Emerging as Leaders in Autism Research and Practice: Using the Data-Driven Intervention Process

Data-Driven Intervention

To accomplish this aim, we propose the data-driven intervention process (Schaaf, 2011) to systematically guide practitioners’ reasoning, creating a seamless link from assessment to hypothesis generation 1 to intervention strategies that can be methodically tested with defined outcome measures. This work builds on Blanche’s (2001, 2006) work on assessment and hypothesis generation in sensory integration to include a broader application with a focus on systematic identification and measurement of outcomes.

As displayed in Figure 1, the data-driven intervention process uses a series of steps designed to organize and guide the therapist’s reasoning. Identification and measurement of outcomes that are both proximal (i.e., specific factors or mechanisms identified as affecting participation) and distal (i.e., participation behaviors one expects to change in response to the intervention) are key to this process. Identification of proximal factors as well as participation-based outcomes not only provides the structure needed to link the hypothesized factors affecting goal achievement with participation outcomes but also provides the clinician or researcher with a clear rationale for the intervention used. Review, display, and analysis of outcome data provide insight into the efficacy of the chosen intervention strategy.

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Figure 1.

Data-driven intervention process (Schaaf, 2011).

Using this process, the identified hypothesis can be tested and confirmed or revised on the basis of solid data. Thus, the data-driven intervention process allows therapists to systematically identify and test their reasoning process by providing data about the effects of the intervention. Ultimately, this process allows therapists to validate occupational therapy interventions.

In This Issue

Given the significant impact of occupational therapy in autism practice and research (Schaaf & Blanche, 2011), occupational therapists must continue to articulate the unique skills that occupational therapy offers; validate these skills through systematic data collection, display, and analysis; and publish their findings in peer-reviewed, interdisciplinary venues. This issue of AJOT provides that opportunity. We are fortunate to include work from many of the leaders in the field of occupational therapy.

The articles presented in this issue fall broadly into three categories: clinical classifications and subtyping, instrument development, and intervention efficacy. The clinical classification and subtyping articles focus on clustering patterns of behaviors that can be used to guide research and practice by tailoring interventions to address specific symptoms or subgroups. The instrument development articles address instrumentation, providing data on instruments that are psychometrically sound, feasible to administer, and meaningful for occupational therapy. These instruments are essential to the data-driven process.

Articles focusing on intervention efficacy provide evidence for a broad spectrum of occupational therapy interventions, ranging from parent coaching to direct interventions. These articles provide occupational therapists with pivotal data to support the use of evidence-based interventions in a data-driven framework. Finally, “The Issue Is” (Burke & Gitlin, 2012) highlights the value of evidence-based practice and presents theoretical issues to confront when integrating evidence into practice.

We hope that you enjoy this new knowledge and use it to shape your practice with data-driven interventions that allow occupational therapy to continue to hold a central role in the ongoing scientific dialogue on autism.