the intersection of ict and health informatics research

The Intersection of ICT and

Health Informatics Research

The Intersection of ICT and

Health Informatics Research

February 24, 2012

Nawanan Theera-Ampornpunt, MD, PhD

A Few Words About Me...

2003 Doctor of Medicine (1st-Class Honors) Ramathibodi2009 M.S. (Health Informatics) University of Minnesota2012 Ph.D. (Health Informatics) University of Minnesota

Currently• Medical Systems Analyst, Health Informatics Division, Faculty of Medicine Ramathibodi Hospital• Chair-Elect, Student Working Groups, American Medical Informatics Association

SlideShare.net/Nawananwww.tc.umn.edu/~theer002groups.google.com/group/ThaiHealthIT

Outline

• What Is Informatics?• Informatics vs. ICT• Some Research Areas in Informatics• The Road Ahead

Biomedical & Health Informatics• “[T]he field that is concerned with the

optimal use of information, oftenaided by the use of technology, to improve individual health, health care, public health, and biomedical research” (Hersh, 2009)

• “[T]he application of the science of information as data plus meaningto problems of biomedical interest” (Bernstam et al, 2010)

DIKW Pyramid

Wisdom

Knowledge

Information

Data

Wisdom

Knowledge

Information

Data

DIKW Pyramid

Informatics

ICT

Knowledge Management

Task-Oriented View of Informatics

Collection Processing

Storage

Utilization

Communication/Dissemination/

Presentation

Informatics As A Field

Shortliffe (2002)

Public Health Informatics

Hersh (2009)

Informatics and Other Fields

Biomedical/Health

Informatics

Computer & Information

Science

Engineering

Cognitive & Decision Science

Social Sciences

(Psychology, Sociology, Linguistics,

Law & Ethics)

Statistics & Research Methods

Medical Sciences &

Public Health

Management

Library Science,

Information Retrieval, KM

And More!

Why Informatics ≠ ICT

People

Techno-logyProcess

Why Informatics ≠ ICT

Informatics

Information & Communications Technology

Why We Need Informatics in Health Care?


#1. Because information is everywhere in health care

Manufacturing

Source: Guardian.co.uk

Source: Cablephet.com

Banking

Source: nj.com

Health care

Why Healthcare Isn’t Like Any Others?

• Life-or-Death• Many & varied stakeholders• Strong professional values• Evolving standards of care• Fragmented, poorly-coordinated systems• Large, ever-growing & changing body of

knowledge• High volume, low resources, little time

Source: nj.com

Why Healthcare Isn’t Like Any Others?

• Large variations & contextual dependence

Source: nj.com

Input Process Output

Patient Presentation

Decision-Making

Biological Responses


#2. Because health care is complex and difficult to

automate

Why Adopting Health IT?

“Computerize”“Go paperless”

“Digital Hospital”

“Modernize”

“Get a HIS”

“Have EMRs”

“Share data”

Some Quotes• “Don’t implement technology just for

technology’s sake.”• “Don’t make use of excellent technology.

Make excellent use of technology.”(Tangwongsan, Supachai. Personal communication, 2005.)

• “Health care IT is not a panacea for all that ails medicine.” (Hersh, 2004)

• “We worry, however, that [electronic records] are being touted as a panacea for nearly all the ills of modern medicine.”(Hartzband & Groopman, 2008)

Health IT: What’s In A Word?

Health InformationTechnology

Goal

Value-Add

Tools


#3. Because unlike other industries, the goal is

HEALTH

To Err Is Human

• Perception errors

Source: interaction-dynamics.com

• Lack of Attention

Source: aafp.org

To Err Is Human

• Cognitive Errors - Example: Decoy Pricing

The Economist Purchase Options

• Economist.com subscription $59• Print subscription $125• Print & web subscription $125

Ariely (2008)

16084

The Economist Purchase Options

• Economist.com subscription $59• Print & web subscription $125

6832

# of People

# of People

To Err Is Human

Clinical Decision Support Systems (CDSSs)

