Supporting Data-Based Decision-Making for Caregivers through Embedded Capture and Access

Abstract

Abstract. The care of individuals with concerns about development, health, and wellness is often a difficult, complicated task and may rely on a team of diverse caregivers. There are many decisions that caregivers must make to ensure the best care and health monitoring are administered. For my dissertation work, I am exploring the use of embedded capture and access to support decisionmaking for caregivers. Embedded capture and access integrates simple and effective capture and useful access, including trending information and rich data, into existing work practices. I hypothesize that this type of technology encourages more frequent access of evidence, increases collaboration amongst caregivers, and increases confidence with which care decisions are made. I am exploring this work through real world deployments of new embedded capture and access applications in the domains of caring for individuals with autism and tracking the development of newborn children.