Data Quality Tools & Best Practices Matthew D. Simmonds, Simtech Solutions Inc.

Slides:



Advertisements
Similar presentations
Presents: The Blue Ridge H M I S.
Advertisements

Introduction to Homeless Management Information Systems (HMIS)
2014 HUD Data Standards. New & Active Clients All ESG, CoC and SSVF funded agencies are required to begin collecting data on new and active clients based.
North Carolina Housing Conference October Focus of COC.
HUD Homeless Program Data
Denver’s Road Home EVALUATION SYSTEM. Evaluation System Parameters Population Counts Change in Status Change in Income Access to Service Frequency of.
September 18-19, 2006 – Denver, Colorado Sponsored by the U.S. Department of Housing and Urban Development Garbage In, Garbage Out: Strategies to Ensure.
HMIS Homeless Management Information System. MISSION To provide standardized and timely information to improve access to housing and services, and strengthen.
2015 Point In Time Count: Broward County CoC Plan to End Homelessness
Homeless Management Information System Donna Curley – HMIS Project Manager.
HMIS Fundamentals HMIS Data Standards for VA Community Contract Programs.
Homeless Management Information Systems (HMIS) Data Quality: Practical Strategies and Tips for Improving Data Quality.
Supportive Services for Veteran Families (SSVF) Data
Supportive Services for Veterans and Families Developing a Comprehensive Data Quality (DQ) Plan Updated 9/14.
Data Standards and Reporting 2014 Updates and what they mean for your programs.
HMIS Homeless Management Information System Part 1: Privacy & Ethics.
MDHI Community Meeting on HMIS Priority Communities Initiative May 13 th and 14 th, 2015.
1 The Point in Time Enumeration Process in Washington, D.C. Darlene Mathews The Community Partnership for the Prevention of Homelessness
Think Change Be Change Lead Change CT PIT 2013 Program Staff Training January 2013 Training PowerPoint Provided by CCEH CT Coalition to End Homelessness.
2014 Homeless Management Information Systems (HMIS) Data Standards for ESG Presented by Melissa Mikel September
Supportive Services for Veteran Families (SSVF) Data Data Collection & Reporting: Basics Updated 5/22/14.
Conducting Better Point-in-Time Counts of Homeless Persons Erin Wilson Abt Associates Inc. Washington, DC July 9, 2007.
Supportive Services for Veteran Families (SSVF) Data HMIS Lead and Vendor Training Updated 9/14.
It’s Not Just Numbers: Implementing Point-in-Time Counts, Using HMIS, and Ensuring Data Accuracy Erin Wilson, Abt Associates Inc. Julie Eberbach, Iowa.
Think Change Be Change Lead Change CT PIT 2014 Permanent Housing Project Training January 2014 Training PowerPoint Provided by CCEH CT Coalition to End.
Supportive Services for Veteran Families (SSVF) Data Data Collection & Reporting Basics.
Think Change Be Change Lead Change CT PIT 2014 Emergency Shelter Project Staff Training January 2014 Training PowerPoint Provided by CCEH CT Coalition.
STATEWIDE COORDINATED ASSESSMENT WORKING GROUP June 5, 2013.
Supportive Services for Veterans and Families Developing a Comprehensive Data Quality (DQ) Plan.
Think Change Be Change Lead Change CT PIT 2014 Transitional Housing Project Training January 2014 Training PowerPoint Provided by CCEH CT Coalition to.
2010 Florida HMIS Conference 1. Using HMIS to Inform Performance Measurement Outcomes Objective: –Enhance awareness and understanding on using HMIS to.
Community Perspective: Using Research and Technology to Identify Effective Solutions to Prevent and End Homelessness Michelle Hayes, The Cloudburst Group.
New England Region Homeless Management Information System PATH Integration Into HMIS Richard Rankin, Data Remedies, LLC Melinda Bussino, Brattleboro Area.
Point-in-Time Count January What Does It Mean to Count Homeless People? A “count” = collecting information about the sheltered and unsheltered homeless.
Tools and Techniques for Ensuring an Accurate HMIS Implementation Matthew D. Simmonds President Simtech Solutions, Inc. October 12, 2012.
HMIS Data Quality Training 211 Orange County. Learning Objective This training is scheduled for 2 hours. Objective 1.Teach users how to find deficiencies.
New England Regional Point in Time Report How Data Can Inform a Regional Approach to Preventing & Ending Homelessness Matthew D. Simmonds, Simtech Solutions.
Distributed Reporting A Cost-Effective Alternative to Data Warehousing Matt Simmonds, Simtech Solutions John Yazwinski, Father Bill’s & MainSpring.
2014 HMIS Data Standards Overview HMIS Data Standards Background – Key resources – Implementation Timeline – Revision Process Overview of Key.
Conducting, Analyzing and Using Point-in-Time Counts Point-in-Time Counts Presented By: Matthew D. Simmonds, Simtech Solutions Inc. NAEH Annual Conference.
Friday, February 13, 2009 Matthew D. Simmonds, President, Simtech Solutions Inc. National Alliance to End Homelessness Conference.
Tuesday, November 18, 2008 Robert Pulster, Executive Director of the Governor’s Interagency Council on Housing and Homelessness & Matthew D. Simmonds,
Regional Approaches to Coordinated Assessment, Prioritization and Housing Placement Eddie Barber, Simtech Solutions Inc. Gary Sanford, Metro Denver Homeless.
PIT/HIC Data Entry and Reporting
KYHMIS BOS Quarterly Meeting
Point in Time Count/Housing Inventory Count Presentation
What is the Continuum of Care?
HUD Advanced Homeless Data Users Meeting
AHAR (2016) Orientation Michigan Statewide Homeless Management Information System Overview of the Data Collection and Submission Process for the Annual.
Detroit Continuum of Care (CoC) 2017 HIC and PIT Count
2017 HIC & PIT January 26, 2017.
Welcome to the Webinar Once you join the webinar you will need select the phone symbol on the top left hand corner of the screen to connect the audio Select.
Restructure & Repurpose 2017
Connecticut Coalition to End Homelessness
2017 HMIS Data Standard Changes
What is the Homeless Point-in-Time Count?
Minnesota’s Homeless Management Information System (HMIS)
Minnesota’s Homeless Management Information System (HMIS)
Featuring: Kristen Rodgers, CommUnityCare Street Medicine Team
September 15, 2008 Resource Coordinator Training
Annual Homeless Point-in-Time & Housing Inventory Count
Ending Family Homelessness in Cuyahoga County
Annual Performance Report (APR) Training:
Point in Time Count & Housing Inventory Count Final Report 2018
Programs Serving Residents Experiencing Homelessness
KC METRO HMIS Training PATH.
Working Together: Domestic Violence and Homelessness Services Coordination: Connecticut’s Approach July 25, 2018.
KC METRO HMIS Training Emergency Shelter.
Blue Ridge Behavioral Healthcare
HMIS Support.
Presentation transcript:

