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iHT2 Health IT Summit San Francisco 2013 - Christopher Chute, Division of Biomedical Informatics, Mayo Clinic, Presentation “Strategies for the ICD-10 Transition”


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Christopher Chute, MD, DrPH …

Christopher Chute, MD, DrPH
Chair, Division of Biomedical Informatics
Mayo Clinic

Presentation “Strategies for the ICD-10 Transition”

Participants will have an understanding of the background and implications of three key areas:

∙ How Meaningful Use standards impact ACO operations and success
∙ What role will the new ICD-10 play in providers understanding their practice and outcomes
∙ What are the future directions in ICD-11 and beyond Meaningful Use Phase 3

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  • Biomedical Informatics; CG Chute © Mayo Clinic College of Medicine 2004
  • Classifications and Terminology CG Chute © Mayo Clinic College of Medicine 2007
  • Green already exists in ICD-10 either explicitly or implicitly – Content model specifies them in a more systematic way Orange rubrics are new – they exist in some specialty adaptations already such as oncology, mental health, neurology, dermatology, Functioning Properties is also new – to allow joint use of ICD and ICF
  • SHARPn is committed to open-source resources that can industrially scale to address barriers to the broad-based, facile, and ethical use of EHR data for secondary purposes SHARPn will collaborate to create, evaluate, and refine informatics artifacts that advance the capacity to efficiently leverage EHR data to improve care, generate new knowledge, and address population needs. SHARPn will make these artifacts available to the community of secondary EHR data users as open-source tools, services, and scalable software.
  • Transcript

