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This research seeks to identify online participants’ disposition and skills. A prototype dashboard and annotation scheme were developed to support facilitators and several computational predictors were identified that show statistically significant correlations with dialogue skills as observed by human annotators.

BACKGROUND Patient portals are becoming more common, and with them, the ability of patients to access their personal electronic health records (EHRs). EHRs, in particular the free-text EHR notes, often contain medical jargon and terms that are difficult for laypersons to understand. There are many Web-based resources for learning more about particular diseases or conditions, including systems that directly link to lay definitions or educational materials for medical concepts. OBJECTIVE Our goal is to determine whether use of one such tool, NoteAid, leads to higher EHR note comprehension ability. We use a new EHR note comprehension assessment tool instead of patient self-reported scores. METHODS In this work, we compare a passive, self-service educational resource (MedlinePlus) with an active resource (NoteAid) where definitions are provided to the user for medical concepts that the system identifies. We use Amazon Mechanical Turk (AMT) to recruit individuals to complete ComprehENotes, a new test of EHR note comprehension. RESULTS Mean scores for individuals with access to NoteAid are significantly higher than the mean baseline scores, both for raw scores (P=.008) and estimated ability (P=.02). CONCLUSIONS In our experiments, we show that the active intervention leads to significantly higher scores on the comprehension test as compared with a baseline group with no resources provided. In contrast, there is no significant difference between the group that was provided with the passive intervention and the baseline group. Finally, we analyze the demographics of the individuals who participated in our AMT task and show differences between groups that align with the current understanding of health literacy between populations. This is the first work to show improvements in comprehension using tools such as NoteAid as measured by an EHR note comprehension assessment tool as opposed to patient self-reported scores.

An important learning skill is the ability to make abstractions, i.e., to construct classification schemes which highlight similarities and differences. In this paper we shall outline the content of a undergraduate course which attempts to teach this skill in the context of teaching introductory LISP programming and problem solving. The key to this enterprise has been the development of: 1. a taxonomy of problems , i.e., a classification scheme which groups problems into classes based on specific criteria, and 2. a set of plans , i.e., abstractions, each of which captures the essential features of a class of problems, and corresponding solution programs . We begin by developing a scheme for classifying the problems usually offered as exercises in introductory LISP courses. We then examine the LISP programs which solve problems in the various classes and abstract higher level structures called 'plans.' Here we view a plan as a program template plus comments describing the goals and reasons for the various expressions in the template. Next, we build on the set of plans to include new problems. Finally, we speculate briefly on the utility of our taxonomy with respect to programming in languages such as FORTRAN, APL, or PASCAL.