Transcript Slides - CHiR - Arizona State University

CHiR-Arizona HealthQuery
Winter 2012 Stakeholder Meeting
January 23, 2012
center for health information & research
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
Welcome/Introductions - Attendees
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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
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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
Director Search Update
Rolf Halden
center for health information & research
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.
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.
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.
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.
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: [email protected]. 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.
AZHQ: Review of Data Algorithm & 2011
Discoveries
Diana Petitti
center for health information & research
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.
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.
Matching /De-duplication Example
Name
SSN
ICD-9
Record 1
Diana
Petitti
925-47-2221
410.xx
Record 2
Diana
Petitti
001-47-1234
410.xx
• 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
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.
What We Have Already Done
• Identified data NOT affected by algorithm
• Characterized the problem for data affected
Data NOT Affected by Algorithm
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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
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.
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.
Larger Implications
• Without a unique identifier (and
authentication), accuracy of matching / deduplication 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.
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.
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.
Project Results
Asthma Utilization in AHCCCS Members
Zachary Ortiz
(see separate slide show)
center for health information & research
Spring Meeting – May 21st (tentative)
Thank you!
center for health information & research