Translating Precision Medicine Into Action: Big Data at the Point of Care

I N S T I T U T E F O R I N F O R M A T I C S | W A S H I N G T O N U N I V E R S I T Y S C H O O L O F M E D I C I N E

Translating Precision Medicine Into Action:

Big Data at the Point of Care

Philip R.O. Payne, PhD, FACMI

Robert J. Terry Professor and Director, Institute for Informatics

Washington University School of Medicine

Professor of Computer Science and Engineering

Washington University School of Engineering and Applied Science


COI and Disclosures• Federal Funding: NCI, NLM, NCATS, AHRQ

• Additional Research Funding: BAH, SAIC, Rockefeller Philanthropy Associates, AcademyHealth, Pfizer, Hairy Cell Leukemia Foundation

• Academic Consulting: Case Western Reserve University, Cleveland Clinic, Columbia University, Stonybrook University, University of Kentucky, West Virginia University

• International Partnerships: Soochow University (China), Fudan University (China), Clinica Alemana (Chile), Universidad de Chile (Chile)

• Other Consulting/Honoraria: American Medical Informatics Association (AMIA), Institute of Medicine (IOM), Geisinger Health System

• Editorial Boards: Journal of the American Medical Informatics Association, Journal of Biomedical Informatics, eGEMS, BMC Medical Informatics and Decision Making (Section Editor, Healthcare Information Systems)

• Corporate: Signet Accel LLC (Founder), Signet Innovations LLC (Advisor), Aver, Lumeris


Precision Medicine: Using Data, Treating Individuals

We’re taking about the idea that we’re all

different individuals, and the best way to

keep us healthy or to treat us when we’re

sick is to take account of those individual

differences. Whether that’s an

understanding if you have cancer what

exactly is going on in your cancer cells, or

whether if it giving you the right drug at the

right dose for you, let’s understand how to

do that better. We have tried to do things

like that over decades, but we haven’t really

had the tools. The time is now to really

make that opportunity become a reality.

Source: Francis Collins (January, 2015)


Genomics: Decoding the Human “Blueprint”

[Decoding the human genome sequence] is

the most significant undertaking that we

have mounted so far in an organized way in

all of science. I believe that reading our

blueprints, cataloguing our own instruction

book, will be judged by history as more

significant than even splitting the atom or

going to the moon.

Source: Francis Collins (May, 1998)


EHRs and Clinical Decision Support: Delivering the Right Knowledge at the Point-of-Care

“61% of physicians felt their EHR improved

the quality of care they delivered to

patients, but only 1 in 3 said it had

improved their job satisfaction, and 1 in 5

said they would go back to paper if they

could. Tellingly, the more advanced the

EHR; for example, systems that offered

reminders, alerts, and messaging capability,

the greater the unhappiness.”

“people working collaboratively with

technology are far more effective than

either people or technology alone.”

Source: Robert Wachter (The Digital Doctor: Hope, Hype, and Harm at the Dawn of Medicine's Computer Age; 2015


The Role of Biomedical Informatics in Bridging Precision Medicine, Genomic Medicine, EHRs, and Decision Support: A Systems Approach

Computational and Information

Sciences

Driving Biological and

Clinical Problems

Social, Cultural, and

Human Factors

Biomedical Informatics


Why Is Now the Time To Bring Genomics to the Point-of-Care?

5 Trends Shaping the Translation of Precision Medicine Into Action

1. Decreasing Cost and Increasing Accessibility of Sequencing

Source: NHGRI, 2016

2. Expanding Knowledge of How to Interpret Genomes for Clinical Use

ClinVar (https://www.ncbi.nlm.nih.gov/clinvar/) at the National Center for Biotechnology Information (NCBI) is a freely available archive for interpretations of clinical significance of variants for reported conditions. The database includes germline and somatic variants of any size, type or genomic location. Interpretations are submitted by clinical testing laboratories, research laboratories, locus-specific databases, OMIM®, GeneReviews™, UniProt, expert panels and practice guidelines.

