3/21/2014. Leveraging HIT to Enhance Data Quality and Public Reporting: Structured Reporting

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1 Leveraging HIT to Enhance Data Quality and Public Reporting: Structured Reporting James E. Tcheng, MD, FACC, FSCAI, FESC Professor of Medicine Professor of Community and Family Medicine (Informatics) Duke Clinical Research Institute Duke University Medical Center and Health System Leveraging HIT to Enhance Data Quality and Public Reporting: Structured Reporting James E. Tcheng, MD, FACC, FSCAI, FESC COI / relevant relationships: American Board of Internal Medicine Philips Medical Systems Grant support: FDA (R24 grants) Leveraging HIT to Enhance Data Quality and Public Reporting Aspirational the promise Technical a bit of informatics Practical how will this affect me? 1

2 Aspiration If you can dream it, you can do it. Walt Disney The Virtuous Cycle of Quality Califf RM et al, Health Affairs, 2007 From Data to Knowledge to Outcomes Advancing the science of medicine from data to evidence to guidelines Advancing the practice of medicine from guidelines to care delivery to performance improvement while improving efficiency, effectiveness, and outcomes enabling the workforce leveraging existing information harnessing the river of data 2

3 Aspiration If you don t know where you are going, chances are you will end up somewhere else. Yogi Berra If you don t know where you are going, any road will take you there. Lewis Carroll Health IT Opportunities Patient Care Clinical care Access to documentation Explicit, data-specific documentation Better resource utilization Augmented care Facilitated communication Research Criteria-based performance trials Experiential / observational studies Large simple / pragmatic / streamlined trials Evolution of Clinical & Research HIT Infrastructure PLATFORM Clinical Clinical Research OWNERSHIP Paper Records Paper Case Report Form Electronic Health Records Electronic Data Capture But still limited Clinical databases - optimized for transactions of patient-centric care Research databases - optimized for external validity and analysis Clinical Clinical Research Practitioners Academia / Industry Health Care Entities Control and dissemination of data is limited by owners, restricting development of the Learning Health Care System 3

4 Health IT Opportunities - Populations Systems-based care Variations in practice, performance improvement Patient-reported information / outcomes / follow-up Public reporting Regulatory Lower cost / shorter time to approvals Meet increasing demands for evidence (and stakeholders are also increasing!) Pharmacovigilance, device surveillance Professional Customized learning Maintenance of Certification / Licensure Transformation of Clinical Evidence Generation Secular Changes Outcomes Past placebo control Present comparative effectiveness Future costeffectiveness & surveillance? Desired Future economic value Structure & Methods isolated, confounded observational clinical trials complex, consolidated & randomized clinical trials digital hospital clinical records; systematic reviews & controlled observational registries Scope product performance patient outcomes: case studies/series population outcomes: evidence-based medicine & large simple trials Hypothesis single hypothesis / static answer continuous update / Bayesian Learning Health System Excellence & Mediocrity A society which scorns excellence in plumbing simply because it is plumbing, but rewards mediocre philosophy simply because it is philosophy, will soon become a society in which neither its pipes nor its theories will hold water. John W. Gardner (1961) HEW Secretary under Lyndon Johnson Engineer of the Great Society (Medicare, PBS) 4

5 What is Informatics? the sciences devoted to the gathering, storing, management and analysis of data, the conversion of that data into information, and the transformation of information into knowledge the discipline dealing with the resources, devices, and methods required to optimize the acquisition, storage, retrieval, and use of information in health and biomedicine a broad academic field encompassing humancomputer interaction, information science, information technology, algorithms, and social science Wikipedia Informatics Is The science of information Exchange, Use, and Reuse of Data Requires Shared, Uniform Data Definitions 5

6 What is a Data Element? Question or prompt May have associated controlled terminology Value, result or answer May have associated controlled terminology HCV status: Data Element May have associated controlled terminology A data element is a question value pair Considered the smallest meaningful unit of data exchange Formally defined in ISO/IEC and Typically have a unique identifier, a definition, and valid values Interpretation requires context (e.g., date/time of collection, method of measurement, or person, place or thing to which the data pertains) Class Name Type Alias Anatomy of a Data Element Heart Failure \ NYHA Class Definitions Coding Instructions Vocabulary Representation Maps Citation Tagged Values Name: HL7 RIM Value:RIM Mapping: observation.value Condition: Where observation.code = "heart failure class" Tagged Values Name: CDISC SDTM CDISC SDTM: FA.FATESTCD = HFCLASS, FA.FATEST = Heart Failure Class, FA.OBJ = Heart Failure WHERE Avoid Qualifiers MH.TERM = Heart Failure Tagged Values - Name: CADSR Local Value Domain Value: NYHAClassType Preprocedure Tagged Values Name: Property Concept Code Value C-##### - Most recent... Tagged Value: PropertyConceptPreferredName Value Type Tagged Value: Name: PropertyQualifierConceptCode1 Value C##### - Date of Nahm M, McCourt B Vocabulary Projects ACC/AHA Top 100 EHR Terminology Weintraub WS et al., JACC 2011; 5: NCRI Cardiology Clinical Trials Terminology Anderson HV et al., JACC 2013; 61: IHE Cath Report Content (CRC-technical supplement) Coming soon: ACC/AHA/SCAI Cardiac Cath Structured Reporting HPS ACC/AHA/ASE/FDA Echo Terminology ACC/AHA/FDA Cardiovascular Endpoints NCDR Consolidated Data Dictionary 6

