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Medical Informatics:Opportunities for Improving Patient Care
Ben Shneiderman [email protected] @benbendc
Founding Director (1983-2000), Human-Computer Interaction LabProfessor, 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.orgwww.cs.umd.edu/hcil/sharp
Medical Lab Tests
Tracking & Retrospective Analysis
www.cs.umd.edu/hcil/sharp
Medication Reconciliation: Current Form
www.cs.umd.edu/hcil/sharp
Univ of Maryland HCIL tasks - Missing Laboratory Reports - Medication Reconciliation - Alarms and Alerts Management
www.youtube.com/watch?v=ZGf1EiuIIIM
Prototype by Tiffany ChaoVIDEO 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
10
Example # 1: Twinlist running on Microsoft Amalga PlatformContact: 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
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
www.cs.umd.edu/hcil/lifelines2 /eventflow /pairfinder
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
Reducing Wrong Patient Errors: Animated Transitions & Photos
BONUS
Reducing Wrong Patient Errors: Animated Transitions & Photos
Reducing Wrong Patient Errors: Animated Transitions & Photos
Error Recognition Rate for each Group
63%
43%
36%
7%
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
CombinedPhotoAnimationControl
(Taieb-Maimon, Plaisant & Shneiderman, 2012)
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
Control Photo Combined
36%
Animation
7%
43%
63%
Reducing Wrong Patient Errors: Animated Transitions & Photos
www.cs.umd.edu/hcil/sharp
Take Away Messages
Healthcare is fertile area for HCI & InfoVisPatient Safety is a national priorityWe 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/lifeflow /eventflow /pairfinder