March 16, 2010: I. SimTranslational eScience Epi – 206 Medical Informatics Translational eScience Ida Sim, MD, PhD March 16, 2010 Division of General Internal

March 16, 2010: I. Sim Translational eScienceEpi – 206 Medical Informatics

Translational eScience

Ida Sim, MD, PhD

March 16, 2010

Division of General Internal Medicine, and Graduate Group in Biological and Medical Informatics

UCSF

Copyright Ida Sim, 2010. All federal and state rights reserved for all original material presented in this course through any medium, including lecture or print.


Some Observations• We reinvent the wheel with every study• We don’t repurpose data efficiently• Research and care are separate, unintegrated• We use computers for data processing, not concept

processing• Research policy emphasizes “let a thousand flowers

bloom” more than coherence and comparability of research results

• It’s logistically hard to work with collaborators• ...


These Problems...

• ....will increasingly limit the clinical and translational research we want and need to do– “The ‘clinical research grid’ is failing” (Crowley, et al, JAMA 2004;

291:1120-1126), Institute of Medicine


Outline

• From Here to There (Web 2.0/3.0 eScience) • Collaborative Care and Web 2.0• Collaborative Research and Web 2.0/3.0

– study interpretation/hypothesis generation

– study design/execution

– publication and dissemination

• Closing the Loop• Class Summary

Here

Virtual Patient

Transactions

Raw data

Medical knowledge

Clinical research

transactions

Raw research

data

Dec

isio

n su

ppor

t

Med

ical

logi

c

PATIENT CARE / WELLNES RESEARCH

Workflow modeling and support, usability, cognitive support, computer-supported cooperative work (CSCW), etc.

CRMSsEHRs


IRB Funding Agency

Study DB

Data analysis

Results reporting

Contract R

esearch O

rganization (C

RO

)

Protocol

Trial DesignSponsorsAcademic PIs

?Site 1 Site 2 Site 3

Site Management Organization (SMO)

Here

Clinic 2008

FrontDesk

Radiology

MedicalInformationBureau

Walgreens

Pharm BenefitManager

Benefits Check(RxHub)

HealthNet

B&T

UCare

Specialist

ReferralAuthorization

Internet Intranet Phone/Paper/Fax

Lab

UniLab

(HL-7)


There?

• Open data/open science on epic scale – everyone produces content

– automated data mining and knowledge discovery across all of biomedicine

– collaborative, flat, fluid, emergent, open participation

– even very esoteric communities can be supported

• “Not your grandfather’s clinical research”


General Drivers of Change

• A “grand convergence” of– maturation of the Internet as connective data technology

– ubiquity of microchips in computers, appliances, and sensors

– explosion of data from everywhere and everything (Big Data)

• For all fields, frontiers of research driven by– ability to do large-scale multi-disciplinary data analysis,

visualization, etc.


Biomedical Drivers of Change• Personalized medicine, geno-pheno correlations

– need genomic and phenotype data in computable form for large-scale small signal correlations

• predictors more likely to be rare vs common variants

• Genomic data will be a commodity– SNPs, whole genome analysis

• Large-scale phenotype is the bottleneck• Requires tighter connection between research and

care – huge volume, complex data that needs to be made sense of


How?

• Combination of web 2.0 and semantic web applied to health and biomedical science – web 2.0: Vague-ish term on emerging web, strongly based

on social computing• people are as important as computers in the network

– semantic web (aka web 3.0): • web 1.0 is a web of documents

• web 3.0 is a web of (computer-understandable) data

• Building the research “cyberinfrastructure” is the single most important challenge confronting the nation’s science laboratories (NSF)

http://www.nsf.gov/news/special_reports/cyber/index.jsp


Big Picture + People

..

....

..

..

....

..

..

VirtualPatient

Transactions

Raw data

Medicalknowledge

Clinicalresearch

transactions

Rawresearch

data

DecisionsupportMedical logic

PATIENT CARE /WELLNES RESEARCH

Workflow modeling and support, usability, cognitive support,computer-supported cooperative work (CSCW), etc.

