1. IT for MDs (Part 2)
Nawanan Theera‐Ampornpunt, MD, PhD
Feb. 20, 2013
Faculty of Medicine Ramathibodi Hospital
SlideShare.net/Nawanan

2. Recap from Part 1
 Information is everywhere in medicine
 Computerizing health care is difficult because of its
complexity
 Health IT has a role because “To Err Is Human”
 Health IT is just a tool. Whether it improves patient
care and outcomes depends on its design and
implementation
2

3. Health IT: What’s In A Word?
Health Goal
Information Value‐Add
Technology Tools
3

4. Fundamental Theorem of Informatics
4
(Friedman, 2009)
(Friedman, 2009)

5. Health IT
Applications
in Hospitals
5

6. Enterprise‐wide Hospital IT
 Master Patient Index (MPI)
 Admission‐Discharge‐Transfer (ADT)
 Electronic Health Records (EHRs)
 Computerized Physician Order Entry (CPOE)
 Clinical Decision Support Systems (CDSSs)
 Picture Archiving and Communication System (PACS)
 Nursing applications
 Enterprise Resource Planning (ERP)
6

7. Departmental IT
 Pharmacy applications
 Laboratory Information System (LIS)
 Radiology Information System (RIS)
 Specialized applications (ER, OR, LR,
Anesthesia, Critical Care, Dietary Services,
Blood Bank)
 Incident management & reporting system
7

8. Hospital Information System
Clinical
Medical ADT Notes
Records
Workflow
Pharmacy IS
Operation Master
Patient LIS
Theatre
Index (MPI)
Order
CCIS
RIS
Scheduling
Portals Billing
PACS
8 Modified from Dr. Artit Ungkanont’s slide

9. EHRs & HIS
The Challenge ‐ Knowing What It Means
Electronic Health
Records (EHRs)
Hospital
Information System
Electronic Medical (HIS)
Records (EMRs)
Electronic Patient
Records (EPRs)
Clinical Information
System (CIS)
Personal Health
Computer‐Based Records (PHRs)
Patient Records
(CPRs)
9

10. EHR Systems
Just electronic documentation?
History Diag‐ Treat‐
...
& PE nosis ments
Or do they have other values?
10

11. Functions that Should Be Part of EHR Systems
 Computerized Medication Order Entry
 Computerized Laboratory Order Entry
 Computerized Laboratory Results
 Physician Notes
 Patient Demographics
 Problem Lists
 Medication Lists
 Discharge Summaries
 Diagnostic Test Results
 Radiologic Reports
11 (IOM, 2003; Blumenthal et al, 2006)

12. Computerized Physician Order Entry (CPOE)
12

13. Computerized Physician Order Entry (CPOE)
Values
No handwriting!!!
 Structured data entry: Completeness, clarity,
fewer mistakes (?)
 No transcription errors!
 Entry point for CDSSs
 Streamlines workflow, increases efficiency
13

14. 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,
(Shortliffe, 1976) treatment options
14

15. Clinical Decision Support Systems (CDSSs)
 Alerts & reminders
 Based on specified logical conditions
 Examples:
 Drug‐allergy checks
 Drug‐drug interaction checks
 Drug‐disease checks
 Drug‐lab checks
 Drug‐formulary checks
 Reminders for preventive services or certain actions
(e.g. smoking cessation)
 Clinical practice guideline integration
15

16. Example of “Alerts & Reminders”
16

17. Clinical Decision Support Systems (CDSSs)
 Evidence‐based knowledge sources e.g. drug
database, literature
 Simple UI designed to help clinical decision making
 E.g., Abnormal Lab Highlights
17

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

19. 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
19

20. 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
20

21. Clinical Decision Support Systems (CDSSs)
PATIENT
Perception Drug‐Drug
CLINICIAN Interaction
Attention Checks
Long Term Memory External Memory
Working
Memory
Knowledge Data Knowledge Data
Inference
DECISION
21

