Best Practices: Data Collection for Coordinated Assessment and Centralized Intake

Best Practices: Data Collection for Coordinated Assessment and Centralized Intake

Data collection is essential to the success of any Centralized Intake and Coordinated Assessment model. Valid data facilitates optimum housing and service patterns, and improves overall systems of care. Continuums of Care (CoCs) equipped with valid data can generate powerful reporting, which in turn improves their ability to obtain funding.

In this article, we’ll discuss 4 data collection best practices for Centralized Intake:

  1. Real-Time Data Entry Capabilities
  2. Dynamic Client Forms
  3. Compliance with HMIS Data Standards
  4. Data Quality Assurance Features

 

1. Real-time Data Entry Capabilities

Real-time data entry occurs when data processing takes place instantaneously upon data entry or receipt of a command. When implemented into an HMIS, real-time data processing enhances service provision (e.g. prevents duplication of services) streamlines referral processes, and allows for accurate bed availability information for precise reservation management.

Real-time reporting is another important real-time data processing feature. Real time reporting systems can process the data as soon as it arrives from the source and then deliver valuable data sets immediately. This is useful in two ways:

  • Trouble shooting. For example, if a discrepancy is found in when generating an Annual Performance Report (APR), the end user can immediately make the data correction, and subsequently re-run the APR. The new data will be presented in the results to provide an accurate final APR.
  • Data Correction. Make data corrections and complete time-sensitive reporting tasks to immediately be observed through reporting and submission.

 

2. Dynamic Client Forms

Display logic can be used to create dynamic client forms that automatically adapt as the end user enters information. It allows the form to display data fields conditionally, based on information entered into previous data fields. Display logic works through hide/show functionality that operates in response the information entered by the end user.

For example, clicking “Yes” to Veteran Status should automatically expand the intake form to display additional Veteran questions. Similarly, if a client indicates that they are female, then the form should automatically display an additional pregnancy question (this logic should also be based on the female client’s age).

Note that display logic should be applied to all forms, including intake forms, assessment forms, and program management forms (i.e. program entry, status, exit, and follow-up forms).

 

3. Compliance with HMIS Data Standards

Data collection must comply with the most recent HMIS Data Standards. This includes features and functionality that meet all Universal Data Elements, Program-Specific Data Elements, and Meta Data Elements requirements per HUD. This means that the HMIS should be equipped with screen templates and program/service configuration functionality that adheres to these regulations.

The HMIS must also support data entry that complies with all regulations surrounding the Federal Partner Program-Specific Data Elements for the following program categories:

  • CoC Programs
  • PATH Programs
  • VA/SSVF Programs
  • RHY Programs
  • HOPWA Programs
  • RHSP Programs

All forms should be fully customizable and easy for the System Administrator to create autonomously. This is achieved through screen and field editors. Such tools enable the System Administrator to set field requirements, enable display logic, and create customized data fields with varying response options e.g. picklist, checkbox, etc.) in ways that ensure the correct data will be corrected and made available for reporting purposes.

 

4. Data Quality Assurance Features

Data quality is the foundation of a successful Centralized Intake system; insufficient data quality impedes CoC funding initiatives. As such, the HMIS must be equipped with a targeted set of data quality assurance features.

Below are several examples:

  • Data entry validations, logic, and mandatory input fields built into all forms
  • Error checking functions that can identify out of range values and missing data with direct feedback to the user completing data entry.
  • Data dictionary that defines all data elements as well as primary and foreignkey associations
  • Data cleaning tools, including those that facilitate the non-duplication of records
  • Technical safeguards to ensure a high level of client confidentiality, specifically related 
to backend server(s) and data encryption and transmission

 

In Sum…

Implementing these data collection best practices will ensure that accurate and timely information is available to use for rapid assessment and service provision. These best practices enable CoCs to prioritize services and make the best use of their resources. CoCs that are able to collect data in an accurate, valid, reliable, and timely manner will be able to procure the true benefits that the Centralized Intake and Coordinated Assessment model has to offer.