Integrating evidence based medicine and em rs

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  • 1. Integrating Evidence-Based Medicine and EHRs:“Nextgen” Clinical Decision Support for Genomically-enabled Healthcare Daniel Masys, MD Affiliate Professor Biomedical & Health Informatics University of Washington, Seattle CMIO Leadership Forum October 5, 2012
  • 2. Topics• The goal• The challenges to be addressed on the way to the goal• Partial progress toward a decision-support infrastructure for ‘precision healthcare’• Currently unmet needs – a plea for your help
  • 3. A deceptively simple goal• Do the right thing, and only the right thing, and do it every time for every individual –Therapeutic subset: right dose of the right drug for the right patient at the right time
  • 4. The baseline: The gap between what we know and what we do6,712 Individuals in 12 Cities Only 54.9% received recommended care Only 54.9% received recommended preventive care Only 53.5% received recommended acute care Only 56.1% received recommended chronic careExamples: Hip Fracture 22.8% (Range 6.2-39.5%) Atrial Fibrillation 24.7% Depression 57.2% Senile Cataract 78.7% (Best performance) McGlynn, et. al., NEJM 2003;348:2635-45
  • 5. Rising expectations that systems should work• IOM Reports – 14 reports now in the Quality Chasm series – Current mantra: “Learning Healthcare Systems”• Persistent media attention
  • 6. Problems
  • 7. Process Errors• Majority of errors do not result from individual recklessness, but from flaws in health system organization (or lack of organization).• Failures of information management are common: – illegible writing in medical records – lack of integration of clinical information systems – inaccessibility of records – lack of automated allergy and drug interaction checking
  • 8. Persistentmedia attention
  • 9. If all of these studies on healthcarequality are correct, the problems aretoo big to name, and would notleave the message-giver intact Don Berwick (Outgoing) Administrator, Centers for Medicare & Medicaid Services
  • 10. If airlines were run like healthcare...• Pilots would build and maintain their own airliners• Navigation instruments would be available but not used (“just another case of Denver…”)• There would be no ‘second pair of eyes’ (ATC) watching each flight’s progress• Ticket = seat only. Pilots would separately bill for piloting services two months after the flight• There would be no National Aerospace Course Guidance infrastructure embedded in GPS’s and onboard navigation computers linked to autopilots• Pilots would leave the plane before all accidents• The transportation system would be no safer or consistent than any single practitioner participating in it
  • 11. Systems Design Issues in Healthcare • Current practice largely depends upon the clinical decision making capacity and reliability of autonomous individual practitioners, for classes of problems that routinely exceed the bounds of unaided human cognition Masys DR. Effects Of Current And Future Information Technologies On The Health Care Workforce. Health Affairs, 2002 Sept-Oct; 21(5):33-41.
  • 12. Why?• In the absence of facts, opinion prevails (85% of healthcare) - T. Clemmer, M.D.• “A Thousand Doctors, A Thousand Opinions” - French proverb• “Instead of teaching doctors to be intelligent map readers, we have tried to teach every one to be a cartographer.” - L. Weed, M.D.• “We need ‘just in time’ education. In medical school we teach ‘just in case’ education. - William Stead, M.D.
  • 13. Inescapable Conclusion• Health care in the 20th Clinical Century and before was Events hopelessly bound by reliance on imprecise phenotypic manifestations of disease and a thousand year old Guild and Apprenticeship model of Molecular medical reasoning and Events education
  • 14. The Genome Sequence is at hand…so?“The good news is that we have the human genome. The bad news is it’s just a parts list”
  • 15. The Promise• Molecular and clinical biomarkers for health conditions individuals either have or are susceptible to • Includes traditional healthcare history, physical findings, diagnostic imaging, standard clinical laboratories • Increasingly: large volumes of molecular data – Structural genomics: DNA in residence (~22,000 genes) – Functional genomics: genes switched on (1-2% active) – Proteomics (400,000 proteins from 22,000 genes)
  • 16. The Promise, cont’d• Precision Health Care• Pharmacogenomics – “The right dose of the right drug for the right patient at the right time” – Drug development: • Avoid drugs likely to cause side effects • Re-investigate “back- burner” drugs • Develop entirely new drugs targeting fundamental disease processes "Heres my sequence...” New Yorker, 2000
  • 17. Tsunami Forecast: Big Data Ahead in Healthcare
  • 18. The need for patient-specific clinical decision support in the era of precision medicine 1000 Facts per Decision Proteomics and other effector molecules 100 Functional Genetics: Gene expression profiles 10 Structural Genetics: e.g. SNPs, haplotypesDecisions byclinical Human Cognitivephenotype Capacityi.e., traditionalhealth care 1990 2000 2010 2020
  • 19. A three step approach to managingand using personal genomic data for decision support Step 1:Get the data into Electronic Health Records (EHRs) in a usable form
  • 20. Most common current method for delivery of DNA analysis into clinical operations
  • 21. Problems with treating genomic analysis in same fashion as otherprofessionally interpreted clinical data• Lossy compression: many DNA features observed, only a few clinically relevant reported, remainder discarded• Interpretation inextricably bound together with primary observations in a document format• Document reporting format not amenable to parsing for automated machine interpretation and decision support• Much more unknown than known about genomic effects, and science changing rapidly
