Roseann C. Schaaf; Creating Evidence for Practice Using Data-Driven Decision Making. Am J Occup Ther 2015;69(2):6902360010. https://doi.org/10.5014/ajot.2015.010561
Download citation file:
© 2020 American Occupational Therapy Association
To realize the American Occupational Therapy Association’s Centennial Vision, occupational therapy practitioners must embrace practices that are not only evidence based but also systematic, theoretically grounded, and driven by data related to outcomes. This article presents a framework, the Data-Driven Decision Making (DDDM) process, to guide clinicians’ occupational therapy practice using systematic clinical reasoning with a focus on data. Examples are provided of DDDM in pediatrics and adult rehabilitation to guide practitioners in using data-driven practices to create evidence for occupational therapy.
Identify participation challenges and goals.
Describe the current level of functioning in each area.
Identify factors that may interfere with participation for each identified goal by making observations; taking the client’s history; and having discussions with the client, family members, teachers, and others.
Conduct standardized and systematic assessments. Use specific assessments to evaluate the potential factors that affect each occupational challenge. The choice of assessment tools is based on information gleaned from Steps 1 and 2 and is guided by the practitioner’s clinical reasoning and theoretical perspective. Assessment data are summarized and guide the development of the hypotheses.
Identify strengths and barriers to participation. Ascertain the individual and environmental (social, physical, and cultural) strengths that can be used to support participation in meeting goals and the environmental factors that may be barriers to successful participation.
Generate specific hypotheses regarding the factors that affect successful participation by using assessment findings.
Design the intervention. Develop and explicate specific evidence-based activities and strategies so they can be replicated. Document the frequency, intensity, and time course of these activities and strategies.
Identify the proximal and distal outcomes that will be used to monitor progress toward goals. These outcomes are directly related to the hypothesized factors affecting participation and include individual and environmental strengths and barriers. Proximal outcomes are the identified factors that affect participation (e.g., poor praxis, decreased cognition or motivation, poverty of movement, spasticity, difficulty processing and integrating sensation; Melnyk & Morrison-Beedy, 2012). Distal outcomes are the skills, abilities, and behaviors that are expected to change in response to the intervention (Melnyk & Morrison-Beedy, 2012). These outcomes are directly related to the participation challenges and goals identified in Step 1.
Conduct the intervention.
Collect, display, and analyze data with a chart, bar graph, line graph, or table for analysis.
Monitor progress. Modify hypotheses and intervention as needed on the basis of outcome data. Additional assessments may be performed to further the development of the hypothesis.
A systematic reasoning process that includes collection, display, and analysis of outcome data can scientifically validate occupational therapy practice.
DDDM provides a mechanism to create evidence through practice by utilizing data to guide and measure practice.
Occupational therapy practitioners’ expertise in facilitating participation and measurement of participation-based outcomes is essential to validate practice.
This PDF is available to Subscribers Only
For full access to this pdf, sign in to an existing account, or purchase an annual subscription.