Eye-Tracking and Retrospective Verbal Protocol to Support Information Systems Design

Abstract

As capabilities for storing and sharing electronic health data expand, it becomes increasingly important to classify, prioritize, and contextually link patient health data. Providers using electronic health records (EHR) complain that it is difficult for them to rapidly find information they need and that key information can be missed. Unfortunately, phases of human-centered design that support defining information needs and developing an understanding of the work environment are sometimes shortchanged. We present a knowledge elicitation approach for the purposes of supporting EHR design that combines the rich contextual data gained through recording information use activities during actual patient care followed by retrospective verbal protocol interviews of the provider as they watch and listen to playback of their information use activities. As a means of analyzing qualitative data collected through techniques such as these, we apply grounded theory based content analysis techniques. The goal of our research is the development of principles to support better organization and prioritization in the presentation of electronic health data. As a secondary objective, we also seek to identify criteria that, with computational algorithms, may be used to prioritize specific data in context to support intelligent contextualized information display. In this paper, we present details of our methods (data collection and analyses), preliminary findings that have resulted from these efforts, and specific advantages and disadvantages to our approach.