7 Why are we doing this? Goals of Health Information Standards Interoperability the ability to exchange information between organizations Comparability the ability to ascertain the equivalence of data from different sources Data Quality the measurement of accessibility, completeness, accuracy and precision (and more) Levels of Interoperability Basic allows data to be exchanged between computer systems Word processing documents, text messages Functional describes the standard syntax (format) of the data Document templates, forms, data structures Message standards Semantic requires use of standardized content (vocabularies) within the data structure Comparability Meaning of the data is consistent when shared among different parties Common terminology required Should work in the background Words are not enough Codes uniquely identify terms Vocabulary specialized, precise terms that remove ambiguity Ontology describes nature of entities and their relations Classification groups related terms 7

8 Potential Uses of Health Data are Constrained by Data Quality Factors Data quality issues Different for client/patient communications vs. clinical decision support vs. epidemiological analysis Often constrained by external forces e.g. criteria for diagnosis often differs from the criteria for reporting Impact on secondary use Unidirectional effect of data consolidation Detailed General General Detailed The Big 5 and their owners International Classification of Diseases Clinical Modification (ICD-9/10-CM) World Health Organization National Center for Health Statistics (clinical modification) Current Procedural Therapy (CPT) American Medical Association Focus on reimbursement SNOMED Clinical Terms (SNOMED CT) International Health Terminology Standards Development Organization (IHTSDO) Logical Observation Identifiers, Names and Codes (LOINC) Regenstrief Institute for Healthcare Focus on recording RxNorm clinical content National Library of Medicine Practical The way to get started is to quit talking and begin doing. Walt Disney 8

9 Documentation Directions Create structured documents where there is inherently structured content (e.g., procedure notes) Data (not words) populate report Data management by all members of the team Create (only) elements of structure in documents not inherently structured (e.g., clinic / hospital notes) (Limited) data summative assessments (e.g. CCS class) (Limited) lexicon (<100 critical data elements for cardiology) Data management by all members of the team SPEED, efficiency, accuracy, productivity, repetition From Cacophony to Symphony What is Structured Reporting? Not just the table-based, formatted report this is a paradigm shift HIT-enabled workflow all who handle data are responsible for the data Requires re-engineering of HIT systems, implementation science, administrative support Just the facts no more Dictaphone Final report within minutes of procedure What Is HIT-Enabled Workflow? Extends across the continuum of care outpatient, inpatient, transitions Focuses on data interchange, data quality ADT (Admission, Discharge, Transfer) interfaces Orders in, results out, billing interfaces Codified, structured data Re-use of data (collect once, use many) Complies with standards e.g, DICOM, IHE profiles, CV vocabularies Recognizes all potential consumers Clinicians (efficiency, decision support, communication, HIE) Quality and outcomes reporting, registries, public health Business of medicine (pre-cert / Appropriate Use, billing) 9

10 29 Be a poet, not Procedure a novelist Reports Data from ADT, EHR, hemo systems Limited text 10

11 Be a poet, not a novelist Data input via tree Data from hemo system Limited text Procedure Reports Be a poet, not a novelist Cardiac Cath Structured Reporting Health Policy Statement Proposing a standard for both data content and report format for cardiovascular catheterization Representation: ACC, SCAI, AHA, STS, SIR, SVS, ISACHD, IASC, CCS, APCS, HL7, IHE, DICOM, AACN Extending, updating the 2008 Structured Reporting in CV Imaging HPS (Douglas et al.) Clinician lens (workflow, report format) rather than technical (DICOM-SR, IHE profiles) 33 11

12 Typical Current State Enterprise Information Systems Clinical Data Repository (EHR) Decision Support Repository Registration (ADT), Accounts, Scheduling, Labs, Pharmacy, CPOE, Inventory, Interfaces Modality A Management Measurements Analysis Reports Image processing Modality B Management Measurements Analysis Reports Image processing Modality C Management Measurements Analysis Reports Image processing ETC PACS PACS PACS CVIS Future State? Enterprise Information Systems Clinical Data Repository (EHR) Decision Support Repository Integration Broker Registration (ADT), Accounts, Scheduling, Labs, Pharmacy, CPOE, Inventory, Interfaces Meta-data / resources Cardiovascular Information System OP Admission Discharge OP History, ECG, medications, events ALL Modality Management Measurements Analysis Reports Image processing CPACS - Enterprise DATA MD task worklist, reporting, esignature, communications engine, admininstration Consistent MD experience Pre-cert / LCD / Appropriate use Clinical decision support Scheduling / White Board Registry / quality reporting Modality Plug and Play Philosophy, Roles, Responsibilities Save money. Live Better. Let s Build Something Together. The Magic of Macy s. Act???? once. Benefit all. 12

13 Science and Quality Lifelong Learning Aggregate Data Data Review Design and/ or Approval of National Initiatives NCDR Registries Individual Member Data Teach Aligned: QI Knowledge Skill Performance Improvement Member Portfolio MOC Part II Part IV Collaboration National Specialty Boards Cost Value Tradeoff Optimum value Starting point Usefulness of data Adverse impact on usability Paper Electronic free text Adapted from B. Middleton Partially structured Rigidly structured Looking for the New Paradigm Data entered once, validated frequently Data flows across platforms Data views are customizable Data is easily retrievable and supports composite views Data supports clinical decision-making 13

14 Health IT Opportunities Learning What Works IOM 2011 Effectiveness over efficacy (value) Keeping pace with technology - need for timely data Continuity among, across studies / observations Dissemination and implementation 14