HomeMahoning CoC HMISKB Articles that Apply to Mahoning CoC HMISMahoning HMIS Data Quality Standards

1.1. Mahoning HMIS Data Quality Standards

I. Introduction

This document describes the Homeless Management Information System (HMIS) data quality standards and the data quality monitoring plan for the Mahoning County Continuum of Care (Mahoning County CoC). This document was developed by the Mahoning County CoC HMIS Advisory Committee for the Mahoning County CoC, in coordination with the HMIS participating agencies and community service providers. These HMIS Data Quality Standards and the related data quality monitoring plan will be updated annually, considering the latest HMIS data standards and the Ohio Mahoning County CoC Performance Management Plan.

A. Applicability of the HMIS Data Quality Standards

This HMIS Data Quality Standards document applies to all HMIS participating agencies located within the Ohio Mahoning County CoC, regardless of funding source. No Ohio Mahoning County CoC HMIS participating provider is exempt from the standards or process laid out in this document.

B. What is an HMIS

An HMIS is a locally administered, electronic data collection system that stores longitudinal person-level information about the individuals who access homeless and other human services in a community. Each CoC receiving Housing and Urban Development (HUD) funding is required to implement an HMIS to capture standardized data about all persons accessing the homeless and at-risk of homelessness assistance system. Furthermore, elements of HUD’s annual CoC Program competition are directly related to a CoC’s progress in implementing its HMIS.

In addition to CoC Programs and state-funded homeless programs, HMIS accommodates the following programs:

C. HMIS Data and Technical Standards

In 2004, HUD published HMIS Data and Technical Standards in the Federal Register. The Standards defined the requirements for data collection, privacy safeguards, and security controls for all local HMIS. In March 2010, HUD published changes in the HMIS Data Standards Revised Notice incorporating additional data collection requirements for the Homelessness Prevention and Rapid Re-Housing Program (HPRP) funded under the American Recovery and Reinvestment Act (ARRA). In 2014, HUD published the 2014 Data and Technical Standards1 , which accommodates more programs, like SSVF, RHY, PATH, and HOPWA, as well as removed references to HPRP.

D. What is Data Quality?

Data quality is a term that refers to the reliability and validity of client-level data collected in HMIS. It is measured by the extent to which the client data in the system reflects actual information in the real world. With good data quality, the CoC can “tell the story” of the population experiencing homelessness. The quality of data is determined by assessing certain characteristics about the data such as timeliness, completeness, and accuracy. In order to assess data quality, a community must first think about what data quality means and document this understanding in a data quality plan.

E. What are Data Quality Standards?

Data quality standards set expectations for the quality of data entered into the HMIS and provide guidance to HMIS participating providers on how to capture and enter reliable and valid data for persons accessing the homeless assistance system.

F. What is a Data Quality Monitoring Plan?

A data quality monitoring plan is a set of procedures that outlines a regular, on-going process for analyzing and reporting on the reliability and validity of the data entered into the HMIS at both the program and aggregate system levels. A data quality monitoring plan is the primary tool for tracking and generating information necessary to identify areas for data quality improvement.

II. Data Quality Standards

All Ohio Mahoning County CoC HMIS participating providers must strive to adhere to the following data quality standards. These standards are in addition to those identified by HUD in the HMIS Data and Technical Standards. HMIS Users and program staff should be familiar with both sets of requirements.

A. Data Timeliness

Entering data in a timely manner can reduce human error that occurs when too much time has elapsed between the data collection, or service transaction, and the data entry. Ideally, the data is entered during intake, but that is not always possible. The individual doing the data entry may be relying on handwritten notes or their own recall of a case management session, a service transaction, or a program exit date; therefore, the sooner the data is entered, the better chance the data will be correct. Timely data entry also ensures that the data is accessible when it is needed, either proactively (e.g. monitoring purposes, increasing awareness, meeting funded requirements), or reactively (e.g. responding to requests for information, responding to inaccurate information).

1. Data Timeliness Standard

All required data elements for each program type must be entered within seven days (including weekends and holidays) of the client entering the program. Any client updates that occur during the program stay should be entered into HMIS within seven days of data collection. Client records must be closed within seven days of the client exiting the program.