    • 1. Biomedical Informatics Strategies for the ICD-10 Transition with Comments on Meaningful UseChristopher G. Chute, MD DrPHProfessor Biomedical InformaticsVice-Chair, Data GovernanceMayo ClinicChair, International Classification of Disease RevisionMember, HIT Standards Committee, HHS/ONCChair, ISO Technical Committee 215 on Health Informatics IHT2, San Francisco 26 March, 2013
    • 2. Biomedical Informatics Declarations• No real or apparent financial conflicts of interest • All products are open-source• Comments represent beliefs of the author• He has an odd sense of humor
    • 3. Biomedical Informatics Key Learning Objectives (they made me do this slide)• How Meaningful Use standards impact ACO operations and success• What role will the new ICD-10 play in providers understanding their practice and outcomes• What are the future directions in ICD-11 and beyond Meaningful Use Phase 3 © Mayo Clinic College of Medicine 2010 3
    • 4. Biomedical Informatics Health Care Is An Information Intensive Industry• Control of Health Care Costs ...• Improved Quality of Care ...• Improved Health Outcomes ...• Appropriate Use of Health Technology...• Compassionate Resource Management...... depend upon information… Ultimately Patient Data 4
    • 5. Biomedical Informatics Biocomputing Unimaginable potential, next 50 years• Moore’s law still reigns • 1012 fold increase in computing power / 50 yrs • World-class Supercomputers of 1990 • Game platforms and cell phones of 2012 (GPUs)• Extensions to networks, memory, & storage 110 baud to 100Gb backbone (109 / 50 yrs) Vacuum tubes to 100Gb RAM (1011 / 50 yrs) Paper-tape to Pb drives (1015 / 50 yrs) Cloudy computing (103 / 3 yrs)• Synergistic Summary – 1050 / 50 yrs 5
    • 6. Biomedical Informatics Big Science Collaboration and Semantics• Communication of findings and results • Human publication • Sharing of data resources as building blocks • Foundation for incremental, big-science• Harnessing computing requires formalization • Data format and structures • Discrete terms, vocabulary, ontology • Non-ambiguous concepts, non-overlapping terms • Information models and problem architectures • Standards, conventions, and shared context 6
    • 7. Biomedical Informatics Comparable and Consistent Data• Inferencing from data to information requires sorting information into categories • Statistical bins • Machine learning features• Accurate and reproducible categorization depends upon semantic consistency• Semantic consistency is the vocabulary problem • Almost always manifest as the “value set” problem 7
    • 8. Biomedical Informatics From Practice-based Evidence to Evidence-based Practice Clinical Registries et al.Data Databases Shared Semantics Inference Medical Knowledge Patient StandardsEncounters Terminologies & Data ModelsDecision Expert Clinical Knowledge Guidelinessupport Systems Management Foundations for Learning Health System 8
    • 9. Biomedical Informatics World Health Organization; ICD-11 9
    • 10. Biomedical Informatics World Health Organization; ICD-11 10
    • 11. Biomedical Informatics Meaningful Use 13 January, 2010 Reporting InteroperabilityRequirements Standards 169pp 35pp 11
    • 12. Biomedical Informatics The Challenge• Most clinical data in the United States is heterogeneous – non-standard • Within Institutions • Between Institutions• Meaningful Use is mitigating, but has not yet “solved” the problem • Achieving standardization in Meaningful Use is sometimes minimized
    • 13. Biomedical Informatics ACO Operations and Success• Learning Health System • Commitment to discovering best practice • Critical examination of practice• ACO rationale • Align incentives for reimbursement • Practice integration, communication, exchnage • Quality measurement, ultimate improvement • Best Practice Discovery, monitor adherence • Improve health, wellness, disease management • Reduce overall costs © Mayo Clinic College of Medicine 2010 13
    • 14. Biomedical Informatics What Does It Take To Become Learning Health System• Culture, Culture, and Culture• Data issues • Comparable and consistent data representation • Comprehensive and longitudinal EMR/EHR • Data quality, reliability, and completeness • Curation, monitoring, management • Near real-time warehouse • Analytic capacity • First order Decision Support implementation © Mayo Clinic College of Medicine 2010 14
    • 15. Biomedical Informatics What Does It Take To Become Learning Health System• Culture, Culture, and Culture• Data issuesComparable and consistent data representation • Comprehensive and longitudinal EMR/EHR • Data quality, reliability, and completeness • Curation, monitoring, management • Near real-time warehouse • Analytic capacity © Mayo Clinic College of Medicine 2010 15
