The document provides an overview of hospital information systems, describing how they use information and communication technologies to help clinicians provide high quality, safe, and efficient care by reducing errors and improving access to patient information, with examples including electronic health records, computerized physician order entry, clinical decision support systems, and other applications.

1. Overview of
Hospital Information Systems
Nawanan Theera-Ampornpunt, M.D., Ph.D.
Department of Community Medicine
Faculty of Medicine Ramathibodi Hospital
March 3, 2014
SlideShare.net/Nawanan

2. A Bit About Myself...
2003
2009
2011
2012
M.D. (First-Class Honors) (Ramathibodi)
M.S. in Health Informatics (U of MN)
Ph.D. in Health Informatics (U of MN)
Certified HL7 CDA Specialist
Deputy Executive Director for Informatics (CIO/CMIO)
Chakri Naruebodindra Medical Institute
• Lecturer, Department of Community Medicine
Faculty of Medicine Ramathibodi Hospital
Mahidol University
•
nawanan.the@mahidol.ac.th
http://groups.google.com/group/ThaiHealthIT
2

3. Outline
•
•
•
•
•
•
Healthcare & Information
Why We Need ICT in Healthcare
Health IT
Hospital Information Systems
Health Information Exchange
Q&A
3

4. Let’s start with
something simple...
4

5. What Clinicians Want?
To treat & to
care for their
patients to their
best abilities,
given limited
time &
resources
Image Source: http://en.wikipedia.org/wiki/File:Newborn_Examination_1967.jpg (Nevit Dilmen)
5

6. High Quality Care
•
•
•
•
•
•
Safe
Timely
Effective
Patient-Centered
Efficient
Equitable
Institute of Medicine, Committee on Quality of Health Care in America. Crossing the quality chasm:
a new health system for the 21st century. Washington, DC: National Academy Press; 2001. 337 p.
6

7. Information is Everywhere in Healthcare
Shortliffe EH. Biomedical informatics in the education of
physicians. JAMA. 2010 Sep 15;304(11):1227-8.
7

8. “Information” in Medicine
Shortliffe EH. Biomedical informatics in the education of physicians. JAMA. 2010 Sep 15;304(11):1227-8.
8

9. Why We Need ICT
in Healthcare?
#1: Because information is
everywhere in healthcare
9

10. Landmark IOM Reports
(IOM, 2000)
(IOM, 2001)
(IOM, 2011)
10

11. Patient Safety
• To Err is Human (IOM, 2000) reported
that:
– 44,000 to 98,000 people die in U.S.
hospitals each year as a result of
preventable medical mistakes
– Mistakes cost U.S. hospitals $17 billion to
$29 billion yearly
– Individual errors are not the main problem
– Faulty systems, processes, and other
conditions lead to preventable errors
Health IT Workforce Curriculum Version
3.0/Spring 2012
Introduction to Healthcare and Public Health in the US: Regulating Healthcare - Lecture d
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12. IOM Reports Summary
• Humans are not perfect and are bound to
make errors
• Highlight problems in U.S. health care
system that systematically contributes to
medical errors and poor quality
• Recommends reform
• Health IT plays a role in improving patient
safety
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13. To Err is Human 1: Attention
Image Source: (Left) http://docwhisperer.wordpress.com/2007/05/31/sleepy-heads/
(Right) http://graphics8.nytimes.com/images/2008/12/05/health/chen_600.jpg
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14. To Err is Human 2: Memory
Image Source: Suthan Srisangkaew, Department of Pathology, Facutly of Medicine Ramathibodi Hospital, Mahidol University
14

15. To Err is Human 3: Cognition
• Cognitive Errors - Example: Decoy Pricing
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# of
People
16
0
84
# of
People
68
32
Ariely (2008)
15

16. Cognitive Biases in Healthcare
“Everyone makes mistakes. But our
reliance on cognitive processes prone to
bias makes treatment errors more likely
than we think”
Klein JG. Five pitfalls in decisions about diagnosis and prescribing. BMJ. 2005 Apr 2;330(7494):781-3.
16

