“Occupational Therapy is Making”: Clinical Rapid Prototyping and Digital Fabrication

Splint that has been 3D printed in a material of an appropriate skin color and fit to a client's hand.

Lyme Disease’s Impact

An ongoing, and very personal thread of research that our group engages in (due to my own journey with Lyme Disease, which I occasionally blog about here) is research into the impacts of Lyme Disease and opportunities for helping to support patients with Lyme Disease. From a patient perspective, Lyme disease is as tough to deal with as many other more well known conditions [1].

Lyme disease can be difficult to navigate because of the disagreements about its diagnosis and the disease process. In addition, it is woefully underfunded and understudied, given that the CDC estimates around 300,000 new cases occur per year (similar to the rate of breast cancer) [2].

Bar chart showing that Lyme disease is woefully under studied.

As an HCI researcher, I started out trying to understand the relationship that Lyme Disease patients have with digital technologies. For example, we studied the impact of conflicting information online on patients [3] and how patients self-mediate the accessibility of online content [4]. It is my hope to eventually begin exploring technologies that can improve quality of life as well.

However, one thing patients need right away is peer reviewed evidence about the impact that Lyme disease has on patients (e.g. [3]) and the value of treatment for patients (e.g. [4]). Here, as a technologist, the opportunity is to work with big data (thousands of patient reports) to unpack trends and model outcomes in new ways. That research is still in the formative stages, but in our most recent publication [4] we use straightforward subgroup analysis to demonstrate that treatment effectiveness is not adequately captured simply by looking at averages.

This chart shows that there is a large subgroup (about a third) of respondents to our survey who reported positive response to treatment, even though the average response was not positive.

There are many opportunities and much need for further data analysis here, including documenting the impact of differences such as gender on treatment (and access to treatment), developing interventions that can help patients to track symptoms, manage interaction within and between doctors, and navigate accessibility and access issues.

[1] Johnson, L., Wilcox, S., Mankoff, J., & Stricker, R. B. (2014). Severity of chronic Lyme disease compared to other chronic conditions: a quality of life survey. PeerJ2, e322.

[2] Johnson, L., Shapiro, M. & Mankoff, J. Removing the mask of average treatment effects in chronic Lyme Disease research using big data and subgroup analysis.

[3] Mankoff, J., Kuksenok, K., Kiesler, S., Rode, J. A., & Waldman, K. (2011, May). Competing online viewpoints and models of chronic illness. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 589-598). ACM.

[4] Kuksenok, K., Brooks, M., & Mankoff, J. (2013, April). Accessible online content creation by end users. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 59-68). ACM.

 

Infant Oxygen Monitoring

Hospitalized children on continuous oxygen monitors generate >40,000 data points per patient each day. These data do not show context or reveal trends over time, techniques proven to improve comprehension and use. Management of oxygen in hospitalized patients is suboptimal—premature infants spend >40% of each day outside of evidence-based oxygen saturation ranges and weaning oxygen is delayed in infants with bronchiolitis who are physiologically ready. Data visualizations may improve user knowledge of data trends and inform better decisions in managing supplemental oxygen delivery.

First, we studied the workflows and breakdowns for nurses and respiratory therapists (RTs) in the supplemental oxygen delivery of infants with respiratory disease. Secondly, using end-user design we developed a data display that informed decision-making in this context. Our ultimate goal is to improve the overall work process using a combination of visualization and machine learning.

Visualization mockup for displaying O2 saturation over time to nurses.
Visualization mockup for displaying O2 saturation over time to nurses.