  • 22. Output of workshop on “Integration of Genetic Test Results into Electronic MedicalRecords” convened by the National Heart Lung and Blood Institute, Bethesda, MDAugust 2-3, 2011
  • 23. 7 desiderata for molecular variation data in EHRs1. Lossless data compression from (high volume) primary observations to clinically relevant subsets.2. Since methods will change, molecular lab results carry observation methods with them (LOINC model)3. Compact representation of clinically actionable subsets for optimal performance (clinician thinkspeed = 250msec)4. Simultaneously support for human-viewable formats (with links to interpretation) and formats interpretable by decision support rules.5. Separate primary sequence data (remain true if accurate) from clinical interpretations of them (will change with rapidly changing science)6. Anticipate the boundless creativity of Nature: multiple somatic genomes, multiple germline genomes for each individual over their lifetime.7. Support both individual care and discovery science
  • 24. Structured keywords for clinical decision InterpretiveIntrepretations support (e.g., C*2*2CLM fires decision codesof primary data rule for CYP2C19*2 homozygotes at time of clopidigrel prescribing). Diagnostic (expect rapid change) A few tens of bytes each. Interpretations (PDF reports). A few kilobytes each. Layered classes of Personal molecular differences represented in EHR as computed EHR-relevant data offset from a Clinical Standard Reference Genome (CSRG) =~1% of genome/ proteome. Primary A few megabytes.Observations. Ifaccurate, keep Consensus full personal germline and somatic sequence(s) forever and metadata: a few gigabytes each 30x+ nextgen reads: hundreds of gigabytes/few terabytes
  • 25. A three step approach to managingand using personal genomic data for decision support Step 2: Create a people and technology infrastructure to use the data for decision support
  • 26. Example of rule-based clinical decision support (CDS) today(version 2.0 CDS: present problem along with solution to problem)
  • 27. Chem7 Panel (BUN, Creat, Lytes, Gluc) Effect of New orders/day decision support: show provider most recent value of same Stopped orders/day test Neilson, EG, et al Ann Intern Med. 2004; 141: 196-204
  • 28. Effect of Computerized Provider Order Entry (CPOE) with CDS at Vanderbilt 35 30.1 30Errors per 100 Orders 25 20 15 10 6.8 5 2.2 1.3 0.2 0.1 0 pre-CPOE post-CPOE Potential ADEs Medication Prescribing Errors Rule Violations Potts, A. et al. PEDIATRICS 2004;:113:59-63
  • 29. An example of progress towardsoperational genomically enabled clinical decision support
  • 30. Vanderbilt PREDICT projectPharmacogenomic Resource for Enhanced Decisions In Care and Treatment. Go-live date: September 2010 Replicate literature DNA association Evidence review Guidance: effect in by P&T sub- implementation Professional local committee societies, FDA biobank • Drug-genotype Prospective pair in EMR Follow outcomes Genotyping: • Other genotypes • Is dose changed? (e.g. Illumina outside EMR • Are outcomes affected? ADME panel) • Point of care • What do patients think? decision support Pulley JM et al. Operational Implementation of Prospective Genotyping for Personalized Medicine: The Design of the Vanderbilt PREDICT Project. Clin Pharmacol Ther. 2012 May 16
  • 31. As seen by providers at the moment of prescribing:
  • 32. Electronic Medical Record genomic data as viewed by providers
  • 33. Genomic data as viewed by patients
  • 34. The face of personalized medicine
  • 35. A three step approach to managingand using personal genomic data for decision support Step 3: Scale the decision support up toenable all providers and all patientsand families to benefit, via a public information infrastructure
  • 36. Context for what follows
  • 37. 20th Century course guidance 21st Century course guidance
  • 38. A Systems Approach to Scaling-up• A National Healthcare Course Guidance infrastructure (analogous to FAA course guidance database for aviation) 1. A continuously updated Public Library of clinical decision support ‘packages’ (a federally supported information commons): Wikipedia for decision support. 2. Event monitors embedded in EHR systems: healthcare autopilots and “guardrails” 3. System-generated alerts at the “teachable moment” of diagnostic testing and therapy ordering 4. Automated tracking of outcomes vs. provider decisions: a learning healthcare system
  • 39. Your help needed: Currently Unmet Infrastructure Needs• Standards for electronic “decision support packages” containing: 1. Recognition logic for conditions of interest as represented in EHR systems 2. Guidance for target users (clinician, patient, family) 3. Recognition logic for “closed loop decision support”: process or outcome measure to monitor, along with record of whether clinician accepted or rejected guidance• A Decision Support Public Library of clinical decision support ‘packages’ representing best practice• Decision support authoring systems to enable local ‘best practice rules committees’ to easily import, review, and implement decision support packages received from the Decision Support Public Library
  • 40. It Takes a Village… Thanks to:• Vanderbilt PREDICT and • NHLBI EHR technical EHR team desiderata co-authors – Dan Roden, MD – Gail Jarvik, MD, PhD, – Josh Denny, MD, MS Nick Anderson PhD, Neil – Jim Jirjis, MD Abernethy PhD – UW – Kevin Johnson, MD, MS – Isaac Kohane, Harvard – Jill Pulley, MBA – Marc Hoffman, Cerner – Dana Crawford, PhD – Howard Levy, Johns Hopkins – Dina Paltoo, George Papanicolau, NIH