hmis for point in time data collection september 13-14, 2005 st. louis, missouri sponsored by the...
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HMIS for Point In Time Data Collection
September 13-14, 2005St. Louis, Missouri
Sponsored by the U.S. Department of Housing and Urban Development
Lea Jackson, Kansas City Metro Area
Linda Murray, Fresno, CA
Cindy Namer, State of Maine
Darlene Mathews, Washington, DC
Kansas City Continuum of CarePoint in Time Count
September 13-14, 2005St. Louis, Missouri
Sponsored by the U.S. Department of Housing and Urban Development
Lea Jackson Kansas City Metro Area
September 13-14, 2005 St. Louis, MissouriSponsored by the U.S. Department of Housing and Urban Development
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Homeless Counts in Kansas City
• Point in Time still collected on paper• Network not originally geared as an HMIS• 200 online agencies• 90% real time bed coverage• Shelter counts available any time by running
reports for “Bed Night” service• Homelessness often documented by Emergency
Assistance agencies
September 13-14, 2005 St. Louis, MissouriSponsored by the U.S. Department of Housing and Urban Development
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Collection Considerations
• Can we ever escape entirely from paper collection forms?• How to combine & “de-duplicate” data from more than one
information system?• Street Count best practices
• How many volunteers? • Mode of collection – Paper or PDA?
• Is anyone using HMIS to collect the Housing Inventory section of the report?
• What is important to your agencies and their local funders?
Fresno Madera Continuum of CarePoint in Time Count
September 13-14, 2005St. Louis, Missouri
Sponsored by the U.S. Department of Housing and Urban Development
Linda C. MurrayInformation Technology Services Manager
Housing Authorities of the City and County of FresnoFresno Continuum of Care, CA
September 13-14, 2005 St. Louis, MissouriSponsored by the U.S. Department of Housing and Urban Development
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Homeless Count and Survey
• 2003-Homeless Access to Care SurveyPoint in Time - Physical Count
Fresno County Human Services System
• 2005-Homeless Street Survey and Gaps AnalysisPoint in Time - Physical Count and HMIS
City of Fresno Consolidated Plan
September 13-14, 2005 St. Louis, MissouriSponsored by the U.S. Department of Housing and Urban Development
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Survey Plan
• Identify Survey Coordinator
• Identify Media Coordinator
• Identify Sponsor or Source for Funding-Printing, Incentives
• Define Data Collection and Reporting Goals
• Develop Survey w/ Data Elements
September 13-14, 2005 St. Louis, MissouriSponsored by the U.S. Department of Housing and Urban Development
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Survey Plan (cont.)
• Define Goals for Outreach-Media, Count, Target Areas and Populations
• Develop List of Locations
• Develop Volunteer List
• Solicit/Acquire Incentives
• Train Surveyors
• Conduct Point in Time Count
September 13-14, 2005 St. Louis, MissouriSponsored by the U.S. Department of Housing and Urban Development
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Technology
Technology Available for Data Intake
• Intake Form Scanner• Fingerprint Scanner• PDA• Card Swiping• Bar Code Readers
Technology Available for Count
• Scanner for Surveys• HMIS Data Reports
September 13-14, 2005 St. Louis, MissouriSponsored by the U.S. Department of Housing and Urban Development
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For More Information Visit our Website at:
www.thecontinuumofcare.org
State of MainePoint in Time Count
September 13-14, 2005St. Louis, Missouri
Sponsored by the U.S. Department of Housing and Urban Development
Cindy NamerState of Maine
September 13-14, 2005 St. Louis, MissouriSponsored by the U.S. Department of Housing and Urban Development
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Maine PIT History
• Most comprehensive version in Maine has been done by City of Portland for several years
• In the last 2 years, we have accomplished a statewide survey collaborating with the 3 Continua
• Now looking to integrate core purpose data collection into statewide HMIS.
September 13-14, 2005 St. Louis, MissouriSponsored by the U.S. Department of Housing and Urban Development
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HMIS – Point In Time Integration Advantages
• Collect a universal set of information in conjunction with what HUD is already requiring.
• Allows for data analysis for any “point in time” as opposed to being limited to one 24-hour period
• Will more effectively capture the true # of homeless clients that float in and out of homeless situations throughout the year
• Will reduce # of person hours and paperwork needed to complete the current Point In Time Survey
• In Maine, integration will alleviate agency staff burden by also facilitating the semi-annual survey and the monthly occupancy reporting required for funding.