Data Quality Tools & Best Practices Matthew D. Simmonds, Simtech Solutions Inc.

The Different Levels of Data Cleansing Continuum 2 Org 2Org 1Org 3Org 4Org 5Org 6 Continuum 3Continuum 1 Program 1Program 2Program 3

State Level Data Quality Techniques Periodically run reports to compare counts to housing inventory. Periodically run reports to compare counts to housing inventory. Annually run reports to compare counts to point in time figures. Annually run reports to compare counts to point in time figures. Increase the focus on data quality and provide tools and training to assist agencies improve this quality. Increase the focus on data quality and provide tools and training to assist agencies improve this quality. Increase reporting requirements via data feeds and reduce the reliance on paper. Increase reporting requirements via data feeds and reduce the reliance on paper. Check data completion stats regularly and create performance benchmarks by program type. Check data completion stats regularly and create performance benchmarks by program type. Tie HMIS to billing. Tie HMIS to billing.

Sample M-5 Data Completion Stats Report

Capacity Analysis (HMIS Vs. Housing Inv)

Auditing and Cleansing at the CoC Level Create a subcommittee to address data quality within the CoC and dedicate time at CoC meetings to share updates. Create a subcommittee to address data quality within the CoC and dedicate time at CoC meetings to share updates. Implement interim reporting “dry runs” and use HMIS to track program performance. Implement interim reporting “dry runs” and use HMIS to track program performance. Develop customizable CoC wide common intake documents that match your HMIS software as well as your local reporting needs. Develop customizable CoC wide common intake documents that match your HMIS software as well as your local reporting needs. Introduce peer-to-peer training and assistance. Introduce peer-to-peer training and assistance. Consider HMIS implementation status when ranking projects. Consider HMIS implementation status when ranking projects. Self-audit by comparing 1 day APR reports against PIT count. Self-audit by comparing 1 day APR reports against PIT count.

Sample HMIS vs. Point in Time Analysis

Data Quality at the Agency Level Data Quality at the Agency Level Dedicate at least 1 staff person to the task of ensuring the data is being collected in a timely and accurate fashion. Dedicate at least 1 staff person to the task of ensuring the data is being collected in a timely and accurate fashion. Implement interim reporting “dry runs”. Implement interim reporting “dry runs”. Use HMIS as a management tool. Use HMIS as a management tool. Customize your CoC-wide common intake documents to match your agency’s unique needs. Customize your CoC-wide common intake documents to match your agency’s unique needs. Introduce peer-to-peer training and assistance. Introduce peer-to-peer training and assistance. Run a client served report on a daily basis and compare it to the counts from the bed register. Run a client served report on a daily basis and compare it to the counts from the bed register.

Data Quality at the Agency Level Data Quality at the Agency Level Check for invalid data conditions: Long Term Disability = No and any disabilities = Yes Note: mental health and substance abuse as duration should be long term. Long Term Disability = No and any disabilities = Yes Note: mental health and substance abuse as duration should be long term. Long Term Disabilty = No and Collecting SSDI = Yes. Long Term Disabilty = No and Collecting SSDI = Yes. Client = Inactive, any other data = Active. Client = Inactive, any other data = Active. Social Security # is not null and SSN Data Quality Code = blanks. Social Security # is not null and SSN Data Quality Code = blanks. Pregnancy = Yes and Gender = Male. Pregnancy = Yes and Gender = Male. Chronically Homeless = Yes and all disabilities = No. Chronically Homeless = Yes and all disabilities = No. Household ID not null and program type = individual shelter. Household ID not null and program type = individual shelter. SSN or Zip Code Data Quality code does not match value in SSN or zip code field. SSN or Zip Code Data Quality code does not match value in SSN or zip code field.

Ensuring an Accurate Chronic Count If the client has a disability (F=Yes), and they either had 4 or more homeless episodes (G>=4) OR were homeless for greater than 1 year (J>365), and are 18 years old or older (K>=18), then count them as chronically homeless. If the client has a disability (F=Yes), and they either had 4 or more homeless episodes (G>=4) OR were homeless for greater than 1 year (J>365), and are 18 years old or older (K>=18), then count them as chronically homeless.

Use ID Cards or Name Tags to Reduce Duplicates

Use an Offline Bed Register