Quarterly Data Quality: Reading the Rubric

The Quarterly Data Quality (QDQ) process is one composed of several steps: Enter data into Minnesota’s HMIS; run the QDQ report; update data; and finally, submit scores. The formulas used to calculate the scores are based on fixed logic and work the same way each time the QDQ report is run. What can be confusing at times is how data is defined as missing or incongruent. This is where the QDQ Rubric comes in.

Rubric Overview

The rubric looks at three scoring categories: Completeness, Consistency & Accuracy, and Timeliness.

• Completeness: The report checks that all expected data can be found in a client’s record. Scores are based on the project type of the project. Pages 5-11 of the QDQ Rubric show the details of the completeness score.
  
  EXAMPLE: Client records in permanent housing projects will be assessed on whether there is a Housing Move-In Date (HMID). There would be no check on HMID for client records in emergency shelter projects.

• Consistency and Accuracy: The report checks that all aspects of a client’s profile and assessment data agree with each other, and that there are no contradictions among the data. The rubric defines what the contradictions are in the data. Pages 12-13 of the QDQ Rubric show the details of the consistency and accuracy score.
  
  EXAMPLES:

  • A Housing Move-In Date that is before a project start date is a contradiction.

  • A minor’s client record containing a “Yes” answer for the Veteran Status question is a contradiction.

• Timeliness: The report is making a calculation between when the Entry Date for a project and the timestamp of the data. Back Date Mode does not change the data entry timestamp. It is when the fingers physically type the data. The score varies by project type. Pages 14-15 of the QDQ Rubric show the details of the timeliness score.
  
  EXAMPLE: If the Entry Date is 5 April 2023 and the data is entered on 12 April 2023, the time difference is 7 days. For a permanent housing project, the Timeliness score would be 87%. For a transitional housing project, the Timeliness score would be 76.5%.

Some of these components are more straightforward to audit and measure than others, and that reality is reflected in the scoring. The scoring is also informed by HUD’s vision for the future of HMIS as outlined in HUD’s SNAPS Office Data TA Strategy to Improve Data and Performance. Scores are calculated on a percentage basis, so that, though the point total for permanent housing programs is higher, those programs will not automatically score higher than other programs.

Every year ICA asks for your input on the QDQ rubric. Your ideas and input on the scoring rubric are important. They help make QDQ a better process. The individual data elements are not all evaluated the same way. Some of them are more straightforward to audit and measure than others, and that reality is reflected in the scoring.  

So, if you feel there is a particular scoring of a data element that needs more scrutiny, or a score category in general that could use a change, please let us know. We cannot guarantee that every request will become a change to the rubric, but we want to make sure the rubric is reflecting a measurement of quality data. ICA’s QDQ team and the QDQ monitoring partners will review the suggestions and update the scoring by October 2023.   

How To Provide Feedback  

You can share your thoughts about the QDQ rubric by submitting a feedback form: Click here to access that form. You are invited to use the feedback form to submit any thoughts about QDQ, not just those related to the rubric. Please make all submissions by September 1st, 2023. 

To submit ideas for updates to the Scoring Rubric, respond to the first prompt with the text “2023 Rubric Review”.

The Quarterly Data Quality process is a collaborative process. Making it work better is in all our interests, and your ideas matter.