1. Data Quality 201: Bed Coverage and Strategies to Improve
Data Quality 201: Bed Coverage and Strategies to Improve. – the community experience.
Mike Lindsay, ICF
Alissa Parrish, ICA

2. Data Quality 201: Bed Coverage
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

3. Session Overview/Agenda
Data Quality 201: Bed Coverage
Mike Lindsay, ICF
Alissa Parrish, ICA
Session Overview/Agenda
SNAPS Data Strategy Discussion
HMIS Participation
Strategies
Community Examples

4. 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

5. 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

6. 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

7. 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)

8. SNAPS Strategic Goal #2 cont.
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

9. SNAPS Strategic Goal #2 cont.
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

10. 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.

11. HMIS Participation Data Quality 201: Bed Coverage Mike Lindsay, ICF
Alissa Parrish, ICA
HMIS Participation

12. What’s the Effect of Low HMIS Bed Coverage?
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

13. What’s the Effect of Low HMIS Bed Coverage? cont.
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

14. Bed Coverage Rate – The Basics
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

15. Factors Leading to Poor 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

16. Factors Leading to Poor HMIS Bed Coverage cont.
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

17. Factors Leading to Poor HMIS Bed Coverage cont.
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

18. How? Data Quality 201: Bed Coverage Mike Lindsay, ICF
Alissa Parrish, ICA
How?

19. Increasing HMIS Bed Coverage
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

20. Increasing HMIS Bed Coverage cont.
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

21. Engaging Faith Based Organizations
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

22. Talking Points: Engaging Faith Based Organizations
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

23. Additional Tips to Consider
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

24. Additional Tips to Consider cont.
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

25. Can Technology Improve Coverage?
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

26. Community Examples Data Quality 201: Bed Coverage Mike Lindsay, ICF
Alissa Parrish, ICA
Community Examples

27. Anchorage CoC – Rescue Mission
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

28. Anchorage CoC – Rescue Mission
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

29. Anchorage CoC – Locally Funded Projects
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

30. Alaska BoS CoC – Overall Coverage
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

31. Boise City / Ada County CoC – Non-HUD-funded Mission
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

32. Boise City / Ada County CoC –
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

33. Any More ??? Alissa Parrish, alissa.parrish@icalliances.org
Data Quality 201: Bed Coverage
Mike Lindsay, ICF
Alissa Parrish, ICA
Any More ???
Alissa Parrish,
Mike Lindsay,