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Data-Driven Decision Making

Data-Driven Decision Making 2 nd Annual Growing School-Based Mental Health Summit Wisconsin Dells, WI June 13, 2017. Elizabeth Connors, Ph.D. Assistant Professor, Division of Child and Adolescent Psychiatry Center for School Mental Health University of Maryland School of Medicine.

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Data-Driven Decision Making

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  1. Data-Driven Decision Making 2nd Annual Growing School-Based Mental Health SummitWisconsin Dells, WIJune 13, 2017 Elizabeth Connors, Ph.D. Assistant Professor, Division of Child and Adolescent Psychiatry Center for School Mental Health University of Maryland School of Medicine

  2. How we often feel after attending a conference…

  3. How we sometimes feel when we return to work…

  4. Objectives • Define DDDM and Data • Review Action Steps for Making Use of Data • Review Performance Indicators and Best Practices • Minneapolis, MN Example • Change Idea Activity

  5. What is Data-Driven Decision Making? • DDDM is the process of using observations and other relevant data/information to make decisions that are fair and objective. • DDDM can help inform decisions related to appropriate student supports and be used to monitor progress and outcomes across multiple tiers (mental health promotion to selective and indicated intervention).

  6. What are Data? • Data are information! ….gathered from multiple sources such as: • students grades • performance tests • behavior checklists • attendance • suspensions • office referrals

  7. “We have oceans of data, what can it tell us about our students that will help set priorities and improve our schools?” – School District Official quoted in Parke, 2012

  8. Making Use of District and School Data • Identify broad area of interest • Create specific questions • Establish roles and trust • Make decisions about the sample, time frame and methodology • Format and present results • Outcomes and further directions

  9. Factors that Impact Using District Data • Leadership • Coherency of goals • Data system itself • Data management/analysis knowledge or skills • Quality, timeliness and perceptions of data • Time and resource support to personnel

  10. DDDM Indicators • Use data to determine services Examples: attendance, discipline referrals, classroom observation • Monitor individual students Examples: “Stepping” students up or down between tiers based on data to show their progress where they were placed.

  11. DDDM Indicators • Aggregate student mental health data …to share with stakeholders and make decisions about mental health service planning and implementation? • Disaggregate student mental health data …to examine school mental health outcomes based on sub population characteristics?

  12. Best Practices to Match Intervention with Need • Use of multiple data sources • Use of validated screening/assessment/survey tool (s) appropriate to your student population • Consistent and systematic process of using screening and assessment data to “triage” students into appropriate levels of support

  13. CSMH, 2016

  14. SUMMARY OF FREE ASSESSMENT MEASUREScsmh.umaryland.edu (Clinician Resources tab) • A t • Assessment measures that are in the public domain (free of charge) and available online for clinicians. • Table of Contents: • Clinical Measures – Global • Clinical Measures – Problem Area Specific • Academic Measure Copies of these instruments can be found here: • https://drive.google.com/folderview?usp=sharing&id=0B0GTQg4639jjVGMyd3RaOHhCQXc& ddrp=1#

  15. Definition and Purpose of Fidelity Monitoring • Fidelity assessment = indicators of doing what is intended. • This requires: A) knowing what is intended B) having some way of knowing the extent to which a person did what was intended in daily practice • FM can help you learn WHY what you’re doing or HOW you’re doing it is leading to the OUTCOMES you observe. http://implementation.fpg.unc.edu/book/export/html/626

  16. Best Practices for Fidelity Monitoring • Tools, procedures and resources to measure: • Adherence to intervention content • what is being implemented • Quality of program delivery • manner in which facilitator delivers/implements program • Logistics • conducive implementation environment • number/length of sessions implemented

  17. Balancing Fidelity and Adaptation During implementation of a selected prevention or intervention, it is important to balance: • Fidelity – degree to which a program or practice is implemented as intended • Adaptation – how much, and in what ways, a program or practice is changed to meet local circumstances https://www.samhsa.gov/capt/applying-strategic-prevention-framework/step4-implement#fidelity-adaptation

