1 / 57

Visualizing Electronic Health Records for Diverse Applications and Interfaces

Explore the research and challenges in visualizing electronic health records across diverse users, applications, and interfaces. Discover tools and techniques for meaningful visual displays and interaction in healthcare data management.

billw
Download Presentation

Visualizing Electronic Health Records for Diverse Applications and Interfaces

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Visualization ofElectronic Health RecordsBen Shneiderman ben@cs.umd.edu @benbendcFounding Director (1983-2000), Human-Computer Interaction LabProfessor, Department of Computer ScienceMember, Institute for Advanced Computer StudiesUniversity of MarylandCollege Park, MD 20742

  2. Visualization ofElectronic Health Records@benbendcUniversity of MarylandCollege Park, MD 20742

  3. Interdisciplinary research community - Computer Science & Info Studies - Psych, Socio, Poli Sci & MITH (www.cs.umd.edu/hcil)

  4. Design Issues Input devices & strategies Keyboards, pointingdevices,voice Direct manipulation Menus, forms, commands Output devices & formats Screens, windows, color, sound Text, tables, graphics Instructions, messages, help Collaboration &Social Media Help, tutorials, training Search www.awl.com/DTUI Fifth Edition: 2010 • Visualization

  5. HCI Pride: Serving 5B Users Mobile, desktop, web, cloud  Diverseusers: novice/expert, young/old, literate/illiterate, abled/disabled, cultural, ethnic & linguistic diversity, gender, personality, skills, motivation, ...  Diverse applications:E-commerce, law, health/wellness, education, creative arts, community relationships, politics, IT4ID, policy negotiation, mediation, peace studies, ...  Diverse interfaces: Ubiquitous, pervasive, embedded, tangible, invisible, multimodal, immersive/augmented/virtual, ambient, social, affective, empathic, persuasive, ...

  6. Information Visualization & Visual Analytics • Visual bands • Human percle • Trend, clus.. • Color, size,.. • Three challe • Meaningful vi • Interaction: w • Process mo 1999

  7. Information Visualization & Visual Analytics • Visual bandwidth is enormous • Human perceptual skills are remarkable • Trend, cluster, gap, outlier... • Color, size, shape, proximity... • Three challenges • Meaningful visual displays of massive da • Interaction: widgets & window coordinati • Process models for discovery 1999 2004

  8. Information Visualization & Visual Analytics • Visual bandwidth is enormous • Human perceptual skills are remarkable • Trend, cluster, gap, outlier... • Color, size, shape, proximity... • Three challenges • Meaningful visual displays of massive data • Interaction: widgets & window coordination • Process models for discovery 1999 2004 2010

  9. Treemap: Gene Ontology • Space filling • Space limited • Color coding • Size coding • - Requires learning (Shneiderman, ACM Trans. on Graphics, 1992 & 2003) www.cs.umd.edu/hcil/treemap/

  10. Treemap: Smartmoney MarketMap www.smartmoney.com/marketmap

  11. Market falls steeply Feb 27, 2007, with one exception

  12. Market mixed, February 8, 2008 Energy & Technology up, Financial & Health Care down

  13. Market rises, September 1, 2010, Gold contrarians

  14. Treemap: WHC Emergency Room (6304 patients in Jan2006) Group by Admissions/MF, size by service time, color by age

  15. Treemap: WHC Emergency Room (6304 patients in Jan2006) (only those service time >12 hours) Group by Admissions/MF, size by service time, color by age

  16. Information Visualization: Mantra • Overview, zoom & filter, details-on-demand • Overview, zoom & filter, details-on-demand • Overview, zoom & filter, details-on-demand • Overview, zoom & filter, details-on-demand • Overview, zoom & filter, details-on-demand • Overview, zoom & filter, details-on-demand • Overview, zoom & filter, details-on-demand • Overview, zoom & filter, details-on-demand • Overview, zoom & filter, details-on-demand • Overview, zoom & filter, details-on-demand

  17. Information Visualization: Data Types 1-D Linear Document Lens, SeeSoft, Info Mural 2-D Map GIS, ArcView, PageMaker, Medical imagery 3-D World CAD, Medical, Molecules, Architecture Multi-VarSpotfire, Tableau, Qliktech, Visual Insight Temporal LifeLines, TimeSearcher, Palantir, DataMontage Tree Cone/Cam/Hyperbolic, SpaceTree, Treemap Network Pajek, UCINet, NodeXL, Gephi, Tom Sawyer InfoVizSciViz. infosthetics.com visualcomplexity.com eagereyes.org flowingdata.com perceptualedge.com datakind.org visual.ly visualizing.org infovis.org

  18. Obama Unveils “Big Data” Initiative (3/2012) Big Data challenges: • Developing scalable algorithms for processing imperfect data in distributed data stores • Creating effective human-computer interaction tools for facilitating rapidly customizable visual reasoning for diverse missions. http://www.whitehouse.gov/sites/default/files/microsites/ostp/big_data_press_release_final_2.pdf `