17. Cognitive Biases in Healthcare
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.
17

18. Cognitive Biases in Healthcare
Croskerry P. The importance of cognitive errors in diagnosis and strategies to minimize them.
Acad Med. 2003 Aug;78(8):775-80.
18

19. Common Errors
• Medication Errors
– Drug Allergies
– Drug Interactions
• Ineffective or inappropriate treatment
• Redundant orders
• Failure to follow clinical practice guidelines
19

20. Why We Need ICT
in Healthcare?
#2: Because healthcare is
error-prone and technology
can help
20

21. Why We Need ICT
in Healthcare?
#3: Because access to
high-quality patient
information improves care
21

22. Health IT
Use of information and communications
technology (ICT) in health & healthcare
settings
Source: The Health Resources and Services Administration, Department of
Health and Human Service, USA
Slide adapted from: Boonchai Kijsanayotin
22

23. Health IT: What’s in a Word?
Health
Information
Technology
Goal
Value-Add
Tools
23

24. “Health” in “Health IT”
• Patient’s Health
• Population’s Health
• Organization’s Health
(Quality, Reputation & Finance)
24

25. Various Forms of Health IT
Hospital Information System (HIS)
Computerized Provider Order Entry (CPOE)
Electronic
Health
Records
(EHRs)
Screenshot Images from Faculty of Medicine Ramathibodi Hospital, Mahidol University
Picture Archiving and
Communication System
(PACS)
25

26. Still Many Other Forms of Health IT
Biosurveillance
mHealth
Personal Health Records
(PHRs) and Patient Portals
Images from Apple Inc., Geekzone.co.nz, Google, HealthVault.com and American Telecare, Inc.
Telemedicine &
Telehealth
26

27. Values of Health IT
• Guideline adherence
• Better documentation
• Practitioner decision making or
process of care
• Medication safety
• Patient surveillance & monitoring
• Patient education/reminder
27

28. Enterprise-wide Hospital IT
•
•
•
•
•
•
Master Patient Index (MPI)
Admit-Discharge-Transfer (ADT)
Electronic Health Records (EHRs)
Computerized Physician Order Entry (CPOE)
Clinical Decision Support Systems (CDS)
Picture Archiving and Communication System
(PACS)
• Nursing applications
• Enterprise Resource Planning (ERP) - Finance,
Materials Management, Human Resources
28

29. Departmental IT in Hospitals
• 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
29

30. EHRs & HIS
The Challenge - Knowing What It Means
Electronic Health
Records (EHRs)
Hospital
Information System
(HIS)
Electronic Medical
Records (EMRs)
Electronic Patient
Records (EPRs)
Computer-Based
Patient Records
(CPRs)
Personal Health
Records (PHRs)
Clinical Information
System (CIS)
30

31. Computerized Provider Order Entry (CPOE)
31

32. Computerized Provider Order Entry (CPOE)
Values
• No handwriting!!!
• Structured data entry: Completeness, clarity,
fewer mistakes (?)
• No transcription errors!
• Streamlines workflow, increases efficiency
32

33. Stages of Medication Process
Ordering
CPOE
Transcription
Dispensing
Administration
Automatic
Medication
Dispensing
Electronic
Medication
Administration
Records
(e-MAR)
Barcoded
Medication
Dispensing
Barcoded
Medication
Administration
33

34. Clinical Decision Support Systems (CDS)
• The real place where most of the
values of health IT can be achieved
(Shortliffe, 1976)
– Expert systems
• Based on artificial intelligence,
machine learning, rules, or
statistics
• Examples: differential
diagnoses, treatment options
34

35. Clinical Decision Support Systems (CDS)
– Alerts & reminders
• Based on specified logical conditions
• Examples:
– Drug-allergy checks
– Drug-drug interaction checks
– Reminders for preventive services
– Clinical practice guideline integration
35