Source: Landrum MJ, Lee JM, Benson M, Brown G, Chao C, Chitipiralla S, Gu B, Hart J, Hoffman D, Hoover J, Jang W, Katz K, Ovetsky M, Riley G, Sethi A, Tully R, Villamarin-Salomon R, Rubinstein W, Maglott DR. ClinVar: public archive of interpretations of clinically relevant variants. Nucleic Acids Res. 2016 Jan 4;44(D1):D862-8. doi: 10.1093/nar/gkv1222. PubMed PMID:26582918

https://www.ncbi.nlm.nih.gov/clinvar/

3. Growth in Pharmacogenomics and Precision Therapeutics

Stratification of:• Responders• Non-Responders• Potential AEs

Source: Werner HM, Mills GB, Ram PT. Cancer systems biology: a peek into the future of patient care?. Nature reviews Clinical oncology. 2014 Mar 1;11(3):167-76.

4. Improving Sophistication of Clinical Decision Support (CDS) Tools

Source: eclinicalworks.com

5. Advances in Computation and Artificial Intelligence


Challenges and Opportunities Surrounding the Delivery of Genomic

Data to Patients and Providers

A Brief History of Clinical Decision Support and Genomic Medicine


Bayes Theorem: The Probabilistic Foundation for Computational Approaches to Predicting Outcomes

Thomas Bayes (1701-1761)English statistician, philosopher

and Presbyterian minister

Source: http://www.gaussianwaves.com


Artificial Intelligence: The Rise, and Fall, and Rise Again of Smart Machines (a selected history)

• Ancient History• 4th Century BC: Aristotle invents syllogistic logics (deductive reasoning)• 17th Century: Sir Samuel Morland invents the first arithmetical machines• 19th Century: Charles Babbage invents the programmable mechanical calculating machine (the

analytical engine)

• Modern History• 1945: Vannevar Bush publishes “As We May Think”, predicting a future where machines help

humans to make decisions• 1950: Alan Turing proposes what is now known as the Turing Test• 1956: John McCarthy coins the term “Artificial Intelligence” • 1957: The General Problem Solver (GPS) is demonstrated by Newell, Shaw and Simon• 1958: John McCarthy invents the LISP programming language• 1962: Arthur Samuel from IBM demonstrates the first game playing program (checkers)• 1970’s: The “AI Winter”• 1980’s: Machine learning and artificial neural networks (ANNs) see increasing use• Today: Broad adoption of cognitive computing (including deep learning), information retrieval,

natural language processing, and “bots”• 2014: First (substantiated) claim of a computer passing the Turing Test (a chat “bot” named “Eugene

Goostman”)


The Evolution of Clinical Decision Support

Leeds Abdominal Pain

SystemMYCIN HELP

Embedded Systems within

EMRs/EHRs

• Focus on diagnosing abdominal pain

• Used Bayesian probability theory

• Pre-dated widespread use of computers in clinical environment

• Focus on managing infections

• Introduced forward and backwards chaining

• Provided both certainty factors as well as an explanatory facility

1960s 1970s 1970s-80s 1990s - Present

F.T. de DombalUniversity of Leeds

• General alerting system (critical values, etc.)

• Integrated with a clinical information system (early EMR)

• Employed the Arden Syntax to encode rules

• Largely proprietary and specific to vendor environments

• Limited to basic logical operations if-then-else

• Require discrete data to trigger rules

E.H. ShortliffeStanford University

H. WarnerLDS Hospital (InterMountain)


The Basic Structure of a Decision Support System (CDSS)

Knowledge Base (KB)

Inference EngineData Sources

Knowledge Management Tools

User Interface

• Messaging• Application Integration• Mobile• Other modalities…

Critical Lab Value

Interpretation of Value(Severity, Importance, Communications Mapping)

Alert Sent to Provider(s)


Data Sources: Volume, Velocity, and Variability

• EMRs/EHRs

• PHRs

• Ancillary Systems

• Sensors and Telemetry

• Mobile Devices

• Reference Data Sets

• Canonical Knowledge

Source: Forbes, “Scientists Save Healthcare (But They're Not From Med School)” (2013)


Inference Engine Design: What Kind of Reasoning Process is Best?