Stage of Data Entry

Number of Days to Enter Data (including weekends and holidays)

Program Entry 2

7

Update data during program stay

7

Program Exit

7

Table 1

B. Data Completeness

All data entered into the HMIS must be complete. Missing or incomplete data (e.g., missing digit(s) in a Social Security Number (SSN), missing the year of birth, missing information on disability or veteran status) can negatively affect the ability to provide comprehensive care to clients.

1. Data Completeness Standard

The percentage of required data elements identified as ‘missing’ or ‘client doesn’t know/client refused’ should be no more than 0% to 10%, depending on project type and data element. (See Table 2 for details.)

The Mahoning County CoC has established an acceptable range of ‘missing’ and ‘client doesn’t know/client refused’ responses, depending on the data element and the type of project entering data. The percentages listed in the last two columns represent the maximum percentages allowed.

Data Elements*

Applicability of Standard by Project Type

Missing – Max Allowed


Client Doesn’t Know/ Refused – Max Allowed

All Data Elements Except those listed below

All Projects 3

0%

2%

Veteran Status

All Projects

0%

0%

Relation to Head of Household

All Projects

0%

0%

Client Location

All Projects

0%

0%

Disabling Condition (Adults)

All Projects

0%

5%

Social Security Number

SSVF Projects

0%

0%

Income as a Percent of AMI

SSVF Projects

0%

0%

VAMC Station Code

SSVF Projects

0%

0%

Domestic Violence

All HUD Projects

0%

5%

Move-In Date at Exit

All RRH Projects

0%

0%

Destination

ES Projects Only

10%

2%

All Projects except ES

2%

2%

Housing Assessment at Exit

HOPWA and Prevention Only

2%

2%

Table 2

*End Users can find their Data Completeness measures in the Data Quality – All Workflows report.

C. Data Accuracy

Information entered into the HMIS needs to be valid, i.e. it needs to accurately represent information on the people that enter any of the homeless service programs contributing data to HMIS. Inaccurate data may be intentional or unintentional. In general, false or inaccurate information is worse than incomplete information, since with the latter, it is at least possible to acknowledge the gap. Thus, it should be emphasized to clients and staff that it is better to enter nothing than to enter inaccurate information. To ensure the most up-to-date and complete data, data correction should be performed once the error(s) is detected.

All data entered into the HMIS shall be a reflection of information provided by the client, as documented by the intake worker or otherwise updated by the client and documented for reference. Recording inaccurate information is strictly prohibited, except in cases where a client refuses to provide correct personal information (see Anonymous Clients section below).

1. Data Accuracy Standard:

The percentage of clients showing in each of the Data Quality Measurements for Accuracy should be no more than 0-3%, depending on project type and the measurement. (See Table 3 for details.)

Data Quality Measurements for Accuracy*

Applicability of Standard by Project Type

Max Allowed


All Data Accuracy measures in the Data Quality reports not listed below

All Projects

3%

Duplicate Entry Exits

All Projects

0%

Future Entry Exits

All Projects

0%

Incorrect Entry Type

All Projects

0%

Mismatched Household IDs

All Projects

0%

Children Only Households

All Projects except RHY

0%

Missing Head of Household

All Projects

0%

Needs without Services

PATH, RHY, and Housing Stabilization projects only

0%

Service Dates fall outside of Entry and Exit Dates

PATH, RHY, and Housing Stabilization projects only

0%

Open Services

PATH, RHY, and Housing Stabilization projects only

0%

Missing Entry Exits

PATH, RHY, and Housing Stabilization projects only

0%

Table 3

D. Bed/Unit Utilization Rates

One of the primary features of an HMIS is the ability to record the number of client stays or bed nights at a homeless assistance project. The count of clients in a project on a given night is compared to the number of beds reported in the Housing Inventory Chart (HIC) to return the agency’s Bed Utilization percentage. The generally acceptable range of bed utilization rates for established projects is 65% - 105%.

The CoC recognizes that new projects may require time to reach the projected occupancy numbers and will not expect them to meet the utilization rate requirement during the first operating year.

