Friday, November 11, 2016

HMIS and Bed Usage Rates

Image: Kitwe Co, UK


If we may now turn from the soap opera that has become the MWHI Task Force, there are a couple of matters related to data-collection that are worth discussing: HMIS and coordinated entry. 

As some of you may be aware, CANDO last month offered up to the Task Force, sua sponte, a recommendation that the Task Force advocate for a systematic approach to improving homeless services delivery, starting with wider use of Servicepoint, a Homeless Management Information System (HMIS) application in use in Oregon, and the development of a coordinated entry system.  Because you can't advocate for something you don't understand, and understanding the importance of these tools might be difficult if you don't live in the world of homeless services delivery, we decided to blog about them.  And because they're kind of dry subjects, we're spreading them over two blogs. 

As a preliminary matter, HMIS and Servicepoint have been mentioned at several Task Force and committee meetings, accompanied by considerable misinformation.  We will not attempt here to sort out the confusion we heard at those meetings, except to say it seemed most often to come from the mouths of those with "a little learning" a long time ago.

If you're feeling wonky, you can read about HMIS and coordinated entry generally at the HUD Exchange (linked above), but you don't need to do that to understand these blogs.

HMIS is concerned primarily with housing, a "home" being the natural focus of a "home"-less service delivery system.  HUD requires every continuum of care (CoC) every year to inventory the housing available in their homeless assistance program and issue a Housing Inventory Count report.

If a program/provider collects bed-usage data in their CoC's HMIS application, HUD counts the beds as "covered."  A high bed coverage rate means the data will be meaningful, and will allow the CoC to better compete for HUD funding.  Here's what the HMIS-coverage situation looks like in Region 7, based on the last bed inventory count:

Region 7 2016 Housing Programs

Program
*
Fam Units
Fam Beds
Child Only Beds
Adult Only Beds
Total Beds
S
Salem IHN

4
14


14
S
Center for H&S
DV
3
13

2
15
S
UGM Simonka

5
18

78
96
S
UGM Men’s




114 (+94 overfl)
208
S
Sable House
DV



10
10
S
Polk County




3
3
S
NWHS SOS



15

15
S
Total Beds





361


Program
*
Fam Units
Fam Beds
Child Only Beds
Adult Only Beds
Total Beds
TH
St Francis

14
46


46
TH
St Joseph

17
51


51
TH
Salvation Army




84
84
TH
Shangri-La

2
4

14
18
TH
Grace House




9 (W)
9
TH
UGM Men’s




56 (M)
56
TH
Titus Hse
AD



6
6
TH
Shelly’s Hse
CH



17 (W)
17
TH
Rstration Hse
CH



48 (M)
48
TH
HOB
V



5 (M)
5
TH
NWHS



8

8
TH
Total Beds





351


Program
*
Fam Units
Fam Beds
Adult Only Beds
Total Beds
PSH
SHA-CHDV

3
15
6
21
PSH
SHA-VASH

6
20
48
68
PSH
NWHS-HOAP



9
9
PSH
Shangri-La 0

4
8
12
20
PSH
Shangri-La 2

3
5
4
9
PSH
Shangri-La B



5
5
RRH
CAA-SSVF

2
8
3
11
RRH
CAA-OHCS

3
10
7
17
RRH
CAA-ARCHES

10
23
20
43
RRH
CAA-ARCHES

2
4
3
7

Total Beds




210

S-Shelter  TH-Transitional Housing   PSH-Permanent Supportive Housing   RRH-Rapid Re-Housing   DV-domestic violence   AD-alcohol/drug addiction  CH-criminal history  V-veterans  SHA-Salem Housing Authority   
CH&S-Center for Hope and Safety  CAA-MWVCommunity Action Agency     HH-Household   NWHS-Northwest Human Services      HOB-Home of the Brave (closed summer 2016)  CHDV-Chronically Homeless Disabled Vet SSVF-Supportive Svces for Vet Families

Not included, Father Taafee Homes, Woodmansee Community, River of Life House, Safe Families, Polk CDC, Oxford Houses

The programs shaded in the chart above were not using Servicepoint when the chart was made (and still are not, as far as we know).  Based on this data, Region 7's HMIS bed coverage rates looks like this:

Housing Type
Total Beds in HIC
Total DV Beds in HIC
Total HMIS Beds
HMIS Coverage Rate
Shelter
361
25
14
4%
TH
351
0
124
35%
PSH
132
0
34
26%
RRH
78
0
78
100%

These rates are so low (RRH excepted) as to limit the usefulness of the data as systems measures.  Region 7's rates also drag down ROCC's rates (see below) (though Region 7 isn't the only one doing this), and limits the usefulness of ROCC's data as systems measures.  For that reason,  they also harm ROCC's ability to compete on a national level for CoC Program funding.

ROCC's 2016 Consolidated Application


ROCC's 2016 Consolidated Application

For the past several years, HUD has steadily raised the bar as CoCs improve their data collection and fine-tune their tools and methods.  If a CoC's bed coverage rate is below a certain percentage, the CoC "loses points" and below-par data won't be accepted for HUD's statistical purposes.   Here to the left is what ROCC told HUD this year about its low bed coverage rates (BOS CoC is another name for ROCC).

Translated, ROCC is acknowledging that we've not been able to persuade non-grantee providers (i.e., providers who don't receive HUD funding) to use Servicepoint, but we are trying, however ineffectually.

In Region 7, efforts to persuade are so, uhm, subtle as to be imperceptible.  Political pressure is needed to raise awareness of the problem and a way around the barriers to non-grantee use, which come down to these: 1) license fees ($350/yr), 2) staff time (a few hours/wk), 3) philosophical objections, 4) culture of non-cooperation among providers.   

In case you're wondering what this kind of data could tell us, at right is a draft of the most recent report on ROCC's Bed Coverage Rate and Bed Utilization Rate.  The latter, if based on accurate data, is one indication of how efficiently our programs are operating, whether they're meeting the needs of the homeless community, etc.

However, the BUR here is not based on reliable data, because the BCRs for ESFAM, ESIND, THFAM and THIND are too low, and they're too low because too few providers participated in data collection.   

The other measures (Average HH Size, Length of Stay), are similarly unreliable.

(When HUD has finished reviewing the data in this report, asterisks will appear by "ESFAM, ESIND, THFAM and THIND" to indicate those coverage rates are unacceptable.) 

You don't have to be a data scientist to see how accurate data could assist communities -- this community -- to improve delivery of homeless services, even to the point of ending it, functionally anyway, which means services are reasonably available to meet need.  We don't have to require our neighbors to live in the woods or on the streets before we make services available to them, but we do have to work together in ways that may be unfamiliar if we are going to be effective.  Everyone agrees we haven't been effective.  Let's decide to change that.

Next: HMIS and Coordinated Entry

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