized approach can be applied to multiple clinical areas
Hripcsak et al, PNAS, 2016
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Complementary evidence to inform the patient journey
Clinical characterization:
What happened to them?
Patient-level prediction:
What will happen to me?
Population-level effect estimation:
What are the causal effects?
inference causal inference
observation
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21
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Observational research results in literature
22
85% of exposure-outcome pairs have p < 0.05
29,982 estimates 11,758 papers
What’s going wrong? • Observational study bias • Publication bias • P-hacking
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Observational research in depression
23
1,935 estimates
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What if we considered all outcomes?
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Acute liver injury Hypotension
Acute myocardial infarction Hypothyroidism
Alopecia Insomnia
Constipation Nausea
Decreased libido Open-angle glaucoma
Delirium Seizure
Diarrhea Stroke
Fracture Suicide and suicidal ideation
Gastrointestinal hemorrhage Tinnitus
Hyperprolactinemia Ventricular arrhythmia and sudden cardiac death
Hyponatremia Vertigo
Duloxetine vs. Sertraline for these 22 outcomes:
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What if we consider all treatments?
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Type Class Treatment Drug Atypical Bupropion Drug Atypical Mirtazapine Procedure ECT Electroconvulsive therapy Procedure Psychotherapy Psychotherapy Drug SARI Trazodone Drug SNRI Desvenlafaxine Drug SNRI duloxetine Drug SNRI venlafaxine Drug SSRI Citalopram Drug SSRI Escitalopram Drug SSRI Fluoxetine Drug SSRI Paroxetine Drug SSRI Sertraline Drug SSRI vilazodone Drug TCA Amitriptyline Drug TCA Doxepin Drug TCA Nortriptyline
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Large-scale estimation for depression
• 17 treatments • 17 * 16 = 272 comparisons • 22 outcomes • 272 * 22 = 5,984 effect size estimates • 4 databases so far (Truven CCAE, Truven MDCD,
Truven MDCR, Optum) • 4 * 5,984 = 23,936 estimates
NOT DATA MINING - Each analysis following best practice in causal inference
26
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Estimates are in line with expectations
27
11% of exposure-outcome pairs have calibrated p < 0.05
In literature, 85% have p < 0.05
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OHDSI’s recommended best practices for population-level effect estimation
Evidence Generation
• Write and share protocol
• Open source study code
• Use validated software
• Replicate across databases
Evidence Evaluation
• Produce standard diagnostics
• Include negative controls
• Create positive controls
• Calibrate confidence interval and p-value
Evidence Dissemination
• Don’t provide only the effect estimate
• Also share protocol, study code, diagnostics and evaluation
• Produce evidence at scale
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Complementary evidence to inform the patient journey
Clinical characterization:
What happened to them?
Patient-level prediction:
What will happen to me?
Population-level effect estimation:
What are the causal effects?
inference causal inference
observation
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Populations can be used to accurately predict outcomes for individuals
1.00 0.90 0.80 0.70 0.60 0.50
CCAE
M
DCD
MDC
R O
PTU
M
AUC AMI
Hypothyroidism
Stroke
Diarrhea
Nausea
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Building the LHC of observational research?
31