HMIS Data Quality
Please sign up for Data Quality Training Sessions here. Note that these sessions are provided on a first-come, first-served basis.
Data Quality Resources
WHAT IS DATA QUALITY?
Data quality is a term that refers to the reliability and validity of client-level data collected in the HMIS. It is measured by the extent to which the client data in the system reflects actual information in the real world. No data collection system has a quality rating of 100%. However, to meet the goals set forth by the statewide implementation when presenting accurate and consistent information on homelessness, it is critical that the HMIS have the best possible representation of reality as it relates to persons experiencing homeless and the projects that serve them. Specifically, the goal is to record the most accurate, consistent and timely information in order to draw reasonable conclusions about the extent of homelessness and the impact on the homeless service system. To that end, each CoC will collectively assess the quality of our data by examining characteristics such as timeliness, completeness, and accuracy.
WHY IS DATA QUALITY IMPORTANT?
Data quality is vitally important to the success of the HMIS and the programs that use this database. HUD monitors the quality of the HMIS data through programs by the AHAR and NOFA. If the quality of the data is poor, HUD may refuse to grant funding or trim future funding. Consequently, if this happens, these funding cuts could negatively affect program(s) throughout the CoC. Since it is imperative that the data is correct, HMIS Agency providers and the HMIS Staff from each CoC should be working diligently on adhering to the HUD data standards in order to ensure all reports are complete, consistent, accurate, and timely across the entire statewide implementation.
TIMELINESS OF DATA ENTRY
Entering data in a timely manner can reduce human error that occurs when too much time has elapsed between the data collection/service transaction and the data entry. The individual doing the data entry has to rely on handwritten notes or their own recall of a case management session, a service transaction, or a project exit date; therefore, the sooner the data is entered, the better the chance the data will be correct. Timely data entry ensures accessibility of information for the entire CoC.
All data entered into the HMIS must be complete. Completeness is the level at which a field has been answered in whole or in its entirety. Measuring completeness can ensure that client profiles are accurately answered in whole and that an entire picture of the client situation emerges. Partially complete or missing data (e.g., missing digit(s) in a SSN, missing the year of birth, missing information on disability or veteran status) can negatively affect each CoC’s ability to provide comprehensive care to clients. Incomplete data results in an inaccurate picture of the need in each CoC, directly affecting services in individual communities necessary to permanently house clients. It is every HMIS end user’s responsibility to report an accurate picture of populations served to facilitate accurate reporting and analysis.
All Participating Agencies across each CoC should work consistently to reduce duplication in HMIS by following workflow practices outlined in training. HMIS end users are trained to search for existing clients in the system, across multiple parameters, before adding a new client into the system. Client data can be searched by Client ID, Name, Social Security Number, and Client Alias. End Users are trained to follow this protocol when adding a new client in the system.
Data consistency will ensure that data is understood, collected, and entered consistently across all projects in the HMIS. Consistency directly affects the accuracy of data; if an end user collects all of the data, but they don't collect it in a consistent manner, then the data may not be accurate. All data in HMIS shall be collected and entered in a common and consistent manner across all projects. To that end, all end users will complete an initial training before accessing the live HMIS system. All HMIS end users must recertify their knowledge of consistency practices on an annual basis.
Accurate data ensures that the HMIS is the best possible representation of reality as it relates to persons experiencing homelessness and the programs serving them on a day-to-day basis. Accuracy can be difficult to assess as it depends on the client providing correct data and the intake worker’s ability to document and enter the data accurately. Accuracy is best determined by comparing records in the HMIS to paper records, or the records of another reliable provider. For example, a SSN in question can be compared to a paper case file or SSI benefit application. In-person interviews, with clients participating in projects who are utilizing the HMIS, are another method for assessing accurate data entry. Evaluation for accurate documentation of case management, service transactions and referrals in the HMIS can be assessed by client interviews.