    • 16. Biomedical Informatics Meaningful Use and Comparable Data• Phase I – an into• Phase II – dawn of effective standards• Phase III – deployment of useful comparability• Biannual follow-ons – ratcheting consistency• Learning Health System cannot be supported by Meaningful Use standards alone • Not yet sufficient granularity/specificity © Mayo Clinic College of Medicine 2010 16
    • 17. Biomedical Informatics Clinical Terminology and Classification• SNOMED-CT – evolving into utility • Not a human entry terminology • Increasingly comprehensive and rational • Platform for data comparability • Meaningful Use Phase II requirement (problem list)• Consolidated CDA • Vast improvement over CCD • Specifies Value Sets • NLM National Value-set Center © Mayo Clinic College of Medicine 2010 17
    • 18. Biomedical Informatics So, Where’s ICD10 in All This? © Mayo Clinic College of Medicine 2010 18
    • 19. Biomedical Informatics © Mayo Clinic College of Medicine 2010 19
    • 20. Biomedical Informatics Short Summary of Those Reasons for delaying ICD-10-CM1. Meaningful Use phase I2. Meaningful Use phase II3. Meaningful Use phase III• Requirement to implement SNOMED CT • Phase II for problem lists• Limited clinical benefit• Substantial cost• ICD-10 is already 25 years old © Mayo Clinic College of Medicine 2010 20
    • 21. Biomedical Informatics Some Observations on American Clinical Modifications of the ICDICD-9-CM Diagnoses codes ICD-10-CM Diagnoses codes • Roughly 13,000 codes • Roughly 68,000 codes • Injury codes: 15% • Injury codes: 60% • Disease codes: 8,500 • Disease codes: 18,000 • Permuted by: • Right, Left, Both, Unspecified • First Episode, subsequent • Adds up to 6 codes per disease • Done for a bit more than half disease codes 21
    • 22. Biomedical InformaticsHow Well do Clinical Classifications Work?• Study of Coding efficacy to measure content capture Chute et al., JAMIA 1996;3(3):224-233 • Text reports from four academic medical centers• Data parsed into 3,061 clinical observations• Coded twice by national panel of experts• Updated re-coding using most current versions of ICD-10-CM and ICD-9-CM - 2010 • Coding over-read by CDC/NCHS • National Center for Health Statistics
    • 23. Biomedical Informatics Scoring Example ICD-9-CM2 <Primary Diagnosis>:melanoma0 <Extent>:Clark’s level 20 <Quantitative>:0.84 mm [depth of invasion]2 <Histology>:superficial spreading1 <Anatomy>:thigh0 <Topology>:leftCode: 172.7 | Malignant melanoma of skin, lower limbCode: M8743/3 | Superficial spreading melanoma
    • 24. Biomedical InformaticsComparison of Clinical Content Coverage between ICD-9-CM and ICD-10-CM
    • 25. Biomedical Informatics © Mayo Clinic College of Medicine 2010 25
    • 26. Biomedical Informatics ICD-10 – The good part Roughly 2% of Expansion Attributable to• Administration• Imaging• Radiation Oncology• Physical Rehabilitation• Diagnostic Audiology © Mayo Clinic College of Medicine 2010 26
    • 27. Biomedical Informatics International Classification of Disease ICD11 Use Cases• Scientific consensus of clinical phenotype• Public Health Surveillance • Mortality • Public Health Morbidity• Clinical data aggregation • Metrics of clinical activity • Quality management • Patient Safety • Financial administration • Case mix • Resource allocation 27
    • 28. Biomedical Informatics Traditional Hierarchical System ICD-10 and family 28
    • 29. Biomedical Informatics Addition of structured attributes to conceptsConcept name Definition Language translationsPreferred string Language translationsSynonyms Language translationsIndex Terms 29
    • 30. Biomedical Informatics THE CONTENT MODEL Any Category in ICD is represented by:1. ICD Concept Title 7. Causal Properties 1.1. Fully Specified Name 7.1. Etiology Type 1.2 Preferred Name 7.2. Causal Properties - Agents 1.3 Synonyms 7.3. Causal Properties - Causal Mechanisms 7.4. Genomic Linkages2. Classification Properties 7.5. Risk Factors 2.1. Parents 2.2 Type 8. Temporal Properties 2.3. Use and Linearization(s) 8.1. Age of Occurrence & Occurrence Frequency 8.2. Development Course/Stage3. Textual Definition(s) 9. Severity of Subtypes Properties 10. Functioning Properties4. Terms 10.1. Impact on Activities and Participation 4.1. Base Index Terms 10.2. Contextual factors 4.2. Inclusion Terms 10.3. Body functions 4.3. Exclusions5. Body System/Structure 11. Specific Condition Properties 5.1. Body System(s) 11.1 Biological Sex 5.2. Body Part(s) [Anatomical Site(s)] 11.2. Life-Cycle Properties 5.3. Morphological Properties 12. Treatment Properties6. Manifestation Properties 13. Diagnostic Criteria 6.1. Signs & Symptoms 6.2. Investigation findings
    • 31. Biomedical Informatics Addition of semantic arcs - OntologyRelationshipsLogical DefinitionsEtiologyGenomicLocation LateralityHistologySeverityAcuity 31
    • 32. Biomedical Informatics Serialization of “the cloud” Algorithmic Derivation 32
    • 33. Biomedical InformaticsLinear views may serve multiple use-cases Morbidity, Mortality, Quality, … 33
