SNAPS Data Strategy and Bed Coverage

Data Quality 201: Bed Coverage and Strategies to

Improve. – the community experience

.

Mike Lindsay, ICF

Alissa Parrish, ICA

Learning Objectives

•

Understanding SNAPS Data Strategy and

relationship to Bed Coverage

•

Understand the core elements, definitions, and

metrics of bed coverage

•

Review strategies to increase bed coverage in a

meaningful way

•

Discuss the connection between bed coverage

and all other components of data quality

Data Quality 201: Bed Coverage

Mike Lindsay, ICF

Alissa Parrish, ICA

Session Overview/Agenda

•

SNAPS Data Strategy Discussion

•

HMIS Participation

•

Strategies

•

Community Examples

Data Quality 201: Bed Coverage

Mike Lindsay, ICF

Alissa Parrish, ICA

SNAPS Data Strategy to Improve Data

And Performance

Data Quality 201: Bed Coverage

Mike Lindsay, ICF

Alissa Parrish, ICA

SNAPS Data Strategy to Improve Data

And Performance

•

The Office of Special Needs Assistance Programs (SNAPS) has

defined a set of goals it believes represent where the field and

Federal government can be in 3 – 5 years.  Every CoC should

consider the following:

•

How closely their CoC/HMIS implementation is to achieving

the vision and strategies

•

If these federal priorities align with their local efforts

•

Barriers they may be facing to implement the vision and

strategies

Data Quality 201: Bed Coverage

Mike Lindsay, ICF

Alissa Parrish, ICA

SNAPS Strategic Goal #1

•

Communities use their data to optimize

systems of care through making ongoing

system performance improvements and

determining optimal resource allocation

Data Quality 201: Bed Coverage

Mike Lindsay, ICF

Alissa Parrish, ICA

SNAPS Strategic Goal #2

•

Communities operate data systems that allow

for 

accurate, comprehensive, and timely data

collection

, usage, and reporting

•

Key Characteristic – Bed Coverage across CoC

(funded and unfunded)

Data Quality 201: Bed Coverage

Mike Lindsay, ICF

Alissa Parrish, ICA

SNAPS Strategic Goal #2 cont.

•

Majority

 of CoCs in 3 – 5 years Goals for

Quality Data

•

100% of ALL homeless providers contribute

data to HMIS

Data Quality 201: Bed Coverage

Mike Lindsay, ICF

Alissa Parrish, ICA

SNAPS Strategic Goal #2 cont.

•

Advanced

 CoCs in 3 – 5 years Goals for Quality

Data

•

100% homeless providers and non-

homeless providers contribute to 

shared

data environment

Data Quality 201: Bed Coverage

Mike Lindsay, ICF

Alissa Parrish, ICA

SNAPS Strategic Goal #3

•

Federal government coordinates to receive and

use data to make informed decisions in

coordination with other data sets, across and

within agencies.

Data Quality 201: Bed Coverage

Mike Lindsay, ICF

Alissa Parrish, ICA

HMIS Participation

Data Quality 201: Bed Coverage

Mike Lindsay, ICF

Alissa Parrish, ICA

What’s the Effect of Low HMIS Bed

Coverage?

•

Low HMIS Bed Coverage prevents many

communities from understanding the true

nature and extent of homelessness

•

Prevents accurate Federal, State, and Local

Reporting

•

Prevents the development of a data informed

decision making culture

Data Quality 201: Bed Coverage

Mike Lindsay, ICF

Alissa Parrish, ICA

What’s the Effect of Low HMIS Bed

Coverage? cont.

•

Clients can get “lost” in the homeless services

system

•

If the community is using HMIS for

Coordinated Entry, low bed coverage could

have a significant effect on “inactivity” for the

purposes of CE

•

Difficult to look at overall client movement

through the system and system effectiveness

Data Quality 201: Bed Coverage

Mike Lindsay, ICF

Alissa Parrish, ICA

Bed Coverage Rate – The Basics

•

HMIS bed coverage rate refers to the proportion of beds in

a community that participate in HMIS. The HMIS bed

coverage rate is equal to the total number of HMIS-

participating beds divided by the total number of beds in a

community.

Example:

Total Homeless Beds = 150

Homeless Beds in HMIS = 45

Bed Coverage = 45/150 = 30% HMIS Bed Coverage

Data Quality 201: Bed Coverage

Mike Lindsay, ICF

Alissa Parrish, ICA

Factors Leading to Poor HMIS Bed

Coverage

•

Lack of agency resources

•

Staffing

•

Too few, too little time

•

Limited computer skills

•

Shelter may be dependent on homeless ‘volunteers’

•

Don’t see the need for HMIS (management/staff)

•

Technology

•

Few computers, may not be adequate for HMIS

•

Limited access to internet

•

Existing HMIS software may not be adequate for high

volume, high turnover shelters

Data Quality 201: Bed Coverage

Mike Lindsay, ICF

Alissa Parrish, ICA

Factors Leading to Poor HMIS Bed

Coverage cont.

•

Many housing programs are not required to participate in HMIS and

choose

 not to participate.

•

Secular organizations with limited resources

•

Faith Based Organizations (FBOs)

•

Rescue Missions affiliated with the Association of

Gospel Rescue Missions (AGRM):

•

AGRM has about 300 member Missions

•

About 70% of these Missions accept no federal funding

•

They provide an estimated 11% of ES and TH beds

nationwide

•

Often satisfied with Mission focused software (about 100

Missions)

•

Non-affiliated Missions

•

Other FBOs

Data Quality 201: Bed Coverage

Mike Lindsay, ICF

Alissa Parrish, ICA

Factors Leading to Poor HMIS Bed

Coverage cont.

•

Many housing programs are not required to participate in HMIS and

choose

 not to participate.

