IOM Report: Health IT and Patient Safety

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Medical Informatics: Opportunities for Improving Patient Care Ben Shneiderman [email protected] @benbendc Founding Director (1983-2000), Human-Computer Interaction Lab Professor, Department of Computer Science Member, Institute for Advanced Computer Studies

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Medical Informatics: Opportunities for Improving Patient Care Ben Shneiderman [email protected] @ benbendc Founding Director (1983-2000), Human-Computer Interaction Lab Professor, Department of Computer Science Member, Institute for Advanced Computer Studies. - PowerPoint PPT Presentation

Transcript of IOM Report: Health IT and Patient Safety

Page 1: IOM Report: Health IT and Patient Safety

 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

Page 2: IOM Report: Health IT and Patient Safety

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

Page 3: IOM Report: Health IT and Patient Safety

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

Page 4: IOM Report: Health IT and Patient Safety

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)

    

Page 5: IOM Report: Health IT and Patient Safety

Medical Lab Tests

Tracking

Sharpc.orgwww.cs.umd.edu/hcil/sharp

Page 6: IOM Report: Health IT and Patient Safety

Medical Lab Tests

Tracking & Retrospective Analysis

www.cs.umd.edu/hcil/sharp

Page 7: IOM Report: Health IT and Patient Safety

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

Page 8: IOM Report: Health IT and Patient Safety

Prototype by Tiffany ChaoVIDEO AVAILABLE AT www.cs.umd.edu/hcil/sharp

Prototype by Tiffany Chao VIDEO: www.cs.umd.edu/hcil/sharp

Page 9: IOM Report: Health IT and Patient Safety

Prototype by Tiffany Chao VIDEO: www.cs.umd.edu/hcil/sharp

Page 10: IOM Report: Health IT and Patient Safety

Successful adoption efforts

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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

Page 11: IOM Report: Health IT and Patient Safety

Patient Histories: LifeLines

www.cs.umd.edu/hcil/lifelines2

Page 12: IOM Report: Health IT and Patient Safety

Patient Histories: LifeLines2

www.cs.umd.edu/hcil/lifelines2

Page 13: IOM Report: Health IT and Patient Safety

Patient Histories: LifeFlow

www.cs.umd.edu/hcil/lifeflow

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Patient Histories: EventFlow

www.cs.umd.edu/hcil/eventflow

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Patient Histories: PairFinder

www.cs.umd.edu/hcil/pairfinder

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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

Page 17: IOM Report: Health IT and Patient Safety

Reducing Wrong Patient Errors: Animated Transitions & Photos

BONUS

Page 18: IOM Report: Health IT and Patient Safety

Reducing Wrong Patient Errors: Animated Transitions & Photos

Page 19: IOM Report: Health IT and Patient Safety

Reducing Wrong Patient Errors: Animated Transitions & Photos

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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

Page 21: IOM Report: Health IT and Patient Safety

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


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