1. Funding was provided by a contract from AcademyHealth. Additional support was provided by AHRQ 1R01HS019912-01 (Scalable PArtnering Network for CER: Across Lifespan, Conditions, and Settings), AHRQ 1R01HS019908 (Scalable Architecture for Federated Translational Inquiries Network), and NIH/NCRR Colorado CTSI Grant Number UL1 RR025780 (Colorado Clinical and Translational Sciences Institute). Data Model Considerations for Clinical Effectiveness Researchers Michael G. Kahn 1,3,4, Deborah Batson 4, Lisa Schilling 2 1 Department of Pediatrics, University of Colorado, Denver 2 Department of Medicine, University of Colorado, Denver 3 Colorado Clinical and Translational Sciences Institute 4 Department of Clinical Informatics, Children’s Hospital Colorado Electronic Data Methods (EDM) Forum Stakeholder Symposium Building an Electronic Clinical Data Infrastructure to Improve Patient Outcomes 23 June 2012 Michael.Kahn@ucdenver.edu

2. Disclosures Presentation based on EDM Forum commissioned paper: “Data model considerations for clinical effectiveness researchers” Medical Care 50(9) 2012 S60-S67 DOI: 10.1097/MLR.0b013e318259bff4. (publish ahead of print). 2

3. What is a data model & why should I care? A data model determines: – What data elements can be stored –What relationships between data can be represented –Technical stuff: data type, allowed ranges, required versus optional (missingness) You should care because it determines: –How easy can data be recorded –How easy can data be extracted –Contributes to data quality 3

4. Visit- versus Patient-centric data models 4

5. Query Complexity: “For each patient, how many medications where filled over a period of time?” 5 Four-table join Three-table join

6. Query Complexity: “Average number of prescriptions written per visit?” 6 Two-table join Three-table join + Date comparisons

7. SAFTINet Asthma Cohort Definition 1.Adults (ages 18 and over) as of Jan 1, 2009 receiving care in selected sites who: –Have had at least 2 visits separated by at least 30 days coded as 493.xx in the 18 months prior to July 1, 2011, OR –A single diagnosis of 493.xx AND two filled prescriptions for an asthma maintenance medication separated by at least 30 days in the past 12 months. 2.Exclusion criteria: Patients with other concomitant chronic lung disease –Cystic fibrosis –COPD, emphysema, chronic bronchitis –Alpha-1-antitrypsin deficiency –Pulmonary fibrosis –Active TB 7

8. Additional Data Model Requirements for SAFTINet Create patient-level analytic data sets Calculate ages to the year for adults, and to smaller units of measurement for children Calculate prescribed drug intervals (drug exposures) Link data across disparate data sources Use standardized terminologies to take advantage of conceptual hierarchies and relationships Identify a patient as being part of a defined cohort Support limited data sets compliant with HIPAA 8

9. Key questions for a data model From Jeff Brown regarding FDA Sentinel Initiative*: 1.What does the system need to do? 2.What data are needed to meet system needs? 3.Where will the data be stored? 4.Where will the data be analyzed? 5.Is a common data model needed, and if so, what will the model look like? 9 *Brown JS, Lane K, Moore K, Platt R. Defining and evaluating possible database models to implement the FDA Sentinel initiative. U.S. Food and Drug Administration; May 2009 2009.

10. Eight dimensions of data models Modified from Moody and Shanks* DimensionOriginal definitionRecasted definition for CER 1. CompletenessDoes the data model contain all user requirements? Can the data model store and retrieve data to meet investigator CER needs? 2. IntegrityDoes the data model conform to the business rules and processes to guarantee data integrity and enforce policies? Does the data model enforce meaningful data relationships and constraints that uphold the intent of the data’s original purpose, i.e., clinical care, billing? 3. FlexibilityDoes the data model deal with changes in business and/or regulatory change? Can new data elements and relationships be added if project scope or if regulatory rules (e.g., patient identification) changes? 4. Understandability Are the concepts and structures in the data model easily understood? Do the concepts, structures and relationships make sense to investigators, data managers, and statisticians? 10 *Moody DL, Shanks GG. Improving the quality of data models: Empirical validation of a quality management framework. Information Systems. 2003;28:619-650.

11. DimensionOriginal definitionRecasted definition for CER 5. CorrectnessDoes the data model conform to the rules of the data modeling technique? Does the model conform to good data modeling practices such as limited data storage redundancy? 6. SimplicityDoes the data model contain the minimum possible entities and relationships? Are concepts represented as straightforwardly as possible? Are all data element necessary? 7. IntegrationIs the data model consistent with the rest of the organization’s data? Do all of the various data domains, such as demographics, observations, labs and medications “hang together” in a consistent and logical fashion? 8. Implementability Can the data model be implemented within existing time, budget, and technology constraints? Can the data model be implemented and maintained by current and future partners given anticipated budgets, time, and technical constraints? 11 Eight dimensions of data models Modified from Moody and Shanks* *Moody DL, Shanks GG. Improving the quality of data models: Empirical validation of a quality management framework. Information Systems. 2003;28:619-650.

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13. Potential data models considered by SAFTINet NameDeveloping entityInitial Purpose Observational Medical Outcomes Project (OMOP) Foundation of the NIHComparative Drug Outcomes Studies Virtual Data Warehouse (VDW) HMO Research NetworkDistributed data warehouse to allow comparative studies across collaborating sites: HMORN, CRN, Oregon CTRI i2b2Partners HealthcareInformatics framework for clinical and biological data integration OpenMRSRegenstrief InstituteOpen source enterprise medical record system platform OpenEHROpenEHR FoundationSemantically-enabled open source health computing platform 13

14. Summary Findings None of the existing publicly-available data models met all requirements License-free, flexibility, active community and willingness to collaborate were key features for SAFTINet Each project has different requirements and priorities. There is no best model for all potential CER uses 14

15. Funding was provided by a contract from AcademyHealth. Additional support was provided by AHRQ 1R01HS019912-01 (Scalable PArtnering Network for CER: Across Lifespan, Conditions, and Settings), AHRQ 1R01HS019908 (Scalable Architecture for Federated Translational Inquiries Network), and NIH/NCRR Colorado CTSI Grant Number UL1 RR025780 (Colorado Clinical and Translational Sciences Institute). Data Model Considerations for Clinical Effectiveness Researchers Questions? Electronic Data Methods (EDM) Forum Stakeholder Symposium Building an Electronic Clinical Data Infrastructure to Improve Patient Outcomes 23 June 2012 Michael.Kahn@ucdenver.edu