1. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Werner CEUSTERS, MD Director, Ontology Research Group Center of Excellence in Bioinformatics and Life Sciences University at Buffalo, NY, USA Pre-Conference Short course EMR/EHRs: Utilization of Electronic Health Record (EHR) Data for Clinical Research How to Overcome the Lack of Data Interoperability and Data Quality (Long Version) Monday, February 6, 2012, 2:00-6:00 PM, Miami, FL

2. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Short personal history 1959 - 2012 1977 1989 1992 1998 2002 2004 2006 1993 1995

3. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Benefits of Electronic Health Records (EHRs) for providers and their patients: –Complete and accurate information, shared, coordinated, –Better access to information, when and where needed, –Patient empowerment, proactive, consent. ONCHIT: http://healthit.hhs.gov

4. New York State Center of Excellence in Bioinformatics & Life Sciences R T U ONCHIT’s Legislation and Regulations The Health Information Technology for Economic and Clinical Health (HITECH) Act allows HHS to promote health information technology (HIT) to improve health care quality, safety, and efficiency. Results : –Incentive Program for EHRs issued by CMS: Stage I requirements for certified EHR technology in order to qualify for the payments: ‘Meaningful Use’ – 2011-2012; –Standards and Certification Criteria for EHRs; –Request for Comment - Stage 2 Definition of Meaningful Use in 2013 - 2014. ONCHIT: http://healthit.hhs.gov

5. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Examples of Meaningful Use (MU) criteria CPOE for Medication orders, Drug-drug/drug-allergy interaction checks, Record demographics, Report Quality Committee Measures, Maintain active medication list, Maintain active medication allergy list, Record vital signs, Record smoking status.

6. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Examples of imposed standards Patient summary record: –HL7 CDA R2 (CCD) or ASTM E2369 (CCR). Problem list: –ICD-9-CM or SNOMED CT®. Procedures: –ICD-9-CM or HCPCS + CPT-4. Laboratory orders and results: –LOINC®.

7. New York State Center of Excellence in Bioinformatics & Life Sciences R T U MU and Drug/Disease related research Interesting MU requirements: –coded … problem list of diagnoses, drug-drug and drug-allergy interaction checks, –medication list, –medication allergies, vital signs: height, weight, blood pressure, laboratory test results, demographic data: preferred language, insurance type, gender, race, ethnicity, date of birth, and date and cause of death in the event of mortality.

8. New York State Center of Excellence in Bioinformatics & Life Sciences R T U The problem: Meaningful Use criteria and certified EHRs have fallacies 1.Crippled idea about ‘problem list of diagnoses’, 2.Conflation of diagnosis and disease/disorder, 3.The structure of EHR data (information model) is not close enough to the structure of that what the data are about, 4.Unjustified belief that the use of unambiguous codes renders EHR data unambiguous, 5.Mandated xchange standards cause information loss.

9. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Crippled idea about ‘problem list of diagnoses’ Basis of Problem List: –Larry Weed’s Problem Oriented Medical Record Each medical record should have a complete list of all the patient's problems, including both clearly established diagnoses and all other unexplained findings that are not yet clear manifestations of a specific diagnosis. Includes: –diagnosis− physical finding –lab abnormality− physiologic finding –social issue− symptom –demographic issue Weed LL. Medical records that guide and teach. N Engl J Med. 1968 Mar 14;278(11):593-600.

10. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Conflation of diagnosis and disease/disorder The disorder is thereThe diagnosis is here The disease is there

11. New York State Center of Excellence in Bioinformatics & Life Sciences R T U MU criteria and certified EHRs have fallacies 1.Crippled idea about ‘problem list of diagnoses’ 2.Conflation of diagnosis and disease/disorder 3.The structure of EHR data (information model) is not close enough to the structure of that what the data are about

12. New York State Center of Excellence in Bioinformatics & Life Sciences R T U EHR Information Models (simplified) patient diagnosis drug finding encounterpatient diagnosis drug finding

