Health informatics is the application of information science to address problems in healthcare. It involves using technology and data to improve individual health as well as healthcare systems. The adoption of health IT aims to enhance quality, safety, efficiency and reduce costs. Key health IT tools discussed include electronic health records, clinical decision support systems, computerized physician order entry, and health information exchange. The document outlines the benefits and challenges of implementing health IT to transform healthcare delivery.

1. SlideShare.net/Nawanan
Health f
H lth IInformatics:
ti
The Next “Stethoscope” in Healthcare
p
Nawanan Theera-Ampornpunt, MD, MS

2. Healthcare &
Health
H lth IT

3. Manufacturing
g
Source: Guardian.co.uk

4. Banking
g
Source: Cablephet.com

5. Healthcare
Source: nj.com

6. Why Healthcare Isn’t Like Any Others?
• Life-or-Death
• Many & varied stakeholders
• Strong professional values
• Evolving standards of care
• Fragmented, poorly coordinated
Fragmented poorly-coordinated systems
• Large, ever-growing & changing body of knowledge
• High volume, low resources, little time
Source: nj.com

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

8. But...Are We That Different?
Banking
Input Process Output
Transfer
Location A
Location A Location B
Location B
Value‐Add
‐ Security
‐CConvenience
i
‐ Customer Service

9. But...Are We That Different?
Manufacturing
Input Process Output
Raw
Raw Assembling Finished
Finished
Materials Goods
Value‐Add
‐ Innovation
‐ Skills
‐ QA

10. But...Are We That Different?
Healthcare
Input Process Output
Sick Patient Patient Care Well Patient
Value‐Add
‐ Medical technology & medications
g
‐ Clinical knowledge & skills
‐ Quality of care; process improvement
‐ Information

11. Information is Everywhere
y

12. Various Forms of Health IT
Hospital Information System (HIS) Computerized Provider Order Entry (CPOE)
Electronic
Health
Records Picture Archiving and
g
(EHRs) Communication System
(PACS)

13. Still Many Other Forms of Health IT
Health Information
Exchange (
g (HIE))
m-Health
m Health
Biosurveillance
Personal Health Records
(PHRs)
Telemedicine &
Information Retrieval Telehealth
Images from Apple Inc., Geekzone.co.nz, Google, PubMed.gov, and American Telecare, Inc.

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

15. Some Quotes
• “Don’t implement technology just for technology s sake.”
Don t 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)

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

17. Dimensions of Quality Healthcare
• Safety
• Timeliness
• Effectiveness
• Efficiency
• Equity
E it
• Patient-centeredness
at e t ce te ed ess
(IOM, 2001)

18. Value of Health IT
• Guideline
G ideline adherence
• Better documentation
• Practitioner decision making or process of care
• Medication safety
• Patient surveillance & monitoring
• Patient ed cation/reminder
education/reminder

19. Fundamental Theorem of Informatics
(Friedman, 2009)

20. Is There A Role for Health IT?
(IOM, 2000)

21. Landmark IOM Reports
(IOM, 2000) (IOM, 2001)

22. Landmark IOM Reports: Summary
• Humans are not perfect and are bound to make errors
p
• High-light problems in the U.S. health care system that
systematically contributes to medical errors and poor
quality
• Recommends reform that would change how health
care works and how technology innovations can help
improve quality/safety

23. Why We Need Health IT
• Health care is very complex (and inefficient)
• Health care is information-rich
• Quality of care depends on timely availability &
quality of information
• Clinical knowledge body is too large
• Short time during a visit
• Practice guidelines are put “on-the-shelf”
• “To err is human”

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

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

26. To Err Is Human
• 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
Economist.com $59 68
• Print & web subscription $125 32
(Ariely, 2008)

27. What If This Happens in Healthcare?
• It already h
l d happens....
(Mamede et al., 2010; Croskerry, 2003; Klein, 2005)
• What if health IT can help?