• Descriptive logic• If-then-else

• Branching

• Probabilistic reasoning• Bayesian statistics

• Rule-based systems• Hybrid rules that reflect

probability (aka “certainty”)

• Ontology-driven systems• Inferring logical rules “on

demand”

Source: "Brain-Computer Interface Systems - Recent Progress and Future Prospects", book edited by Reza Fazel-Rezai


Representing CDSS Knowledge (1): Arden Syntax

• CDSS rules organized into Medical Logic Modules (MLMs)

• MLM’s include:• Maintenance data (e.g., sources)• Human readable version of rule• Formal logic• Definition of action to be taken

• Challenges:• Execution environments• Support within commercial

EMR/EHR platforms• Potential complexity of rule logic

(e.g., the “curly brace problem”)


Representing CDSS Knowledge (2): Semantic Languages

Source: www.w3.org


Delivering Information to the Right Person in the Right Place at the Right Time

Source: www.drchrono.com


Understanding Context: Distributed Cognition


What About Genomic Medicine?

Source: www.illumina.com


Clinical Sequencing in One Slide…

Source: McDonnel Genome Institute, CIViC Project


Challenges to Clinical Sequencing

• Evolving technologies and needs• Targeted panels• Exomes• Whole Genomes

• Reimbursement• Validation of tests

• research vs. clinical• Constantly changing knowledge

base concerning variant significance

• Pathogenic• Likely pathogenic• Uncertain significance• Likely benign• Benign

• Genomic literacy• Patients• Providers• Other decision makers

Evidence framework. This chart organizes each of the criteria by the type of evidence as well as the strength of the criteria for a benign (left side) or pathogenic (right side) assertion. Evidence code descriptions: BS, benign strong; BP, benign supporting; FH, family history; LOF, loss of function; MAF, minor allele frequency; path., pathogenic; PM, pathogenic moderate; PP, pathogenic supporting; PS, pathogenic strong; PVS, pathogenic very strong.

Source: Standards and guidelines for the interpretation of sequence variants: a joint consensus recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology (2015)


Many Sources of Canonical Knowledge

Source: Ramos EM, Din‐Lovinescu C, Berg JS, Brooks LD, Duncanson A, Dunn M, Good P, Hubbard TJ, Jarvik GP, O'donnell C, Sherry ST. Characterizing genetic variants for clinical action. InAmerican Journal of Medical Genetics Part C: Seminars in Medical Genetics 2014 Mar 1 (Vol. 166, No. 1, pp. 93-104).


What is Reportable or Actionable?

Source: Kalia SS, Adelman K, Bale SJ, Chung WK, Eng C, Evans JP, Herman GE, Hufnagel SB, Klein TE, Korf BR, McKelvey KD. Recommendations for reporting of secondary findings in clinical exome and genome sequencing, 2016 update (ACMG SF v2. 0): a policy statement of the American College of Medical Genetics and Genomics. Genetics in Medicine. 2016 Nov 17.

59 reportable findings

The exome of the human genome consists of roughly 180,000 exons constituting about 1% of the total genome.


Logistical Issues: How To Store Genomic Data

Source: Kho AN, Rasmussen LV, Connolly JJ, Peissig PL, Starren J, Hakonarson H, Hayes MG. Practical challenges in integrating genomic data into the electronic health record. Genetics in Medicine. 2013 Sep 26;15(10):772-8.


Connecting Genomics Data to CDSS and the EHR

Technical desiderata for the integration of genomic data into Electronic Health Records:1) Maintain separation of primary molecular observations

from the clinical interpretations of those data2) Support lossless data compression from primary

molecular observations to clinically manageable subsets

3) Maintain linkage of molecular observations to the laboratory methods used to generate them

4) Support compact representation of clinically actionable subsets for optimal performance

5) Simultaneously support human-viewable formats and machine-readable formats in order to facilitate implementation of decision support rules

6) Anticipate fundamental changes in the understanding of human molecular variation

7) Support both individual clinical care and discovery science

Source: Masys DR, Jarvik GP, Abernethy NF, Anderson NR, Papanicolaou GJ, Paltoo DN, Hoffman MA, Kohane IS, Levy HP. Technical desiderata for the integration of genomic data into Electronic Health Records. Journal of biomedical informatics. 2012 Jun 30;45(3):419-22.


