2.01 Driving Change with System Performance Measures NAEH 2017

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Presentation transcript:

2.01 Driving Change with System Performance Measures NAEH 2017

Driving Change with SPMs HUD Systems Performance Measures Length of Time Homeless- average and median Numbers of Homeless People and Newly Homeless Returns to Homelessness Increases in Earned and Benefit Income Exits to and Retention in Permanent Housing Requirement - not just an exercise What are the numbers telling us? Why collect all this data? What do we do with it? How do we make it meaningful? Address Gaps, Priorities, Capacity, Emerging Needs, P&P

Drive Change with System Performance Measures NAEH 2017 Tess Colby Manager Community Services Programs Tess.Colby@co.pierce.wa.us July 18, 2017

Pierce County, WA COC COC has about 2,400 permanent and temporary beds $11.3 million in 2017 COC - $3.2 million ESG - $390,000 WA State - $3.5 million Local - $4.2 million PIT (2017): 1,321 persons Increase in unsheltered and chronic In 2016 9,838 unduplicated persons sheltered/housed (60% increase over 2012) Coordinated Entry, Prioritization Population = 832,000 City of Tacoma = 205,000 HUD Median Income = $74,500 13.1% below poverty level Market Rent 2br = $1,360 FMR 2 br = $1,142 Vacancy rate = below 3% Walk thru COC data Increase in persons served since 2010 – used data to analyze our system performance, especially TH - conversion increased system capacity to serve 7/18/17

Performance Measures - Sample Performance Based Contracts Outcome Measures System Performance Measures All Hearth Measures Coordinated Entry Measures, e.g., referral acceptance rates, length of time to Expenditure of Contract Rate Big Three Length of Time in Program Exits to Permanent Housing Returns to Homelessness 7/18/17

Sample dashboard – tracks project outcomes against universal outcomes for the intervention type – in this case, RRH Picks up main HEARTH measures, plus the our local ones like contract expenditure CLICK Draw attention to two measures – two line graphs on left show how long it takes to move into housing; bar graph on right shows how long people remain in the RRH program, and how many people exit the program and retain permanent housing. Examples of why context and overall system goals are important to keep in mind when looking at data. Rents have increased by over 25% in the last year, so it’s much harder to find units – thus, longer search times We implemented CES with prioritization in late 2016, so programs are receiving referrals of persons with much higher housing barriers, so time in program has increased a little, but retention of permanent housing upon exit has really taken a dive. Tells us we need to rethink our provider training and RRH policies now that RRH is serving a more vulnerable population. 7/10/17

This is the 2nd page of the dashboard – sharing a bit of additional detail. CLICK – we wanted to take a few of the outcomes and disaggregate by race – in particular, exits to perm housing and returns to homelessness – and where there are discrepancies, as there are in this instance (lower Perm housing exit rate and higher return rate for African Americans). In both of these examples, the data doesn’t really give us an answer – it tells us the questions to ask. That’s what ultimately leads to change and improved performance. 7/10/17

So Much Data, So Little Time! Data paralysis: we can extract it… now what?? Data Story Telling: translating numbers to words Data Sharing: helping providers use their own data Finally, Data Challenges – so many! Our top three are: data paralysis. HMIS is a wealth of data, which can be slied and diced so many different ways. So how do we figure out where to focus our time? It depends on what story you want to tell. We’ve found that we are spending way more time now talking about messaging, and selecting data that helps support that messaging… even if the data shows a need for improvement. Because that’s always part of the story. And third, when we started sharing the dashboards with provider agencies, we realized that we needed to teach many of them how to read them, how to interpret them and what they could do with them. SO that’s a whole new set of skills we are developing! AND, we also got a lot of suggestions about what OTHER reports we could create for them, so now we’re helping agencies learn how to do their own data analysis. Since sharing the data with providers we’ve found that agencies are more invested in understanding their outcomes and improving them. 7/18/17

Kevin Finn, MSW, LISW-S President/CEO kfinn@end-homelessness.org 513.263.2780

Roles within our CoC HUD Continuum of Care (OH-500) Unified Funding Agency HUD Emergency Solutions Grant HUD Housing Opportunities for Person with AIDS (HOPWA) State of Ohio Housing Crisis Response Program (HCRP) Hamilton County – Indigent Care Levy funds City of Cincinnati – Human Services funding Collaborative Funding Model - Privately Raised Funding for Community Initiatives $ 18.5 million $ 1.2 million $ 900,000 $ 400,000 $ 2 million $ 420,000 $ 750,000 Outcomes Data + Community Input = Funding Allocation

Cincinnati/Hamilton County CoC (OH 500) Population within our CoC 806,631 Population of “Greater Cincinnati” 2,114,580 U.S. Census Bureau's formal name for the area is the Cincinnati–Middletown, OH–KY–IN Metropolitan Statistical Area. “Greater Cincinnati” encompasses our CoC, & portions of 3 Balance of State CoCs (Ohio, Indiana, Kentucky)

System Performance: What we are doing well… Following the Data Allocation processes use System Performance measures Using data analytics company to analyze HMIS data & compare data to other data sets Working Groups (PSH, RRH, Street Outreach, etc.) compare outcomes data quarterly; identify best practices. Use data to target Shelter Diversion resources. Using data to forge partnerships (PHA, across CoCs, etc.) Signs of improvement Returns to homelessness- Street Outreach returns down from 25% to 13% (with Coordinated Entry & CABHI). 12% increase in Street Outreach exits to PH. Reductions in the number of people becoming homeless for the first time

System Performance: Where we suck… Length of Time People Remain Homeless Has actually increased by 2 days (???) Fits & starts with the RRH model Landlords, landlords, landlords… Employment & Income measures Almost all measures have actually decreased May be due to implementation of Coordinated Entry system- housing our most chronic clients Homelessness as a Regional Issue We are helping run Coordinated Entry in BOS KY CoC People moving across the river: impacts recidivism, returns to homelessness, documentation of CH status

Coalition for the Homeless Eva Thibaudeau, LCSW Coalition for the Homeless Greater Houston, TX

The Way Home CoC is: City of Houston (see right) Harris County Population 4.59M 1777 sq. miles Houston Pasadena Fort Bend County Population 741,237 885 sq. miles Montgomery County Population 1M 507 sq. miles Conroe

% Successful Exits to PSH & RRH* Eva’s Thought Bubble: WHAT???!!!!! Our data analysis shows success! The PIT is steadily dropping! HELP!!! Houston—we have a problem! FY2016 SO = 16% ES/TH/PH = 29% *Related factors: Auto-exits from day/overnight shelter = no data High volume of shelter users = volume of auto exits High volume of light touch users = volume of auto exits Outreach team comfort w/ HMIS in the field = training opps

For more information email: info@thewayhomehouston.org

Discussion and Q&A Driving Change with System Performance Measures