Sailesh Ramakrishnan

Autominder: an intelligent cognitive orthotic system for people with memory impairment

The world’s population is aging at a phenomenal rate. Certain types of cognitive decline, in particular some forms of memory impairment, occur much more frequently in the elderly. This paper describes Autominder, a cognitive orthotic system intended to help older adults adapt to cognitive decline and continue the satisfactory performance of routine activities, thereby potentially enabling them to remain in their own homes longer. Autominder achieves this goal by providing adaptive, personalized reminders of (basic, instrumental, and extended) activities of daily living. Cognitive orthotic systems on the market today mainly provide alarms for prescribed activities at fixed times that are specified in advance. In contrast, Autominder uses a range of AI techniques to model an individual’s daily plans, observe and reason about the execution of those plans, and make decisions about whether and when it is most appropriate to issue reminders. Autominder is currently deployed on a mobile robot, and is being developed as part of the Initiative on Personal Robotic Assistants for the Elderly (the Nursebot project).

Oncology thinking cap: scaffolded use of a simulation to learn clinical trial design.

Background: Physicians often are called on to participate in and interpret clinical trials, but their training in this area may not provide them with the inquiry skills that are needed. Simulations have the potential to be a promising tool for helping medical students learn the skills involved in clinical trial design. However, simulations may be complex and require additional scaffolding to support learning. Description: The goal of this study was to teach aspects of cancer clinical trial design through the scaffolded use of a simulation, the Oncology Thinking Cap. The software-based scaffolding provided guidance in designing the trial. Subsequently, the simulation allowed students to run the designed trial, which produces detailed patient histories. This feedback then could be used to redesign the trial. Evaluation: Twenty-four 4th-year medical students were asked to design a clinical trial in advance, on paper, to test a new anticancer drug. Student groups then designed and simulated running the clinical trial assisted by the software environment. Instructional effectiveness was measured using a pretest-posttest design that included having students (a) write a group research proposal and (b) individually critique a flawed proposal. At the group level (N = 6 groups), students demonstrated a 34% increase in the number of elements of a clinical trial that they included in their research proposals. At the individual level (N = 24), students improved by 48% in their critiques of flawed proposals. Conclusions: Scaffolding embedded in the simulator is a promising approach to helping students learn about clinical trial design.