Where clinicianswant to stay

EHRs

CTMSs

Primary Care MD

Patient

Principal Investigator


Outline







Collaborative Care

• “Upskilling” all participants– almost 50% of Americans have 1 or more chronic conditions

• chronic diseases account of >75% of total medical costs

– not enough primary care or specialists for chronic disease management

– must increase knowledge of entire care team (e.g., families)

• Beyond the EHR (i.e., beyond record-keeping)• Must support collaborative care

– messaging, task management, shared conceptualization of problem/education, group decision making, secure distributed permissioned access

– contextualized to work and living for all team members


Web 2.0 in Health• Vague-ish term on emerging web, strongly based on

social computing– people are as important as computers in the network

• Several principles– user-generated content

– harness power/wisdom of crowds

– openness

– architecture of participation

– niche markets

(P. Anderson, What is Web 2.0? JISC Tech and Standards Watch, Feb 2007)


User-Generated Content

• Anyone anywhere is a source of content– YouTube, Flickr, Wikipedia. citizen journalism, blogs

– e.g., http://PatientsLikeMe.com

• Exists in parallel with (trumps?) Old/Main Stream Media (MSM), hierarchical information sources– NIH MedlinePlus http://www.nlm.nih.gov/medlineplus/

– WebMD.com

http://www.nlm.nih.gov/medlineplus/


Power/Wisdom of Crowds

• Tapping into distributed intelligence of people– wikipedia (as accurate as Encyclopedia Britannica)– www.intrade.com: “stock market” for health care reform

passage– e.g., Google Flu

• Use distributed machine and people resources– parallel computing for cheap: donate your PC cycles to find

signs of intelligence from outer space• http://setiathome.berkeley.edu/

• Crowdsourcing: e.g., http://www.answers.com/– 250,300 questions in health

http://wiki.answers.com/Q/FAQ/431

http://setiathome.berkeley.edu/


Openness

• Dimensions of openness– open source: computer code open to all for wisdom of

crowds to improve (e.g., VistA VA EHR system)– open access: no restrictions on use or distribution of content – open participation: everyone can participate

• communal management, flat hierarchies, consensus emergent decision-making

• Allows “mash-ups” of freed data– http://www.googlelittrips.com/GoogleLit/Home.html for

Aeneid, Grapes of Wrath, user-generated road trips...

- e.g., http://healthmap.org/en http://www.nature.com/avianflu/google-earth/index.html

http://www.googlelittrips.com/GoogleLit/Home.html

http://healthmap.org/en

http://www.nature.com/avianflu/google-earth/index.html


Architecture of Participation

• Network externalities concept: “the service automatically gets better the more people use it” e.g., – fax machines, cell phones...the more the better– Google search

• the more “link paths” people tread, the richer the data for the Google search algorithm

– Amazon book ratings, Netflix ratings

• Anonymity important for this to happen in healthcare– whoissick.org/sickness/– better epi data if everyone contributed to public health data

• 1-3% refuse to share clinical data for research


Niche Markets• “The web” is unlimited resource

– can service even extremely small market niches

• Shape of the web: the “long tail”

where traditional focus is

with infinitely long tail, majority of action is here

# p

eo

ple

market niche/things being done


Niche Markets in Health

• Rare diseases– PatientsLikeMe

• Geographic, ethnic, other niches– Russian-speaking boy scouts with ADHD in rural Montana


Outline







Study Interpretation/Hypothesis Generation

• New hypotheses arise from examining prior data and knowledge– clinical data, e.g.,

• claims data

• EHR data/data warehouses

– research data (aka the literature)• basic science research results (e.g., animal studies)

• clinical research (e.g., RCTs, GWAS, observational studies)

– all other data


MICU

FinanceResearch

QA

IntegratedData Repository

Internet

ADT Chem EHR XRay PBM Claims

• autofeed nightly, data stored securely with backup

Data Mining in IDR


Data Mining

• The process of automatically discovering useful information in large data repositories– predictive: find variables to predict unknown or future

variables• e.g,. classification of people into likely tax cheaters, credit risks

• e.g., who is at risk of ER bounce-backs?

– descriptive: finding human-interpretable patterns that describe the data

• clustering: e.g., network analysis of depression trials in ClinicalTrials.gov


Anti-depressants vs. Herbals

H i g h C o - o c c u r r e n c e

o f A n t i d e p r e s s a n t s

L o w C o - o c c u r r e n c e o f

A n t i d e p r e s s a n t s a n d

N a t u r a l S u p p l e m e n t s


Hypothesis Generation from Clinical Data

• Background data mining algorithms running on IDR– “promising findings” put up on a website where UCSF

researchers can “vote” on their interest and/or examine

• Let non-researchers nominate hypotheses– e.g., a window in Epic for clinicians to suggest a research

question

• Collect different data to drive data mining– e.g., patients can twitter adverse symptoms, may lead to

earlier detection for adverse effects of new drugs?