22. Clinical Decision Support Systems (CDSSs)
PATIENT
Perception Clinical Practice
CLINICIAN Guideline
Reminders
Attention
Long Term Memory External Memory
Working
Memory
Knowledge Data Knowledge Data
Inference
DECISION
22

23. 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
23

24. 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”
24 (Friedman, 2009)

25. Health IT for Medication Safety
Ordering Transcription Dispensing Administration
Automatic Electronic
CPOE
Medication Medication
Dispensing Administration
Records
(e‐MAR)
Barcoded
Medication Barcoded
Dispensing Medication
Administration
25

26. Clinical Decision Support Systems (CDSSs)
Some risks
 Alert fatigue
26

27. Workarounds
27

28. Unintended Consequences of Health IT
 “Unanticipated and unwanted effect of
health IT implementation” (ucguide.org)
 Resources
 www.ucguide.org
 Ash et al. (2004)
 Campbell et al. (2006)
 Koppel et al. (2005)
28

29. Unintended Consequences of Health IT
Ash et al. (2004)
29

30. Unintended Consequences of Health IT
 Errors in the process of entering and retrieving information
 A human‐computer interface that is not suitable for a highly
interruptive use context
 Causing cognitive overload by overemphasizing structured and
“complete” information entry or retrieval
 Structure
 Fragmentation
 Overcompleteness
Ash et al. (2004)
30

31. Unintended Consequences of Health IT
 Errors in the communication and coordination process
 Misrepresenting collective, interactive work as a linear, clearcut,
and predictable workflow
 Inflexibility
 Urgency
 Workarounds
 Transfers of patients
 Misrepresenting communication as information transfer
 Loss of communication
 Loss of feedback
 Decision support overload
 Catching errors
Ash et al. (2004)
31

32. Unintended Consequences of Health IT
 Errors in the communication and coordination process
 Misrepresenting collective, interactive work as a linear, clearcut,
and predictable workflow
 Inflexibility
 Urgency
 Workarounds
 Transfers of patients
 Misrepresenting communication as information transfer
 Loss of communication
 Loss of feedback
 Decision support overload
 Catching errors
Ash et al. (2004)
32

33. Unintended Consequences of Health IT
Campbell et al. (2006)
33

34. Unintended Consequences of Health IT
Campbell et al. (2006)
34

35. Unintended Consequences of Health IT
Koppel et al. (2005)
35

36. Unintended Consequences of Health IT
Koppel et al. (2005)
36

37. Critical Success Factors in Health IT Projects
Communications of plans & progresses
Physician & non‐physician user involvement
Attention to workflow changes
Well‐executed project management
Adequate user training
Organizational learning
Organizational innovativeness
Theera‐Ampornpunt (2011)
37

38. Health IT Successes & Failures
38 Kaplan & Harris‐Salamone (2009)

39. Health IT Successes & Failures
What success is
 Different ideas and definitions of success
 Need more understanding of different stakeholder
views & more longitudinal and qualitative studies of
failure
What makes it so hard
 Communication, Workflow, & Quality
 Difficulties of communicating across different
groups makes it harder to identify requirements and
understand workflow
39 Kaplan & Harris‐Salamone (2009)

40. Health IT Successes & Failures
What We Know—Lessons from Experience
 Provide incentives, remove disincentives
 Identify and mitigate risks
 Allow resources and time for training, exposure, and
learning to input data
 Learn from the past and from others
40 Kaplan & Harris‐Salamone (2009)

41. Health IT Change Management
41 Lorenzi & Riley (2000)

42. Health IT Change Management
42 Lorenzi & Riley (2000)

43. Health IT Change Management
43 Lorenzi & Riley (2000)

44. Health IT Change Management
44 Lorenzi & Riley (2000)

45. Considerations for a successful
implementation of CPOE
Considerations
Motivation for implementation
CPOE vision, leadership, and personnel
Costs
Integration: Workflow, health care processes
Value to users/Decision support systems
Project management and staging of implementation
Technology
Training and Support 24 x 7
Learning/Evaluation/Improvement
45 Ash et al. (2003)