13. New York State Center of Excellence in Bioinformatics & Life Sciences R T U MU criteria and certified EHRs have fallacies 1.Crippled idea about ‘problem list of diagnoses’ 2.Conflation of diagnosis and disease/disorder 3.The structure of EHR data (information model) is not close enough to the structure of that what the data are about 4.Unjustified belief that the use of unambiguous codes renders EHR data unambiguous

14. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Using generic representations for specific entities is inadequate 557204/07/199026442006closed fracture of shaft of femur 557204/07/199081134009Fracture, closed, spiral 557212/07/199026442006closed fracture of shaft of femur 557212/07/19909001224Accident in public building (supermarket) 557204/07/199079001Essential hypertension 093924/12/1991255174002benign polyp of biliary tract 230921/03/199226442006closed fracture of shaft of femur 230921/03/19929001224Accident in public building (supermarket) 4780403/04/199358298795Other lesion on other specified region 557217/05/199379001Essential hypertension 29822/08/19932909872Closed fracture of radial head 29822/08/19939001224Accident in public building (supermarket) 557201/04/199726442006closed fracture of shaft of femur 557201/04/199779001Essential hypertension PtIDDateSNOMED CT codeNarrative 093920/12/1998255087006malignant polyp of biliary tract

15. New York State Center of Excellence in Bioinformatics & Life Sciences R T U The problem: Meaningful Use criteria and certified EHRs have fallacies 1.Crippled idea about ‘problem list of diagnoses’, 2.Conflation of diagnosis and disease/disorder, 3.The structure of EHR data (information model) is not close enough to the structure of that what the data are about, 4.Unjustified belief that the use of unambiguous codes renders EHR data unambiguous, 5.Mandated exchange standards cause information loss.

16. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Is this possible? The answer of HIT industry. http://www.interoperabilityshowcase.com/docs/webinarArchives/2010_Webinar_Series_Review_PCD_Domain_2010-8-3f.pdf

17. New York State Center of Excellence in Bioinformatics & Life Sciences R T U I respectfully disagree … Standards? –No shortage indeed, but: too many, too low quality, because, too much ad hoc. Availability of ‘the’ technology? –Focus on providing patches for old technology rather than developing better systems from solid foundations. This holds for both Healthcare IT and Semantic Web Technology.

18. New York State Center of Excellence in Bioinformatics & Life Sciences R T U The consequences for EHR data Lack of data quality: –mostly billing oriented  insufficient clinical detail useful for clinical research, –poor provenance tracking  hearsay indistinguishable from first hand signs and symptoms, –hard for algorithms to distinguish differences in opinions from differences in reality. Lack of reliable interoperability amongst EHRs and between EHRs and clinical trial systems.

19. New York State Center of Excellence in Bioinformatics & Life Sciences R T U In a global perspective too large a number of players (clinicians, patients, payers, industry, …) with competing agendas, insufficient global coordination, overestimation of the value of terminologies and concept-based ontologies, inconsistent, inadequate and badly documented standards, shortage and too rapid turnover of trained personnel who can span the divide between IT and biological and clinical expertise, standards development organizations such as ANSI do not hide the fact that the principle focus of their work is on implementer needs, no incentives for the EHR industry to do better. Ceusters W, Smith B. Semantic Interoperability in Healthcare - State of the Art in the US - a Position Paper, March 2010. http://www.referent-tracking.com/RTU/sendfile/?file=ARGOS-SemOp-US2.pdf

20. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Can we do something about it? In ‘Principles for Success’ in Health IT, Stead and Lin argue in favor of radical change: Principle 6: Architect Information and Workflow Systems to Accommodate Disruptive Change »Organizations should architect health care IT for flexibility to support disruptive change rather than to optimize today’s ideas about health care. Principle 7: Archive Data for Subsequent Re-interpretation »Vendors of health care IT should provide the capability of recording any data collected in their measured, uninterpreted, original form, archiving them as long as possible to enable subsequent retrospective views and analyses of those data. NOTE NOTE: ‘See, for example, Werner Ceusters and Barry Smith, “Strategies for Referent Tracking in Electronic Health Records” Journal of Biomedical Informatics 39(3):362-378, June 2006.’ Willam W. Stead and Herbert S. Lin, editors; Committee on Engaging the Computer Science Research Community in Health Care Informatics; National Research Council. Computational Technology for Effective Health Care: Immediate Steps and Strategic Directions (2009)

