Stella Performance Strategy & Analysis Tool June 5 & 6, 2019 1
Stella Performance Webinar Overview • Future of Homeless Service Data • Introduction to Stella and the Performance and Modeling modules • Stella Performance Module Demonstration • Accessing Stella P and Key Concepts • Q & A
Our Goal • To end homelessness, every community needs to be able to implement a systemic response that ensures homelessness is prevented whenever possible or, if it can’t be prevented, it is a rare, brief, and one-time experience. 3
Future of Homeless Service Data
The Future of Homeless Service Data • More strategic • Advanced tools • Actionable data 5
More Strategic 6 6
Strategic Investment in Capacity • Bed Coverage • AHAR submission • Data Quality • Loss of HMIS funding • Staffing to end user ratio • HMIS funding to end user ratio • HMIS Lead Evaluation • HMIS Use of a Data Quality Plan • HMIS End User Training 7
Strategic Guidance HUD permits providers to use and disclose or share data without consent for the purposes below as long as your privacy notice clearly articulated the use and disclosure. This can include uses and disclosures for: - Provision or coordination of services - Carrying out administrative functions - Payment or reimbursement functions - Meeting legal requirements - Averting a serious threat to health or safety - Reporting abuse, neglect or domestic violence - Research purposes 8
Advanced Tools 9
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Actionable Data 11
Stella Strategy & Analysis Tool
Introducing Stella A strategy and analysis tool that helps CoCs understand how their system is performing and model an optimized system that fully addresses homelessness. Stella Performance Module Stella Modeling Module • Stella P relies on dynamic visuals • Stella M assists CoCs to explore of CoCs ’ data to illustrate how how resource investment decisions households move through the amplify system capacity to end homeless system, and to highlight homelessness. outcome disparities. • Starts with homeless needs and • Does the analytical heavy lifting, so performance goals, and helps the your CoC can focus on planning community transform those needs and improving your crisis response into a series of resource investment system. decisions.
Stella Performance • To develop effective performance improvement strategies you need to understand how your system is performing now • Producing data visualizations requires significant investment of resources, not all CoCs have analytic expertise needed to develop and maintain dashboards • Stella P provides a common visual language about the main performance measures to support data informed decision making 14
From LSA to Stella Detailed Downloadable HDX 2.0 Display Analysis Tables Demographics by household type for people experiencing sheltered homelessness, using RRH, and using PSH CoC-Level HMIS Data System Use by household Stella Performance Demographics types and population groups (Now) Length of Time Homeless and in the System Housing Outcomes Returns to Homelessness Stella Modeling (Later) 15
Data Informed Decision Making Performance data helps CoCs evaluate: • Progress toward the goal of making homelessness rare, brief and one- time • Contributions of individual projects to system performance But data should not be the only factor in CoC decision making about resources and policies. Data should be placed in the context of: o System design (ex. participants targeted to be served by a project) o Best practices/research (ex. Housing First, cost effectiveness) Data informed decision making considers the broader context and plans for system change that are not reflected in data about the current system. This is in contrast to data driven decision making which relies only on data. 16
Accessing Stella P & Key Concepts 17
Accessing Stella • Set up HDX 2.0 account if needed • Upload official or local submission LSA dataset to HDX 2.0 • Prepare LSA dataset for Stella P o See Preparing LSA Files for Stella P Guide for more information • Choose a LSA dataset on the Stella P page of HDX 2.0
LSA Files and Stella Performance • Any LSA file can be prepared for • Caution should be used in viewing in Stella P interpreting system performance o See the Preparing LSA Files for from LSA files with outstanding Stella P on the Stella HUD LSA code and data quality issues. Exchange page • Users can prepare files for Stella P https://www.hudexchange.info/ to explore the charts and begin homelessness-assistance/stella/ planning of how to incorporate Stella P into system planning activities. • Once the official FY2018 LSA submission file is available, CoCs may want to remove earlier files from Stella P to prevent sharing of information with unresolved programming or data quality errors. 19
Stella P Basics • Households not people – more important for system planning purposes • Data from ES/SH, TH, RRH and PSH projects entering data into HMIS o No SO data (except self-reported time if selected) o No SSO data • System level exits – last exit to a destination outside the system during the report period o No agency or project level information • System level performance for all households experiencing homelessness – not project performance about participants
Understanding LSA/Stella P and System Performance Measures LSA and System Performance Measure (SPM) reports use different logic: • LSA universe is households, SPM universe is people served • Both report time homeless prior to report period: LSA allows for 7 day gap, SPM does not • LSA looks at returns by household, it will not count returns by people who were in the original household but now are in a different household. The SPMs look at returns by person, as a result they count all returns. While the measures aren’t exactly the same, improvement shown in the LSA should translate to improvement in the SPMs
Data Quality and Stella P • Several types of data quality issues – impact performance in different ways o Missing data in client record – impacts specific measures and filters (data quality insights are about this issue) o HMIS coverage – missing projects results in incomplete performance data (ex. missing ES/SH & TH impacts Days Homeless and Returns, missing PH projects impacts Exits and Returns) o Unknown data – impacts specific measures and filters (ex. Unknown destination at exit impacts Exits and Returns) • Resource to improve data quality: CoC Data Quality Brief 22
Stella P P Me Meas asures 23
Stella P Dashboard 24
Homeless 25
Exits 26
Returns 27
Household Types Population Groups Stella Household Types & Population Groups 28
Pathways 29
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Performance Insights • Stella P has built in logic to identify data quality issues and performance that is outside defined performance expectations. • Users will be able to create their own insights to flag performance that is not consistent with their expectations for their system. • Insights can be pulled into an action plan as the first step in system improvement planning. 32
Insights 33
Stella P Resources Getting Started Understanding Performance • Introductory Webinar • Performance Analysis and Improvement Webinars • Stella P Start Up Guide • HUD’s Communities of Practice • Stella P Prezi • System Map Video • Stella Reference Guide Stella Performance HUD Exchange Page: http://www.hudexchange.info/homelessness-assistance/stella/
QUESTIONS? Submit questions after the webinar to the HDX AAQ Desk 35
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