1. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Inter Ontology 2008 Harmonization of Patient Assessment Instruments in the USA: a Compelling Case for Realism-Based Ontology Tokyo, Japan, February 27, 2008 Werner CEUSTERS Center of Excellence in Bioinformatics and Life Sciences Ontology Research Group University at Buffalo, NY, USA

2. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Presentation Summary (1) In the US, several patient assessment instruments are used for a variety of purposes: –Outcome analysis –Pay for performance –Determination of next level of care The sort of instrument used depends on the care facility: –Skilled nursing facilities (SNF) –Internal Rehabilitation Facilities (IRF) –Home Health Agency (HH) –…

3. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Presentation Summary (2) All instruments are developed –to describe (more or less) the same sort of phenomena –but in such a way that the data (descriptions) obtained are not comparable, and cannot semantically integrated Some ‘solutions’ are underway, but they suffer from the same problems. We propose a method based on realist ontology that is capable to provide semantic interoperability of the various instruments.

4. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Part 1 Healthcare in the US: organization monitoring cost & quality

5. New York State Center of Excellence in Bioinformatics & Life Sciences R T U The cost of health care in the US U.S. Department of Health and Human Services - Centers for Disease Control and Prevention - National Center for Health Statistics. Health, United States, 2007.

6. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Health spending as a percent of GDP Medicare Payment Advisory Commission. A Data Book: Healthcare Spending and the Medicare Program. June 2007.

7. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Major care organization types in the US Acute Care Hospital Long Term Care Hospital Skilled Nursing Facility Inpatient Rehabilitation Facility Home Health Agency Outpatient Rehabilitation

8. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Medicare spending for Post-Acute Care (1999-2005) Medicare Payment Advisory Commission. A Data Book: Healthcare Spending and the Medicare Program. June 2007. http://www.medpac.gov/documents/Jun07DataBook_Entire_report.pdf. Last accessed: December 21, 2007. SNF HHA IRF LTCH total

9. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Growth in post-acute care providers Medicare Payment Advisory Commission. A Data Book: Healthcare Spending and the Medicare Program. June 2007. Differences in availability do not alone explain the differences in costs.

10. New York State Center of Excellence in Bioinformatics & Life Sciences R T U An attempt to study and explain the differences U.S. Department of Health and Human Services - Assistant Secretary for Planning and Evaluation - Office of Disability, Aging and Long-Term Care Policy. A Study Of Stroke Post-Acute Care Costs And Outcomes: Final Report. December 2006

11. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Some findings (1) There exists a wealth of patterns in Post Acute Care

12. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Typical stroke patient trajectories Acute Care Hospital Long Term Care Hospital Skilled Nursing Facility Inpatient Rehabilitation Facility Home Health Agency Outpatient Rehabilitation

13. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Re-admissions Acute Care Hospital Long Term Care Hospital Skilled Nursing Facility Inpatient Rehabilitation Facility Home Health Agency Outpatient Rehabilitation

14. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Patterns of Post-Acute Care for Stroke Victims Following Discharge from Acute Hospital and Admission to Nursing Home U.S. Department of Health and Human Services - Assistant Secretary for Planning and Evaluation - Office of Disability, Aging and Long-Term Care Policy. A Study Of Stroke Post-Acute Care Costs And Outcomes: Final Report. December 2006

15. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Patterns of Post-Acute Care for Stroke Victims Following Discharge from Acute Hospital and Admission to Home Health U.S. Department of Health and Human Services - Assistant Secretary for Planning and Evaluation - Office of Disability, Aging and Long-Term Care Policy. A Study Of Stroke Post-Acute Care Costs And Outcomes: Final Report. December 2006

16. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Some findings (1) A wealth of patterns in PAC –Nearly 170 different PAC patterns were identified in 90 days. –Sixty percent of IRF admissions used a second PAC provider and 30 percent used three or more in 90 days. Influence of patient characteristics, e.g.: –Patients admitted to HH from IRF were similar to patients admitted to OP from IRF with respect to pre- morbid status, cognition, and most functional measures following their stroke, but they had lower incomes.

17. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Some findings (2) Remarkable differences in outcomes: –Relative to patients discharged to HH following IRF, outcomes for patients admitted to outpatient care following IRF were comparable with respect to 90-day residence and significantly better in two dimensions of functional recovery, even after risk adjustment. Resource utilization and costs –Relative to IRF→OP costs, total cost per PAC episode was $2,200 higher and total cost per 90 days was $5,200 higher for IRF→HH patients. Despite the lower costs, IRF→OP patients received about 40 therapy visits in contrast to 21 therapy visits for IRF→HH patients in PAC episodes with comparable duration. However, average PAC beneficiary costs were $400 higher for the IRF→OP group.

18. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Major problem in this context: A uniform set of core measures is required to assess PAC outcomes for patients admitted to single or multiple PAC settings.

19. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Current Tools for Measuring Patients Across the Continuum in Medicare nAcute Hospitals  no standard tool nLong-Term Care Hospitals  no standard tool nInpatient Rehabilitation Facilities  IRF-PAI nSkilled Nursing Facilities  MDS 2.0 (MDS 3.0) nHome Health Agencies  OASIS nSwing-bed hospitals  SB-MDS nPatient generated  PROMIS (under development)

20. New York State Center of Excellence in Bioinformatics & Life Sciences R T U MDS 2.0 – Fragment from Section C

21. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Commonalities amongst instruments Use Classical Test Theory Each instrument has a fixed set of questions presented to the individual All items are asked irrespective of relevance Individual’s score is dependent on items of the particular assessment –Assessments with challenging items -individuals get low score and assessments with easy items get high scores –Scores are incomparable across instruments and settings

22. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Common Domains in Current Assessment Tools nAdministrative Information nSocial Support Information nMedical Diagnosis/Conditions nFunctional Limitations –Physical –Cognitive Kramer A and Holthaus D (eds.), Uniform Patient Assessment for Post-Acute Care; Final Report. Jan 25, 2006. http://www.bu.edu/hdr/documents/QualityPACFullReport.pdf. Last accessed: Dec 18, 2007.

23. New York State Center of Excellence in Bioinformatics & Life Sciences R T U But unfortunately: many differences exist … Individual items to measure each concept Scales used to measure each item Look-back or assessment periods Unidimensionality of individual items Kramer A and Holthaus D (eds.), Uniform Patient Assessment for Post-Acute Care; Final Report. Jan 25, 2006. http://www.bu.edu/hdr/documents/QualityPACFullReport.pdf. Last accessed: Dec 18, 2007.

24. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Differences amongst instruments (1) Kramer A and Holthaus D (eds.), Uniform Patient Assessment for Post-Acute Care; Final Report. Jan 25, 2006. http://www.bu.edu/hdr/documents/QualityPACFullReport.pdf. Last accessed: Dec 18, 2007.

25. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Differences amongst instruments (2) Kramer A and Holthaus D (eds.), Uniform Patient Assessment for Post-Acute Care; Final Report. Jan 25, 2006. http://www.bu.edu/hdr/documents/QualityPACFullReport.pdf. Last accessed: Dec 18, 2007.

26. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Functional Item Comparisons Assessment dayvaries8 OASIS Past 5 days812 MDS 3.0 Past 3 days718 IRFPAI Assessment Periods Scale Levels No. of Functional Items Tools

27. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Functional Scales Unknown1= Total Asst. 0= Activity NA 5= totally bathed by other8= Activity NA2=Maximal Asst. 25% 4= unable, bathes in bed/chair 4= Total Dependence3= Moderate Assistance 50% 3= participates but req. other person 3= Extensive Asst (3+ times/week) 4=Minimal Assistance 25% 2= with person (reminders, access, reach difficult areas 2= Limited Asst. (guided maneuvering) 5=Supervision 1= with devices, independent 1= Supervision6=Modified (device) 0= bathe independent tub/shower 0= Independent7= Complete independence OASISMDSIRF-PAI

28. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Intermediate conclusion After two decades of measurement development, multiple approaches, millions of dollars of funding, expenditure of much intellectual capital and new technologies to develop a uniform measure, we do not have standardized measurement and reporting necessary to evaluate quality of post-acute care or to make informed policy decisions. Duncan P. & Velozo C. Measurement and Methodology.

29. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Part 2 Current ‘solutions’

30. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Chronic Condition Data Warehouse Alignment of CMS data by means of unique beneficiary key involving 5% sample of Medicare beneficiaries since Jan 1, 1999; 21 chronic condition subpopulations: –AMI, cataract, diabetes, stroke, …; Contains the MDS, IRF-PAI, …, data for patients having been cared for in these institutions Available (for fee) from ResDAC Iowa Foundation for Medical Care

31. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Deficit Reduction Act of 2005 Congressional mandate to establish a PAC Payment Reform Demonstration by January 2008 to examine cost and outcomes across different post acute sites –Single comprehensive assessment at acute hospital discharge –Standardized assessment in all PAC settings to measure health and functional status and other treatment factors –Collection of information on resources/patient Report to be submitted to Congress in 2011.

32. New York State Center of Excellence in Bioinformatics & Life Sciences R T U CMS ‘Post Acute Demonstration’ Three components: –Development of a Patient Assessment Instrument –Development of a web-based, electronic reporting system –Implementation of a Payment Reform Demonstration

33. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Development Strategy Identify critical areas/domains for measuring case- mix acuity, resource use, or outcomes Review existing legacy tools (MDS, IRFPAI, OASIS), other leading measurement tools (PROMIS, COCOA-B, VA) and existing tools in LTCHs and acute hospitals Propose core data set that can be used to standardize information at hospital discharge and across all PAC settings

34. New York State Center of Excellence in Bioinformatics & Life Sciences R T U CARE has been developed and testing begins, but … –its design does not appear to yield an indication of a patient’s level of medical necessity for post-acute care; –a significant amount of patient information needed for a CARE assessment would have to be accessed from patients’ paper medical records and other varied hospital systems; –some of the proposed measurement scales are different than those currently used by post-acute providers; –questions such as “Would you be surprised if the patient was readmitted to an acute care hospital in the next 6 months?” and “Would you be surprised if the patient were to die in the next 12 months?” would force discharge planners to rely in part on subjective judgment.

35. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Overall result Existing and future data collections are hard to compare or integrate. If CARE comes in use (2012 ?), there will be one instrument “across the lifespan”, but it is not backward compatible with existing datasets. Success of CARE will depend largely on the ability to exchange information with EHR systems.

36. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Part 3 Towards a solution based on Realist Ontology

37. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Hypotheses Available tool and systems fail due to: –Incompatibility because developed with insufficient (if any at all) ontological insight; –Inadequacy of mappings based on terminology; –Confusion between information models and models of the corresponding reality. An adequate ontology can get more out existing data and help in building better future data sets.

38. New York State Center of Excellence in Bioinformatics & Life Sciences R T U For an ontology to be ‘adequate’ … … in the health domain, it should be built around a core of representational units which describe phenomena as they exist on the side of the patient and of the patient’s environment. Smith B, Ceusters W. Ontology as the Core Discipline of Biomedical Informatics: Legacies of the Past and Recommendations for the Future Direction of Research. In: Gordana Dodig Crnkovic and Susan Stuart (eds.) Computing, Philosophy, And Cognitive Science - The Nexus and the Liminal, Cambridge: Cambridge Scholars Press, 2007;:104-122.

39. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Questions to be addressed To what extent do the mentioned outcome assessment tools and related data sets overlap? Given that these distinct artifacts refer to the entities on the side of the patient primarily implicitly, indirectly and in different ways, how can this overlap be made explicit and quantifiable? How can this overlap be made understandable to software agents in such a way that they can use the corresponding representations of the entities as a basis for meaningful computations, including making comparisons and deriving new information? How can we demonstrate that our approach is successful? How can we ensure that our work is extensible and the methodology applied easy transferable to other measurement tools and data sets?

40. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Proposal 1.build a patient-centric ontology covering the entities in reality that must exist as referents for those terms (included constituent parts of compound terms) that are shared by at least two of the assessment systems and related datasets. 2.define in the terms of this ontology the dictionaries and data- elements of the listed systems, as well as relevant portions of the ICF, of the Common Data Elements of the National Institute of Neurological Disorders and Stroke (NINDS) and of SNOMED CT; 3.validate the ontology through several independent methods, including the degree to which it serves linkage of the available datasets, in the context of patients who suffered from stroke; 4.provide documentation on how to use the ontology in relationship with Electronic Health Records and other Health Information Technology systems.

41. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Methodology Dissemination & awarenessAim4T9 DocumentationAim4T8 Statistical validationAim3T7 Validation through case report annotationAim3T6 Publication in standard formatsAim1T5 Vocabulary developmentAim2T4 Data sets - ontology bridgingAim1T3 Ontology developmentAim1T2 Terminological alignmentAim2T1 TypeAimTask

42. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Terminological alignment TransferBed, Chair, Wheelchair (Transfer) Toilet (Transfer) Tub, Shower (Transfer) Bowel ContinenceBowel Management Bladder ContinenceBladder Management Toilet UseToileting DressingDressing-Upper Body Dressing-Lower Body Bathing Personal HygieneGrooming Bed Mobility Eating MDS ItemsIRF-PAI Items

43. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Alignment with other systems too International Classification of Functioning, Disability and Health (ICF) –From World Health Organization SNOMED CT ‘Common Data Elements’ –National Institute of Neurological Disorders and Stroke (NINDS)

44. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Alignment with SNOMED and ICF : feasibility Consolidated Health Informatics. Standards Adoption Recommendation: Disability. Sep 22, 2005.

45. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Health Condition ( disorder/disease ) Environmental Factors Personal Factors Body function&structure (Impairment) Activities (Limitation) Participation (Restriction) Interaction of entities as perceived in ICF (2001)

46. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Ontology building basics: three levels of reality 1.The world exists ‘as it is’ prior to a cognitive agent’s perception thereof; 2.Cognitive agents build up ‘in their minds’ cognitive representations of the world; 3.To make these representations publicly accessible in some enduring fashion, they create representational artifacts that are fixed in some medium. Smith B, Kusnierczyk W, Schober D, Ceusters W. Towards a Reference Terminology for Ontology Research and Development in the Biomedical Domain. Proceedings of KR-MED 2006, November 8, 2006, Baltimore MD, USA

47. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Three levels of reality 1.The world exists ‘as it is’ prior to a cognitive agent’s perception thereof; Smith B, Kusnierczyk W, Schober D, Ceusters W. Towards a Reference Terminology for Ontology Research and Development in the Biomedical Domain. Proceedings of KR-MED 2006, November 8, 2006, Baltimore MD, USA

48. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Reality exist before any observation R

49. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Reality exist before any observation R And also most structures in reality are there in advance.

50. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Three levels of reality 1.The world exists ‘as it is’ prior to a cognitive agent’s perception thereof; 2.Cognitive agents build up ‘in their minds’ cognitive representations of the world; Smith B, Kusnierczyk W, Schober D, Ceusters W. Towards a Reference Terminology for Ontology Research and Development in the Biomedical Domain. Proceedings of KR-MED 2006, November 8, 2006, Baltimore MD, USA

51. New York State Center of Excellence in Bioinformatics & Life Sciences R T U The ontology author acknowledges the existence of some Portion Of Reality (POR) R B

52. New York State Center of Excellence in Bioinformatics & Life Sciences R T U R B Some portions of reality escape his attention.

53. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Three levels of reality 1.The world exists ‘as it is’ prior to a cognitive agent’s perception thereof; 2.Cognitive agents build up ‘in their minds’ cognitive representations of the world; 3.To make these representations publicly accessible in some enduring fashion, they create representational artifacts that are fixed in some medium. Smith B, Kusnierczyk W, Schober D, Ceusters W. Towards a Reference Terminology for Ontology Research and Development in the Biomedical Domain. Proceedings of KR-MED 2006, November 8, 2006, Baltimore MD, USA

54. New York State Center of Excellence in Bioinformatics & Life Sciences R T U R He represents only what he considers relevant O B #1 RU 1 B1 RU 1 O1 Both RU 1 B1 and RU 1 O1 are representational units referring to #1; RU 1 O1 is NOT a representation of RU 1 B1 ; RU 1 O1 is created through concretization of RU 1 B1 in some medium.

55. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Thus... These concretizations are NOT supposed to be the representations of these cognitive representations; “concept representation” We should not be in the business of

56. New York State Center of Excellence in Bioinformatics & Life Sciences R T U But beware ! These concretizations are NOT supposed to be the representations of these cognitive representations; They are representations of the corresponding parts of reality –They are like the images taken by means of a high quality camera;

57. New York State Center of Excellence in Bioinformatics & Life Sciences R T U They are not (or should not be) like the paintings of Salvador Dali Non-canonical (although nice looking) anatomy

58. New York State Center of Excellence in Bioinformatics & Life Sciences R T U A realist view of the world The world consists of –entities that are Either particulars or universals; Either occurrents or continuants; Either dependent or independent; and, –relationships between these entities of the form e.g. is-instance-of, e.g. is-member-of e.g. isa (is-subtype-of) Smith B, Kusnierczyk W, Schober D, Ceusters W. Towards a Reference Terminology for Ontology Research and Development in the Biomedical Domain. Proceedings of KR-MED 2006, November 8, 2006, Baltimore MD, USA

59. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Top-Level BFO Continuant Occurrent (always dependent on one or more independent continuants) Independent Continuant Dependent Continuant RoleFunctionPropensity

60. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Steps in ontology building 1.For all terms identified in T1, find the entities in reality that are directly denoted; 2.Determine the top categories these entities belong to; 3.Determine for any dependent entity: If process: the continuants that participate in it If dependent continuant: the continuant upon which it depends 4.For any entity determined in step 3, go to step 2. Rudnicki R, Ceusters W, Manzoor S, Smith B. What Particulars are Referred to in EHR Data? A Case Study in Integrating Referent Tracking into an Electronic Health Record Application. In Teich JM, Suermondt J, Hripcsak C. (eds.), American Medical Informatics Association 2007 Annual Symposium Proceedings, Biomedical and Health Informatics: From Foundations to Applications to Policy, Chicago IL, 2007;:630-634.

61. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Building the MDS Ontology MDS Ontology U2U2 U3U3 U5U5 U4U4 U6U6 MDS 1 MDS 2 MDS 3 MDS 4 MDS 5 MDS 6 … U 11 U7U7 U 14 U 13 U 10 U 12 MDS terms U 17 U 16 U1U1 U9U9 U8U8 BFO Class-relations

62. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Adding the other assessments instruments U2U2 U1U1 U7U7 U 17 U9U9 U3U3 U5U5 U4U4 U6U6 U 11 U 10 U 14 U 12 U 13 U…U… OPO Ontology (MDS + CARE +…) MDS 1 MDS 2 MDS 3 MDS 4 MDS 5 MDS 6 … … MDS terms U 16 U8U8 BFO

63. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Adding the other assessments instruments U2U2 U1U1 U7U7 U 17 U9U9 U3U3 U5U5 U4U4 U6U6 U 11 U 10 U 14 U 12 U 13 U…U… OPO Ontology (MDS + CARE +…) MDS 1 MDS 2 MDS 3 MDS 4 MDS 5 MDS 6 … … … CARE 1 CARE 2 CARE 3 CARE 4 MDS terms CARE terms U 15 U 16 U8U8 BFO

64. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Semantic integration of the CCW data Purpose: –Better comparability –Statistical validation of the ontology Explanation of observed correlations between assessment data elements Finding patient subpopulations exhibiting correlations which are near- significant without the ontology, but significant with the ontology Two level integration: –Type level : poor man’s linkage –Particular level: rich man’s linkage

65. New York State Center of Excellence in Bioinformatics & Life Sciences R T U ‘Poor man’s’ data linkage U2U2 U1U1 U7U7 U 17 U9U9 U3U3 U5U5 U4U4 U6U6 U 11 U 10 U 14 U 12 U 13 U…U… MDS Ontology MDS 1 MDS 2 MDS 3 MDS 4 MDS 5 MDS 6 … … MDS terms U 16 U8U8 pt4pt3 Patient data

66. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Data linkage using multiple instruments

67. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Problems with this level Exclusive focus on universals, ignoring that in data collection (almost) everything is about particulars. Therefore Referent Tracking must be brought in the picture.

68. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Referent Tracking solves this problem: It is true that: –(1) ‘All Americans have one mother’ –(2) ‘All Americans have one president’ But: –(1) ‘all Americans have a distinct mother’ –(2) ‘all Americans have a (numerically) identical president’

69. New York State Center of Excellence in Bioinformatics & Life Sciences R T U The essence of Referent Tracking Keeping track of particulars By means of singular and globally unique identifiers (#1, #2, #3, …) That function as surrogates for these entities in information systems, documents, etc And are managed IN a referent tracking system. Ceusters W. and Smith B. Tracking Referents in Electronic Health Records. In: Engelbrecht R. et al. (eds.) Medical Informatics Europe, IOS Press, Amsterdam, 2005;:71-76

70. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Essentials of Referent Tracking Generation of universally unique identifiers; deciding what particulars should receive a IUI; finding out whether or not a particular has already been assigned a IUI (each particular should receive maximally one IUI); using IUIs in the EHR, i.e. issues concerning the syntax and semantics of statements containing IUIs; determining the truth values of statements in which IUIs are used; correcting errors in the assignment of IUIs.

71. New York State Center of Excellence in Bioinformatics & Life Sciences R T U From ‘poor man’s’ to ‘rich man’s’ data linkage U2U2 U1U1 U7U7 U 17 U9U9 U3U3 U5U5 U4U4 U6U6 U 11 U 10 U 14 U 12 U 13 MDS Ontology MDS 1 MDS 2 MDS 3 MDS 4 MDS 5 MDS 6 … MDS terms U 16 U8U8 pt4pt3 Patient data formula

72. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Rich man’s data linkage: focus on particulars U6U6 U 11 MDS 3 MDS 4 pt4pt3 pt4 IUI-1 U6U6 IUI-2IUI-3 U 11 IUI-4IUI-5 pt3 Instance-of Particular relations

73. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Many more combinations possible The terms used in MDS 4 denote distinct particulars related to both patients One of the terms used in MDS 4 denotes the same particular for both patients

74. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Many more combinations possible If the same MDS (containing several referring terms) applies to different patients at t1, either –All terms denote always distinct particulars ‘patient is able to recall what he did yesterday’ –Some terms denote the same particular ‘patient is able to remember who he met yesterday’ If the same MDS applies to the same patient at distinct times: –Some terms may/may not denote the same particular ‘patient recognizes his room mate’ If the same term occurs in distinct MDS –May/may not denote the same particular (at any time)

75. New York State Center of Excellence in Bioinformatics & Life Sciences R T U Conclusion Need for cost containment without sacrificing quality in the US is well understood; The need for assessment instruments that span a patient’s life is recognized as well; Role of terminologies is overestimated, probably because of dramatic shortcomings in EHR information models; Realist ontology, as a method and application, may be the missing link.