External Memory

Knowledge Data

Long Term Memory

Knowledge Data

Inference

DECISION

PATIENT

Perception

Attention

WorkingMemory

CLINICIAN

From a teaching slide by Don Connelly, 2006


External Memory

Knowledge Data

Long Term Memory

Knowledge Data

Inference

DECISION

PATIENT

Perception

Attention

WorkingMemory

CLINICIAN Abnormal lab

highlights


External Memory

Knowledge Data

Long Term Memory

Knowledge Data

Inference

DECISION

PATIENT

Perception

Attention

WorkingMemory

CLINICIAN Drug-Allergy Checks


External Memory

Knowledge Data

Long Term Memory

Knowledge Data

Inference

DECISION

PATIENT

Perception

Attention

WorkingMemory

CLINICIAN

Drug-Drug Interaction

Checks


• CDSS as a replacement or supplement of clinicians?– The demise of the “Greek Oracle” model (Miller & Masarie, 1990)

The “Greek Oracle” Model

The “Fundamental Theorem”

Friedman (2009)


#4. Because health care is error-prone and technology

can help

Some Research Areas in Informatics

Research Agenda for Thailand’s Informatics

http://www.slideshare.net/nawanan/research-topics-for-informatics-in-the-context-of-thailand

Health IT Adoption & Use: Underlying Assumption

Systems Analysis, Design & Coding

Adoption Use Outcomes

ICT’sFocus Informatics Focus

Underlying Assumption

Individual Adoption & use

• Better clinical outcomes• Improved patient satisfaction• More provider productivity/satisfaction

Organizational Adoption & Use

• Improved operational efficiency• More patients• Reduced costs/increased revenues

Societal Adoption & Use

• Better individual health/quality of life• Better population health• Long-term cost savings

Areas of IT Adoption Research

Adoption

• Describe the state of adoption in a specific setting

• Compare adoption in 2 settings

• Identify facilitators and barriers of IT adoption

Use

• Describe the state of health IT use in a specific setting

• Compare use in 2 settings

• Identify facilitators and barriers of IT use

• Determine if/when adoption will lead to use

Outcomes

• Determine if/when IT adoption & use will lead to better outcomes(+ what outcomes?)

• Compare impacts of same health IT in different settings

• Reveal mechanisms/pathways that translate adoption & use to outcomes

Example of Health IT Adoption Studies

Adoption Studies: Descriptive Aspect

Unpublished contents on this slide were removed. Please contact the speaker at

[email protected] for more information.

Adoption Studies: Theoretical Aspect

Unpublished contents on this slide were removed. Please contact the speaker at

[email protected] for more information.

Evaluation Studies of Health IT: Benefits of Health IT

Kaushal et al. (2003)

Risks of Health IT• Alert fatigue

Workarounds

Evaluation Studies of Health IT: Risks of Health IT

Koppel et al. (2005)


Han et al. (2005)


Ash et al. (2004)

Public Health Informatics

Yasnoff et al. (2001)

Consumer Health Informatics

Kaelber et al. (2008)

The Road Ahead for ICT & Informatics

IBM’s Watson

Image Source: socialmediab2b.com

Rise of the Machines?

Image Source: englishmoviez.com

Data Mining in Health Care

Image Source: Dr. Kumar @ UMN

mHealth & Social Media

ICT in Emergencies & Disasters

http://www.kromchol.com/

http://dds.bangkok.go.th/Canal/


http://www.thaiflood.com


http://www.youtube.com/user/roosuflood


https://www.facebook.com/groups/mophwarroomcoordination/


Biosurveillance

Source: Google.org/FluTrends

Google Flu Trends (Biosurveillance)

Other Intersecting Areas

Image Source: Dr. Kumar @ UMN

• Natural Language Processing (NLP)• Knowledge Representation & Semantics• Standards, Vocabularies, Ontologies• Bioinformatics• Telemedicine/Telehealth, Bio-sensing• Information Retrieval• Ethical, Legal & Social Issues (ELSI)

What Will The Future Be for Health Care?

Intelligent & helpful

machines

Machines with a human touch

Machines that replace humans

HAL 9000 Data David NS-5

Dangerous killer machines

Let’s Shape the Future Together!