28. U.S.’s Efforts on Health IT Adoption
?
“...We will make wider use of electronic records and
We
other health information technology, to help control
costs and reduce dangerous medical errors.”
President George W. Bush
Sixth State of the Union Address, January 31, 2006
Source: Wikisource.org Image Source: Wikipedia.org

29. Public Policy in Informatics: A US’s Case
1991: IOM s CPR Report published
1991: IOM’s CPR Report published
1996: HIPAA enacted
2000‐2001: IOM’s To Err Is Human &
Crossing the Quality Chasm published
2004: George W. Bush’s Executive Order
establishing ONCHIT (ONC)
2009‐2010: ARRA/HITECH Act &
“Meaningful use” regulations
Meaningful use regulations

30. U.S. Adoption of Health IT
Ambulatory (Hsiao et al, 2009) Hospitals (Jha et al, 2009)
Basic EHRs w/ notes 7.6%
Comprehensive EHRs
p 1.5%
CPOE 17%
• U.S. lags behind other Western countries
(Schoen et al, 2006;Jha et al, 2008)
• Money and misalignment of benefits is the biggest
reason

31. We Need “Change”
“...we need to upgrade our medical
records by switching from a p p to
y g paper
an electronic system of record
keeping...”
President Barack Ob
P id t B k Obama
June 15, 2009

32. The Birth of “Meaningful Use”
“...Our recovery plan will invest in
yp
electronic health records and new technology
that will reduce errors, bring down costs,
ensure privacy, and save lives.
ensure privacy and save lives ”
President Barack Obama
Address to Joint Session of Congress
Address to Joint Session of Congress
February 24, 2009
Source: WhiteHouse.gov

33. American Recovery & Reinvestment Act
• Contains HITECH Act
(
(Health Information Technology for Economic and
gy
Clinical Health Act)
• ~ 20 billion dollars for Health IT investments
• Incentives & penalties for providers

34. National Leadership
Office f th N ti
Offi of the Nationall Coordinator for Health Information
C di t f H lth I f ti
Technology (ONC -- formerly ONCHIT)
David Blumenthal, MD, MPP
National Coordinator for
Health Information Technology
(2009 - Present)
Photo courtesy of U.S. Department of Health & Human Services

35. What is in the HITECH Act?
(Blumenthal, 2010)

36. “Meaningful Use”
g
Pumpkin “Meaningful Use”
of a Pumpkin
Image Source & Idea Courtesy of Pat Wise at HIMSS, Oct. 2009

37. “Meaningful Use” of Health IT
g
Stage 1
Stage 1
‐ Electronic capture of Better
health information
‐ Information sharing
Stage 3
St 3
Health
‐ Data reporting
Stage 2
Use of
EHRs to
to
Use of EHRs improve
to improve outcomes
processes of
care
(Blumenthal, 2010)

38. Health IT
Applications
A li ti

39. Enterprise-wide Hospital IT
• Master Patient Index (MPI)
• Admit-Discharge-Transfer (ADT)
• Electronic Health Records (EHRs)
• C
Computerized Ph i i O d E t (CPOE)
t i d Physician Order Entry
• Clinical Decision Support Systems (CDSSs)
pp y
• Picture Archiving and Communication System (PACS)
• Nursing applications
• Enterprise Resource Planning (ERP))
l (

40. Departmental IT
• Pharmacy applications
y pp
• Laboratory Information System (LIS)
• Specialized applications (ER, OR, LR, Anesthesia,
Critical Care, Dietary Services, Blood Bank)
• Incident management & reporting system

41. EHRs & HIS
The Challenge ‐ Knowing What It Means
Electronic Health
Records (EHRs)
Hospital Information
Hospital Information
System (HIS)
Electronic Medical
Records (EMRs)
Records (EMRs)
Electronic Patient
Records (EPRs)
Clinical Information
System (CIS)
Personal Health
Computer‐Based
C B d Records (PHRs)
Patient Records
(CPRs)

42. EHR Systems
Just electronic documentation?
History Diag‐ Treat‐
...
& PE nosis ments
Or d th have th l ?
O do they h other values?