September 13-14, 2005 St. Louis, MissouriSponsored by the U.S. Department of Housing and Urban Development
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HMIS – Point In Time Integration Disadvantages
• At a minimum, all agencies providing emergency shelter must be online and actively submitting data to an HMIS
• At a minimum, street outreach programs must be refined, documented, and occurring on a regular, ongoing basis.
• Agencies must be performing ongoing data quality procedures.
September 13-14, 2005 St. Louis, MissouriSponsored by the U.S. Department of Housing and Urban Development
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Data as an Ongoing Process
• Do analysis of your current Point In Time Survey• Why are you collecting the data in your PIT Survey?• Can you get the information somewhere else?• Pick the experts – if you are contracting with someone to
organize your Survey, utilize their skills in this analysis.• Review your reporting expectations
• Who are your audiences?• What are you trying to say to them?• What is the format you want to use to communicate
information?• Who is going to be responsible for communicating the
information and how often?
September 13-14, 2005 St. Louis, MissouriSponsored by the U.S. Department of Housing and Urban Development
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Process for Integrating Point In Time with HMIS
• Map the data elements you are trying to collect in a PIT Survey to the HUD data elements, or your jurisdictions current required data set.• See handout # 1 for Maine’s mapping
• Have your CoC(s) review data elements that cannot be mapped to your current required data set, and assist them in determining if the data is valuable enough to collect on a daily basis.• See handout # 2 for questions under review
• Develop a timeline for implementation• Identify areas needing refinement or coverage and
integration in a methodical manner
September 13-14, 2005 St. Louis, MissouriSponsored by the U.S. Department of Housing and Urban Development
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Implementation and Beyond
• Develop a timeline for implementation• Do at least one survey both manually and through the HMIS• Make sure that your subsystems are in place and ready to
collect or accept data• Identify areas needing refinement or coverage and
integration in a methodical manner• Look for inconsistencies between the HMIS data and the
manual survey• Are there calculation or interpretation inconsistencies?• Are you collecting information from other sources?
– If so, why?– Assess the value and pertinence of the additional data
in the context of your current survey expectationsExample: Collecting information from local Community Action
Agencies may be easily accessible, but these are more likely to be serving At Risk of Homeless clients, as opposed to actual homeless clients. While this is valuable information, the Point In Time survey may not be the best avenue for collection or reporting
September 13-14, 2005 St. Louis, MissouriSponsored by the U.S. Department of Housing and Urban Development
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Maine’s Current Timeline
• Data collection of PIT data elements implemented by September 2005
• Dual Survey (manual and HMIS) in January 2006
• Refinement of data collection process
• Release of Annual Report on Homelessness by March 2006
HMIS for Point In Time Data Collection in Washington, D.C.
Darlene Joseph MathewsThe Community Partnership for the Prevention of
HomelessnessWashington, D.C.
September 13-14, 2005St. Louis, Missouri
Sponsored by the U.S. Department of Housing and Urban Development
September 13-14, 2005 St. Louis, MissouriSponsored by the U.S. Department of Housing and Urban Development
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Point In Time in Washington, D.C.
• The District of Columbia has conducted Point in Time enumeration for five years.
• The report is produced by the Metropolitan Washington Council of Governments, and the Community Partnership’s Deputy Director has served as the principal author and analyst.
• The enumeration covers Washington D.C. and the surrounding region which includes eight smaller Continua of Care.
September 13-14, 2005 St. Louis, MissouriSponsored by the U.S. Department of Housing and Urban Development
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Creating a Roadmap to Successful Data Collection
• Determine what you seek to track.
• In D.C. we looked at demographic information on the homeless population broken down by:
• Chronically Homeless (2002 and forward)• Emergency Shelters• Transitional Shelters• Permanently Housed
• And categorized by Individuals or Persons in Families
September 13-14, 2005 St. Louis, MissouriSponsored by the U.S. Department of Housing and Urban Development
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Additional Categories & Subpopulations Tracked
• Housing Needs• Employment Status• Gender• Chronic Homeless Status• Physical Disability
• Substance Abuse• Seriously Mentally Ill Dual
Diagnosis• Veteran• HIV/AIDS Status• Domestic Violence• Youth• Chronic Health Problem• Language Minority
September 13-14, 2005 St. Louis, MissouriSponsored by the U.S. Department of Housing and Urban Development
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Develop Goals for Analysis
• Accurate distribution of homeless population across the region
• Changes and trends in the population over the time
• Meaningful Gaps Analysis
• Provide the COG Board and public with good information about the homeless population in the D.C. area.
