# Presentation: 038302

## HMIS as a Tool for Informing CoC Planning

- HMIS as a Tool for Informing CoC Planning

## Welcome and Introduction

- 	Facilitator
*	**Matt White, National HMIS TA Initiative, Abt Associates*
- 	HUD Presenter
*	**Mike Roanhouse, Office of Special Needs Assistance Programs, HUD *
*	*Presenters* *
*	**Michelle Hayes, National HMIS TA Initiative, Abt Associates**Darlene Mathews, The Community Partnership, Washington, D.C. **Matt Berg, City of Philadelphia, Office of Supportive Housing*

**Notes:**
Facilitator:

Welcome session participants.

Introduce self, HUD presenter, and CoC representative.

Housekeeping details:

Session is structured to involve participants in a discussion and to allow time for questions and answers, following a relatively short presentation.  Please hold questions and comments until the discussion and question and answer portions of the session.

Session evaluations have been placed on each seat.  The evaluations can be completed at the end of the session and left on the chairs or deposited in a designated spot for collection by conference staff. 

After the session, participants should proceed to the Grand Ballroom Foyer for the morning break and return to this room for the CoC Management session.

## Key CoC Planning Questions

- What programs and/or combinations of services are most effective at preventing and ending homelessness?
- What is the utilization rate of beds for homeless individuals and families?
- How many persons are living on the streets and in shelter?
- How many unduplicated people are served by our CoC?
- How do we know that programs are meeting their stated objectives? How do we make priority decisions for funding under SuperNOFA?

## Putting the puzzle pieces together

- Making sense of HUD data requirements:
- Housing Inventory Charts
- Point in Time Counts
- HMIS counts and
- Benchmarking and measuring progress in CoC Strategic Plan
- Prioritization decisions in SuperNOFA application

## Housing Inventory Charts 

- Purpose: to understand bed capacity of the shelter system at the local and national level
- Data Required
- Annual capacity with seasonal and overflow beds for:
- emergency shelter
- transitional housing, and
- permanent supported housing
- Program level data on location
- Capacity
- Target population served
- Extent of HMIS data collection

## Importance of HIC for CoCs

- Annual reassessment of capacity to provide beds to homeless individuals and families regardless of funding source
- Geocode (zip code) information information allows for location trending, can be correlated with last known address to identify gaps in service location
- HMIS participation and number of beds entering data into system feed into calculations of coverage (i.e. knowing when the data are valid and reliable)
- Over time you can document system change trends

## Purpose of HIC for HUD 

- Annual reassessment of capacity to provide beds to homeless individuals and families regardless of funding source
- Simply understanding the capacity of the system is a very powerful statistic
- Geocode (zip code) identifies over/under served neighborhoods or locations
- HMIS participation and number of beds entering data into system feed into calculations of coverage and generation of Annual Homeless Assessment Report (AHAR)
- Over time you can document shifts in overall system change (i.e. reduction in shelter beds increase in permanent housing beds)

## Point In Time Counts 

- Purpose
- To calculate a one day point in time count of sheltered and unsheltered homeless persons
- Data required:
- Statistically reliable or estimated counts of sheltered and unsheltered adults, children, and youth recorded in populations/subpopulations chart

## Importance of PIT Counts for CoCs 

- One day count of homeless men, women, and children in the CoC
- Count of persons living on the street that may not come in contact with other CoC programs
- Capacity and utilization of individual shelter/housing programs at one point in time
- Annual one day figure that can be used to document trending (annual increase/decrease of homeless population)

## Importance of PIT Count for HUD

- Annual calculation of sheltered and unsheltered homeless persons across the nation
- Allows for trending analysis of increase/decrease in nation’s homeless population
- Documents number of persons not served in shelter/housing
- Limitations to use as a result of allowable “estimate”

## HMIS 

- Purpose
- To capture client level data over time on the characteristics and service needs of homeless population to inform CoC planning and decision making and development of Annual Homeless Assessment Report
- Data, Privacy, and Security Requirements
- As specified in Data and Technical Standards Final Notice: www.hud.gov/offices/cpd/homeless/hmis/standards/

## Importance of HMIS for CoCs

- Annual client level data on all clients served across all programs in CoC
- Characteristics and length of stay
- Service utilization across programs
- Unduplicated count of clients served
- Allows CoC to evaluate effectiveness of individual programs and prioritize decisions in SuperNOFA process
- Ability to benchmark and measure progress on local CoC plans to end homelessness

## Importance of HMIS for HUD

- Annual CoC level data on unduplicated count of clients served with service use patterns
- Generation of Annual Homeless Assessment Report
- Ability to meet GPRA goals
- Ability to benchmark and measure progress on Administration Goals

## Community Uses of HMIS 

- District of Columbia
- Project Prioritization in SuperNOFA
- Point in Time Counts
- Philadelphia
- Bed capacity management
- Ongoing program and system level monitoring
- Performance contracting

## Uses of HMIS for SuperNOFA Project and Generation of Point in Time Counts 

- Darlene Joseph Mathews
- The Community Partnership for the
- Prevention of Homelessness
- www.community-partnership.org
- Washington, D.C.

