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A Visual Analytics Framework for Emergency Room Clinical Encounters

A Visual Analytics Framework for Emergency Room Clinical Encounters. Zhiyuan Zhang 1 , Supriya Garg 1 , Arunesh Mittal 1 , Alex Dimitriyadi 1 , IV Ramakrishnan 1 , Rong Zhao 1 , Klaus Mueller 1 , Asa Viccellio 2. 1 Department of Computer Science 2 Department of Emergeny Medicine.

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A Visual Analytics Framework for Emergency Room Clinical Encounters

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  1. A Visual Analytics Framework for Emergency Room Clinical Encounters Zhiyuan Zhang1, Supriya Garg1, Arunesh Mittal1, Alex Dimitriyadi1, IV Ramakrishnan1, Rong Zhao1, Klaus Mueller1, Asa Viccellio2 1 Department of Computer Science 2 Department of Emergeny Medicine

  2. Medical Record • As old as Hippocrates (around 400 BC) • father of Western medicine • Should: • accurately reflect the course of disease • indicate the probable cause of disease

  3. Paper-Based Medical Record

  4. Electronic Medical Record (EMR) • Transaction-driven: documentation + tables • Non-intuitive interfaces • Fragmented display of patient information

  5. Typical Information Flow in the ER Current History Past History Family History Social History Review of System Order Laboratory Data Obtain Patient’s Information Physical Examination Take appropriate action within an appropriate time Request and obtain consultation Reevaluate and readjust therapy and diagnosis

  6. Typical Information Flow in the ER Current History Past History Family History Social History Review of System Current History Past History Family History Social History Review of System Order Laboratory Data Obtain Patient’s Information Current History Past History Family History Social History Review of System Physical Examination Order Laboratory Data Obtain Patient’s History Current History Past History Family History Social History Review of System Physical Examination Order Laboratory Data Obtain Patient’s History Physical Examination Order Laboratory Data Obtain Patient’s Information Physical Examination Take appropriate action within an appropriate time Request and obtain consultation Reevaluate and readjust therapy and diagnosis Take appropriate action within an appropriate time Request and obtain consultation Reevaluate and readjust therapy and diagnosis Take appropriate action within an appropriate time Request and obtain consultation Reevaluate and readjust therapy and diagnosis Take appropriate action within an appropriate time Request and obtain consultation Reevaluate and readjust therapy and diagnosis

  7. System Objectives • Support and enhance the clinical decision-making • Simple Interfaceto make data and information exploration easier • Assimilation of data from different sources • Visualization and visual reasoning is key • Ease of data and information access is key

  8. System Overview

  9. The 5-WScheme • Use a strongly structured paradigm, the 5-W • WHO : the patient and the history • WHAT : Symptoms, tests and results, diagnosis, treatmentsand medications, etc. • WHERE : locations (when appropriate) of the WHATon the human body • WHEN : time and duration of the WHAT • WHY : cause and effect of the various WHAT constituents

  10. Visualize the 5-W Scheme • WHO : Basic Info and Vital Signs • WHAT : Integrated in WHERE, WHEN, and WHY. • WHERE : Human map • WHEN : Time line • WHY : Directed causal graph

  11. Visualize Who, What and Where (0) Start Screen

  12. Visualize Who, What and Where (a) Results of triage (name, weight, age, vitals with high fever)

  13. Visualize Who, What and Where (b) Populating the spatial map with symptoms (head problems)

  14. Visualize Who, What and Where (c) Adding past history (lung cancer in remission)

  15. Visualize Who, What and Where (d) Social history indicates patient has a history of smoking

  16. Visualize Who, What and Where (e) The diagnostic process begins, immediate actions

  17. Visualize Who, What and Where (e) The diagnostic process begins, using the Diagnostic Sandbox

  18. Visualize Who, What and Where (f) The diagnostics determines epidural hematoma

  19. Visualizing When • Each entry is classified into: • Symptoms; Tests; Diagnosis; Treatments.

  20. Visualizing When • Each sub-track within one track represents an event.

  21. Visualizing When • Each sub-track has an explanation.

  22. Visualizing When • Abnormal results or severe symptoms are highlighted in red to draw the physician’s attention.

  23. Visualizing When • Brushing operation

  24. Visualizing Why • Symptom->Test/Data->Diagnosis->Treatment/Medication • A modified version of the force-directed layout • All symptoms have incoming edges from a node representing a visit to the physician. • A convex hull is drawn for each visit.

  25. Visualizing Why • Enable quick mental analysis • Help detect errors in diagnoses

  26. Visualize Why

  27. Visualizing Why Case Study

  28. Visualize Why

  29. Conclusions • Implemented an emerging visual analytics system • For clinical encounters in emergency room scenarios • Clinician and patient-focused • Unifies all EMR information fragments into a single interactive visual framework • Voice and multi-touch interaction capabilities

  30. Current/Future Work • Improvements, fine-tuning, and additional features • Include more formal user and affordance studies to fine-tune the various modules of our system. • A more geometrically structured temporal plot for the casual graph. • Treatment Outcome • Integrate more analytics in the system.

  31. Thanks For Listening…

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