Krist Wongsuphasawat John Alexis Guerra Gomez Catherine Plaisant Taowei David Wang Ben Shneiderman Meirav Taieb-Maimon Presented by Ren Bauer

 Motivation  Related Work ◦ Shortcomings  Visualization Techniques  Evaluation ◦ Case Studies ◦ User Study

 Washington Hospital Center ◦ Dr. Phuong Ho ◦ Bounce Backs ◦ Anomalous Patient Transfer Patterns  Previously viewed sequences in an MS Excel spreadsheet  Needed a more efficient option

 Temporal ◦ Timelines ◦ Spirals  Hierarchical ◦ Trees ◦ Icicle Plots

 Temporal Data Visualization

 Hierarchical Data Visualization

 Developed at the University of Maryland  Data mining tool focused on providing an overview of events ◦ Scales to any number of records ◦ Summarizes all possible sequences ◦ Highlights temporal spacing of events within sequences

 Input records  Form timelines  Combine common events  Form LifeFlow Representation

 Case Study 1: Medical Domain ◦ One dataset included 7,041 patients  ER patients from Jan 2010 ◦ Most Common: Arrival->ER->Discharge-Alive  4,591 (65.20%) ◦ 193 (2.74%) Patients LWBS, 38 (0.54%) AWOL  Can be compared with hospital standard for quality control

 Case Study 1: Medical Domain ◦ Interesting Patterns  Arrival->ER->Floor->IMC/ICU  “Step up”  Went from floor to ICU more quickly then floor to IMC  Captured screenshots to compare with standards ◦ Found 6 patients experiencing “bounce backs” ◦ Anomalous sequences  Patients being accepted into the ICU after being pronounced dead…

 Case Study 1: Medical Domain ◦ Measuring Transfer Time  Easy to make queries such as: “If patients went to the ICU, what was the average transfer time from the ER to the ICU?” ◦ Comparison  Hypothesis about IMC patients being transferred more quickly based on time of day  Found no significant difference

 Case Study 2: Transportation Domain ◦ 8 Traffic Response Agencies at U Maryland ◦ Noticed many incidents lasting 24 hours  12:30am Apr 10 th to 11:45pm Apr 10 th  Probable data entry error ◦ Ranked agencies based on performance  Fastest (Agency C) 5 minutes  Immediate Clearances  slowest (Agency G) 2 hours 27 minutes  Actually ranked fairly well for “incident”

 User Study  10 Grad students examining 91 medical records ◦ 12 minute training video ◦ 15 simple to complex tasks  “Where did patients usually go after they arrived”  “Retrieve IDs of all patients with this transfer pattern” ◦ Most tasks performed in under 20 seconds ◦ Final Task: 10 minutes to find 3 anomalies intentionally put in data  All students found first 2, most saw third but weren’t sure it was anomalous

 Motivation ◦ Need an efficient tool to compare sets of sequences ◦ Previous work insufficient  Solution ◦ LifeFlow visualization suite  Evaluation ◦ Case studies show usefulness ◦ User study shows usability

 Some of this information could be found with methods as simple as SQL searches, is this technology really necessary? ◦ What kind of information could it not help us find?  Traffic agencies were ‘ranked’ based on response time, but further investigation revealed these rankings may not mean anything, what are the dangers of technology such as this?