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

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Presentation on theme: "March 16, 2010: I. SimTranslational eScience Epi – 206 Medical Informatics Translational eScience Ida Sim, MD, PhD March 16, 2010 Division of General Internal."— Presentation transcript:

1 March 16, 2010: I. SimTranslational eScience Epi – 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, All federal and state rights reserved for all original material presented in this course through any medium, including lecture or print.

2 March 16, 2010: I. SimTranslational eScience Epi – 206 Medical Informatics 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...

3 March 16, 2010: I. SimTranslational eScience Epi – 206 Medical Informatics 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: ), Institute of Medicine

4 March 16, 2010: I. SimTranslational eScience Epi – 206 Medical Informatics 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

5 Here Virtual Patient Transactions Raw data Medical knowledge Clinical research transactions Raw research data Decision support Medical logic PATIENT CARE / WELLNES RESEARCH Workflow modeling and support, usability, cognitive support, computer-supported cooperative work (CSCW), etc. CRMSsEHRs

6 March 16, 2010: I. SimTranslational eScience Epi – 206 Medical Informatics IRBFunding Agency Study DB Data analysis Results reporting Contract Research Organization (CRO) Protocol Trial Design Sponsors Academic PIs ? Site 1Site 2 Site 3 Site Management Organization (SMO) Here

7 March 16, 2010: I. SimTranslational eScience Epi – 206 Medical Informatics 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”

8 March 16, 2010: I. SimTranslational eScience Epi – 206 Medical Informatics 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.

9 March 16, 2010: I. SimTranslational eScience Epi – 206 Medical Informatics 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

10 March 16, 2010: I. SimTranslational eScience Epi – 206 Medical Informatics 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)

11 March 16, 2010: I. SimTranslational eScience Epi – 206 Medical Informatics Big Picture + People.. Primary Care MD Patient Principal Investigator

12 March 16, 2010: I. SimTranslational eScience Epi – 206 Medical Informatics 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

13 March 16, 2010: I. SimTranslational eScience Epi – 206 Medical Informatics 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

14 March 16, 2010: I. SimTranslational eScience Epi – 206 Medical Informatics 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)

15 March 16, 2010: I. SimTranslational eScience Epi – 206 Medical Informatics User-Generated Content Anyone anywhere is a source of content –YouTube, Flickr, Wikipedia. citizen journalism, blogs –e.g., Exists in parallel with (trumps?) Old/Main Stream Media (MSM), hierarchical information sources –NIH MedlinePlus –WebMD.com

16 March 16, 2010: I. SimTranslational eScience Epi – 206 Medical Informatics 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 Crowdsourcing: e.g., –250,300 questions in health

17 March 16, 2010: I. SimTranslational eScience Epi – 206 Medical Informatics 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...http://www.googlelittrips.com/GoogleLit/Home.html - e.g.,

18 March 16, 2010: I. SimTranslational eScience Epi – 206 Medical Informatics 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

19 March 16, 2010: I. SimTranslational eScience Epi – 206 Medical Informatics 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 # people market niche/things being done

20 March 16, 2010: I. SimTranslational eScience Epi – 206 Medical Informatics Niche Markets in Health Rare diseases –PatientsLikeMe Geographic, ethnic, other niches –Russian-speaking boy scouts with ADHD in rural Montana

21 March 16, 2010: I. SimTranslational eScience Epi – 206 Medical Informatics 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

22 March 16, 2010: I. SimTranslational eScience Epi – 206 Medical Informatics 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

23 March 16, 2010: I. SimTranslational eScience Epi – 206 Medical Informatics MICU Finance Research QA Integrated Data Repository Internet ADT ChemEHRXRayPBMClaims autofeed nightly, data stored securely with backup Data Mining in IDR

24 March 16, 2010: I. SimTranslational eScience Epi – 206 Medical Informatics 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

25 March 16, 2010: I. SimTranslational eScience Epi – 206 Medical Informatics Anti-depressants vs. Herbals

26 March 16, 2010: I. SimTranslational eScience Epi – 206 Medical Informatics 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?

