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

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

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

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

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)

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

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

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.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Any More ??? Alissa Parrish, alissa.parrish@icalliances.org 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