Theera-Ampornpunt N. The intersection of ICT and health informatics research. Presented at: Faculty of ICT, Mahidol University; 2012 Feb 24; Bangkok, Thailand.

1. The Intersection
of ICT and
Health
Informatics
Research
February 24, 2012
Nawanan Theera-Ampornpunt, MD, PhD

2. A Few Words About Me...
2003 Doctor of Medicine (1st-Class Honors) Ramathibodi
2009 M.S. (Health Informatics) University of Minnesota
2012 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/Nawanan
www.tc.umn.edu/~theer002
groups.google.com/group/ThaiHealthIT

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

4. Biomedical & Health Informatics
• “[T]he field that is concerned with the
optimal use of information, often
aided 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 meaning
to problems of biomedical interest”
(Bernstam et al, 2010)

5. DIKW Pyramid
Wisdom
Knowledge
Information
Data

6. DIKW Pyramid
Wisdom
Knowledge
Knowledge Management
Information Informatics
Data ICT

7. Task-Oriented View of Informatics
Collection Processing Utilization
Communication
Storage /Dissemination/
Presentation

8. Informatics As A Field
Shortliffe (2002)

9. Public Health Informatics
Hersh (2009)

10. Informatics and Other Fields
Social
Sciences Statistics &
(Psychology,
Sociology, Research
Linguistics, Methods
Cognitive & Law & Ethics) Medical
Decision Sciences &
Science Public Health
Engineering Management
Library
Computer & Biomedical/
Science,
Information Health
Information
Science Informatics
Retrieval, KM
And More!

11. Why Informatics ≠ ICT
People
Techno-
Process logy

12. Why Informatics ≠ ICT
Informatics
Information & Communications T
echnology

13. Why We Need Informatics
in Health Care?

15. Why We Need Informatics
in Health Care?
#1. Because information is
everywhere in health care

16. Manufacturing
Source: Guardian.co.uk

17. Banking
Source: Cablephet.com

18. Health care
Source: nj.com

19. 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

20. Why Healthcare Isn’t Like Any Others?
• Large variations & contextual dependence
Input Process Output
Patient Decision- Biological
Presentation Making Responses
Source: nj.com

21. Why We Need Informatics
in Health Care?
#2. Because health care is
complex and difficult to
automate

22. Why Adopting Health IT?
“Go paperless” “Computerize”
“Get a HIS”
“Digital Hospital”
“Have EMRs”
“Modernize”
“Share data”

23. 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)

24. Health IT: What’s In A Word?
Health Goal
Information Value-Add
Technology Tools

25. Why We Need Informatics
in Health Care?
#3. Because unlike other
industries, the goal is
HEALTH

26. To Err Is Human
• Perception errors
Source: interaction-dynamics.com

27. To Err Is Human
• Lack of Attention
Source: aafp.org

28. To Err Is Human
• Cognitive Errors - Example: Decoy Pricing
# of
The Economist Purchase Options People
• Economist.com subscription $59 16
• Print subscription $125 0
• Print & web subscription $125 84
# of
The Economist Purchase Options People
• Economist.com subscription $59 68
32 Ariely (2008)
• Print & web subscription $125

29. Clinical Decision Support Systems (CDSSs)
PATIENT
Perception
CLINICIAN
Attention
Long Term Memory External Memory
Working
Memory
Knowledge Data Knowledge Data
Inference
DECISION
From a teaching slide by Don Connelly, 2006

30. Clinical Decision Support Systems (CDSSs)
PATIENT
Perception
CLINICIAN Abnormal
Attention lab
highlights
Long Term Memory External Memory
Working
Memory
Knowledge Data Knowledge Data
Inference
DECISION

31. Clinical Decision Support Systems (CDSSs)
PATIENT
Perception
CLINICIAN Drug-
Attention Allergy
Checks
Long Term Memory External Memory
Working
Memory
Knowledge Data Knowledge Data
Inference
DECISION