  19. EHRs: Temporal categorical data Category Numerical Event Patient ID: 45851737 Stock: Microsoft Event 04/26/2010 10:00 31.03 04/26/2010 10:15 31.01 04/26/2010 10:30 31.02 04/26/2010 10:45 31.08 04/26/2010 11:00 31.16 12/02/2008 14:26 Arrival 12/02/2008 14:36 Emergency12/02/2008 22:44 ICU 12/05/2008 05:07 Floor 12/14/2008 06:19 Exit Time Arrival Emergency ICU Floor Exit A type of time series

  20. Patient Histories: Our Research www.cs.umd.edu/hcil/toolname

  21. Patient Histories: Our Research www.cs.umd.edu/hcil/toolname

  22. LifeLines: Patient Histories www.cs.umd.edu/hcil/lifelines

  23. Patient Histories: Our Research www.cs.umd.edu/hcil/toolname

  24. LifeLines2: Align-Rank-Filter & Summarize www.cs.umd.edu/hcil/lifelines

  25. LifeLines2: Align-Rank-Filter & Summarize www.cs.umd.edu/hcil/lifelines2

  26. Patient Histories: Our Research www.cs.umd.edu/hcil/toolname

  27. Similan: Search www.cs.umd.edu/hcil/similan

  28. Patient Histories: Our Research www.cs.umd.edu/hcil/toolname

  29. LifeFlow: Aggregation Strategy Temporal Categorical Data (4 records) LifeLines2 format Tree of Event Sequences LifeFlow Aggregation www.cs.umd.edu/hcil/lifeflow

  30. LifeFlow: Interface with User Controls

  31. Patient Histories: Our Research www.cs.umd.edu/hcil/toolname

  32. EventFlow: Original Dataset

  33. LABA_ICSs Merged

  34. SABAs Merged

  35. Align by First LABA_ICS

  36. Reduce Window Size

  37. Original Dataset

  38. Discovery Process: Systematic Yet Flexible Preparation • Own the problem & define the schedule • Data cleaning & conditioning • Handle missing & uncertain data • Extract subsets & link to related information

  39. Discovery Process: Systematic Yet Flexible Preparation • Own the problem & define the schedule • Data cleaning & conditioning • Handle missing & uncertain data • Extract subsets & link to related information Purposeful exploration – Hypothesis testing • Range & distribution • Relationships & correlations • Clusters & gaps • Outliers & anomalies • Aggregation & summary • Split & trellis • Temporal comparisons & multiple views • Statistics & forecasts

  40. Discovery Process: Systematic Yet Flexible Preparation • Own the problem & define the schedule • Data cleaning & conditioning • Handle missing & uncertain data • Extract subsets & link to related information Purposeful exploration – Hypothesis testing • Range & distribution • Relationships & correlations • Clusters & gaps • Outliers & anomalies • Aggregation & summary • Split & trellis • Temporal comparisons & multiple views • Statistics & forecasts Situated decision making - Social context • Annotation & marking • Collaboration & coordination • Decisions & presentations

  41. UN Millennium Development Goals To be achieved by 2015 • Eradicate extreme poverty and hunger • Achieve universal primary education • Promote gender equality and empower women • Reduce child mortality • Improve maternal health • Combat HIV/AIDS, malaria and other diseases • Ensure environmental sustainability • Develop a global partnership for development

  42. 30th Anniversary!!! www.cs.umd.edu/hcil@benbendc

  43. Office of National Coordinator: SHARP Strategic Health IT Advanced Research Projects - Security of Health Information Technology - Patient-Centered Cognitive Support - Healthcare Application and Network Platform Architectures - Secondary Use of EHR Data Univ of Maryland HCIL tasks - Missing Laboratory Reports - Medication Reconciliation - Wrong Patient Errors www.cs.umd.edu/hcil/sharp

  44. Medication Reconciliation: Current Form Univ of Maryland HCIL tasks - Missing Laboratory Reports - Medication Reconciliation - Alarms and Alerts Management www.cs.umd.edu/hcil/sharp www.youtube.com/watch?v=ZGf1EiuIIIM

  45. Twinlist: Medication Reconciliation “Best reconciliation app I have ever seen” Dr. Shawn Murphy, PartnersHealthcare & Harvard Medical “Super-cool demo” Dr. Jonathan Nebeker, Univ of Utah & VA “Twinlist concept is brilliant” Dr. Kevin Hughes, Harvard Medical School Tiffany Chao, Catherine Plaisant, Ben Shneideman Based on class project of : Leo Claudino, SamehKhamis, RanLiu, Ben London, Jay Pujara Students of CMSC734 Information Visualization class www.youtube.com/watch?v=YoSxlKl0pCo

  46. Twinlist: Medications Grouped

More Related