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IDR Snapshot:
Quantitative Assessment Methodology
Evaluating Size and Comprehensiveness of
an Integrated Data Repository

           Vojtech Huser, MD PhD
            James J Cimino, MD
           Laboratory for Informatics Development
                     NIH Clinical Center
                             NLM
Research question
• How can you evaluate an IDR?
                • What makes a good IDR ? (for research)?


                • IDR A(in 2007) vs. IDR A (in 2012)
                • IDR A vs. IDR B




Vojtech Huser                      http://code.google.com/p/idrsnapshot/   2
Vojtech Huser   http://code.google.com/p/idrsnapshot/   3
Motivation/Assumptions
• Improving IDR
      – while acknowledging the a-priori limitations
• Ideal IDR for a researcher
      – versus existing data
• General measure on whole-IDR level
      – Not research project specific
• Build on CTSA IDR surveys from 2007, 2008
  and 2010
Vojtech Huser         http://code.google.com/p/idrsnapshot/   4
Examples
• Academic medical center
                • with limited number of outpatient clinics
                    – University of Utah


• Integrated delivery network
                • Outpatient and inpatient records
                    – Partners Healthcare


• HMO
                • Health Plan component
                    – Kaiser Permanente South California


Vojtech Huser                     http://code.google.com/p/idrsnapshot/   5
Target level (researcher-facing schema)
• Level 1: Epic Clarity (6000+ tables)

• Level 2: Clinical Data Repository (add data from
  GE Centricity (outpatient) (plus other sources)

• Level 3: subset of CDR for health plan members
  only

• Level 4: i2b2, or VDW, or OMOP, or XYZ
     (Deduce, Further, BTRIS)



Vojtech Huser                   http://code.google.com/p/idrsnapshot/   6
Beyond core data sources
      – Diagnoses, Procedures, Labs
      – ADT data (admission, ICU)
      – Visit data

• Less-common data sources
      – Insurance history data
      – Over the counter drug data
      – Death certificate data
                    – Link to other sources
      – Pharmacy dispensing data
      – Out of network claims data
                • Health Plan data
                         » Heath Assessment questionnaire data
                         » Out of network pharmacy refills data


Vojtech Huser                        http://code.google.com/p/idrsnapshot/   7
Measures
• Glasgow comma scale of 10 vs. 5
• Apgar score of 7 vs. 5
•

• Good measure:
      – Intuitive to interpret (count of patients)
      – facilitates monitoring and improvement
      – does not place any arbitrary value on individual
        measure components
                – (e.g., value of 10 years of medication history vs. 10 years of
                  weight/height history).


Vojtech Huser                  http://code.google.com/p/idrsnapshot/               8
“Consortia made easy(ier)”




Vojtech Huser   http://code.google.com/p/idrsnapshot/   9
Results   (site A, site B)




               Vojtech Huser, IDR Snapshot
                                                 10
          http://code.google.com/p/idrsnapshot
Vojtech Huser   http://code.google.com/p/idrsnapshot/   11

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Vojtech Huser IDR Snapshot

  • 1. IDR Snapshot: Quantitative Assessment Methodology Evaluating Size and Comprehensiveness of an Integrated Data Repository Vojtech Huser, MD PhD James J Cimino, MD Laboratory for Informatics Development NIH Clinical Center NLM
  • 2. Research question • How can you evaluate an IDR? • What makes a good IDR ? (for research)? • IDR A(in 2007) vs. IDR A (in 2012) • IDR A vs. IDR B Vojtech Huser http://code.google.com/p/idrsnapshot/ 2
  • 3. Vojtech Huser http://code.google.com/p/idrsnapshot/ 3
  • 4. Motivation/Assumptions • Improving IDR – while acknowledging the a-priori limitations • Ideal IDR for a researcher – versus existing data • General measure on whole-IDR level – Not research project specific • Build on CTSA IDR surveys from 2007, 2008 and 2010 Vojtech Huser http://code.google.com/p/idrsnapshot/ 4
  • 5. Examples • Academic medical center • with limited number of outpatient clinics – University of Utah • Integrated delivery network • Outpatient and inpatient records – Partners Healthcare • HMO • Health Plan component – Kaiser Permanente South California Vojtech Huser http://code.google.com/p/idrsnapshot/ 5
  • 6. Target level (researcher-facing schema) • Level 1: Epic Clarity (6000+ tables) • Level 2: Clinical Data Repository (add data from GE Centricity (outpatient) (plus other sources) • Level 3: subset of CDR for health plan members only • Level 4: i2b2, or VDW, or OMOP, or XYZ (Deduce, Further, BTRIS) Vojtech Huser http://code.google.com/p/idrsnapshot/ 6
  • 7. Beyond core data sources – Diagnoses, Procedures, Labs – ADT data (admission, ICU) – Visit data • Less-common data sources – Insurance history data – Over the counter drug data – Death certificate data – Link to other sources – Pharmacy dispensing data – Out of network claims data • Health Plan data » Heath Assessment questionnaire data » Out of network pharmacy refills data Vojtech Huser http://code.google.com/p/idrsnapshot/ 7
  • 8. Measures • Glasgow comma scale of 10 vs. 5 • Apgar score of 7 vs. 5 • • Good measure: – Intuitive to interpret (count of patients) – facilitates monitoring and improvement – does not place any arbitrary value on individual measure components – (e.g., value of 10 years of medication history vs. 10 years of weight/height history). Vojtech Huser http://code.google.com/p/idrsnapshot/ 8
  • 9. “Consortia made easy(ier)” Vojtech Huser http://code.google.com/p/idrsnapshot/ 9
  • 10. Results (site A, site B) Vojtech Huser, IDR Snapshot 10 http://code.google.com/p/idrsnapshot
  • 11. Vojtech Huser http://code.google.com/p/idrsnapshot/ 11

Notas del editor

  1. Introduction into the problem, some thoughts on how can we measure this, some results, questions to the audience
  2. Respiration – strong cry vs. weak irregular breathing (takes one point)Appearance – pink extremities, pale/blue all over (takes two points)