Designing and Implementing a Data Quality Assurance Program for your CoC’s HMIS Michael Lindsay, ICF International Natalie Matthews, Abt Associates Inc.

Learning Objectives Understand all components of a Data Quality Assurance Program and how this work fits into the overall efforts of the CoC Discuss the roles that CoCs, HMIS Leads, and agencies play in implementing a Data Quality Assurance Program Learn from the challenges faced by other communities implementing a Data Quality Assurance Program

Session Overview/Agenda Purpose and Intent of a Data Quality Program Review each of the 4 components of a DQ Program Discuss roles, responsibilities and potential next steps for your community

Definition of Data Quality Data quality refers to the reliability and comprehensiveness of your community’s data (as collected in HMIS) Do you have sufficient data to accurately reflect the demographics, needs and outcomes of persons experiencing homelessness? Components of data quality include Timeliness Completeness Accuracy Consistency

Current Requirements for DQ Section 4.2.2 of the HMIS Technical Standards (2004) “PPI collected by a CHO must be relevant to the purpose for which it is to be used. To the extent necessary for those purposes, PPI should be accurate, complete and timely.”

Current Reporting on DQ Traditionally, HUD reporting on data quality has focused on the rates of completeness (by data element).

Proposed and Forthcoming Guidance Section 580.37 of the HMIS Proposed Rule (2011) “..HMIS Leads must set data quality benchmarks for CHOs, including bed coverage rates and service-volume coverage rates.” With the emergence of System-level reporting and the maturity of HMIS implementations across the country, both funders and communities have started to place a greater emphasis on data quality, with many CoCs integrating data quality into their project ranking and scoring process and developing their own, more detailed data quality plans that extend beyond

Data Quality Plans In anticipation of the HMIS Final Rule, and in response to NOFA scoring criteria for the CoC Program, many CoCs have created data quality plans Plans often consist of Baseline expectations for completeness, timeliness Monitoring protocols for reviewing accuracy Who has a DQ plan? How do you monitor for compliance with DQ plan? Who monitors agencies on DQ?

So why a DQ Program? We argue that while having a good data quality plan in place is key, it is only the beginning and does not ensure that your CoC will have more accurate and reliable data None of us want that DQ Plan to become just another stack of paper’s that sits on someone’s desk. That’s where having a clearly defined and active Data Quality Pro

Elements of Data Quality Program CoC HMIS Data Quality Plan Enforceable agreements Monitoring and reporting Compliance processes

Connection to CoC’s Overall Efforts Data Quality Program 1. CoC commitment to improving DQ 2. CoC’s HMIS DQ Plan & Enforceable Agreements 3. Monitoring & Reporting Compliance Processes A lot of the work of the CoC (including coordinated entry and system performance measures), has started to be framed with the practice of collective impact. We’ll spend a few moments now talking about what collective impact is, and how it translates into the work we’re discussing here on the development of a data quality program. The first condition that must be in place for collective impact is a Common Agenda. In the context of HMIS Data Quality, that common agenda is the commitment of the CoC to have the most accurate and meaningful data as possible. Next is a set of Shared Measures to engender shared accountability. This is he written document that you create to outline how you will achieve that—your CoC’s HMIS Data Quality Plan. DISCUSS MORE W/MIKE to think through how and if to fold this in. Is it helpful? 4. CoC, Agency and HMIS Leadership Efforts 5. HMIS Lead’s administration of HMIS

Preparing for the DQ Program

Identifying Your Baseline Important to take stock of where you are now Do you know how many of the homeless assistance and homelessness prevention projects in your CoC, are actively participating in HMIS? Baseline for bed coverage Have you recently run data completeness reports for your full HMIS implementation? Baseline data completeness When CoC leaders, project staff and HMIS Lead staff review reports, does the data seem accurate? Baseline for accuracy

Step 1: Ensure CoC’s Commitment Important to clarify up front what the expectations are for the data quality program CoC will need to review and approve the DQ Plan CoC should also be heavily involved in determining expectations for monitoring and compliance This work cannot and should not fall just on the shoulders of the HMIS Lead Agency

Key Considerations in Step 1 How will the CoC enforce expectations for data quality? Will these expectations extend to all homeless assistance and homeless prevention programs in the community? How frequently will the CoC leadership review data quality reports and data analysis?