36. Example of “Reminders”
36

37. More CDS Examples
• Reference information or evidencebased knowledge sources
–
–
–
–
Drug reference databases
Textbooks & journals
Online literature (e.g. PubMed)
Tools that help users easily access
references (e.g. Infobuttons)
37

38. Infobuttons
Image Source: https://webcis.nyp.org/webcisdocs/what-are-infobuttons.html
38

39. Other CDS Examples
• Pre-defined documents
–
–
–
–
Order sets, personalized “favorites”
Templates for clinical notes
Checklists
Forms
• Can be either computer-based or
paper-based
39

40. Order Sets
Image Source: http://www.hospitalmedicine.org/ResourceRoomRedesign/CSSSIS/html/06Reliable/SSI/Order.cfm
40

41. Other CDS Examples
• Simple UI designed to help clinical
decision making
– Abnormal lab highlights
– Graphs/visualizations for lab results
– Filters & sorting functions
41

42. Abnormal Lab Highlights
Image Source: http://geekdoctor.blogspot.com/2008/04/designing-ideal-electronic-health.html
42

43. Clinical Decision Making
PATIENT
Perception
CLINICIAN
Attention
Long Term Memory
Knowledge
Working
Memory
Data
External Memory
Knowledge
Data
Inference
DECISION
Elson, Faughnan & Connelly (1997)
43

44. Clinical Decision Making
PATIENT
Perception
CLINICIAN
Attention
Long Term Memory
Knowledge
Working
Memory
Data
Abnormal lab
highlights
External Memory
Knowledge
Data
Inference
DECISION
44

45. Clinical Decision Making
PATIENT
Perception
CLINICIAN
Attention
Long Term Memory
Knowledge
Working
Memory
Data
Drug-Allergy
Checks
External Memory
Knowledge
Data
Inference
DECISION
45

46. Clinical Decision Making
PATIENT
Perception
CLINICIAN
Attention
Long Term Memory
Knowledge
Working
Memory
Data
Drug-Drug
Interaction
Checks
External Memory
Knowledge
Data
Inference
DECISION
Elson, Faughnan & Connelly (1997)
46

47. Clinical Decision Making
PATIENT
Perception
CLINICIAN
Attention
Long Term Memory
Knowledge
Working
Memory
Data
Clinical Practice
Guideline
Reminders
External Memory
Knowledge
Data
Inference
DECISION
Elson, Faughnan & Connelly (1997)
47

48. Clinical Decision Making
PATIENT
Perception
CLINICIAN
Attention
Long Term Memory
Knowledge
Working
Memory
Data
External Memory
Knowledge
Inference
Data
Diagnostic/Treatment
Expert Systems
DECISION
Elson, Faughnan & Connelly (1997)
48

49. Proper Roles of CDS
• CDSS as a replacement or supplement of
clinicians?
– The demise of the “Greek Oracle” model (Miller & Masarie, 1990)
The “Greek Oracle” Model
Wrong Assumption
The “Fundamental Theorem” Model
Correct Assumption
Friedman (2009)
49

50. Unintended Consequences of Health IT
Some risks
• Alert fatigue
50

51. Workarounds
51

52. Health Information Exchange (HIE)
Government
Hospital B
Hospital A
Lab
Patient at Home
Clinic C
52

53. Outline
Healthcare & Information
Why We Need ICT in Healthcare
Health IT
Hospital Information Systems
Health Information Exchange
• Q&A
53

54. Patients Are Counting on Us...
Image Source: http://www.flickr.com/photos/childrensalliance/3191862260/
54

55. More Resources
• American Medical Informatics Association (AMIA)
www.amia.org
• International Medical Informatics Association (IMIA)
www.imia.org
• Thai Medical Informatics Association (TMI)
www.tmi.or.th
• Asia eHealth Information Network (AeHIN)
www.aehin.org
• ThaiHealthIT Google Groups Mailing List
http://groups.google.com/group/ThaiHealthIT
• Thai Health Informatics Academy
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