HMIS Data Quality Monthly Audit
Monthly HMIS audit template for checking completeness, timeliness, and accuracy against HUD data quality benchmarks. Use it to catch missing fields, stale enrollments, and setup errors before they affect reporting.
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Built for: Homeless Services · Continuum Of Care (coc) Programs · Community Action Agencies · Behavioral Health And Housing Navigation
Overview
This template is a monthly HMIS audit worksheet for reviewing whether client records and project setup data are complete, timely, and internally consistent. It is organized the way an HMIS administrator actually works: first confirm the audit scope and pull the data quality report, then review universal data elements, then check entry and exit timeliness, then move into project-specific fields, logical consistency, and setup data such as project type, CoC code, funding sources, and bed inventory.
Use it when you need a repeatable monthly review for HUD-aligned data quality monitoring, CoC oversight, or internal compliance follow-up. It is especially useful when multiple projects feed into the same HMIS and you need one place to document deficiencies, assign corrective actions, and track resolution dates. The template helps you catch missing or unknown values, late data entry, stale open enrollments, duplicate clients, and configuration errors that can distort reports.
Do not use it as a substitute for case management notes or as a one-time cleanup checklist after a system migration unless you also adapt the scope and review period. It is not meant for incident response, privacy breach investigation, or program performance evaluation. If your agency has unique funder rules, local CoC thresholds, or special project types, customize the benchmark fields and add any required local elements before rollout.
Standards & compliance context
- This template supports HUD HMIS data quality monitoring by documenting completeness, timeliness, accuracy, and consistency checks in a repeatable monthly format.
- The project setup section helps verify reporting inputs that affect Continuum of Care oversight and HMIS data submission quality under HUD-aligned practices.
- The corrective action section creates an audit trail that can support internal compliance programs and external monitoring expectations for homeless services providers.
- If your agency also uses broader quality systems, the same structure can be adapted to ISO 9001-style non-conformance tracking or local CoC monitoring procedures.
General regulatory context for orientation only — verify current requirements with counsel or the relevant agency before relying on this template for compliance.
What's inside this template
Audit Scope and Setup
This section defines exactly what was reviewed so the audit is traceable, repeatable, and tied to the correct month, projects, and report snapshot.
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Agency / Organization Name
Enter the full legal name of the agency whose HMIS data is being audited.
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Project(s) Under Review
List all HMIS project names and project IDs included in this audit period.
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Audit Period (Month and Year)
Select the first day of the month being audited.
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Project Type(s) Included
Select all project types represented in this audit.
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HMIS Data Quality Report Pulled Prior to Audit
Confirm that the current month’s HMIS data quality report has been exported from the HMIS system before beginning this audit.
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Auditor Name and Role
Enter the full name and title of the person conducting this audit (e.g., HMIS Administrator, Data Quality Manager).
Universal Data Elements (UDE) Completeness
This section checks whether the core client fields are present and usable, since missing universal elements weaken every downstream report.
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Name (3.01) — Missing/Unknown Rate
Enter the percentage of client records with missing or ‘Data Not Collected’ for Name (Element 3.01). Acceptable: ≤5%.
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Social Security Number (3.02) — Missing/Unknown/Refused Rate
Enter the percentage of records with SSN quality issues (missing, refused, or approximate). Acceptable: ≤20% for SSN due to population sensitivity; flag if >20%.
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Date of Birth (3.03) — Missing/Unknown Rate
Enter the percentage of records with missing or approximate Date of Birth (Element 3.03). Acceptable: ≤5%.
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Race and Ethnicity (3.04) — Missing/Unknown Rate
Enter the percentage of records with missing or ‘Data Not Collected’ for Race and Ethnicity (Element 3.04). Acceptable: ≤5%.
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Gender (3.06) — Missing/Unknown Rate
Enter the percentage of records with missing or ‘Data Not Collected’ for Gender (Element 3.06). Acceptable: ≤5%.
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Veteran Status (3.07) — Missing/Unknown Rate
Enter the percentage of adult records with missing or ‘Data Not Collected’ for Veteran Status (Element 3.07). Acceptable: ≤5%.
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Disabling Condition (3.08) — Missing/Unknown Rate
Enter the percentage of records with missing or ‘Data Not Collected’ for Disabling Condition (Element 3.08). Acceptable: ≤5%.
Program Entry and Exit Data Timeliness
This section matters because late entries and exits create stale records, distort active counts, and undermine monthly reporting accuracy.
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Entries Recorded Within 3 Days of Program Entry — Compliance Rate
Enter the percentage of enrollments entered into HMIS within 3 calendar days of the actual program entry date. Acceptable: ≥90%.
