Developing, Implementing, And Sharing Pharmacogenomics CDS (TBI Panel)

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Developing, Implementing, and Sharing Pharmacogenomics CDS

Robert R. Freimuth, Ph.D.Biomedical Statistics and Informatics,Applied Clinical Informatics

AMIA TBI – March 18, 2013

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Pharmacogenomics (PGx) CDS

• Mayo Clinic PGx CDS

• Case Study: Abacavir

• Challenges

• Challenges for Genomics-Based CDS

• Sharing Knowledge

• Standardization

• Challenges of the Future

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EHR at Mayo Clinic, Rochester

• >12 different systems that store primary data

• GE

• Started from a partnership with HP in late 1980s

• Mainframe design

• Blaze Advisor rule engine

• Built for large-scale financial applications (FICO)

• Designed for complex, multi-faceted problems

• Full forward and backward chaining logic

• Tightly coupled to data sources

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CDS at Mayo Clinic, Rochester

• Developing CDS solutions for >20 years

• Initially developed over home-grown EHR

• Batch-oriented rules for events and conditions

• Standalone systems for different departments• e.g., Pharmacy, Infectious Disease

• Started using Blaze ~2005

• Provided real-time CDS

• >100 high-level rules composed of ~1000 atomic rules

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CDS Rule Development Process

• Synthesis of clinical knowledge

• Committee approvals

• Technical specifications

• UI screen design

• Web service calls, database queries

• Implementation and deployment

• Compiling

• Loading to rule servers on mainframe

• Run modes

• Silent

• "Live"

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Abacavir and HLA-B*5701

• Abacavir

• Nucleoside RT-inhibitor for HIV infection

• HLA-B

• >1500 known variants

• *5701 allele is associated with life-threatening hypersensitivity reactions to abacavir

• Results are either "positive" or "negative"

• At Mayo Clinic, *5701 screening is recommended:

• Prior to initiating treatment with abacavir

• Prior to reinitiation of abacavir in patients of unknown HLA-B*5701 status who have previously tolerated abacavir

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Abacavir-HLA B*5701: Governance

• Executive sponsors:

• Center for Individualized Medicine

• Pharmacogenomics Task Force

• Mayo Pharmacy Formulary Committee

• Disease-Oriented Task Force: ID

• Other committee approvals:

• Practice Convergence Council (MC-CPC)

• MC Clinical Decision Support Subcommittee

• Implementation

• Site-based P&T committees, coordinate with CIM

• Local CDS teams

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Abacavir-HLA B*5701 Workflow

Internal lab result?

External lab result?

What is it?

Capture result

Inbox message

Prompt for test

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Abacavir-HLA B*5701 Results

• Text report

• MCF, MCA, MCHS

• Structured data

• MCR

• Storage of results

• Internal: lab test

• External: custom table

• Added as an allergy

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Challenges: Abacavir

• Currently using the Allergy model

• Can be deleted or altered

• Policy for positive test/deleted allergy

• Not a true allergy

• Reaction has not occurred, but is possible

• Storing external results in a custom table

• Inaccessible by other systems (e.g., pharmacy)

• Providers can't see entries from prior admissions

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Challenges: External Results

• Capture

• Streamline current alert-heavy process

• Representation

• Test orders and results change over time

• Storage

• "Clinical event" (MCF/MCA)

• "Observation/finding" (MCR)

• Exchange

• CDA (partial solution for providers?)

• Clinical notes (patient-reported data)

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Challenges: Enterprise-Level

• Coordination across the Mayo enterprise (multi-site)

• Group formed to focus on challenges related to the implementation of PGx CDS

• Identify issues with our current infrastucture

• Identify bottlenecks in processes

• Propose short and long term solutions

• Consensus for functional requirements

• Abacavir vs. Carbamazepine

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Challenges: Education

• Clinical knowledge

• Proactive: Videos, articles

• On-demand: AskMayoExpert (AME)

• Contextual: Alerts link to AME

• Rule implementation

• Consequences of going against recommendation

• "If the patient has been on abacavir for 10 years, do I still need to order the test?"

• Workflow

• "If I click yes (or no), how many more alerts will pop up?"

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Generalizing

A

T C

G

N

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Measuring Outcomes of PGx CDS

• Evaluating effectiveness

• What data should be captured?

• Case-by-case decision?

• Increased effectiveness of therapy

• Decreased adverse reactions or complications

• Fiscal responsibility

• Over-testing

• Time to resolution

• Increased efficiency of healthcare delivery

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Refining PGx CDS

• Create highly specific triggers

• Limit by drug, genotype

• Ethnicity?

• Requires accurate data, not always available

• Probabilities?

• Allele, adverse reaction, effective treatment

• Context-sensitive

• IL28B, peginterferon, and hepatitis C

• Timing: diagnosis vs. CPOE

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Increasing Complexity of Genomics CDS

• Evolution from "simple" CDS to guided workflows

• Context-sensitive expert systems

• Require provider input

• Prompt for data (e.g., family history)

• Technical limitations in popup alerts

• Important for complex decisions

• Need to capture clinical judgement

• Not limited to genomics…

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Sharing Knowledge…

• Human-readable text

• PharmGKB

• Structured representations

• TPP/CPIC tables

• Semantically computable

• PGx Guideline Repository

• Implementation-independent algorithms

• Generalized syntax, metadata

• Executable code (local adoption)

Tuesday

Posters

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Sharing Knowledge… Is Hard

• Who?

• Many stakeholders

• What?

• Institutional vs. national standards of care

• Not everything is generalizable

• Where?

• Distributed vs. Centralized

• How?

• Knowledgebases

• Code libraries

• Web services

Main

tenance

Governance

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Standardization

• Guideline development and interpretation

• Clinical practice and standard of care

• Allele definitions (molecular)

• Nomenclature is only part of the solution

• Stability

• Inferrence

• Phenotype definitions

• Genotype-phenotype translation

• Test results

• Sequence vs. interpretation

• CDS rules

HealtheDecisions

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Challenges of the Future

• Combinatorial expansion, complexity

• Gene-Drug / Gene-Drug Class

• Gene-Gene-Drug / Pathway-Drug

• Advances in knowledge

• Stable representation of results

• Sequence? Interpretation?

• Reinterpretation process

• Store updated results

• Educate providers

• Advances in technology

• Merge, reconcile results

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Challenges of the Future

• Genome on a thumb drive

• The ultimate in patient-provided data

• Patient mobility

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Acknowledgements – Abacavir Implementation

• Michelle Elliott, MD

• Joseph Paul, MD

• David Blair, MD

• John Black, MD (DLMP)

• Joseph Yao, MD (DLMP)

• Robert Freimuth, PhD (Knowledge Engineering)

• Jyotishman Pathak, PhD (Knowledge Engineering)

• Mark Siska, RPh

• Kelly Wix, DPh

• Pedro Caraballo, MD (GIM)

• Robert Bleimeyer (Blaze, MN)

• Mark Dobie (Blaze, MN)

• Padma Rao (Blaze, MN)

• Liz Clark (Cerner, AZ)

• Gaurav Jain (Cerner, MCHS)

• Charles Pugh (Cerner, FL)

• Cloann Schultz (PM, CIM)

• Caer Vitek (Education, CIM)

• Donald Gabrielson (PM)

• and many others