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This wiki page will facilitate the discussions to determine the approach that Query Health will take to represent queries and its related data models.

In order to perform an analysis of the various models listed below, a set of queries have been selected. These queries along with their details are listed below. Also to aid in the analysis, the S&I Framework CIM can be used as a starting point.

Query 1:

Diabetes: HbA1c Poor Control Quality Measure based on the Expanded Analysis User Story from the Clinical WG.

The Query definition from NQF can be found here: NQF_HQMF_HumanReadable_0059.html
The codes associated with the queries can be found here: NQF_Retooled_Measure_0059.xls

Query 2:

Cholestrol Control Quality Measure from Summer Concert Series "The Hub". The measure definition is annotated as"330-CT" in the following document: TCNY Quality Measures Report v Q4 2009 091028.pdf
A Sample SQL Query for the measure can be found file: 330CTExampleSQL.sql

Query 3:

The third query sample is based on the work performed by i2B2 and others as part of Epidemiology/Health Services Research. This is elaborated in the Hypothesis Generation User Story.

Query Definition: TBD

Query Analysis feature
Code or Object Oriented Data Model (e.g hQuery)
SQL or Relational Data Model (e.g i2B2)
Query Representation using standards
Represent 3 useful queries to understand
  • Ease of query creation and representation
  • Performance of the queries
Query 1:
Query 2:

EMF with Transform to hQuery
Discussion (blog post)
EMF with Transform to XQuery
EMF with Transform to SQL
Pictorial representation of the data model required for the sample queries

Models are all simple, see Models for Query Health
EMF references CIM model, transform uses hQuery model.
How does the approach deal with data from C32 at run time and at loading time
hQuery converts C32 into a JSON representation that is stored in a repository.

Any of a number of ways. One implementation uses XQuery and a collection of CCD documents, and other assume data has been loaded into a database in some way.
How does the approach deal with arbitrary data
Arbitrary data would require implementation of an importer to convert it into the expected JSON format.

New models and/or vocabulary easily adopted.
How will the approach address model extensions like new requirements, new relationships, attributes etc.
Patient data is represented using a JavaScript API backed by a JSON data structure. The JSON and API may be freely extended to accommodate new data without impacting existing users of the data. The JSON repository is schema-free so no configuration changes are required.

See above.