Moving Beyond Hastiness and Superficiality: Being an Ethical Technical Communicator via Digital Communication Technologies in Health Care

This thesis examines digital technologies used by technical communicators in healthcare settings. I show that technical communicators, who function as users, advocators and evaluators, need a useable framework for ethical engagement with digital technologies, which integrally affect the physician-patient relationship. Therefore, I apply rhetorical methodology by producing useable knowledge and phenomenological methodology by examining lived experiences of technical communicators. Substantiation comes from theories spanning technical communication, philosophy, and composition studies. Evidence also emerges from qualitative interviews with communication professionals working in healthcare; my concerns arise from personal experiences with electronic recordkeeping in the exam room. This thesis anticipates challenging the presumed theory-practice divide while encouraging greater disciplinary reciprocity. Because technical communication infuses theory into productive capacity, this thesis presents the tripartite summons of the ethical technical communicator: to exercise critically-reflective action that safeguards the physician-patient relationship by ways of using digital technologies, advocating for audiences, and evaluating digital technologies.

GENERATING PLANS IN CONCURRENT, PROBABILISTIC, OVER-SUBSCRIBED DOMAINS

Planning in realistic domains typically involves reasoning under uncertainty, operating under time and resource constraints, and finding the optimal subset of goals to work on. Creating optimal plans that consider all of these features is a computationally complex, challenging problem. This dissertation develops an AO* search based planner named CPOAO* (Concurrent, Probabilistic, Over-subscription AO*) which incorporates durative actions, time and resource constraints, concurrent execution, over-subscribed goals, and probabilistic actions. To handle concurrent actions, action combinations rather than individual actions are taken as plan steps. Plan optimization is explored by adding two novel aspects to plans. First, parallel steps that serve the same goal are used to increase the plan’s probability of success. Traditionally, only parallel steps that serve different goals are used to reduce plan execution time. Second, actions that are executing but are no longer useful can be terminated to save resources and time. Conventional planners assume that all actions that were started will be carried out to completion. To reduce the size of the search space, several domain independent heuristic functions and pruning techniques were developed. The key ideas are to exploit dominance relations for candidate action sets and to develop relaxed planning graphs to estimate the expected rewards of states. This thesis contributes (1) an AO* based planner to generate parallel plans, (2) domain independent heuristics to increase planner efficiency, and (3) the ability to execute redundant actions and to terminate useless actions to increase plan efficiency.