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Inferring visual behaviour from user interaction data on a medical dashboard

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Paper presented at the 2018 International Conference on Digital Health. Available at https://doi.org/10.1145/3194658.3194676

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Inferring visual behaviour from user interaction data on a medical dashboard

  1. 1. Inferring Visual Behaviour from User Interaction Data on a Medical Dashboard! Ainhoa Yera, Javier Muguerza, Olatz Arberlaitz, Iñigo Perona Faculty of Informatics – University of the Basque Country UPV/EHU Richard Keers, Darren Ashcroft Division of Pharmacy and Optometry – University of Manchester Caroline Jay, Markel Vigo School of Computer Science – University of Manchester Richard Williams, Niels Peek Division of Informatics, Imaging and Data Sciences – University of Manchester Paper available at https://doi.org/10.1145/3194658.3194676
  2. 2. Motivation! •  Health data + software •  Proactive management of population health care •  SMASH a primary care intervention –  Medical dashboard –  Patient safety –  PINCER indicators1 •  Conditions vs medications: CKD and NSAIDs •  Demographics vs medications: Woman, smoker, +35, CHC –  Pharmacists and GPs –  Deployed in Salford (UK) 1: Avery et al. (2012) A pharmacist-lead information technology intervention for medication errors. The Lancet 379 1
  3. 3. SMASH screens! 5 - Trends 3 - Visualisation 1 - Landing page 4 - Patients 6 - Information 2 - Indicators 2
  4. 4. Problem! •  Information overload problem •  Addressing high information density in medical decision making tools? •  “The right amount of information” •  Adaptive user interfaces –  What are these information needs? –  What’s taxing decision making of clinicians? •  Visual behaviour is proxy of interest and information overload “I just need the right amount of information” Louise, GP at Greater Manchester 3
  5. 5. Visual behaviour as a proxy of interest and information overload! 4
  6. 6. Problem! •  Adaptive user interfaces –  What are these information needs? –  What’s taxing decision making of clinicians? •  Visual behaviour is proxy of interest and information overload •  Eye-trackers not expected beyond the lab setting •  RQ: can we link user interaction data to visual behavior? “I just need the right amount of information” Louise, GP at Greater Manchester 5
  7. 7. Collected data! •  Laboratory (N=6) –  Interaction logs and eye-tracking data –  Typical tasks •  Remote (N=35) –  Interaction logs –  10 months data (Jan-Oct 2016) 6
  8. 8. Computed metrics! •  User interaction metrics –  Exploration: mouse hovers between mouse clicks –  Pace: elapsed time between two consecutive mouse clicks •  Gaze metrics –  Fixation duration: values between 50-400ms •  Vectors of aggregations –  V1: Exploration and pace on SMASH (length: 2) –  V2: Exploration and pace per screen (length: 7x2) –  G: Median fixation duration per AOI (length: 9) 7
  9. 9. Analysis: laboratory participants! •  Finding individuals with similar interactive behaviours –  Clustering analysis on log data (V1 and V2) •  k=3 optimal when applying silhouette analysis on Cluster Validity Indexes, k: 3..10 •  Euclidean distance •  Same results for V1 and V2 •  Hierarchical clustering including the centroids •  Neighbour-joining method k-means, k=3, Euclidean Log data: clustering on V Log data: clustering on V P3 P1 P4 P5 P6 P2 P1 P2 P3 P4P5 P6 P6 P2 C3 P4 P5 C1 P3 P1 C2 0.00.20.40.60.81.01.21.4 Cluster Dendrogram DM_dmedEu2 Height 8
  10. 10. Analysis: laboratory participants! •  Finding individuals with similar interactive behaviours –  Correlation analysis on fixation durations (G) –  Same pairs as those emerging from interactive behaviour Log data: clustering on V Log data: clustering on V P3 P1 P4 P5 P6 P2 Log data: clustering on V Gaze data: correlation on G P3 P1 P4 P5 P6 P2 9
  11. 11. Analysis: lab + remote participants! •  Finding individuals with similar interactive behaviours –  Lab (N=6) and remote (N=35) altogether –  Clustering analysis on log data, Euclidean distance –  k=3 optimal when applying silhouette analysis on Cluster Validity Indexes, k: 3..10 All= lab + remote (N=41) Log data: clustering on V P1 P2 P3 P4P5 P6 P6 10
  12. 12. Laboratory (N=6) All= lab + remote (N=41) Gaze data Log data Main outcome/hypothesis: those individuals belonging to a cluster in the remote group have similar visual behaviours to that of laboratory users Log data Hypothesis: Gaze data 11
  13. 13. Outcomes! •  In terms of interactive behaviours clusters indicate –  P2 and P6: slow pace and high exploration –  P1 and P3: high pace and high exploration –  P4 and P5: high pace and low exploration •  In terms of visual behaviours (ie cognitive load) –  P2 and P6: less load when looking at indicators –  P1, P3, P4, P5: high cognitive load on indicators and data table 12
  14. 14. Conclusions! •  There is a relationship between interactive behaviour and visual behaviour •  We could infer visual behaviour by monitoring interaction data •  Results not completely conclusive but promising •  We have a higher certainty 13
  15. 15. Questions? Inferring Visual Behaviour from User Interaction Data on a Medical Dashboard! 15 markel.vigo@manchester.ac.uk @markelvigo

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