  18. FM Method and Timing • HOW? Fidelity can be monitored through 3 approaches: • Direct Observation • Record Review • Asking Others • WHAT? And assessed in 3 broad categories: • Context • Content • Competence • WHEN? Fidelity is monitored: • Before implementation • During implementation • After implementation

  19. Best Practices for Aggregating Student Mental Health Data • Uniform data definitions • Uniform data collection procedures • Central data collection system • Tracking and analysis of multiple outcomes salient to each specific service/support including both mental health and educational outcomes. • Uniform process to analyze and review data on a consistent basis (monthly, quarterly, annually).

  20. Best Practices for Aggregating Student Mental Health Data • Process/protocol to share tailored data reports with diverse stakeholder groups including school board, local and state education authority, funders, service providers, school staff, students and families • Process/protocol to use aggregated data at each tier to make decisions about mental health service implementation and planning

  21. Best Practices for Disaggregating Student Mental Health Data • Examine mental health or other progress and outcome data characteristics based on sub-population characteristics • Age/Grade • Free and reduced price lunch • Race/ethnicity • Other pertinent sub-population characteristics

  22. Action Step #1: Evaluate Current DDDM Processes and Purposes in Your System • Meet with your team and evaluate what DDDM will be used for (e.g., intervention selection, monitoring student progress, system-wide program evaluation) • Evaluate whether these purposes reflect your current needs and identify any gaps

  23. Action Step #2: Identify the Data You Want or Need • Identify existing data sources. Which sources are currently used? Which sources are available but might be used more regularly or strategically? • Identify desired data sources that are not yet available. Can you make plans to obtain them?

  24. Action Step #3: Put Your Data to Use! • Identify team(s) that will use data • At the individual student, classroom, grade, school, or system level: • Identify/prioritize area of concern • Select practical data sources and collection methods • Collect and examine data • Implement interventions to address the problem • Collect data throughout intervention to monitor progress • Meet regularly as a team to review data • Evaluate data sharing practices across team members • Ensure that you use a data management system that allows for data sharing and analysis

  25. Hennepin County/ Minneapolis Public Schools Words of Wisdom • Screening and Assessment Doesn’t Happen in a Vacuum • People are making the decisions based on services, resources, and supports in their building • Without a system, all students will be triaged to Tier 3 • Objective: Truly developing MTSS incorporating use of data in decision making District specific context (that limits “traditional” universal MH screening): large district, not a lot of momentum for universal MH screening, haven’t found our perfect screener yet based on small tests of change we ran

  26. But…they’re not giving up!

  27. Current Activities to Support a MTSS with active DDDM in Minneapolis • Still keeping an eye out for a tier 1 tool to triage to tier 2 • Training in early warning signs for mental health concerns – annually for teachers • Collaboratively developing Decision Making Guides to triage students • Encouraging school buildings to build up Tier 2 • Helping school buildings really develop their teaming strategies (focusing in on SHAPE Teaming Domain)

  28. What are your DDDM change ideas? • How do YOU track student progress in Tier 2 and 3 services? What improvements could you feasibly start testing? • Consider the following: • Scope and Scale: What is the school vs district role? • Data Tools: What data tools do you use/ are you looking for? • Decision Making/Teaming Process: How do you make decisions about, and tracks and document decisions about: • Students referred for MH services? • Students served by/enrolled in MH services? • Student progress in Tier 2/3 services?

  29. Activity • Brainstorm 1 change idea to improve your school or district data driven decision making • What is the change? • How will it lead to improvement? • What are 1-2 small scale tests to get started with this change?

  30. Questions/Comments? Center for School Mental Health http://csmh.umaryland.edu Email: csmh@psych.umaryland.edu Phone: (410) 706-0980 Elizabeth Connors, Ph.D. econnors@som.umaryland.edu 443-801-3254

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