46. Minimizing MD’s Change Resistance
 Involve physician champions
 Create a sense of ownership through
communications & involvement
 Understand their values
 Be attentive to climate in the organization
 Provide adequate training & support
46 Ash et al. (2003)

47. Reasons for User Involvement
 Better understanding of needs & requirements
 Leveraging user expertise about their tasks & how
organization functions
 Assess importance of specific features for prioritization
 Users better understand project, develop realistic
expectations
 Venues for negotiation, conflict resolution
Sense of ownership
 Pare & Sicotte (2006): Physician ownership
important for clinical information systems
47 Ives & Olson (1984)

48. Gartner Hype Cycle
Image source: Jeremy Kemp via http://en.wikipedia.org/wiki/Hype_cycle
48 http://www.gartner.com/technology/research/methodologies/hype‐cycle.jsp

49. Rogers’ Diffusion of Innovations: Adoption Curve
Rogers (2003)
49

50. Balanced Focus of Informatics
People
Techno‐
Process
logy
50

51. A Physician’s Story...
51

52. Summary
 Health IT applications vary in their “mechanisms of
actions” (improvements in outcomes).
 Health IT can lead to “unintended consequences”
and bad outcomes if poorly designed,
implemented, or managed.
 Realizing benefits of health IT depends on critical
success factors, mostly management aspects
(including change management, project
management)
52

53. Q & A...
Download Slides
SlideShare.net/Nawanan
Contacts
nawanan.the@mahidol.ac.th
www.tc.umn.edu/~theer002
groups.google.com/group/ThaiHealthIT
53

54. References
 Ash JS, Berg M, Coiera E. Some unintended consequences of information
technology in health care: the nature of patient care information system‐
related errors. J Am Med Inform Assoc. 2004 Mar‐Apr;11(2):104‐12.
 Ash JS, Stavri PZ, Kuperman GJ. A consensus statement on considerations
for a successful CPOE implementation. J Am Med Inform Assoc. 2003 May‐
Jun;10(3):229‐34.
 Campbell, EM, Sittig DF, Ash JS, et al. Types of Unintended Consequences
Related to Computerized Provider Order Entry. J Am Med Inform Assoc.
2006 Sep‐Oct; 13(5): 547‐556.
 Friedman CP. A "fundamental theorem" of biomedical informatics. J Am
Med Inform Assoc. 2009 Apr;16(2):169‐70.
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55. References
 Ives B, Olson MH. User involvement and MIS success: a review of research.
Manage Sci. 1984 May;30(5):586‐603.
 Kaplan B, Harris‐Salamone KD. Health IT success and failure:
recommendations from the literature and an AMIA workshop. J Am Med
Inform Assoc. 2009 May‐Jun;16(3):291‐9.
 Koppel R, Metlay JP, Cohen A, Abaluck B, Localio AR, Kimmel SE, Strom BL.
Role of computerized physician order entry systems in facilitating
medication errors. JAMA. 2005 Mar 9;293(10):1197‐203.
 Lorenzi NM, Riley RT. Managing change: an overview. J Am Med Inform
Assoc. 2000 Mar‐Apr;7(2):116‐24.
55

56. References
 Miller RA, Masarie FE. The demise of the "Greek Oracle" model for medical
diagnostic systems. Methods Inf Med. 1990 Jan;29(1):1‐2.
 Rogers EM. Diffusion of innovations. 5th ed. New York City (NY): Free
Press;2003. 551 p.
 Riley RT, Lorenzi NM. Gaining physician acceptance of information
technology systems. Med Interface. 1995 Nov;8(11):78‐80, 82‐3.
 Theera‐Ampornpunt N. Thai hospitals' adoption of information technology:
a theory development and nationwide survey [dissertation]. Minneapolis
(MN): University of Minnesota; 2011 Dec. 376 p.
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