1. Query Health Technical WG 10/27/2011

2. Agenda TopicTime Allocation Administrative Stuff and Reminders2:00 – 2:05 pm F2F Recap2:05 – 2:15pm Query Analysis – Queries Overview2:15 – 2:30pm Transport Analysis Deep Dive2:30 – 3:00pm

3. F2F Recap Doug F shared his thoughts on Query Health Technical Solution Reviewed the Abstract Model and had a good discussion on Query Types Had an overview of the CIM from Holly Miller and Russell Leftwich DECISION: Discussed technical decision making criteria and reached agreement on criteria. We had 6 different technical approaches that were presented by summer concert series participants!! Thank you We had a WG Vote to determine which ones would be a good starting candidates for a solid technical foundation for Query Health – DECISION: hQuery, i2B2 and PopMedNet have been selected as starting candidates – Each one have their strengths and weaknesses and need further evaluation WG will continue further evaluation of the above three and their approaches using specific topics and detailed criteria for the topics. The first two topics chosen are – Query Analysis – Transport Analysis WG volunteers will fill out the detailed criteria for the two topics and further analysis will be performed on the WG calls.

4. Query Analysis Query Analysis Matrix http://wiki.siframework.org/Query+Health+-+Query+and+Data+Model+Analysis Overview of Queries for analysis Sample Queries based on Query Health User Stories, NQF/MU Stage 1 measures, research work performed by Health Services Research.

5. Query Analysis – Query set 1 Based on Query Health Expanded Diabetes Analysis User Story http://wiki.siframework.org/Query+Health+Expanded+Analysis+User+Story Hba1c > 9.0% (NQF0059/MU Stage 1) LDL Control < 100 mg/dl (NQF0064/MU Stage 1)

6. Query Analysis – Query 2 The Hub Query Examples – QM18 Simple query which is used as a Quality Measure by the hub Cholesterol Control: Male patients ≥ 35 years of age and female patients ≥ 45 years of age without Diabetes Mellitus or Ischemic Vascular Disease who have a total cholesterol < 240 or LDL < 160 measured in the past 5 years. The last 5 years is calculated from the most recent patient visit within the reporting period. Has a Temporal nature to it, uses encounter based data and is based on the following data elements: Encounters, Birth Year, Sex, Problem List, Labs Codes and Concepts: ICD9 Codes: DM, IVD, Ttl, CHOL LOINC Codes: LDL, Ttl, CHOL

7. Query Analysis – Query 3 Hypothesis Generation Query based on work from i2B2 as part of Epidemiology/Health Services Research: http://care.diabetesjournals.org/content/33/3/526.full.pdf http://wiki.siframework.org/Query+Health+Hypothesis+Generation+User+Story+Pr oposal Sample Query: The retrospective cohort analysis (n=34,253) included all patients aged 18 years identified by an ICD-9 code for Diabetes Mellitus (250.XX) or an A1C of 6.0% and at least one record of prescription of an oral diabetes medication as an outpatient or dispensation as an inpatient, between 1 January 2000 and 31 December 2006. Analyses focused on three classes of diabetic medications: sulfonylureas, the biguanide metformin, and the thiazolidinediones, rosiglitazone and pioglitazone. We excluded patients receiving either metformin or thiazolidinedione who had a diagnosis of polycystic ovaries but not diabetes. We used a cumulative temporal approach to ascertain the calendar date for earliest identifiable risk associated with rosiglitazone compared with that for other therapies. For each patient, duration of exposure to individual diabetes medications was assessed in 6-month increments during which only one of the four medications was prescribed. Patients receiving multiple medications under consideration were excluded. Events were associated with a particular medication only when the prescription or dispensation occurred within 6 months before the documented myocardial infarction. If a patient did not have any activity for a 6-month observation period but resumed activity in the following period, than the particular 6-month observation period with no activity was excluded from analysis.

8. Transport Analysis Deep Dive Transport Analysis Matrix: http://wiki.siframework.org/Query+Health+Transport+Analysis

9. Next Steps Complete Query Analysis Matrix Transport Analysis Next Steps