Medical Informatics:
Opportunities for Improving Patient Care
Ben Shneiderman
ben@cs.umd.edu
@benbendc
Founding Director (1983-2000), Human-Computer Interaction Lab
Professor, Department of Computer Science
Member, Institute for Advanced Computer Studies
IOM Report: Health IT and Patient Safety
Problem
- Poorly designed health IT can create new hazards
in the already complex delivery of care.
- Dosing errors, failure to detect life-threatening illnesses &
delaying treatment due to poor HCI or loss of data
have led to serious injury & death.
Recommendations:
- User-centered design & human factors
applied to health IT
www.iom.edu/Reports/2011/
Health-IT-and-Patient-Safety-Building-Safer-Systems-for-Better-Care.aspx
Strategic Health IT Advanced Research Projects (SHARP):
Office of National Coordinator
- Security of Health Information Technology
- Patient-Centered Cognitive Support
- Healthcare Application and Network Platform Architectures
- Secondary Use of EHR Data
Sharpc.org
www.cs.umd.edu/hcil/sharp
HCIL’s Research Initiatives
Medical Lab Tests
- Tracking Completion in Complex Workflows
- Guidelines for Design of Tables & Actions
- Retrospective Analysis
Medication Reconciliation
Patient Histories – Analysis & Search
- Temporal Event Sequences
- Point & Interval Events
Wrong Patient Errors (BONUS)
Medical Lab Tests
Tracking
Sharpc.org
www.cs.umd.edu/hcil/sharp
Medical Lab Tests
Tracking & Retrospective Analysis
www.cs.umd.edu/hcil/sharp
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
Prototype by Tiffany Chao
VIDEO AVAILABLE AT
www.cs.umd.edu/hcil/sharp
Prototype by Tiffany Chao VIDEO: www.cs.umd.edu/hcil/sharp
Prototype by Tiffany Chao VIDEO: www.cs.umd.edu/hcil/sharp
Successful adoption efforts
Example # 1: Twinlist running on
Microsoft Amalga Platform
Contact: Hank Rappaport
Example #2: Twinlist adapted for problem list
reconciliation in cancer risk assessment
(Hughes riskApps™ - an EHR module)
Contact: Kevin S. Hughes, Massachusetts General Hospital
10
Patient Histories: LifeLines
www.cs.umd.edu/hcil/lifelines2
Patient Histories: LifeLines2
www.cs.umd.edu/hcil/lifelines2
Patient Histories: LifeFlow
www.cs.umd.edu/hcil/lifeflow
Patient Histories: EventFlow
www.cs.umd.edu/hcil/eventflow
Patient Histories: PairFinder
www.cs.umd.edu/hcil/pairfinder
Temporal Event Sequences
Tool
Event Types
Records
Display
LifeLines
Points & intervals
One
One record
LifeLines2
Points
Many
Many records
Similan
Points
Many
Many records
LifeFlow
Points
Many
Many records &
aggregate
EventFlow
Points & intervals
Many
Many records &
aggregate
PairFinder
Points
Many
Temporal record
distributions
www.cs.umd.edu/hcil/lifelines2
/eventflow
/pairfinder
Reducing Wrong Patient Errors:
Animated Transitions & Photos
BONUS
Reducing Wrong Patient Errors:
Animated Transitions & Photos
Reducing Wrong Patient Errors:
Animated Transitions & Photos
Reducing Wrong Patient Errors:
Animated Transitions & Photos
Error Recognition Rate for each Group
63
%
0.7
63%
43%
0.6
0.5
43%
36%
0.4
36%
0.3
0.2
7%
7%
0.1
0
Control
Control
Animation
Animation
Photo
Photo
Combined
Combined
The combination of animation & photo resulted in a significant increase
in error recognition rate relative to the control & animation groups 
Dramatic implications for commercial systems
(Taieb-Maimon, Plaisant & Shneiderman, 2012)
Take Away Messages
Healthcare is fertile area for HCI & InfoVis
Patient Safety is a national priority
We have tools for analysts
Tomorrow: Workshop on EHR Informatics
Thanks to:
Oracle Corporation
NIH/National Cancer Institute grant RC1-CA147489,
SHARP grant 10510592 – Office of National Coordinator
www.cs.umd.edu/hcil/sharp
www.cs.umd.edu/hcil/lifeflow
/eventflow
/pairfinder