21. New York State Center of Excellence in Bioinformatics & Life Sciences R T U register or annotate clinical data in terms of the Ontology for General Medical Science etiological processdisorderdispositionpathological process abnormal bodily featuressigns & symptomsinterpretive processdiagnosis producesbearsrealized_in producesparticipates_inrecognized_as produces Small steps that help reach that goal (1) http://code.google.com/p/ogms/ Scheuermann R, Ceusters W, Smith B. Toward an Ontological Treatment of Disease and Diagnosis. 2009 AMIA Summit on Translational Bioinformatics, San Francisco, California, March 15-17, 2009;: 116-120.2009 AMIA Summit on Translational Bioinformatics

22. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Motivation Clarity about: –disease etiology and progression –disease and the diagnostic process –phenotype and signs/symptoms

23. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Big Picture

24. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Clinical Picture =def. – A representation of a clinical phenotype that is inferred from the combination of laboratory, image and clinical findings about a given patient. Diagnosis =def. – –A conclusion of an interpretive process that has as input a clinical picture of a given patient and as output an assertion to the effect that the patient has a disease of such and such a type. Example: Diagnosis

25. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Obvious? ‘Diseases and diagnoses are the principal ways in which illnesses are classified and quantified, and are vital in determining how clinicians organize health care.’ Ann Fam Med 1(1):44-51, 2003. ‘MedDRA […] is a standardized dictionary of medical terminology [ … which …] includes terminology for symptoms, signs, diseases and diagnoses.’ Medical Dictionary for Regulatory Activities

26. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Small steps that help reach that goal (2) Use unique identifiers for each entity about which you register data 557204/07/199026442006closed fracture of shaft of femur 557204/07/199081134009Fracture, closed, spiral 557212/07/199026442006closed fracture of shaft of femur 557212/07/19909001224Accident in public building (supermarket) 557204/07/199079001Essential hypertension 093924/12/1991255174002benign polyp of biliary tract 230921/03/199226442006closed fracture of shaft of femur 230921/03/19929001224Accident in public building (supermarket) 4780403/04/199358298795Other lesion on other specified region 557217/05/199379001Essential hypertension 29822/08/19932909872Closed fracture of radial head 29822/08/19939001224Accident in public building (supermarket) 557201/04/199726442006closed fracture of shaft of femur 557201/04/199779001Essential hypertension PtIDDateObsCodeNarrative 093920/12/1998255087006malignant polyp of biliary tract Codes identifying generic terms from OGMS compatible ontologies

27. New York State Center of Excellence in Bioinformatics & Life Sciences R T U However … Generic terms used to denote specific entities do not have enough referential capacity –Usually enough to convey that some specific entity is denoted, –Not enough to be clear about which one in particular. For many ‘important’ entities, unique identifiers are used: –UPS parcels –Patients in hospitals –VINs on cars –…

28. New York State Center of Excellence in Bioinformatics & Life Sciences R T U explicit reference to the concrete individual entities relevant to the accurate description of some portion of reality,... Fundamental goals of Referent Tracking Ceusters W, Smith B. Strategies for Referent Tracking in Electronic Health Records. J Biomed Inform. 2006 Jun;39(3):362-78.

29. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Method: numbers instead of words Ceusters W, Smith B. Strategies for Referent Tracking in Electronic Health Records. J Biomed Inform. 2006 Jun;39(3):362-78. –Introduce an Instance Unique Identifier (IUI) for each relevant particular (individual) entity 78

30. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Small steps that help reach that goal (2) Use unique identifiers for each entity about which you register data 557204/07/199026442006closed fracture of shaft of femur 557204/07/199081134009Fracture, closed, spiral 557212/07/199026442006closed fracture of shaft of femur 557212/07/19909001224Accident in public building (supermarket) 557204/07/199079001Essential hypertension 093924/12/1991255174002benign polyp of biliary tract 230921/03/199226442006closed fracture of shaft of femur 230921/03/19929001224Accident in public building (supermarket) 4780403/04/199358298795Other lesion on other specified region 557217/05/199379001Essential hypertension 29822/08/19932909872Closed fracture of radial head 29822/08/19939001224Accident in public building (supermarket) 557201/04/199726442006closed fracture of shaft of femur 557201/04/199779001Essential hypertension PtIDDateObsCodeNarrative 093920/12/1998255087006malignant polyp of biliary tract IUI-001 IUI-003 IUI-004 IUI-005 IUI-007 IUI-002 IUI-012 IUI-006 Codes identifying the entities to which the generic terms apply

31. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Small steps that help reach that goal (3) #1: this lady #2: “Simpson” #3: “Smith” #4: #1’s mass #5: representation of #4’ at 2010-03-31:08.30 #7: #1’s last name #6: representation of #4’ at 2010-04-14:09.57 #8: this spreadsheet #10: format of entries in #9 #9: this column of #8 #11: owner of #8 #12: copy of #8 send to #13 … Be pedantically precise with data and meta-data

32. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Representations First Order Reality L1. Entities (particular or generic) with objective existence which are not about anything L2. Clinicians’ beliefs about (1) L3. Linguistic representations about (1), (2) or (3) Three levels of reality in Ontological Realism

33. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Data generation and use observation & measurement data organization model development use add Generic beliefs verify further R&D (instrument and study optimization) application Δ = outcome 33

34. New York State Center of Excellence in Bioinformatics & Life Sciences R T U observation & measurement A crucial distinction: data and what they are about data organization model development use add Generic beliefs verify further R&D (instrument and study optimization) application Δ = outcome First- Order Reality Representation is about 34

35. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Ontological Realism makes crucial distinctions Between data and what data are about: –Level 1 entities (L1): everything what exists or existed some are referents (‘are’ used informally) some are L2, some are L3, none are L2 and L3 –Level 2 entities (L2): beliefs all are L1 some are about other L1-entities but none about themselves –Level 3 entities (L3): expressions all are L1, none are L2 some are about other L1-entities and some about themselves 35

36. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Ontological Realism makes crucial distinctions Between data and what data are about; Between continuants and occurrents: –obvious differences: a person versus his life an elevator versus his going up and down space versus time –more subtle differences (inexistent for flawed models e.g. HL7-RIM) : observation (data-element) versus observing diagnosis versus making a diagnosis message versus transmitting a message 36

37. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Small steps that help reach that goal (4) unique identification by means of ‘codes’ unique identification by means of ‘instance unique identifiers’ Design data warehouses that are Basic Formal Ontology and Referent Tracking compatible:

38. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Standard approach for organizing research data Building a huge matrix with patient cases in one dimension and patient characteristics in the other dimension Cases Characteristics ch1ch2ch3ch4ch5ch6... case1 case2 case3 case4 case5 case6...

39. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Standard approach for organizing research data Use statistical correlation techniques to find associations between characteristics and (dis)similarities between cases Cases Characteristics ch1ch2ch3ch4ch5ch6... case1 case2 case3 case4 case5 case6...

40. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Fundamental questions 1.What is a characteristic ? 2.What (sorts of) (clinical trial relevant) characteristics go in here ? 3.How can we make distinct studies comparable? 4.Because such matrices tend to become huge, how can we make analysis feasible ? 5.How can we make results re-usable?

41. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Small steps that help reach that goal (5) Express data in a referential syntax –From: ‘this human being is a 40 year old patient with a stomach tumor’ –To (something like) : ‘this-1 on which depends this-2 and this-3 has this-4’, where –this-1 instanceOf human being at t 1 –this-2 instanceOf age-of-40-years at t 2 –this-2 qualityOf this-1 at t 2 –this-3 instanceOf patient-roleat t 3 –this-3 roleOf this-1at t 3 –this-4 instanceOf tumorat t 4 –this-4 partOf this-5at t 6 –this-5 instanceOf stomachat t 7 –this-5 partOf this-1at t 8 –… Smith B, Ceusters W. Ontological Realism as a Methodology for Coordinated Evolution of Scientific Ontologies. Applied Ontology, 2010;5(3-4):139-188.