## The Community Partnership Background

- Since 1999, the Community Partnership has used various databases and software to manage data
- One of the first “Tier 1” agencies to pilot HMIS
- Today our HMIS has over 200 programs, 300 users and over 50,000 clients entered

## SuperNOFA Measurement Method

- Purpose: To create a comprehensive performance measurement method to inform the funding and ranking process
- Introduced in 2005
- Compares like to like programs
- Sets benchmarks for program outcomes

## Method

- DC organized our renewal programs to create like to like groupings for comparison and ranking
- We set benchmarks for outcomes that were slightly above HUD standards outlined in the NOFA
- We exported all raw data into Excel to perform calculations and analysis
- Each program was given a quantitative score based on these three measures:
- 		1.	Occupancy,
- 		2.	Change in Income,
- 		3a.	Destinations and Exit  or
- 		3b.	Stability in the Program (for PH programs)
- This process was also used to inform the funding of our District programs and used again this year for the NOFA ranking process

## Method, Cont’d

- _Occupancy Score _–Based on client entry and exit information
*Calculation: Number of occupied bed nights/Number of total bed nights in the period*
- _Change in Income_- Based on cash and non-cash benefits tracked through HMIS
*Calculation: Change in the number of income sources for a client after entry/total clients served*
- _Destination-_ Based on Exit Sheet
*Calculation: Positive Destinations/total destinations*
- _Stability-_Based on Entry and Exit Sheets
*Calculation: Length of Stay > 6 months/number of clients in program*

## Sample Occupancy Spreadsheet

## Sample Income Spreadsheet

## Sample Destination Spreadsheet

## Sample Quality Rank Spreadsheet

## Benefits of the NOFA Measurement System 

- Fair and balanced way of comparing like to like programs
- We were able to change benchmarks and thresholds for various levels of the Continuum
- It can give you a good idea of how an individual program is performing as well as an aggregate snapshot of multiple programs

## Steps to Effective Project Prioritization

- _Organization-_We took time to formulate and structure the type of queries and reports that we need to develop for ongoing reporting and measurement
- _Timelines-_We identified the manner in which we want our data presented, the time frame for producing it and the steps in developing it
- _Technical Assistance-_We hired a consultant who completes user trainings every month on HMIS. Training topics range from basic, intermediate and advanced use, to reporting capabilities and better data entry tips.

## Point in Time

- The District of Columbia has conducted Point in Time enumeration for six years
- Aggregate DC numbers are added with regional numbers to produce the Metropolitan Washington Point in Time Report on behalf of the Council of Governments
- Goal is to track trends in distribution of beds, housing needs and other variables
- Data collected from:
- Street Outreach 	Permanently Housed
- Emergency Shelters	Transitional Shelters
- And categorized by Individuals or Persons in Families

**Notes:**
 we are trying to illustrate the “problem” by identifying the amount of  people on the streets in emergency and transitional shelters, but also the “solution” as we add to the numbers of people inside the Continuum residing in permanent supportive housing.

## Additional Categories & Subpopulations Tracked

- Housing Needs
- Employment Status
- Gender
- Chronic Homeless Status
- Physical Disability
- Mental Disability

- Veteran Status
- HIV/AIDS Status
- Domestic Violence
- Youth
- Chronic Health Problem
- Language Minority
- Housing needed today (for gaps analysis)

## Participation Requirements

- All CoC Grantees contractually required to participate and enter data in HMIS
- All private organizations operating programs in the D.C.  Continuum, but not contracted with the Partnership were asked to complete paper forms and spreadsheets with the necessary information.

## The Process 

- Step 1: Custom HMIS Screen
- Custom Point in Time survey assessment that is user friendly and accurately captures all the information we sought to collect
- Step 2: Training and Communication
- The actual count was derived from bed lists so refresher trainings were held on how to enumerate bedlists & use the Survey Assessment
- The process was also reinforced through emails, quick reference guides and ongoing technical assistance
- Contractual obligation to participate in the process

## The Process, Con’t

- Step 3: Data Quality Control
- On January 26, the Partnership ran bed list reports and distributed to all Providers including hypothermia sites.
- Providers were then given two weeks to clean bed lists and complete a survey on each client in their program.
- Point in Time assessments were then run for each residential program
- If the number of assessments were greater or less than the bed list count, Providers were required to go back to their data and correct the data so that the # of assessments = # reflected in the bedlist count for that day.

