Aligning temporal data by sentinel events: discovering patterns in electronic health records

  • Authors:

  • Taowei David Wang;Catherine Plaisant;Alexander J. Quinn;Roman Stanchak;Shawn Murphy;Ben Shneiderman

  • Affiliations:

  • University of Maryland, College Park, MD, USA;University of Maryland, College Park, MD, USA;University of Maryland, College Park, MD, USA;University of Maryland, College Park, MD, USA;Massachusetts General Hospital, Boston, MA, USA;University of Maryland, College Park, MD, USA

  • Venue:
  • Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
  • Year:
  • 2008

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Abstract

Electronic Health Records (EHRs) and other temporal databases contain hidden patterns that reveal important cause-and-effect phenomena. Finding these patterns is a challenge when using traditional query languages and tabular displays. We present an interactive visual tool that complements query formulation by providing operations to align, rank and filter the results, and to visualize estimates of the intervals of validity of the data. Display of patient histories aligned on sentinel events (such as a first heart attack) enables users to spot precursor, co-occurring, and aftereffect events. A controlled study demonstrates the benefits of providing alignment (with a 61% speed improvement for complex tasks). A qualitative study and interviews with medical professionals demonstrates that the interface can be learned quickly and seems to address their needs.