1. HUD Advanced Homeless Data Users Meeting
Thursday, April 24, 2008
Matthew D. Simmonds
President of Simtech Solutions Inc.
&
John Yazwinski
Executive Director of Father Bills & Mainspring and Chairman of the Quincy-Weymouth, MA CoC
HUD Advanced Homeless Data Users Meeting April 24, 2008

2. Community Information
CoC Description - Quincy-Weymouth
Point in Time Count – As of January
30th 2008, 256 persons were
Homeless
General Population Count - 142,013
HUD Advanced Homeless Data Users Meeting April 24, 2008

3. HUD Advanced Homeless Data Users Meeting April 24, 2008
Overview
It is our intent to share with others the data driven approach we have used to expediently address the issues surrounding chronic homelessness in our community. Using both HMIS and non-HMIS data we have been able to accomplish the following:
Identify sub-populations such as chronic homeless and young adults in great need of our attention.
Reduce our housing and non-housing costs.
Show via client surveys a demonstrable improvement in quality of life.
Ensure the quality of the data we are reporting on.
Provide media outlets, grant providers and donors with crucial facts and figures.
Allow us to demonstrate the accomplishment of goals set in our 10 Year Plan.
Improve our point in time counting strategy by adding both map based technologies and Excel based HMIS data auditing tools.
Show substantive data that has been instrumental in facilitating conversations with state institutions in regards to improving their discharge planning.
Reduce the turn around time for completing the Point In Time Chart K to less than 1 day.
Chart the build up of housing units and the corresponding decline in shelter beds.
HUD Advanced Homeless Data Users Meeting April 24, 2008

4. Measurable Outcomes from the 10 Year Plan
Reduce Inappropriate Discharges
Decrease Cost of Emergency Services
Increase Housing
Improve Regional Collaboration and Support
HUD Advanced Homeless Data Users Meeting April 24, 2008

5. HUD Advanced Homeless Data Users Meeting April 24, 2008
Examining the Trends
Over a four year period the shelter population of year olds grew from 137 to 205 representing an increase of 49%. The age group of year olds has had a startling increase of 58.5% with most of the increases occurring after the economic downturn in 2001.
HUD Advanced Homeless Data Users Meeting April 24, 2008

6. Action Step: Reduce Inappropriate Discharges
This data was shared with the Mass. Interagency Council on Homelessness as well as statewide advocacy groups such as Mass Housing and Shelter Alliance.
This research resulted in:
A change in discharge policies from statewide systems of care.
A new Housing First pilot program assisting 20 young adults aging out of state systems.
HUD Advanced Homeless Data Users Meeting April 24, 2008

7. Action Step: Reduce Inappropriate Discharges
HUD Advanced Homeless Data Users Meeting April 24, 2008

8. Action Step: Determine Our Chronic Population
In English - If the client has a disability, and they either had 4 or more homeless episodes OR
were homeless for greater than 1 year, and are 18 years old or older, then count them as
chronically homeless.
In Excel =IF(AND(J1="Yes",OR(K1="Y",AW1>=365),AC1>18),1,0)
J1= Disability from Detail. Any of the disability fields = Yes or Long Term Disability = Yes
K1= 4 or more episodes. Sort by client ID & entry date, increment by 1 for each new date where the start date of next program record > end date of last program record. Therefore we are considering an episode as ANY break in stay.
AW1=Total Length of Stay. Entry Date – Exit Date. If exit is blank use today as a bookend.
AC1=Client Age at Entry. (Entry date – date of birth) /
HUD Advanced Homeless Data Users Meeting April 24, 2008

9. Examining the Trends
Once we identified the chronically homeless we were able to pinpoint
their bed utilization rates and compare that with the utilization rates of
the non-chronic. Our findings were as follows:
Chronic clients served FY04 = 397
Total clients served in FY04 = 1285
% clients that were chronic = 397/1285 or 30.8%
Chronic clients served on 2/1/04* = 72
Total clients served on 2/1/04 = 146
% clients served that were chronic = 72/146 or 49.3%
Less than one third of the total clients
were utilizing roughly half of the bed stays!
* One of several randomly selected dates all of which showed similar results.
HUD Advanced Homeless Data Users Meeting April 24, 2008

10. Action Step: Decrease Cost of Emergency Services
After identifying the issue the continuum moved forward with a pilot Housing First project and studied the before and after results to determine if the model was an effective one. Our findings were as follows:
Cost Benefit Analysis – Shelter Vs. Housing
Hard costs per client at the shelter per year = $14,600.
Hard costs per client at Claremont House per year = $11,195. Total savings per client = $3,405.
Cost Benefit Analysis – Medical Costs
The Claremont House study showed out of 12 women placed emergency room visits dropped from 22 visits prior to housing to 11 after housing and inpatient stays dropped from 44 to 4.
FROM ACTUAL BILLINGS - Dr. Barber from Quincy Medical stated cost savings to the community were roughly $60,000 or $5000 per client for the first year of the study alone.
IF WE HAD TO ESTIMATE - The average cost of inpatient stays in the US was $1023 per day according to the Medical Care Cost Equation Tool (MCCE). According to MEPS the national average cost of an ER visit was $560.  Therefore based on these averages the total savings to the community were $40,964 for inpatient stays and $6160 for ER visits for a total savings of $47124.
HUD Advanced Homeless Data Users Meeting April 24, 2008

11. Action Step: Increase Housing
Closed an emergency shelter due to lack of need and took 35 total beds offline.
2+ years ahead of pace on the 10 year plan goal to build up housing units for the chronically homeless with 52 new units
Quincy beats housing goal: City reports 20% drop in chronic homelessness (Source Patriot Ledger)
HUD Advanced Homeless Data Users Meeting April 24, 2008

12. Action Step: Leverage the Point In Time Count
With the aid of an Excel based reporting tool we were able to generate the point in time report shown here with data compiled from all agencies within a few hours time.
The simplified process has enabled us to implement a “dry run” point in time count without any backlash from the participating agencies.
Year to year comparisons of point in time data have been instrumental in charting trends. Our chronic count has decreased every year for the last four years and we are now seeing more vets than ever.
Using Excel enables us to collect data from non-HUD funded agencies and serves as an effective auditing tool of our HMIS data.
By sharing our point in time info with others around New England and compiling data throughout the region we hope to establish benchmarks to better understand what should be reasonably expected for a community of our size.
HUD Advanced Homeless Data Users Meeting April 24, 2008

13. Action Step: Leverage the Point In Time Count
Point in Time Street Count Map for January 30, 2008
Street Count Map Legend
HUD Advanced Homeless Data Users Meeting April 24, 2008

14. Action Step: Improve Regional Collaboration
Clients Served by Region July 1, 2006 – June 30, 2007
HUD Advanced Homeless Data Users Meeting April 24, 2008

15. Action Step: Improve Regional Collaboration
Application Inventory
Initial Assessment*
HUD HMIS Data Collection & Reporting**
Bed Register**
Non-Homeless Data Collection*
HUD XML and CSV Data Exchange*** 
Custom Assessments***
GIS Mapping*
Agency Directory*
Referral Passing Tools*
Services Tracking***
Housing Inventory Chart Mgmt Tools*
Point In Time Counting Tools*
Advanced Reporting*
PATH Data Collection & Reporting*
Data Quality Monitoring Tools & Reports***
* = AgencyDash.com (non-HMIS)
** = SHORE (HMIS)
*** = Both
Not Homeless
Homeless
XML
XML
XML
XML
XML
SHORE (HMIS)
AgencyDash.com (Non-HMIS)
HUD Advanced Homeless Data Users Meeting April 24, 2008