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Exits Recorded Within 3 Days of Program Exit — Compliance Rate
Enter the percentage of exits entered into HMIS within 3 calendar days of the actual exit date. Acceptable: ≥90%.
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Number of Open Enrollments with No Exit (Stale Records) — Count
Enter the count of enrollments open for more than 90 days with no recorded exit or service activity. Target: 0; flag any count >5 for immediate review.
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Destination at Exit (3.12) — Missing/Unknown Rate
Enter the percentage of exit records with missing, unknown, or ‘Data Not Collected’ for Destination (Element 3.12). Acceptable: ≤5%.
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Reason for Leaving (3.12b) Populated for All Applicable Exits
Confirm that Reason for Leaving is recorded for all applicable project types where required by HUD Data Standards.
Project-Specific and Program Data Elements
This section verifies the fields that drive program-level reporting and funder-specific analysis, including income, benefits, and chronic homelessness status.
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Prior Living Situation (3.917) — Missing/Unknown Rate
Enter the percentage of entry records with missing or ‘Data Not Collected’ for Prior Living Situation (Element 3.917). Acceptable: ≤5%.
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Income and Sources (4.02) at Entry — Missing/Unknown Rate
Enter the percentage of applicable entry records missing Income and Sources data (Element 4.02). Acceptable: ≤5%.
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Income and Sources (4.02) at Annual Assessment / Exit — Missing Rate
Enter the percentage of applicable annual assessment or exit records missing Income and Sources update (Element 4.02). Acceptable: ≤5%.
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Non-Cash Benefits (4.03) — Missing/Unknown Rate
Enter the percentage of applicable records missing Non-Cash Benefits data (Element 4.03). Acceptable: ≤5%.
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Health Insurance (4.04) — Missing/Unknown Rate
Enter the percentage of applicable records missing Health Insurance data (Element 4.04). Acceptable: ≤5%.
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Annual Assessments Completed Within Required Window
Confirm that all clients enrolled for 12+ months have an annual assessment recorded within 30 days before or after their anniversary date, per HUD requirements.
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Chronic Homelessness Status Accurately Documented
Confirm that Chronic Homelessness determination fields (Element 3.917 + disability + length of time) are complete and consistent for all PSH and applicable CoC-funded enrollments.
Data Accuracy and Logical Consistency
This section catches records that may be complete on paper but still fail basic logic checks, such as duplicate clients or impossible dates.
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Duplicate Client Records Identified — Count
Enter the number of confirmed or probable duplicate client records identified in the HMIS system for this audit period. Target: 0.
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Overlapping Enrollment Errors (Same Client, Same Project Type, Same Period)
Enter the count of clients with overlapping enrollments in the same project type during the audit period. These indicate data entry errors. Target: 0.
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Exit Date Before Entry Date — Error Count
Enter the count of enrollment records where the recorded exit date precedes the entry date. This is a critical logical error. Target: 0.
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Head of Household Designation Present for All Households
Confirm that every household enrollment has exactly one client designated as Head of Household. Missing or multiple HOH designations cause APR calculation errors.
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Age-Inconsistent Data Flags (e.g., Minor Recorded as Veteran)
Confirm no records exist where a client’s age is inconsistent with their recorded data (e.g., client under 18 flagged as Veteran, or DOB indicating age >120 years). Answer ‘Yes’ if no such errors exist.
Project Descriptor and Setup Data
This section ensures the HMIS project itself is configured correctly, because setup errors can affect every record tied to that project.
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Project Type Correctly Configured in HMIS
Confirm the project type (ES, TH, PSH, RRH, SO, etc.) is correctly set in the HMIS project setup and matches the grant agreement.
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Continuum of Care (CoC) Code Correctly Assigned
Confirm the correct CoC code is assigned to each project in HMIS setup. Incorrect CoC codes cause misattribution in HUD reporting.
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Funding Sources Accurately Recorded in Project Setup
Confirm all active funding sources (HUD CoC, ESG, SSVF, PATH, RHY, local, etc.) are accurately recorded in the HMIS project setup for the audit period.
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Bed and Unit Inventory (HIC) Data Current and Accurate
Confirm that bed inventory, unit counts, and bed type designations in HMIS match the current Housing Inventory Count (HIC) submission for this project.
Corrective Actions and Follow-Up
This section turns findings into accountable next steps by documenting deficiencies, owners, deadlines, and evidence of resolution.
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Overall Data Quality Assessment
Select the overall data quality rating for this agency/project based on audit findings.
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Summary of Deficiencies Identified
Provide a narrative summary of all data quality deficiencies identified during this audit, including element names, error counts, and affected projects.
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Corrective Action Plan Documented for All Critical Deficiencies
Confirm that a written corrective action plan has been created for every critical deficiency identified in this audit, including responsible staff and target resolution date.
