1. # 1 Clinical Element Models (CEMs) SHARP F2F Meeting Mayo Clinic June 21, 2010 Stanley M Huff, MD

2. # 2 A Simple Model

3. Use of detailed clinical models in SHARP Guide for data normalization widgets Target for structured output from NLP Logical structure for data payload in NHIN Connect services Reference for data that participates in the phenotype logic and queries # 3

4. # 4 Model Classes Created Patient, Employee, Provider, Organization, ContactParty, PatientContact (visit), ServiceDeliveryLocation, AdmitDiagnosis HealthIssue (Problem), Allergy, Intolerance, Document Order –OrderLab, OrderLabMicro, OrderBloodProduct –OrderMedAmb, OrderMedCont, OrderMedInt, OrderMedPCA, OrderMedReg –OrderNutrition, OrderRadiology, OrderNursing, OrderRepiratory, OrderTherapies LabObs, MicroLabObs, Assert, Eval, Meas, Proc Qualifiers, Modifiers (Subject), Attributions, Panels

5. # 5 Model Subtypes Created Number of models created - 4384 –Laboratory models – 2933 –Evaluations – 210 –Measurements – 353 –Assertions – 143 –Procedures – 87 –Qualifiers, Modifiers, and Components Statuses – 26 Date/times – 27 Others – 400+ –Panels – 79

6. # 6 Access to the models Send me an email and I will send you a zip file –stan.huff@imail.org Web browser –www.clinicalelement.comwww.clinicalelement.com –Works best with Mozilla Firefox browser

7. # 7 70 What if there is no model? Dry Weight: Site #1 kg Weight: Site #2 Dry kg Wet Ideal 70 70 70

8. # 8 Relational database implications How would you calculate the desired weight loss during the hospital stay? Patient Identifier Date and TimeObservation TypeObservation Value Units 1234567897/4/2005Dry Weight70kg 1234567897/19/2005Current Weight73kg Patient Identifier Date and TimeObservation Type Weight typeObservation Value Units 1234567897/4/2005WeightDry70kg 1234567897/19/2005WeightCurrent73kg

9. # 9 Model Centered Data Representation Models Models and Concepts SNOMED ICD-10 RxNorm FDB LOINC CPT SNOMED ICD-10 RxNorm FDB LOINC CPT LexGrid Terminology Server Context Specific Mapping Tables ECIS Thesaurus Mayo Thesaurus Internal Terminology (ECIDS) IH Thesaurus

10. # 10 We assume that the model is used in association with a terminology server.

11. Model and Terminology MedicationOrder { drug PenVK, dose 250, route Oral, frequency Q6H, startTime 09/01/95 10:01, endTime 09/11/95 23:59, orderedBy Don Jones, M.D., orderNumber A234567 } Instance data MedicationOrder ::= SET { drug Drug, dose Decimal, route DrugRoute, frequency DrugFrequency, startTime DateTime, endTime DateTime, orderedBy Clinician, orderNumber OrderNumber} Model If the medicationOrder.drug is_a “antibiotic” then notify the infection control officer.

12. # 12 Concept Semantic Network Drugs Antibiotics Analgesics Cardiovascular Penicillins Aminoglycosides CephalosporinsPen VK Amoxicillin Nafcillin

13. # 13 Denormalized Semantic Network Drugshas-childAntibiotics Drugshas-childAnalgesics Drugshas-childCardiovascular Antibioticshas-childPenicillins Antibioticshas-childCephalosporins Antibioticshas-childAminoglycosides Penicillinshas-childPen VK Penicillinshas-childAmoxicillin Penicillinshas-childNafcillin Drugshas-memberAntibiotics Drugshas-memberPenicillins Drugshas-memberPen VK Drugshas-memberAmoxicillin Drugshas-memberNafcillin

14. # 14 Mods and Quals of the Value Choice Mods - Component CE’s which change the meaning of the Value Choice. Quals - Component CE’s which give more information about the Value Choice.

15. # 15 A Panel containing 2 Observations

16. # 16 The use of Qualifiers

17. # 17 The use of Modifiers

18. # 18 XML Model with Term Binding … The name of this model Binding to a single “observable” concept

19. # 19 Binding to a “domain” (value set) Path to the coded element The name of the terminology “domain” that the element is “bound” to

20. # 20 Compiler CEML Source File CE Translator “In Memory” Form HTML UML? openEHR Archetype? HL7 RIM Static Models? Java Class XML Template OWL?

21. # 21 Decomposition Mapping data 138 mmHg SystolicBPRightArmSitting SystolicBPRightArmSittingObs data 138 mmHg quals SystolicBP SystolicBPObs data Right Arm BodyLocation data Sitting PatientPosition Precoordinated Model (User Interface Model) Post coordinated Model (Storage Model)

22. # 22 How much data in a single record? “Chest pain made worse by exercise” –Two events, but very close association –Normally would go into a single finding “Ate a meal at a restaurant and 30 minutes later he felt nauseated, and then an hour later he began vomiting blood.” –Discrete events with known time and potential causal relationships –May need to be represented by multiple associated findings Semantic links are used to represent relationships between distinct event instances

23. # 23 Representation of Semantic Links InstanceId 1RelationshipInstanceId 2 (123) Nauseafollowed-by(987) Vomiting Semantic links can also have certainty and attribution –Certainty –Attribution (who or what asserted the relationship, when, and why?)

24. Area 6 Discussion and Planning # 24

25. Detailed Model Facility a Normalized Data Instances EDW Staging ETL ETL + Rules Analytic Health Repository Decision Support HTACERQICDS Canonical EMR+ Normalized Data Instances Facility b Normalized Data Instances EMR 1a EMR 2a Lab Billing Claims Rx Patient Providr Facility Imaging Sched …. EMR 1b EMR 2b Lab Billing Claims Rx Patient Providr Facility Imaging Sched …. Using NHIN for transmitting data Terminology Services (including CEMs) Terminology, Models, Logic, NLP Semantics, etc. And the Internet for managing Content Internet NHIN NLP Widgets Normalization Widgets NLP Widgets Normalization Widgets

26. Discussion Evaluation projects –Sharing data through NHIN Connect and/or NHIN Direct What, who, when, where? –Comparison of data processed through SHARP to data in existing Mayo and Intermountain data trust, EDW, AHR What, who, when, where? –Others? Evaluation of NLP outputs and value? Focus on a specific domain: X-rays, operative notes, progress notes, sleep studies? Questions –What is the target set of normalization widgets that we want to build? –Can we do the evaluations on de-identified data? –Do we need patient consent to do the evaluations? –Others? # 26