Abstracts

Rationale: Epilepsy patient data stored in the electronic medical record (EMR) data has the potential to offer enhanced patient care, if presented in an easy to understand way. Unfortunately, the large volume of data presented in raw format (as numbers) are hard to process and interpret, thus limiting their use. Data displays and computers can aid the physician during everyday practice. Here, we describe how data displays and decision support systems can work in coordination with each other. We show how patient data and comparative population evidence can be visually displayed in a way that enables the physician to make sense of this information in a fast and intuitive manner. Methods: Many variables are recorded in the EMR of children with epilepsy: seizure types and frequencies, medications, outcomes (measured by increase/decrease in seizure frequencies), potential medication side-effects and their severity. Patient enrollment continues at three different pediatric epilepsy centers. Computational approaches can be used to process, aggregate, and build displays of data. Visual representation help the physician better understand patient data, and aggregated evidence from comparative population. Embedded interactions empower the physician to customize the choice of comparables. The display program shows an approach to make use of the existing EMR resources without having the physician search through the patient's file. Results: We have now collected pilot data on 20 epilepsy patients. Different data views can be built to reveal patterns in the data. Figure 1 shows dashboard with patient-level data, while Figure 2 highlights an approach to display and use comparative population treatment evidence to assist the physician in decision making. Predictive modeling approaches can be use comparable patient data to identify trend in medication response. Within a single dashboard (Figure 1 or Figure 2), different data views linked together give flexibility for data overlay, and for filter and syncing all data views. Such data displays are likely to help the physician gain a quick understanding of the characteristics of the patient data at the time of visit. Such interactive data displays have the potential to reduce information overload, free up the physician's cognition for higher level data processing, and aid informed decision making. Conclusions: This multicenter pilot study is designed to highlight the role that data displays and a clinical decision support program can play to bridge the complementary skill sets of humans and computers (as an external aid) to rapidly derive useful information from a large dataset. External aid in the form of interactive data displays can be used to augment the information processing abilities of the physician and facilitate decision making.