43. Functions that Should Be Part of EHR Systems
• Computerized Medication Order Entry
• Computerized Laboratory Order Entry
• Computerized Laboratory Results
p y
• Physician Notes
• Patient Demographics
P ti t D hi
• Problem Lists
• Medication Lists
• Discharge Summaries
• Diagnostic Test Results
• Radiologic Reports
(IOM, 2003; Blumenthal et al, 2006)

44. Computerized Physician Order Entry
Values
• No handwriting!!!
• Structured data entry: Completeness clarity
Completeness, clarity,
fewer mistakes (?)
• No transcription errors!
• E point for CDSS
Entry i f CDSSs
• Streamlines workflow, increases efficiency

45. Clinical Decision Support Systems (CDSSs)
• The real place where most of the values of health IT can be achieved
– Expert systems
• Based on artificial intelligence, machine learning, rules, or statistics
• Examples: differential diagnoses, treatment options
– Alerts & reminders
• Based on specified logical conditions
• Examples: drug-allergy checks, drug-drug interaction checks,
reminders for preventive services or certain actions (e.g. smoking
cessation), clinical practice guideline integration
– Evidence-based knowledge sources e.g. drug database, literature
– Simple UI designed to help clinical decision making

46. 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

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

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

49. 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

50. Clinical Decision Support Systems (CDSSs)
PATIENT
Perception Clinical
CLINICIAN Practice
Guideline
Attention Reminders
Long Term Memory External Memory
Working
Memory
Knowledge Data Knowledge Data
Inference
DECISION

51. Clinical Decision Support Systems (CDSSs)
PATIENT
Perception
CLINICIAN
Attention
Long Term Memory External Memory
Working
Memory
Knowledge Data Knowledge Data
Inference Diagnostic/Treatment
Expert Systems
DECISION

52. Clinical Decision Support Systems (CDSSs)
• CDSS as a supplement or replacement of clinicians?
– The demise of the “Greek Oracle” model (Miller & Masarie, 1990)
The “Greek Oracle” Model
The “Fundamental Theorem”
(Friedman, 2009)

53. Clinical Decision Support Systems (CDSSs)
Some risks
• Alert fatigue

54. Workarounds

55. Health IT for Medication Safety
Ordering
g Transcription
p Dispensing
p g Administration
Automatic Electronic
CPOE
C O
Medication Medication
Dispensing Administration
Records
(e-MAR)
Barcoded
Medication Barcoded
Dispensing
Di i Medication
Administration

56. Health Information Exchange (HIE)
Government
Hospital A Hospital B
Clinic C
Lab
L b Patient t H
P ti t at Home

57. 4 Quadrants of Health IT
Strategic
Business
Intelligence
g HIE
CDSS
CPOE
Administrative Clinical
VMI EHRs
ERP LIS
ADT
Operational (Theera-Ampornpunt [unpublished], 2010)

58. Health Informatics
As A Field

59. 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,
health care health
and biomedical research” (Hersh, 2009)
• “[T]he application of the science of information as
data l
d t plus meaning t problems of bi di l
i to bl f biomedical
interest” (Bernstam et al, 2010)

60. Data-Information-Knowledge-Wisdom Pyramid
Wisdom
Knowledge
Information
Data

61. Task-Oriented View
Collection Processing Utilization
Communication/
Storage Dissemination/
Presentation

62. M/B/H Informatics As A Field
(Shortliffe, 2002)

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

64. Balanced Focus of Informatics
People
Techno‐
Process logy

65. Informatics & Engineering
Process-focus
Process focus
• Industrial Engineering / Operations Research
g g p
& Management / Business Process Reengineering
Technology-focus
• Computer & Software Engineering
• Biomedical Engineering
• Electrical Engineering