    • 34. Biomedical Informatics Relationship with IHTSDO SNOMED content• IHT (SNOMED) will require high-level nodes that aggregate more granular data • Use-cases include mutually exclusive, exhaustive,… • Sounds a lot like ICD• ICD-11 will require lower level terminology for value sets which populate content model • Detailed terminological underpinning • Sounds a lot like SNOMED• Memorandum of Agreement – July 2010! • WHO right to use for authoring and interpretation 34 ICD11 and SNOMED
    • 35. Biomedical Informatics Potential Future States SNOMED ICD-11 Ghost ICDGhost SNOMED 35
    • 36. Biomedical Informatics Joint Alternate Future ICD-IHTSDO Effort SNOMEDICD-11 36
    • 37. Biomedical Informatics SHARP Area 4: Secondary Use of EHR Data• Agilex Technologies • Harvard Univ.• CDISC (Clinical Data Interchange• Intermountain Healthcare Standards Consortium) • Mayo Clinic• Centerphase Solutions • Mirth Corporation, Inc.• Deloitte • MIT• Group Health, Seattle • MITRE Corp.• IBM Watson Research Labs • Regenstrief Institute, Inc.• University of Utah • SUNY• University of Pittsburgh • University of Colorado
    • 38. Biomedical InformaticsThemes & Projects
    • 39. Biomedical Informatics MissionTo enable the use of Leverage health informatics to:EHR data for secondary •generate new knowledgepurposes, such asclinical research and •improve carepublic health. •address population needsTo support the community •open-source toolsof EHR data consumers by •servicesdeveloping: •scalable software
    • 40. Biomedical Informatics Modes of Normalization• Generally true for both structured and un- structured data• Syntactic transformation • Clean up message formats • HL7 V2, CCD/CDA, tabular data, etc • Emulate Regenstrief HOSS pipeline• Semantic normalization • Typically vocabulary mapping
    • 41. Biomedical Informatics Clinical Data Normalization • Dr. Huff on Data Normalization Stanley M. Huff, M.D.; SHARPn Co-Principal Investigator; Professor (Clinical) - Biomedical Informatics at University of Utah - College of Medicine and Chief Medical Informatics Officer Intermountain Healthcare. Dr. Huff discusses the need to provide patient care at the lowest cost with advanced decision support requires structured and coded data.
    • 42. Biomedical Informatics That Semantic Bit…• Canonical semantics reduce to Value-set Binding to CEM objects• Value-sets should obviously be drawn from “standard” vocabularies • SNOMED-CT and ICD • LOINC • RxNorm • But others required: HUGO, GO, HL7
    • 43. Biomedical Informatics NLP Deliverables and Tools•cTAKES Releases • Smoking Status Classifier • cTAKES Side Effects module • Medication Annotator • Modules for relation extraction•Integrated cTAKES(icTAKES) • an effort to improve the usability of cTAKES for end users•NLP evaluation workbench • the dissemination of an NLP algorithm requires performance benchmarking. The evaluation workbench allows NLP investigators and developers to compare and evaluate various NLP algorithms.•SHARPn NLP Common Type • SHARPn NLP Common Type System is an effort for defining common NLP types used in SHARPn; UIMA framework.
    • 44. Biomedical Informatics High-Throughput Phenotyping• Phenotype - a set of patient characteristics : • Diagnoses, Procedures • Demographics • Lab Values, Medications• Phenotyping – overload of terms • Originally for research cohorts from EMRs • Obvious extension to clinical trial eligibility • Quality metric Numerators and denominators • Clinical decision support - Trigger criteria
    • 45. Biomedical Informatics Drools-based Architecture Jyotishman Pathak, PhD. discusses leveraging open-source tools such as Drools.Clinical Data AccessElement LayerDatabas e Business Logic Transformation Inference Layer Engine (Drools) Service for List of Transform physical Creating Diabetic representation Output (File,  Normalized logical Patients representation (Fact Model) Database, etc)
    • 46. Biomedical Informatics SE MN Beacon – ONC – Community HITHigh-value community-based care delivery model
    • 47. Biomedical Informatics 47
    • 48. Biomedical Informatics SHARP and Beacon Synergies• SHARP will facilitate the mapping of comparable and consistent data into information and knowledge• SE MN Beacon will facilitate the population-based generation of best evidence and new knowledge discovery• SE MN Beacon will allow the application of Health Information Technology to primary care practice • Informing practice with population-based data • Supporting practice with knowledge 48
    • 49. Biomedical Informatics Where is This Going?• Biomedical practice and research are data, information, and knowledge intensive• Comparable and consistent data representation are pre-requisite for efficient clinical analytics• Learning Health Systems will underpin successful ACOs• Meaningful Use is a positive move toward establishing a data framework for Learning Health Systems 49