Toward a national system of assistive technology outcomes measurement.

For more than two decades the assistive technology outcomes literature has featured repeated calls for clinical research to demonstrate the impact of device recommendations, as well as substantial discussion of tools for measuring AT outcomes. Unfortunately, data are still not routinely collected in most AT service delivery settings, which undermines the field. This paper describes a framework for developing a national cloud-based system of AT outcomes measurement that emerged from structured discussions with clinicians, researchers, and manufacturers. Such a system would: (a) allow collection and upload of outcomes data by geographically dispersed researchers, practitioners, and consumers; and (b) enable policymakers, third-party funders, consumers, practitioners, and researchers to retrieve outcomes data for specific disability and/or device groups.

Validating a model of row-column scanning.

PURPOSE: For individuals with severe motor and communicative disabilities, single switch scanning provides a way to access a computer and communicate. A model was developed that utilizes scanning interface settings, error tendencies, error correction strategies, and the matrix configuration to predict a user's communication rate. METHOD: Five individuals who use single switch scanning transcribed sentences using an on-screen keyboard configured with the settings from their communication devices. Data from these trials were used as input to a model that predicted TER for the baseline configuration and at least three other system configurations. Participants transcribed text with each of these new configurations and the predicted TER was compared to the actual TER. RESULTS: Results showed that predicted TER was accurate to within 90% on average. The scan rate was also entered into a previously published model which assumes error-free performance. For our model, the average error for each participant was 10.49%, compared to 79.7% for the model assuming error-free performance. CONCLUSIONS: Our model of row-column scanning was much more accurate than a model that did not consider the likelihood of an error occurring. There is still room for improvement, however, and the results of the study will lead to additional modifications of the model.