66. Summary
• Healthcare will benefit from health IT through
– Information deliveryy
– Process improvement
• The world is moving toward health IT
• H lth iinformatics needs expertise f engineering &
Health f ti d ti from i i
other fields
• Health informatics will be crucial to future’s healthcare

67. Let s
Let’s Build The
Next Generation’s
Healthcare!
H lth !

68. References
• Bernstam EV, Smith JW, Johnson TR. What is biomedical informatics? J Biomed Inform. 2010
Feb;43(1):104‐10.
• Blumenthal D. Launching HITECH. N Engl J Med. 2010 Feb 4;362(5):382‐5.
• Blumenthal D, DesRoches C, Donelan K, Ferris T, Jha A, Kaushal R, Rao S, Rosenbaum S.
Health information technology in the United States: the information base for progress
[Internet]. Princeton (NJ): Robert Wood Johnson Foundation; 2006
• Croskerry P. The importance of cognitive errors in diagnosis and strategies to minimize them.
Acad Med. 2003 Aug;78(8):775‐80. 81 p. Available from:
A d M d 2003 A 78(8) 775 80 81 A il bl f
http://www.rwjf.org/files/publications/other/EHRReport0609.pdf
• Friedman CP. A "fundamental theorem" of biomedical informatics. J Am Med Inform Assoc.
2009 Apr;16(2):169 70.
2009 Apr;16(2):169‐70
• Hersh W. A stimulus to define informatics and health information technology. BMC Med
Inform Decis Mak. 2009;9:24.
• Hsiao C, Beatty PC, Hing ES, Woodwell DA. Electronic medical record/electronic health record
Hsiao C, Beatty PC, Hing ES, Woodwell DA. Electronic medical record/electronic health record
use by office‐based physicians: United States, 2008 and preliminary 2009 [Internet]. 2009
[cited 2010 Apr 12]; Available from: http://www.cdc.gov/nchs/data/hestat/emr_ehr/
emr_ehr.pdf

69. References
• Institute of Medicine, Board on Health Care Services, Committee on Data Standards for
Patient Safety. Key Capabilities of an electronic health record system: letter report [Internet].
f bl f l h lh d l [ ]
Washington, DC: National Academy of Sciences; 2003.
31 p. Available from: http://www.nap.edu/catalog/10781.html
• Jha AK DesRoches CM Campbell EG Donelan K Rao SR Ferris TG Shields A Rosenbaum S
AK, DesRoches CM, Campbell EG, Donelan K, Rao SR, Ferris TG, Shields A, Rosenbaum S,
Blumenthal D. Use of electronic health records in U.S. hospitals. N Engl J Med.
2009;360(16):1628‐38.
• Jha AK, Doolan D, Grandt D, Scott T, Bates DW. The use of health information technology in
, , , , gy
seven nations. Int J Med Inform. 2008;77(12):848‐54.
• Klein JG. Five pitfalls in decisions about diagnosis and prescribing. BMJ. 2005 Apr
2;330(7494):781‐3.
• Mamede S, van Gog T, van den Berge K, Rikers RM, van Saase JL, van Guldener C, Schmidt HG.
Effect of availability bias and reflective reasoning on diagnostic accuracy among internal
medicine residents. JAMA. 2010 Sep 15:304(11):1198‐203.
• Miller RA, Masarie FE Th d i
Mill RA M i FE. The demise of the "Greek Oracle" model for medical diagnostic
f h "G k O l " d lf di l di i
systems. Methods Inf Med. 1990 Jan;29(1):1‐2.
• Schoen C, Osborn R, Huynh PT, Doty M, Puegh J, Zapert K. On the front lines of care: primary
care doctors office systems experiences and views in seven countries Health Aff
care doctors’ office systems, experiences, and views in seven countries. Health Aff
(Millwood). 2006;25(6):w555‐71.
• Shortliffe EH. JBI status report. Journal of Biomedical Informatics. 2002 Oct;35(5‐6):279‐80.