CHiR-Arizona HealthQuery Winter 2012 Stakeholder Meeting

1. CHiR-Arizona HealthQuery Winter 2012 Stakeholder Meeting January 23, 2012

2. Agenda • 11:45 – Welcome/Introductions • 11:50 – Update on Director Search (Rolf Halden) • 12:05 – AZHQ: Review of Data Algorithms & 2011 Discoveries (Diana Petitti) • 12:30 – Project Results (Zachary Ortiz) • Closing Remarks

3. Welcome/Introductions - Attendees • Arizona Medical Board* • Desiree Anthony, Chandler Regional and Mercy Gilbert Medical Centers • Bruce Bethancourt, Banner Medical Group • Twila Burdick, Banner Health System • Kathleen Dowler, Chandler Regional and Mercy Gilbert Medical Centers • Timothy Flood, Arizona Department of Health Services • Pamela Garcia-Filion, Phoenix Children’s Hospital • Marisue Garganta, St. Joseph’s Hospital and Medical Center* • Victoria Grandsoult, Phoenix Children’s Hospital • Rolf Halden, ASU CHiR • Gevork Harootunian, ASU CHiR • Bill Johnson, ASU CHiR • Jeffrey Joyce, Maricopa Integrated Health System • Bill Kirkland, Mountain Park Health Center • Marc Leib, AHCCCS* • Diana Petitti, ASU CHiR • Howard Pitluk, Health Services Advisory Group • Sandra Ramos, Chandler Regional and Mercy Gilbert Medical Centers • Tameka Sama, ASU CHiR • Mark Slater, Scottsdale Healthcare *attended online

4. Director Search UpdateRolf Halden

5. Director Job Posting ARIZONA STATE UNIVERSITY – PROFESSOR AND DIRECTOR OF THE CENTER FOR HEALTH INFORMATION AND RESEACH Arizona State University seeks an energetic, creative, and self-motivated full-time faculty member to fill the vacant position of Director of the Center for Health Information and Research, CHiR. The faculty appointment will be in a program/school appropriate to the candidate’s field.

6. Director Job Posting, cont’d CHiR has established a unique data resource (chir.asu.edu) featuring health information from multiple data partners, including the Arizona Medicaid program a.k.a. Arizona Health Cost Containment Program, AHCCCS, and the Arizona Health Query, AZHQ. The CHiR data resource has been in existence since 1999 and now contains health information from claims, vital records, and other standardized and well-documented health records for residents of Arizona. The resource can be used to conduct population-based research, including health services, epidemiologic, outcomes, and comparative effectiveness research. In addition, CHiR has established collaborative relationships to promote research with the Arizona Department of Health, the Arizona Medicaid program (AHCCCS), major hospital systems, community health centers and other health care entities in Arizona.

7. Director Job Posting, cont’d ASU is committed to expand and grow CHiR, by seeking a visionary leader and building a team of experts to become a unique resource for population health research and to carry out a number of teaching and professional service activities. Opportunities exist to develop synergies with ASU’s strategic partners, e.g., the Mayo Clinic and its Center for the Science of Healthcare Delivery.

8. Director Posting cont’d Qualified candidates will meet the following requirements: • M.D. degree and/or Ph.D. in health informatics and/or health services research, or equivalent • Experience in health data aggregation, management, and analysis • Demonstrated ability to obtain sponsored research grants (e.g., NIH, CDC, AHRQ, PCORI, EPA) • Track record of peer-reviewed publications in high-impact journals • Management and leadership experience • Ability to interact with senior leadership in the health care community • Excellent written and oral communication skills.

9. Director Posting cont’d This tenure-track/tenured position will be filled at a rank commensurate with the candidate’s level of experience and seniority. The preferred starting date for this position is July 2012. Applications will be accepted until the position has been filled; review will begin February 2012. Applicants should submit a curriculum vitae, a 1-2 page statement outlining research and teaching interests, and the names and contact information for 3-5 references via: Tameka.Jackson@asu.edu. ASU is an equal opportunity/affirmative action employer. Additional Comment: If you know of someone who meets these qualifications and is interested in this position, please have them apply per the posting. The position may/may not be tenure track and could be MD/PhD or some other set of applicable credentials.

10. AZHQ: Review of Data Algorithm & 2011 DiscoveriesDiana Petitti

11. Matching /De-duplication Algorithm • The aggregation/integration of data from disparate sources uses algorithms that take information from disparate sources and “decide” whether records from those sources “match” or are a duplicate of a record from another source. • Over the last year, the algorithm used to match/deduplicate data has been under review.

12. Matching /De-duplication Algorithm • It has been determined that the algorithm is not performing optimally and that it results in duplicates for some kinds of record aggregation.

13. Matching /De-duplication Example • Not a match using SSN • Match using name given invalid SSN • If deemed to not be a match (but truly a match), creates a duplicate and double counts myocardial infarction event

14. What We Have Already Done • Made changes to work-in-progress to assure future products are not affected. This includes (but is not limited to) community health needs assessments, special studies and student research. • Completed an assessment of all published papers to identify those that might need to be corrected or withdrawn. One paper identified as possibly affected and further review is in progress. • Assessed reports made available at the CHiR website that might contain misleading information and removed any in this category. One report removed.

15. What We Have Already Done • Identified data NOT affected by algorithm • Characterized the problem for data affected

16. Data NOT Affected by Algorithm • Yuma data • Data for periods before 2005 • AHCCCS data • ADHS data – birth certificate, death certificate, ED and hospitalization • Trauma registry data • Data “matched” by hand • Health care workforce data

17. What We Know about Any Problems • Affects data about children more than adults. • The younger the person, the more the data are affected. • Affects data about Hispanics more than other race/ethnicities.

18. What Else We Are Doing / Will Do • Available to discuss any prior projects and whether and how it might have been affected. • Identified consultants to make independent recommendations on future strategy for matching/de-duplication.

19. Larger Implications • Without a unique identifier (and authentication), accuracy of matching / de-duplication is not 100%. • All databases that aggregate information from disparate sources grapple with this problem. • Current debate in health care reform about a unique identifier for medical care is related.

20. Matching /De-duplication Q&A Q1: What is the current error rate? Answer: For adults, the error rate is ~ 8%. This rate goes down to 4%-5% for adults around age 35 due to more complete data. This rate has been shown to increase in adults around age 65 as this population may report different birthdates (make themselves older), causing linking errors because dates do not match. For children, the error rate is ~ 25%. This rate is highest for ages 0-2 due to incomplete birth records or information that changes later (e.g. SSN and name). Q2: What is the target error rate? Answer: CHiR does not have a set rate. One option could be to set an error rate per project, but that would still be an estimate and not the real error rate based on the outcome of the data matching. One comment stated that an 8%-10% error rate is acceptable, but CHiR believes more research is required as they are not aware of a national or standardized error rate.

21. Matching /De-duplication Q&A, cont’d Q3: Are there auditing tools to check for errors? Answer: Yes, and those tools have been found to reduce the errors. However, AZHQ claims data are not currently in use and they will not be used until the database is restructured. Additional comment: You can be systematic in making errors via computer programming. Therefore hand-matching versus computer matching both have pros and cons, and one method may be preferred over another, depending on the setting and purpose.

22. Project Results Asthma Utilization in AHCCCS MembersZachary Ortiz (see separate slide show)

23. Spring Meeting – May 21st (tentative)Thank you!