42. New York State Center of Excellence in Bioinformatics & Life Sciences R T U The shift envisioned From: –‘this human being is a 40 year old patient with a stomach tumor’ To (something like) : –‘this-1 on which depends this-2 and this-3 has this-4’, where this-1 instanceOf human being at t 1 this-2 instanceOf age-of-40-years at t 2 this-2 qualityOf this-1 at t 2 this-3 instanceOf patient-roleat t 3 this-3 roleOf this-1at t 3 this-4 instanceOf tumorat t 4 this-4 partOf this-5at t 6 this-5 instanceOf stomachat t 7 this-5 partOf this-1at t 8 … denotators for particulars (specific entities)

43. New York State Center of Excellence in Bioinformatics & Life Sciences R T U The shift envisioned From: –‘this human being is a 40 year old patient with a stomach tumor’ To (something like) : –‘this-1 on which depends this-2 and this-3 has this-4’, where this-1 instanceOf human being at t 1 this-2 instanceOf age-of-40-years at t 2 this-2 qualityOf this-1 at t 2 this-3 instanceOf patient-roleat t 3 this-3 roleOf this-1at t 3 this-4 instanceOf tumorat t 4 this-4 partOf this-5at t 6 this-5 instanceOf stomachat t 7 this-5 partOf this-1at t 8 … denotators for appropriate relations

44. New York State Center of Excellence in Bioinformatics & Life Sciences R T U The shift envisioned From: –‘this human being is a 40 year old patient with a stomach tumor’ To (something like) : –‘this-1 on which depends this-2 and this-3 has this-4’, where this-1 instanceOf human being at t 1 this-2 instanceOf age-of-40-years at t 2 this-2 qualityOf this-1 at t 2 this-3 instanceOf patient-roleat t 3 this-3 roleOf this-1at t 3 this-4 instanceOf tumorat t 4 this-4 partOf this-5at t 6 this-5 instanceOf stomachat t 7 this-5 partOf this-1at t 8 … denotators for universals or classes (what is generic) or particulars

45. New York State Center of Excellence in Bioinformatics & Life Sciences R T U The shift envisioned From: –‘this human being is a 40 year old patient with a stomach tumor’ To (something like) : –‘this-1 on which depends this-2 and this-3 has this-4’, where this-1 instanceOf human being at t 1 this-2 instanceOf age-of-40-years at t 2 this-2 qualityOf this-1 at t 2 this-3 instanceOf patient-roleat t 3 this-3 roleOf this-1at t 3 this-4 instanceOf tumorat t 4 this-4 partOf this-5at t 6 this-5 instanceOf stomachat t 7 this-5 partOf this-1at t 8 … time periods (for continuants) when the relationships hold

46. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Relevance: the way RT-representations interact with representations of generic portions of reality instance-of at t #105 caused by

47. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Referent Tracking based data warehousing

48. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Unique identifier for: –each data-element and combinations thereof (L3), –what the data-element is about (L1), –each generated copy of an existing data-element (L3), –each transaction involving data-elements (L1); Identifiers centrally managed in RTS; Exclusive use of ontologies for type descriptions following OBO-Foundry principles; Centrally managed data dictionaries, data-ownership, exchange criteria. General principles of RT-enabled data warehousing (1)

49. New York State Center of Excellence in Bioinformatics & Life Sciences R T U General principles of RT-enabled data warehousing (2) Central inventory of ‘attributes’ but peripheral maintenance of ‘values’; Identifiers function as pseudonyms: –centrally known that for person IUI-1 there are values about instances of UUI-2 maintained by researcher/clinician IUI-3 for periods IUI-4, IUI-5, … Disclosure of what the identifiers stand for based on need and right to know; Generation of off-line datasets for research with transaction-specific identifiers for each element.

50. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Feedback to clinical care Finding ‘similar’ patient cases: –suggestions for prevention, investigation, treatment; ‘Outbreak’ detection; Comparing outcomes; –related to disorders, providers, treatments, … Links to literature; Clinical trial selection; …

51. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Small steps that help reach that goal (6) Link databases to standard ontologies rather than to each other

52. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Discussion points How to convince EHR developers to move their products forward ? How far do we want to go in our own organizations? How to support research that does not promise immediate pay-off?