## Null Value Report

## Process, Con’t

- Step 4: Filtering for Families
- A filter was run to identify children.
- An additional query was run to filter for clients 18 and over to separate children from adults.
- To ensure our numbers were accurate, the total count for children and adults had to equal the sum reported in the bed list. If the numbers didn’t match up, we had to search for the answer.
- Step 5: Findings
- After we were confident in our numbers, we exported data from HMIS into Excel for more in depth data analysis and compiled it with data submitted by private agencies.

## Outreach Agencies

- In 2005 the Partnership created an Outreach Assessment for our providers
- It tracks service transactions for clients
- Outreach Providers were asked to complete a Point in Time Assessment on every client they have served within the last 90 days that they believed to be sleeping on the street on January 25

## Outreach Agencies

- We exported all of the outreach agencies’ information and deduplicated clients using MS Excel
- We then compared our outreach results to our emergency shelter results and were able to deduplicate which clients that were believed to be on the streets actually entered Emergency Shelter

## Difficulties Utilizing HMIS for Point in Time 

- Bed enumeration and basic data entry must be completed properly and monitored often otherwise Point in Time can be extremely difficult.
- Providers were not completing the entire assessment and leaving a lot questions blank. We had to create a null value report to send to the Provider to show them all the questions that needed to be answered.

## Benefits of Using HMIS for Point in Time

- Ultimately it should make the process easier
- Great process to reconcile what we know on on a micro level within our programs and what is in HMIS
- Forces Providers to make sure their data are timely and correct
- Process highlights structural problems with the way HMIS and queries are set up
- Identifies Provider specific problems

## Tips for Conducting a Successful Point in Time

- Have a solid data collection system in place
- Create a framework and timeline for completing tasks
- Have data quality control measures in place to ensure your data are accurate

## Our Results The Problems We Face

- There are **6,157** homeless individuals & persons in families in the District.
- Today in the District of Columbia, 11 of every 1,000 persons are homeless.

## The Progress We are Making

- The District’s Permanent Housing inventory is steadily increasing as indicated in the trend line in the chart below.  Permanent Housing is seen as the SOLUTION to homelessness.
- The District’s 10 year plan goals calls for a continued increase in the development of Permanent Housing units.

## Where We Are Today

- TCP will be undertaking the development of new Custom reports to be used on a quarterly basis to conduct performance measurement using the ART- Advanced Reporting Tool
- To reduce the time intensive aspect of performance measurement we plan on using the scheduling function to run a report on:
- HUD required data elements
- Length of stay, Occupancy and Income
- Report could be run by in the aggregate and broken out by program
- TCP will continue to use HMIS for generation of PIT counts

## City of Philadelphia—Managing for Results with HMIS 

- Matt Berg, City of Philadelphia,
- Office of Supportive Housing

## OSH Agency Functions

- Street Outreach/Chronic Homeless
- Relocation & Prevention
- Intake/Reception
- Emergency Shelter
- Transitional Housing
- Permanent Housing

- Protective Services
- Riverview (Personal Care Facility)
- Case Management
- Domestic Violence Initiatives
- HIV Planning
- Emergency Response

## HMIS Background/Status

- HUD funding applied for and granted
- Requirements, RFP, vendor selection, contracting
- 24 JAD (Joint Application Development) sessions, customization, testing, implementation with parallel systems and nightly batch conversion
- Turned off antiquated “green screen” system in 4/04
- Implemented at Reception Sites
- Implemented at Emergency Shelters
- Beta Testing with Transitional Housing Providers

## Philadelphia’s Homeless System

- Centralized Reception 24/7
- 40 Emergency Shelter programs ~3,000 beds
- 70 Transitional Housing programs ~3,800 beds
- 90 Permanent Supportive Housing programs  ~5,200 beds
- Daytime Intake and Relocation functions staffed by City employees, Shelters and Housing Services are contracted

## Strategic Vision

**HMIS**

**People**

**Stat**

**Performance**

**Based Contracts**

**Ten Year **
**Plan**

**Program**

** ****Monitoring**

** ****and Evaluation**

**Community**

** ****of Practice**

**List of Goals to End Homelessness**

**Goal 1: **

- Open the "back door" out of homelessness

- - ensure that all Philadelphians have a decent, safe,

- accessible and affordable home.