• Counteract the public image that the homeless are primarily street people.
September 13-14, 2005 St. Louis, MissouriSponsored by the U.S. Department of Housing and Urban Development
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The Ultimate Goal
Over time, as we track trends in distribution of beds, housing needs and other variables, 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.
September 13-14, 2005 St. Louis, MissouriSponsored by the U.S. Department of Housing and Urban Development
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Point In Time Configuration
A Hybrid Model
• In 2005, all programs within the Continuum of Care reporting to the Partnership were required to use HMIS to complete the PIT survey.
• 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.
September 13-14, 2005 St. Louis, MissouriSponsored by the U.S. Department of Housing and Urban Development
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The D.C. Point In Time Process Step 1
Create Assessment
Our System Administrator created a Point in Time survey assessment that is user friendly and accurately captures all the information we sought to collect.
September 13-14, 2005 St. Louis, MissouriSponsored by the U.S. Department of Housing and Urban Development
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September 13-14, 2005 St. Louis, MissouriSponsored by the U.S. Department of Housing and Urban Development
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The D.C. Point In Time Process Step 2
Training, Training, Training
•The actual count was derived from bed lists so Providers were first trained on how to enumerate their beds.
•Providers were then trained on how to use the Survey Assessment
•Reducing a high null count by ensuring that each question was answered was instrumental to obtaining an accurate count.
September 13-14, 2005 St. Louis, MissouriSponsored by the U.S. Department of Housing and Urban Development
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The D.C. Point In Time Process Step 3
Data Quality Control
On January 26, D.C. the Partnership ran bed list reports for all Providers including hypothermia sites. These lists were printed out and sent via mail to Providers to verify the names and numbers of persons with their internal rosters.
Providers were then given one month to clean their bed lists and complete a survey assessment on each client in their program.
September 13-14, 2005 St. Louis, MissouriSponsored by the U.S. Department of Housing and Urban Development
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The D.C. Point In Time Process Step 4
Data Quality ControlAfter bed lists were cleaned up and an accurate count of persons was obtained, the Partnership ran the Point in Time assessment for each residential program to make certain that the count of assessments per site was equal to the verified count from the bed list for that day.
If the number of assessments were greater or less than the bed list count, Providers were required to go back to their data, find out why, and correct the data so that:
the # of assessments = # reflected in the bedlist count for that day.
September 13-14, 2005 St. Louis, MissouriSponsored by the U.S. Department of Housing and Urban Development
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The D.C. Point In Time Process Step 5
Check Sum QueriesSix check sum queries were then run to get a count based on bed lists broken down by emergency, transitional and permanent programs and categorized by Individuals of Persons in families.
This sum was checked against the aggregate number of persons that came out of running the Point in Time assessment for each sector of the Continuum.
When numbers did not reconcile, it was often because of internal HMIS architecture issues that the Partnership had to correct.
September 13-14, 2005 St. Louis, MissouriSponsored by the U.S. Department of Housing and Urban Development
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The D.C. Point In Time Process Step 5, Cont’d
Filtering for Families-A tricky business
In order to get accurate information on subpopulations, we had to filter for 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.
September 13-14, 2005 St. Louis, MissouriSponsored by the U.S. Department of Housing and Urban Development
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The D.C. Point In Time Process Step 6
Our 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.
September 13-14, 2005 St. Louis, MissouriSponsored by the U.S. Department of Housing and Urban Development
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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.
• We realized after completing Point in Time that our assessment was date stamped. When we ran the Point in Time on another day using the same assessment, the null values problem re-emerges.
September 13-14, 2005 St. Louis, MissouriSponsored by the U.S. Department of Housing and Urban Development
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Benefits of Using HMIS for Point In Time
• Ultimately it should make the process easier
• Great process to reconcile what we know 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
September 13-14, 2005 St. Louis, MissouriSponsored by the U.S. Department of Housing and Urban Development
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Tips for Conducting a Successful Point in Time
• Have a solid data collection system in place
• Create a framework and timeline for completing tasks
• Take a Point in Time Count more often to keep your Continuum in the practice of keeping track of data elements and avoid null values
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