The Intersection of ICT and Health Informatics Research
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Transcript of The Intersection of ICT and Health Informatics Research
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The Intersection of ICT and
Health Informatics Research
The Intersection of ICT and
Health Informatics Research
February 24, 2012
Nawanan Theera-Ampornpunt, MD, PhD
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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
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Outline
• What Is Informatics?• Informatics vs. ICT• Some Research Areas in Informatics• The Road Ahead
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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)
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DIKW Pyramid
Wisdom
Knowledge
Information
Data
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Wisdom
Knowledge
Information
Data
DIKW Pyramid
Informatics
ICT
Knowledge Management
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Task-Oriented View of Informatics
Collection Processing
Storage
Utilization
Communication/Dissemination/
Presentation
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Informatics As A Field
Shortliffe (2002)
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Public Health Informatics
Hersh (2009)
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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!
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Why Informatics ≠ ICT
People
Techno-logyProcess
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Why Informatics ≠ ICT
Informatics
Information & Communications Technology
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Why We Need Informatics in Health Care?
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Why We Need Informatics in Health Care?
#1. Because information is everywhere in health care
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Manufacturing
Source: Guardian.co.uk
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Source: Cablephet.com
Banking
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Source: nj.com
Health care
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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
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Why Healthcare Isn’t Like Any Others?
• Large variations & contextual dependence
Source: nj.com
Input Process Output
Patient Presentation
Decision-Making
Biological Responses
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Why We Need Informatics in Health Care?
#2. Because health care is complex and difficult to
automate
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Why Adopting Health IT?
“Computerize”“Go paperless”
“Digital Hospital”
“Modernize”
“Get a HIS”
“Have EMRs”
“Share data”
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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)
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Health IT: What’s In A Word?
Health InformationTechnology
Goal
Value-Add
Tools
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Why We Need Informatics in Health Care?
#3. Because unlike other industries, the goal is
HEALTH
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To Err Is Human
• Perception errors
Source: interaction-dynamics.com
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• Lack of Attention
Source: aafp.org
To Err Is Human
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• 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
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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
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Clinical Decision Support Systems (CDSSs)
External Memory
Knowledge Data
Long Term Memory
Knowledge Data
Inference
DECISION
PATIENT
Perception
Attention
WorkingMemory
CLINICIAN Abnormal lab
highlights
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Clinical Decision Support Systems (CDSSs)
External Memory
Knowledge Data
Long Term Memory
Knowledge Data
Inference
DECISION
PATIENT
Perception
Attention
WorkingMemory
CLINICIAN Drug-Allergy Checks
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Clinical Decision Support Systems (CDSSs)
External Memory
Knowledge Data
Long Term Memory
Knowledge Data
Inference
DECISION
PATIENT
Perception
Attention
WorkingMemory
CLINICIAN
Drug-Drug Interaction
Checks
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Clinical Decision Support Systems (CDSSs)
• 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)
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Why We Need Informatics in Health Care?
#4. Because health care is error-prone and technology
can help
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Some Research Areas in Informatics
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Research Agenda for Thailand’s Informatics
http://www.slideshare.net/nawanan/research-topics-for-informatics-in-the-context-of-thailand
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Health IT Adoption & Use: Underlying Assumption
Systems Analysis, Design & Coding
Adoption Use Outcomes
ICT’sFocus Informatics Focus
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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
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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
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Example of Health IT Adoption Studies
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Adoption Studies: Descriptive Aspect
Unpublished contents on this slide were removed. Please contact the speaker at
[email protected] for more information.
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Adoption Studies: Theoretical Aspect
Unpublished contents on this slide were removed. Please contact the speaker at
[email protected] for more information.
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Evaluation Studies of Health IT: Benefits of Health IT
Kaushal et al. (2003)
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Risks of Health IT• Alert fatigue
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Workarounds
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Evaluation Studies of Health IT: Risks of Health IT
Koppel et al. (2005)
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Evaluation Studies of Health IT: Risks of Health IT
Han et al. (2005)
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Evaluation Studies of Health IT: Risks of Health IT
Ash et al. (2004)
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Public Health Informatics
Yasnoff et al. (2001)
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Consumer Health Informatics
Kaelber et al. (2008)
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The Road Ahead for ICT & Informatics
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IBM’s Watson
Image Source: socialmediab2b.com
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Rise of the Machines?
Image Source: englishmoviez.com
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Data Mining in Health Care
Image Source: Dr. Kumar @ UMN
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mHealth & Social Media
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ICT in Emergencies & Disasters
http://www.kromchol.com/
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http://dds.bangkok.go.th/Canal/
ICT in Emergencies & Disasters
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http://www.thaiflood.com
ICT in Emergencies & Disasters
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http://www.youtube.com/user/roosuflood
ICT in Emergencies & Disasters
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https://www.facebook.com/groups/mophwarroomcoordination/
ICT in Emergencies & Disasters
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Biosurveillance
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Source: Google.org/FluTrends
Google Flu Trends (Biosurveillance)
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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)
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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
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Let’s Shape the Future Together!