• claims data




– all other data


Biomedical Research Data

• Biomedical research data repositories– GenBank, UK BioBank, deCODE

– Gene Expression Omnibus (GEO) gene expression and genomic hybridization experiments http://www.ncbi.nlm.nih.gov/geo

– PharmGKB, pharmacogenomics http://pharmgkb.org/

– ClinicalTrials.gov http://clinicaltrials.gov/

• Biomedical literature (i.e., PubMed) • E.g., “human studyome”

– totality of human studies worldwide

– is the scientific foundation for understanding human health and disease and for advancing human health

http://www.ncbi.nlm.nih.gov/geo

http://pharmgkb.org/

http://clinicaltrials.gov/


Sharing Raw Results

46.4 (39.2-51.2) 45.1 (39.9-50.5)

0.83 (0.79-0.99) 0.91 (0.93-1.04)

2.2 (1.7-3.4) 2.7 (1.1 - 4.1)

110 (87-134) 121 (99-129)


Need Standardized Metadata

• Variable names are metadata• MeSH, ICD, SNOMED, etc. are standard clinical vocabularies

– ionized calcium: UMLS code C0373561

Age 46.4 (39.2-51.2) 45.1 (39.9-50.5)

ICa 0.83 (0.79-0.99) 0.91 (0.93-1.04)

Creatinine 2.2 (1.7-3.4) 2.7 (1.1 - 4.1)

Weight (lbs) 110 (87-134) 121 (99-129)


Garlic Chocolate

Age 46.4 (39.2-51.2) 45.1 (39.9-50.5)

ICa 0.83 (0.79-0.99) 0.91 (0.93-1.04)

Creatinine 2.2 (1.7-3.4) 2.7 (1.1 - 4.1)

Weight (lbs) 110 (87-134) 121 (99-129)

Need Metadata About the Study

• Study results = “study data”• Variable names = “study results metadata” • Data about study design = “study metadata”


Garlic Chocolate

Age 46.4 (39.2-51.2) 45.1 (39.9-50.5)

ICa 0.83 (0.79-0.99) 0.91 (0.93-1.04)

Creatinine 2.2 (1.7-3.4) 2.7 (1.1 - 4.1)

Weight (lbs) 110 (87-134) 121 (99-129)

Need Study Design Metadata

• Randomized trial of garlic vs. chocolate for weight loss? Observational study of ionized calcium levels?

• i.e., need data standardized in an ontology of human studies research


Computerizing the Studyome

• Computerize human studies design and results for large-scale discovery, reanalysis, reuse

• Based on Ontology of Clinical Research

http://hsdbwiki.org/




• claims data




– all other data


Data Mining with “Big Data”

• Text mining, data mining, model building across ALL data on web– within and outside biomedicine– supervised (e.g, neural net) and unsupervised (e.g.,

clustering) learning

• Current web is non-semantic– “the web” does not “understand” the meaning of

• content of web pages, or

• data that is sent over the network (e.g., Netflix movie names, or movie content)

– how to go from a web of documents to a web of (computer- understandable) data?


Semantic Web• All content on or sent over the web is expressed

using OWL ontologies– Ontology Web Language, for describing everything, like

“SNOMED for everything”• see OntoWiki, National Center for Biomedical Ontology

• “Intelligent agents” can roam the web doing smart things for you– e.g., booking your summer vacation, making appointment

with the best cardiothoracic surgeon, re-balancing your retirement portfolio

– learning from your actions, acting on your behalf


Semantic Web Databases/Technologies

• www.freebase.com– free + database = absolutely everything in structured,

computable form using OWL ontologies


How Will You be Getting New Ideas?

• Automated discovery of unimaginably large data sets (i.e., the whole web)

• Crowdsourcing– using distributed human intelligence and the wisdom of

crowds to sort the wheat from the chaff

• Will it be better to share your best ideas widely? or to hold them tight?


Outline







A Research Commons• Science Commons: open science data on semantic web

http://sciencecommons.org/ • Health Commons virtual labs vision http://www.healthcommons.net/

– “buy” scientific elements• e.g., PhenX, NHGRI’s common phenotypes for GWAS studies

– https://www.phenxtoolkit.org/

– “buy” scientific services like you shop at Amazon• high-throughput genotyping, array analysis, trial recruitment, survey

design

– assemble your team as needed

– IP, material transfer agreements, etc. all handled by Health Commons framework (like e-commerce)

• Predicated on large-scale, open data

http://sciencecommons.org/

http://www.healthcommons.net/

https://www.phenxtoolkit.org/


On an Open Software Platform

• iPhone-like health care and research “apps”• Clinical research 24/7/without walls• Needs technical standards and a market mechanism• ???