Systematic Approaches to Clinical Phenotyping

Source: Ritchie MD, Holzinger ER, Li R, Pendergrass SA, Kim D. Methods of integrating data to uncover genotype-phenotype interactions. Nature Reviews Genetics. 2015 Feb 1;16(2):85-97.


Integrating Data Across and Between Scales

Source: Ritchie MD, Holzinger ER, Li R, Pendergrass SA, Kim D. Methods of integrating data to uncover genotype-phenotype interactions. Nature Reviews Genetics. 2015 Feb 1;16(2):85-97.


Human Factors (1): Genomic Literacy

Source: Vassy JL, Korf BR, Green RC. How to know when physicians are ready for genomic medicine. Science translational medicine. 2015 May 13;7(287).


Human Factors (2): Patient Engagement & Consent

Source: www.23andme.com

Issues to consider: Patient consent

• ”downstream” impact on family members Impact of variation type

• Constitutional• Post-zygotic or somatic

Communication of relative risk Privacy and confidentiality Direct-to-consumer vs. traditional testing paradigms Scalable return-of-results

Source: www.gizmodo.com


Getting Back to the Point-of-Care: EHR Platform Constraints

Source: 5 Takeaways from Health 2.0's: Accessing & Using APIs from Major EMR Vendors Survey

Source: Mandl KD, Kohane IS. Escaping the EHR trap—the future of health IT. New England Journal of Medicine. 2012 Jun 14;366(24):2240-2.

Source: www.smarthealthit.org


What Happens If We Overcome These Challenges and Realize the Opportunities of Genomics at the

Point-of-Care?

Looking Toward the Future of Precision Medicine


Entering Into a New Information Age in Biomedicine


A Path Forward for Genomic Medicine at the Point-of-Care

Technical desiderata for the integration of genomic data into Electronic Health Records:1) CDS knowledge must have the potential to incorporate

multiple genes and clinical information2) Keep CDS knowledge separate from variant

classification3) CDS knowledge must have the capacity to support

multiple EHR platforms with various data representations with minimal modification

4) Support a large number of gene variants while simplifying the CDS knowledge to the extent possible

5) Leverage current and developing CDS and genomics standards

6) Support a CDS knowledge base deployed at and developed by multiple independent organizations

7) Access and transmit only the genomic information necessary for CDS

Source: Welch BM, Eilbeck K, Del Fiol G, Meyer LJ, Kawamoto K. Technical desiderata for the integration of genomic data with clinical decision support. Journal of biomedical informatics. 2014 Oct 31;51:3-7.

A research agenda for the Precision Medicine, Genomics, and Informatics communities

High throughput clinical phenotyping Multi-scale data integration methods Human factors (genetic literacy, patient

engagement and consent, results reporting)

Regulatory frameworks

A few friendly amendments…

Some Final Thoughts (2): Anticipating and Embracing Evolution in Technologies and Information Needs Is Critical… We Must Focus on Methods


Open Research Questions…

Presentation Models

Knowledge Engineering

Evidence Generation

How do we deliver large amounts of contextualized knowledge at the point-of-care?• Lessons from the social

UX community• Intelligent ”agents”

How do we cope with a constantly changing knowledge-base?• Knowledge “graphs”• Curation tool(s)

How do we increase our understanding of actionable variants in a timely manner?• Mining existing data and

knowledge bases• Phenotyping methods


AcknowledgementsCollaborators:

• Peter J. Embi, MD, MS

• Albert M. Lai, PhD

• Randi Foraker, PhD

• Kun Huang, PhD

• Fuhai Li, PhD

• John C. Byrd, MD

• William E. Carson, MD

• Omkar Lele, MS, MBA

• Tara Borlawsky-Payne, MA

• Marcelo Lopetegui, MD, MS

Funding:

• NIH: NCI, NCATS, NLM

• AHRQ

• Hairy Cell Leukemia Foundation

• Academy Health

Laboratory for Knowledge Based Systems and Engineering (KBASE) @ OSU


Philip R.O. Payne, PhD, [email protected]@prpayne5www.slideshare.net/prpayne5

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Translating Precision Medicine Into Action: Big Data at the Point of Care