Abstract
Background Complex electronic medical records (EMRs) presenting large amounts of data create
risks of cognitive overload. We are designing a Learning EMR (LEMR) system that utilizes
models of intensive care unit (ICU) physicians' data access patterns to identify and
then highlight the most relevant data for each patient.
Objectives We used insights from literature and feedback from potential users to inform the
design of an EMR display capable of highlighting relevant information.
Methods We used a review of relevant literature to guide the design of preliminary paper
prototypes of the LEMR user interface. We observed five ICU physicians using their
current EMR systems in preparation for morning rounds. Participants were interviewed
and asked to explain their interactions and challenges with the EMR systems. Findings
informed the revision of our prototypes. Finally, we conducted a focus group with
five ICU physicians to elicit feedback on our designs and to generate ideas for our
final prototypes using participatory design methods.
Results Participating physicians expressed support for the LEMR system. Identified design
requirements included the display of data essential for every patient together with
diagnosis-specific data and new or significantly changed information. Respondents
expressed preferences for fishbones to organize labs, mouseovers to access additional
details, and unobtrusive alerts minimizing color-coding. To address the concern about
possible physician overreliance on highlighting, participants suggested that non-highlighted
data should remain accessible. Study findings led to revised prototypes, which will
inform the development of a functional user interface.
Conclusion In the feedback we received, physicians supported pursuing the concept of a LEMR
system. By introducing novel ways to support physicians' cognitive abilities, such
a system has the potential to enhance physician EMR use and lead to better patient
outcomes. Future plans include laboratory studies of both the utility of the proposed
designs on decision-making, and the possible impact of any automation bias.
Keywords
electronic health records - intensive care units - data display - user-computer interface
- software design - cognition