•

Section 8 vouchers with a homelessness preference / eligibility

•

HUD-VASH vouchers

•

Transitional Housing beds not CoC-funded

•

Locally-funded rapid rehousing and other permanent housing

projects

Data Quality 201: Bed Coverage

Mike Lindsay, ICF

Alissa Parrish, ICA

How?

Data Quality 201: Bed Coverage

Mike Lindsay, ICF

Alissa Parrish, ICA

Increasing HMIS Bed Coverage

•

For many communities, increased HMIS bed coverage

will not be possible until:

•

Difficult, high volume, high turnover shelters with

limited resources can be successfully integrated

into HMIS, and

•

The community commits to engaging faith based

organizations – often the primary providers of

Emergency Shelter – as full partners in HMIS

Data Quality 201: Bed Coverage

Mike Lindsay, ICF

Alissa Parrish, ICA

Increasing HMIS Bed Coverage cont.

•

For many communities, increased HMIS bed coverage

will not be possible until:

•

Clients served with HUD-VASH vouchers are

entered into the system

•

State and local funders understand the benefits of

HMIS and encourage / require their grantees to use

the system

Data Quality 201: Bed Coverage

Mike Lindsay, ICF

Alissa Parrish, ICA

Engaging Faith Based Organizations

•

Faith-based organizations have different reasons for

gathering data:

•

Ideas of success

•

Long range goals

•

Funding

•

Accountability models (internal and external)

•

Interfaith/community partnerships

•

Denominational viewpoints  (Why do they help

people?)

•

Understanding the realities of faith based partnerships

is critical for success—every situation is unique

Data Quality 201: Bed Coverage

Mike Lindsay, ICF

Alissa Parrish, ICA

Talking Points: Engaging Faith Based

Organizations

•

Why join a community information system /

HMIS?

•

Modifiable and Scalable Systems

•

Information and Resource Sharing

•

Funding Leveraging

•

Collaboration among Service Providers

Data Quality 201: Bed Coverage

Mike Lindsay, ICF

Alissa Parrish, ICA

Additional Tips to Consider

•

Practice relationship building

•

Assist with infrastructure creation within

faith-based community

•

Offer financial incentives

•

Adapt processes to specific agency needs

•

Gain support and insight from colleagues

Data Quality 201: Bed Coverage

Mike Lindsay, ICF

Alissa Parrish, ICA

Additional Tips to Consider cont.

•

Involve non-HMIS participating homeless

service providers in the overall CoC process

and ask them to share their expertise /

knowledge

•

Educate state and local funders about HMIS

•

Provide data / reports / dashboards from

HMIS of interest to non-HMIS participating

entities

Data Quality 201: Bed Coverage

Mike Lindsay, ICF

Alissa Parrish, ICA

Can Technology Improve Coverage?

•

Encourage more participation by reducing HMIS

administrative overhead – 

Make HMIS easier

•

Simplify check-in/check-out process in high volume,

high turnover 

emergency shelters

.

•

Reduce need for staff computer and keyboard skills

•

Run check-in without internet access or HMIS

passwords

•

Reduce/eliminate the need for duplicate data entry

•

Provide useful benefits

 in return for HMIS participation

•

Use HMIS to replace existing, manual reports

•

Provide timely, accurate reporting for donors

•

Document program performance and outcomes

Data Quality 201: Bed Coverage

Mike Lindsay, ICF

Alissa Parrish, ICA

Community Examples

Data Quality 201: Bed Coverage

Mike Lindsay, ICF

Alissa Parrish, ICA

Anchorage CoC – Rescue Mission

•

Ongoing conversation / showing the benefit

to the community / clients served

•

Assisted with data entry into HMIS through

an Agreement with another Agency

•

Discussed transitioning data entry in-house

once the process began

Data Quality 201: Bed Coverage

Mike Lindsay, ICF

Alissa Parrish, ICA

Anchorage CoC – Rescue Mission

More Work to Do

•

Working to simplify / replicate current

internal processes

•

Edited HMIS documents to meet Rescue

Mission needs

•

Ongoing conversations / relationship-building

Data Quality 201: Bed Coverage

Mike Lindsay, ICF

Alissa Parrish, ICA

Anchorage CoC – Locally Funded

Projects

•

CoC Leadership emphasized importance of

HMIS data and encouraged funders to require

the use of HMIS for locally-funded permanent

housing

•

Agencies with the local funding already used

HMIS – easier lift

Data Quality 201: Bed Coverage

Mike Lindsay, ICF

Alissa Parrish, ICA

Alaska BoS CoC – Overall Coverage

•

Education, education, education

•

Seasonal shelter beds important for the CoC’s

coverage

•

CoC Leadership support / encouragement

Data Quality 201: Bed Coverage

Mike Lindsay, ICF

Alissa Parrish, ICA

Boise City / Ada County CoC –

Non-HUD-funded Mission

•

Largest provider of ES and TH beds within the

CoC

•

Invited Rescue Mission leadership to join the

CoC Executive Committee

•

Find connections between Rescue Mission

and other parts of the homeless services

system

Data Quality 201: Bed Coverage

Mike Lindsay, ICF

Alissa Parrish, ICA

Boise City / Ada County CoC –

HUD-VASH

•

HUD-VASH leadership involved in the CoC

Executive Committee

•

Worked through HMIS documents to meet VA

needs

•

Trained staff on HMIS data entry, and provide

ongoing training / technical support

Data Quality 201: Bed Coverage

Mike Lindsay, ICF

Alissa Parrish, ICA

Any More ???

Alissa Parrish, alissa.parrish@icalliances.org

Mike Lindsay, Michael.Lindsay@icf.com

Data Quality 201: Bed Coverage

Mike Lindsay, ICF

Alissa Parrish, ICA