Outline







Content Production• Anyone can produce “content” (researchers, clinicians,

patients, etc.)– clinicians: e.g., www.ganfyd.org, a medical wiki for MDs,

www.sermo.com, etc.– patients: tens of thousands of web sites...– social tagging/social bookmarking (e.g., del.icio.us)

• (content, your-bookmark-tag, your-name) <==> (content, same-bookmark-tag, potential-collaborator)

• All content is open– e.g., Consolidated Appropriations Act of 2007 requires open

online access to NIH funded research– NIH Data Sharing initiative, PubMed Central, etc.

http://www.ganfyd.org/

http://www.sermo.com/


Publication

• Publication is self-controlled– self-archiving, self-publishing in institutional repositories and/or

eScience communities (e.g,. http://escholarship.org/ for UC)– e.g., PLoS One, Nature portals -- “the long tail”

• papers published into PLoS platform

• scientists self-aggregate into (niche) communities

• reader ratings & comments “direct” papers to relevant communities

• evaluation is by # of views, # of comments/citations, ratings, link outs, blog mentions, etc.

• Publications should be in computable form– e.g., using Ontology of Clinical Research for human studies

Disclosure: I’m on PLoS One Advisory Board

http://escholarship.org/


Outline

• From Here to There (web 2.0/3.0 eScience) • Collaborative Care and Web 2.0• Collaborative Research and Web 2.0/3.0






Big Data + Web 2.0 + Web 3.0

..

....

..

..

....

..

..

VirtualPatient

Transactions

Raw data

Medicalknowledge

Clinicalresearch

transactions

Rawresearch

data





EHRs

CTMSs

Primary Care MD

Patient



eCare and eScience

Administrative Clinical Care Research

Physical Networking

Standard Communications Protocols (e.g., HL-7)

PracticeManagement

Systems

EHRExecutionAnalysis

Medical BusinessData Model

Clinical CareData Model

Clinical StudyData Models

Open de-identified repositories

OWL Ontologies of Everything


Collab Care and Research• Beyond data storage, security, and access to smarter knowledge-

based systems• Beyond supporting transactions to supporting collaborative

sense-making– visualization, human and automated pattern matching and testing,

combining multi-disciplinary worldviews

– “marketplace” of ideas, research methods, research tools

• Continuous learning by all participants– teachable moments for new methods, findings, hypotheses

– tighter coupling of front-line clinical evidence needs to research questions


Open Discussion

• How to balance standardization and comparability (e.g., of EHR notes, of research outcomes) with flexibility/innovation?

• Biomedical researchers are conservative– will all this web 2.0/3.0 stuff pass right by us?

• How will this change what you do/how you think, if at all?

• What would you like to see from academia/UCSF to help you stay as competitive in research as possible?

• ???


Outline







Summary• IT focuses on storing, accessing, and exchanging

data • Informatics is use of computers to make sense of

data • The more “computable” the information, the more the

computer can do for us• ...not just us individually, but together as a community

of care and science


Computers Must Interoperate• In a networked world, data and actions must be

shared across people and computers– syntatic interoperation: a common grammar for machines

talking to each other in biomedicine (e.g., HL7)– semantic interoperation: predictable and meaningful

exchange of common meaning• requires standard vocabularies and standard data models

• SNOMED most comprehensive but use is unproven

• Other challenging things that need standardization in biomedicine– “common data elements” in research– a standard EHR data model so all EHRs “look” alike– standard protocol models for human studies, etc.


State of Health IT Use• EHR adoption still low

– barriers include finances, lack of organizational change expertise, fragmentation of health care system, misaligned incentives

• Recovery Act will spur EHR adoption, for good or ill• EHR and data warehouses can but don’t always help

research • Limited success of decision support systems• Fundamental tradeoff of coding effort vs. “smartness”

of system limits both EHR and CDSS return on investment


Take-Home Message• Informatics helps make sense of data and knowledge

– is necessary for better care and research

• Today’s technologies promise transactional support – major barriers are economic, policy, and workflow related

• Need brand new technologies for other 3/4 of Big Picture

• Disruptive change to eScience seems quite possible – as we go from data processing to concept processing– as mobile technologies break down time and space barriers– as social computing takes off


Big Data + Web 2.0 + Web 3.0

..

....

..

..

....

..

..

VirtualPatient

Transactions

Raw data

Medicalknowledge

Clinicalresearch

transactions

Rawresearch

data





EHRs

CTMSs

Primary Care MD

Patient


Documents

March 16, 2010: I. SimTranslational eScience Epi – 206 Medical Informatics Translational eScience Ida Sim, MD, PhD March 16, 2010 Division of General Internal