**Goal 2:**

** **

- Close the "front door" to homelessness -

- implement successful prevention strategies.

**Goal 3:**

** **

- Ensure that no one in Philadelphia needs

- to live on the street.

**Goal 4:**

** **

- Fully integrate all health and social

- services to aid in preventing and addressing

- homelessness.

**Goal 5:**

** **

- Generate the political will, civic support,

- and public and private resources to end

- homelessness.

**Goal 6:**

** **

- Build human capital through excellent

- employment preparation and training programs, and

- jobs at a livable wage.

**Goal 7:**

** **

- Make shelters a dignified place for

- emergency assistance, not a destination.

**Goal 8:**

** **

- Support families and individuals to

- promote long-term independence and eliminate

- recidivism.

**- Philadelphia's Ten Year Plan to End**

**Homelessness Final Draft - Page 3.**

**Consistent**

**with Goals**

**Client**

**Better Off**

**DSS**

**CARES**

## Ten-Year Plan to End Homelessness

- Philadelphia’s Ten-Year Plan to End Homelessness was approved by the Mayor on October 12, 2005
- The Ten Year Plan provides governance for Continuum of Care activities
- 325 community representatives played an active role in creating the Ten-Year Plan
- Data will be used to support plan:
- How  will HMIS/ other data support the Ten-Year Plan?
- How will progress toward meeting goals be tracked using data?

## Realtime Dashboard –Client Demographics

## Realtime Dashboard—Operational Metrics

## PeopleStat Performance Management

- PeopleStat is a performance management process that...
- Uses performance data to inform discussions with providers on “are the people we serve better off?”
- Helps identify both best practices and problematic issues   to shape strategy and guide action
- Paves the way to Performance Based Contracting by developing a reliable data collection and reporting system and valid measures of success
- Helps us end homelessness

## Basic Structure

- Development of Action
- Plans as needed
**PLAN**

- Continuous reporting
- & monitoring**CHECK**

- Implementation of new strategies
- to enhance performance &
- improve client outcomes**DO**

- PeopleStat
- Regular meetings scheduled**ACT**

## Performance Measurement 

**Ask focused questions in progressive steps:**
*What did we do?*
*How well did we do it?*
*Is anyone better off?*
- 	Make progress step by step Starting with question 1

## Success Measurement Model Based on Friedman’s Results Based Accountability (RBA) model (www.resultsaccountability.com & www.raguide.org)

_**How much did we do?**_

_**How well did we do it?**_

_**Is anyone better off?**_

- # Clients    served

- # Activities (units of service)

- % change in a client population

- (Indicators)

- (Performance Measures)

- # clients who experienced improvement

## Our Process

- We are beginning to use HMIS to answer question #1 reliably:           			***What did we do?***
- We are defining and gaining agreement on measures/indicators of success in the PeopleStat meetings
- We intend to progress through the 3 questions step by step

## Insights - Analyzing the Data

- Examine the data together
- Identify trends and patterns
- Breakout data for greater insights
- Discuss reasons for the trends
- Discuss potential ways to improvedata quality, client outcomes and our system

## PeopleStat Data Examples

- Review of system-level and program-level data
- Shelter occupancy trends
- Attendance reporting
- Zip code analysis
- Program utilization rates
- Average length of stay
- Changes in client savings
- Changes in client income

## Data Examples: Census Trends for 2003-2006

## Data Example: Attendance

- Providers A & B are moving towards the goal of having the HMIS and manual client counts in sync
*What is working for these providers?*
- Provider C is not yet hitting the mark...
*How can we support them (training, etc)*

## Data Example: Zipcode AnalysisJanuary 31, 2005-December 31, 2005

**Total HOH: 17,313**
**No Data/Invalid Records: 12,053**
- With more complete data we can make a better argument for allocating resources and facilities based on geographic region
- Focus on Intake collecting this data consistently
- Made Zipcode field mandatory

**1,337**

**819**

**1,****318**

**676**

**774**

## Data Example: Utilization Rates

- Data allows us to see which providers are working at, below, or above capacity
- Enables us to identify client needs (for instance, more Drug & Alcohol programs for Singles) and alter our resources accordingly

## Data Example: Length of Stay

- Of these 7 programs, the average length of stay was 268 days or 9 months
- Suggests management should consider whether 12 months is an appropriate time period

## Data Example: Client Savings

- Client Savings designed to provide assistance after exiting shelter
- Client savings program is mandatory, so Providers A & D must review & explain their data

## Data Example: Client Income

- Represents variance in Client income at program entrance & program exit
- Clients with higher income levels will have more housing options available

## Questions?