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Target Resolution Date for Open Deficiencies
Select the target date by which all identified deficiencies will be resolved and re-verified.
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Agency Data Quality Contact Notified of Findings
Confirm that the agency’s designated HMIS data quality contact has been notified of audit findings and corrective action requirements.
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Supporting Documentation or Screenshots Attached
Attach screenshots of HMIS data quality reports, error logs, or other supporting documentation for this audit.
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HMIS Administrator Signature
HMIS Administrator signature certifying the accuracy of this audit.
How to use this template
- 1. Enter the agency, project list, month, project types, reviewer name, and the HMIS data quality report source so the audit has a clear scope and evidence trail.
- 2. Review each universal data element and record the missing, unknown, or refused rates exactly as they appear in the report, flagging any field that exceeds your local benchmark.
- 3. Check entry and exit timeliness by comparing program dates to HMIS entry and exit timestamps, and note stale open enrollments or missing exit reasons for follow-up.
- 4. Audit project-specific fields and logical consistency by looking for incomplete income, benefits, health insurance, chronic homelessness, duplicate records, and impossible date or age combinations.
- 5. Verify project setup data such as project type, CoC code, funding sources, and bed or unit inventory, then attach screenshots or report excerpts for any configuration defects.
- 6. Document corrective actions, assign owners and target dates, and sign off only after critical deficiencies have a clear resolution path and the agency contact has been notified.
Best practices
- Pull the HMIS data quality report before the audit starts so you are reviewing a fixed snapshot, not a moving target.
- Separate data entry errors from setup errors, because a missing field and a misconfigured project require different corrective actions.
- Flag critical deficiencies immediately when they affect reporting integrity, client tracking, or required project setup fields.
- Use the same benchmark thresholds every month unless your CoC or funder formally changes the standard.
- Review stale open enrollments against program rosters so active clients are not mistakenly treated as closed and vice versa.
- Photograph or screenshot the exact record, report line, or setup screen that supports each finding before the data changes.
- Assign one owner per corrective action and include a target resolution date so follow-up does not stall after the audit meeting.
What this template typically catches
Issues teams running this template most often surface in practice:
Common use cases
Frequently asked questions
What does this HMIS Data Quality Monthly Audit template cover?
It covers the core data quality checks an HMIS administrator needs each month: universal data element completeness, entry and exit timeliness, project-specific fields, logical consistency, and project setup accuracy. It also includes a corrective action section so findings do not stop at identification. This makes it useful both as an audit record and as a follow-up tracker.
How often should this audit be run?
This template is designed for monthly use, which fits the cadence of most HMIS data quality monitoring. Monthly review helps catch missing entries, stale enrollments, and setup issues before they carry into quarterly or annual reporting. If your agency has high turnover or rapid program activity, you may also use it more frequently for internal spot checks.
Who should complete the audit?
An HMIS administrator, data quality lead, or compliance staff member usually completes it. The reviewer should understand HMIS data standards, project setup rules, and how to interpret data quality reports. Program managers can help validate findings, but the audit itself should be owned by someone who can verify system-level data issues.
Does this template align with HUD and CoC data quality expectations?
Yes. The structure is built around HUD HMIS data quality concepts such as completeness, timeliness, accuracy, and consistency, along with project setup checks that affect reporting. It is also useful for Continuum of Care monitoring because it creates a repeatable record of deficiencies and corrective actions. You should still tailor it to local CoC policies and vendor-specific report outputs.
What are the most common mistakes this audit helps catch?
Common issues include missing universal data elements, exits entered late, open enrollments that were never closed, duplicate client records, and incorrect project setup such as the wrong project type or CoC code. It also surfaces logical errors like a minor marked as a veteran or an exit date earlier than the entry date. These are the kinds of problems that can distort reporting even when the program is otherwise operating normally.
Can I customize the thresholds and fields in this template?
Yes. You can adjust the benchmark thresholds, add local data elements, or expand the project-specific section for special funding streams. Many agencies also add fields for reviewer notes, root cause, and staff retraining status. The template is meant to be a starting point, not a fixed compliance form.
What should be attached to support the audit findings?
Attach the HMIS data quality report used for the review, screenshots of problem records when needed, and any notes showing how the issue was verified. Supporting documentation is especially helpful for setup errors, duplicate records, and stale enrollments. Keeping evidence with the audit makes follow-up easier and creates a cleaner compliance trail.
How is this different from ad hoc data cleanup?
Ad hoc cleanup fixes whatever someone notices in the moment, but it does not create a repeatable record of what was checked, what failed, and what was assigned for correction. This template turns cleanup into a documented monthly process with clear accountability. That makes it easier to track trends, prove follow-up, and reduce recurring deficiencies.
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