27 March 16, 2010: I. SimTranslational eScience Epi – 206 Medical Informatics 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

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29 March 16, 2010: I. SimTranslational eScience Epi – 206 Medical Informatics Biomedical Research Data Biomedical research data repositories –GenBank, UK BioBank, deCODE –Gene Expression Omnibus (GEO) gene expression and genomic hybridization experiments –PharmGKB, pharmacogenomics –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

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31 March 16, 2010: I. SimTranslational eScience Epi – 206 Medical Informatics Sharing Raw Results 46.4 ( )45.1 ( ) 0.83 ( )0.91 ( ) 2.2 ( )2.7 ( ) 110 (87-134)121 (99-129)

32 March 16, 2010: I. SimTranslational eScience Epi – 206 Medical Informatics Need Standardized Metadata Variable names are metadata MeSH, ICD, SNOMED, etc. are standard clinical vocabularies –ionized calcium: UMLS code C Age46.4 ( )45.1 ( ) ICa0.83 ( )0.91 ( ) Creatinine2.2 ( )2.7 ( ) Weight (lbs)110 (87-134)121 (99-129)

33 March 16, 2010: I. SimTranslational eScience Epi – 206 Medical Informatics GarlicChocolate Age46.4 ( )45.1 ( ) ICa0.83 ( )0.91 ( ) Creatinine2.2 ( )2.7 ( ) 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”

34 March 16, 2010: I. SimTranslational eScience Epi – 206 Medical Informatics GarlicChocolate Age46.4 ( )45.1 ( ) ICa0.83 ( )0.91 ( ) Creatinine2.2 ( )2.7 ( ) 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

35 March 16, 2010: I. SimTranslational eScience Epi – 206 Medical Informatics Computerizing the Studyome Computerize human studies design and results for large- scale discovery, reanalysis, reuse Based on Ontology of Clinical Research

36 March 16, 2010: I. SimTranslational eScience Epi – 206 Medical Informatics 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

37 March 16, 2010: I. SimTranslational eScience Epi – 206 Medical Informatics 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?

38 March 16, 2010: I. SimTranslational eScience Epi – 206 Medical Informatics 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

39 March 16, 2010: I. SimTranslational eScience Epi – 206 Medical Informatics Semantic Web Databases/Technologies –free + database = absolutely everything in structured, computable form using OWL ontologies

40 March 16, 2010: I. SimTranslational eScience Epi – 206 Medical Informatics 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?

41 March 16, 2010: I. SimTranslational eScience Epi – 206 Medical Informatics 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

42 March 16, 2010: I. SimTranslational eScience Epi – 206 Medical Informatics A Research Commons Science Commons: open science data on semantic web Health Commons virtual labs vision –“buy” scientific elements e.g., PhenX, NHGRI’s common phenotypes for GWAS studies –https://www.phenxtoolkit.org/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

43 March 16, 2010: I. SimTranslational eScience Epi – 206 Medical Informatics 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 ???

44 March 16, 2010: I. SimTranslational eScience Epi – 206 Medical Informatics 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

45 March 16, 2010: I. SimTranslational eScience Epi – 206 Medical Informatics Content Production Anyone can produce “content” (researchers, clinicians, patients, etc.) –clinicians: e.g., a medical wiki for MDs, etc.www.ganfyd.org –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.

46 March 16, 2010: I. SimTranslational eScience Epi – 206 Medical Informatics Publication Publication is self-controlled –self-archiving, self-publishing in institutional repositories and/or eScience communities (e.g,. for UC)http://escholarship.org/ –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

47 March 16, 2010: I. SimTranslational eScience Epi – 206 Medical Informatics 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

48 March 16, 2010: I. SimTranslational eScience Epi – 206 Medical Informatics Big Data + Web Web Primary Care MD Patient Principal Investigator

49 March 16, 2010: I. SimTranslational eScience Epi – 206 Medical Informatics eCare and eScience AdministrativeClinical CareResearch Physical Networking Standard Communications Protocols (e.g., HL-7) Practice Management Systems EHR Execution Analysis Medical Business Data Model Clinical Care Data Model Clinical Study Data Models Open de-identified repositories OWL Ontologies of Everything

50 March 16, 2010: I. SimTranslational eScience Epi – 206 Medical Informatics 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

51 March 16, 2010: I. SimTranslational eScience Epi – 206 Medical Informatics 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? ???

52 March 16, 2010: I. SimTranslational eScience Epi – 206 Medical Informatics 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

53 March 16, 2010: I. SimTranslational eScience Epi – 206 Medical Informatics 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

54 March 16, 2010: I. SimTranslational eScience Epi – 206 Medical Informatics 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.

55 March 16, 2010: I. SimTranslational eScience Epi – 206 Medical Informatics 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

56 March 16, 2010: I. SimTranslational eScience Epi – 206 Medical Informatics 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

57 March 16, 2010: I. SimTranslational eScience Epi – 206 Medical Informatics Big Data + Web Web Primary Care MD Patient Principal Investigator


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