Intelligent Interactive Visual Exploration of Temporal Associations among Multiple Time-oriented Patient Records

Summary

Objectives: To design, implement and evaluate the functionality and usability of a methodology and a tool for interactive exploration of time and value associations among multiple-patient longitudinal data and among meaningful concepts derivable from these data.

Methods: We developed a new, user-driven, interactive knowledge-based visualization technique, called Temporal Association Charts (TACs). TACs support the investigation of temporal and statistical associations within multiple patient records among both con cepts and the temporal abstractions derived from them. The TAC methodology was implemented as part of an interactive system, called VISITORS, which supports intelligent visualization and exploration of longitudinal patient data. The TAC module was evaluated for functionality and usability by a group of ten users, five clinicians and five medical informaticians. Users were asked to answer ten questions using the VISITORS system, five of which required the use of TACs.

Results: Both types of users were able to answer the questions in reasonably short periods of time (a mean of 2.5 ± 0.27 minutes) and with high accuracy (95.3 ± 4.5 on a 0–100 scale), without a significant difference between the two groups. All five questions requiring the use of TACs were answered with similar response times and accuracy levels. Similar accuracy scores were achieved for questions requiring the use of TACs and for questions requiring the use only of general exploration operators. However, response times when using TACs were slightly longer.

Conclusions: TACs are functional and usable. Their use results in a uniform performance level, regardless of the type of clinical question or user group involved.

Keywords

Multiple patients - intelligent visualization - interactive visual data mining - temporal association - temporal abstraction - knowledge-based systems

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