Low-Fidelity Clinical Decision Making Tree

A decision tree to help speech-language pathologists (SLPs) decide the assessments to use and characteristics to look at when working with patients with right-hemisphere dysfunction following a stroke.

Understanding the Needs

The problem

Right hemisphere dysfunction (RHD) is a complex set of symptoms that are challenging for SLPs to assess and treat, especially given disparities in graduate level curriculum and training. Symptoms often go undiagnosed and can cause significant functional limitations for the patient. The clinical decision guide will synthesize research recommendations to guide SLPs through selecting materials and assessing patients appropriately.

The Users

SLPs across diverse care levels (acute care, inpatient rehabilitation, day rehabilitation programs and outpatient) encounter patients who have suffered from strokes on the right side of their brain and may present with these symptoms. Estimations suggest that around 45% of people suffering from strokes are on the right side and 25%-80% of those patients will present with RHD.

The Project

I met with key stakeholders, practice leaders and SLPs to understand the gaps in knowledge and pain points during the assessment process. Chart reviews and discussions regarding patient’s plans of care provided valuable insights to the missing components in the assessment process. Finally, research regarding RHD and recommended treatment guidelines were used to provide a framework.

Synthesized research to match deficit areas with prescribed assessments

Synthesizing and Wireframing

Once the gaps were determined and the research was analyzed, I began sketching what the decision tree would look like. Low-fidelity paper sketches with multiple iterations were completed with a focus on the user flow. As the decision tree would be the deliverable, I wanted to make sure that the user could navigate through the process with ease.

Design Elements

The arrows help guide the user through the chart, however I also wanted to incorporate design elements that incorporated Gestalt Principles. The colors guide the user through general information (yellow), high assessment track information (high) and low assessment track information (green). I also use proximity to guide the user. All general information is neutral, high level assessment is on the top and low level assessment is on the bottom.

Deliverables and Next Steps

Deliverables

At the end of this project, the final products were the research synthetization chart and the decision-tree for clinicians to reference. I also presented this information with case study applications for use of the chart in a 2 hour continuing education course. Finally, this information was assimilated into new hire curriculum to sustain implementation.

Next Steps

I envision this project becoming an living application or as part of a website. Rather than being a decision tree, the application would lead clinicians through the process where scores can be input and items can be selected for the system to guide the user to an appropriate diagnosis.

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Low-Fidelity Prototype in Figma