Project Types

Lowest Acceptable Bed Utilization Rate

Highest Acceptable Bed Utilization Rate

ES, TH, PSH

65%

105%

Table 4

III. Data Quality Monitoring Plan

The following section outlines how MCCCHMIS data quality will be monitored, including adherence to the data quality standards referenced above. Any questions about data entry or policies regarding HMIS should be directed to hmis@cohhio.org.

A. Roles and Responsibilities

HMIS Users: Enter quality data following the relevant workflow issued at HMIS trainings, and adhere to data quality standards outlined in the previous section of this document. Data entry staff/HMIS End users are responsible for checking all relevant Data Quality reports as outlined in this document on a monthly basis, and making corrections or developing corrective action plans to address errors as needed. Additionally, data entry staff/HMIS End users must be responsive to COHHIO HMIS staff when data quality issues are identified, and engage in any needed corrective action.

• Agency Administrators and Users with ART licenses: In addition to the responsibilities assigned to HMIS Users, Agency administrators will run all required reports monthly and compare the results to the data quality standards. The monthly reports agency administrators should run include but are not limited to:

1. Data Quality – All Workflows (for HUD, ESG, CoC, and PATH projects)

2. Data Quality – Services (if necessary) (for HUD, ESG, and PATH projects that use services as part of their workflow)

3. 0261 – RHY Entry Assessment Data Completeness (for RHY projects only)

4. 0262 – RHY Exit Assessment Data Completeness (for RHY projects only)

5. Bed Utilization by Provider (for PSH, TH, ES, SH providers)

6. Desk Time (for all providers)

PATH projects should use the Data Quality reports listed above, but may also have program-specific Data Quality reports that check data elements not included in the reports listed above. Check with an HMIS administrator for more information.

• COHHIO HMIS Staff: Train users on how to correctly enter data into HMIS and how to run reports as necessary, support current users, create and maintain documentation, keeping users informed about any changes, maintain provider data, assist in submitting reports to HUD, and monitor and report on data quality. The COHHIO HMIS Staff will conduct periodic reviews of data quality reports and report any findings to the contact person at the agency responsible for HMIS data entry. Reports will include recommended corrective actions as needed.

B. Compliance

If the agency fails to make corrections when COHHIO HMIS staff has informed them of needed corrective action, or if there are repeated or serious data quality errors, the COHHIO HMIS Staff will notify ODSA, if applicable, and Executive Director about non-compliance with the required HMIS participation.

Several funding sources now consider HMIS data quality when making funding decisions, including ODSA’s Supportive Housing Program, HCRP, and HUD’s CoC Program. Low HMIS data quality performance may result in denial or reductions of this funding.


Appendix A: Terms and Definitions

Client: a person receiving services or housing from the homeless system.

Homeless Management Information Systems (HMIS) – An HMIS is a locally administered, electronic data collection system that stores longitudinal person-level information about the individuals who access homeless and other human services in a community.

HMIS Data Quality – Refers to the reliability and validity of client-level data. HMIS data quality can be measured by the extent to which the client data in the system reflects actual information in the real world.

COHHIO HMIS Staff – Coalition on Housing and Homelessness in Ohio (COHHIO) staff members who have been contracted by the MCCC to provide user training, user support, reporting, analysis, and quality improvement of the HMIS data.

Housing Stabilization projects: The ODSA Homeless Crisis Response Program (HCRP) covers 3 different program types: Emergency Shelter, Rapid Rehousing, and Homelessness Prevention. ODSA uses the term “Housing Stabilization” to refer only to the Rapid Rehousing and Homelessness Prevention programs within the ODSA Homeless Crisis Response Program.

Project Types and Corresponding Funding Sources


1 The HMIS Data and Technical Standards can be found at https://www.hudexchange.info/news/federal-partners-release-final-2014-hmis-data-standards/

2 End Users can find their timeliness measure in the Desk Time report.

3 For PATH data, standards are only applicable to clients who have a date of engagement.


Downloads

This page was: Helpful | Not Helpful
1.4. Visibility Change - December 2016