EBP101

EBP101: Data Driven Decision Making for Pediatric Occupational Therapists

Do standardized tests adequately capture the progress of the students on your caseload? Do you feel confident demonstrating the value of your services by generating and showing evidence of your therapeutic outcomes to teachers, parents, and administrators? If you answered no to either question, you need this course!

This 1-day course provides pediatric occupational therapists with practical solutions and methods for collecting outcomes data for their caseloads in order to monitor client progress and provide evidence for their practice. The beginning of the day starts with a crash course in goal writing, followed by learning multiple methods for gathering objective observational data. Attendees will practice observational note-writing while watching videos. Next, attendees will learn how to graph data in Excel, including showing baseline performance and the addition of new intervention techniques after a plateau. The day concludes with learning how to integrate evidence into progress and annual reports, followed by a special topic: measuring outcomes for large group interventions. The course is designed to be interactive, with real-life examples and hands-on practice throughout. The instructors encourage you to send questions about difficult cases before the course. A course manual and flash drive with useable templates will be provided.

Format: This is a 7-hour course, equivalent to 0.7 CEUs.

Content Level: This course is appropriate for both entry-level and experienced practitioners.

Learning objectives:

At the end of the course, practitioners will be able to:

Identify the elements of measureable goals.
Write measureable SMART goals.
Write goals using the Goal Attainment Scaling (GAS) method.
Demonstrate a variety of observational methods that are used to gather outcomes data.
Understand how to graph outcomes data in Excel.
Understand how to integrate graphs into annual reports.
Understand how to monitor outcomes for large group interventions (e.g., classroom wide or community based interventions).

Agenda

8:00-9:00 Registration. Light breakfast and refreshments will be provided.

9:00-10:30 Introduction to data driven decision making (DDDM) for occupational therapists

Writing objective and measureable goals
Goal Attainment Scaling (GAS)
The role of standardized assessments in DDDM
Operationalizing outcomes

10:30-12:15 Methods for documenting outcomes

Where does DDDM fit within evidence-based practice?
Gathering baseline data
Observational Methods for gathering objective data (e.g., Narrative notetaking, time sampling and duration recording, event sampling/frequency count, Likert rating scales, and single subject designs)
Where do you find the time? Creating data gathering schedules

12:15-1:15 Lunch on your own

1:15-2:30 Using Excel to graph outcomes data

2:30-3:15 Documentation: Including data in progress notes and annual reports

3:15-4:15 Measuring outcomes for large group interventions in the classroom or community

4:15-5:00 Wrapping up – Q and A

Instructor Bios

Alisha Ohl, Ph.D., OT/L, is an occupational therapist and assistant professor, with expertise in psychometrics, pediatric evaluation, autism, and school-based practice. She has over twelve peer-reviewed publications in top occupational therapy journals, and presents her research at multiple conferences annually. Dr. Ohl has practiced in both urban and rural settings, and has held advanced positions as an evaluator and mentor to new therapists. She is passionate about bridging the gap between research and occupational therapy practice.

David Schelly, Ph.D., is a sociologist and assistant professor with expertise in evidence-based practice, social determinants of health, and social epidemiology. His autism research has focused on explaining why childhood autism cases tend to be diagnosed in geographic clusters, such that the “risk” of being diagnosed depends on where one’s parents live. In occupational therapy, Dr. Schelly has written about randomized controlled trial research design in terms of “clinical meaningfulness,” as well as the benefits of considering treatment efficacy using as-treated analyses.