Step 2: Data Quality Plan & Enforceable Agreements DQ Plan should be focused on Defining data quality expectations, by data element and by program type Completeness Timeliness Accuracy Consistency Outlining how data quality will be monitored Who will monitor and when Who will results be reported to

Step 2: Data Quality Plan & Enforceable Agreements Enforceable agreements are critical Need to be completed by all agencies participating in HMIS Should provide guidance on what the consequences are for failure to meet the standards in the DQ Plan Identify the process for notification of failure to meet a standard Lay out the responsibilities of BOTH the HMIS participating agency and the HMIS Lead and CoC

Key Considerations in Step 2 Are the expectations and responsibilities reasonable? Have they been discussed in a public forum, to allow for feedback and to generate buy-in from the CoC? How far back do you need to go in terms of data quality improvements? Are you looking at “old” data? How does poor data quality impact your reporting efforts? PAUSE HERE FOR SOME QUESTIONS/AUDIENCE ENGAGEMENT

Step 3: Monitoring, Reporting & Compliance Processes Once the HMIS Data Quality Plan has been reviewed and approved by the CoC and agreements are in place, it’s time to get out there and implement Will need to train/communicate to agencies and users first, to ensure that all users understand the expectations Encourage the CoC to allow for a grace period Transparency with results is key

Key Considerations in Step 3 Can the HMIS Lead monitor each agency for HMIS data quality compliance on an at least annual basis? Does their monitoring process integrate all 4 elements of data quality? Completeness Accuracy Timeliness Consistency How will monitoring results be shared with the agency? With the CoC?

Step 4: CoC, Agency & HMIS Leadership Efforts Important to celebrate successes and to allow room for growth Make the connection between the HMIS DQ efforts and other CoC lead efforts Impact of improved data quality on the accuracy of System Performance Measures and other local data analysis Impact of improved data quality on the ability to generate a By-Name or Prioritization List, to use HMIS for coordinated entry, etc.

Key Considerations in Step 4 Is everyone at the CoC, agency and HMIS Lead level clear about the role that they play in ensuring data quality? How has this been communicated? How has data quality been integrated into CoC, agency and HMIS meetings? What are the motivations/barriers for getting people on board? Is special outreach or help needed to work with agencies that do not get HUD funding?

Step 5: HMIS Lead’s Administration of HMIS HMIS Lead should complete the monitoring on data quality Will need to run regular data quality reports for agencies to track progress beyond the monitoring visit HMIS Lead is at the center of this work and needs to make these connections to CoC efforts with the community

Key Considerations in Step 5 Is the HMIS Lead regularly communicating about progress and barriers with the data quality program? Has this work become an ongoing effort and is it integrated into the regular operations of the CoC, agencies and HMIS Lead?

What’s Next? Don’t wait!! The quality of your data now will impact your upcoming SPM reports Map out your baseline Discuss these steps with your CoC Review sample HMIS Data Quality Plans (they’re on the web!) Talk to other CoCs about how they’ve done this sort of work Spend time thinking through monitoring and compliance

Resources and Guidance HUD Data Quality Toolkit (2009) https://www.hudexchange.info/resources/documents/huddataqualitytoolkit.pdf HMIS Proposed Rule (2011) https://www.hudexchange.info/resources/documents/HEARTH_HMISRequirementsProposedRule.pdf HMIS Technical Standards (2004)

Additional Questions Natalie Matthews, natalie_matthews@abtassoc.com Mike Lindsay, mlindsay@icfi.com