32. Clinical Decision Support Systems (CDSSs)
PATIENT
Drug-Drug
Perception Interaction
CLINICIAN Checks
Attention
Long Term Memory External Memory
Working
Memory
Knowledge Data Knowledge Data
Inference
DECISION

33. 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)

34. Why We Need Informatics
in Health Care?
#4. Because health care is
error-prone and technology
can help

35. Some Research Areas in
Informatics

36. Research Agenda for Thailand’s Informatics
http://www.slideshare.net/nawanan/research-
topics-for-informatics-in-the-context-of-thailand

37. Health IT Adoption & Use:
Underlying Assumption
ICT’s
Focus Informatics Focus
Systems
Analysis,
Adoption Use Outcomes
Design &
Coding

38. Underlying Assumption
• Better clinical outcomes
• Improved patient satisfaction
Individual • More provider productivity/satisfaction
Adoption & use
• Improved operational efficiency
• More patients
Organizational • Reduced costs/increased revenues
Adoption & Use
• Better individual health/quality of life
• Better population health
Societal • Long-term cost savings
Adoption & Use

39. Areas of IT Adoption Research
Adoption Use Outcomes
• Describe the state of • Describe the state of • Determine if/when IT
adoption in a specific health IT use in a adoption & use will lead
setting specific setting to better outcomes
(+ what outcomes?)
• Compare adoption in 2 • Compare use in 2
settings settings • Compare impacts of
same health IT in
• Identify facilitators and • Identify facilitators and different settings
barriers of IT adoption barriers of IT use
• Reveal
• Determine if/when mechanisms/pathways
adoption will lead to use that translate adoption &
use to outcomes

40. Example of Health IT Adoption Studies

41. Adoption Studies: Descriptive Aspect
Unpublished contents on this slide were
removed. Please contact the speaker at
ranta@mahidol.ac.th for more
information.

42. Adoption Studies: Theoretical Aspect
Unpublished contents on this slide were
removed. Please contact the speaker at
ranta@mahidol.ac.th for more
information.

43. Evaluation Studies of Health IT:
Benefits of Health IT
Kaushal et al. (2003)

44. Risks of Health IT
• Alert fatigue

45. Workarounds

46. Evaluation Studies of Health IT:
Risks of Health IT
Koppel et al. (2005)

47. Evaluation Studies of Health IT:
Risks of Health IT
Han et al. (2005)

48. Evaluation Studies of Health IT:
Risks of Health IT
Ash et al. (2004)

49. Public Health Informatics
Yasnoff et al. (2001)

50. Consumer Health Informatics
Kaelber et al. (2008)

51. The Road Ahead for
ICT & Informatics

52. IBM’s Watson
Image Source: socialmediab2b.com

53. Rise of the Machines?
Image Source: englishmoviez.com

54. Data Mining in Health Care
Image Source: Dr. Kumar @ UMN

55. mHealth & Social Media

56. ICT in Emergencies & Disasters
http://www.kromchol.com/

57. ICT in Emergencies & Disasters
http://dds.bangkok.go.th/Canal/

58. ICT in Emergencies & Disasters
http://www.thaiflood.com

59. ICT in Emergencies & Disasters
http://www.youtube.com/user/roosuflood

60. ICT in Emergencies & Disasters
https://www.facebook.com/groups/mophwarroomcoordination/

61. Biosurveillance

62. Google Flu Trends (Biosurveillance)
Source: Google.org/FluTrends

63. Other Intersecting Areas
• Natural Language Processing (NLP)
• Knowledge Representation & Semantics
• Standards, Vocabularies, Ontologies
• Bioinformatics
• Telemedicine/Telehealth, Bio-sensing
• Information Retrieval
• Ethical, Legal & Social Issues (ELSI)
Image Source: Dr. Kumar @ UMN

64. What Will The Future Be for Health Care?
HAL 9000 Data David NS-5
Intelligent & Machines with a Machines that
Dangerous
helpful human touch replace humans
killer machines
machines

65. Let’s Shape the Future
Together!