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Overview of Health IT
Nawanan Theera-Ampornpunt, M.D., Ph.D.
Faculty of Medicine Ramathibodi Hospital
June 26, 2014
SlideS...
2
Outline
• “Information” in Healthcare
• Health IT & eHealth
• Some Health IT Applications
• A Dream for Healthcare
• Q&A
3
Let’s take a look at
these pictures...
4Image Source: Guardian.co.uk
Manufacturing
5Image Source: http://www.oknation.net/blog/phuketpost/2013/10/19/entry-3
Banking
6ER - Image Source: nj.com
Healthcare (on TV)
7
Healthcare
(At an undisclosed nearby hospital)
8
• Life-or-Death
• Difficult to automate human decisions
– Nature of business
– Many & varied stakeholders
– Evolving sta...
9
Back to
something simple...
10
What Clinicians Want?
To treat & to
care for their
patients to their
best abilities,
given limited
time &
resources
Ima...
11
High Quality Care
• Safe
• Timely
• Effective
• Patient-Centered
• Efficient
• Equitable
Institute of Medicine, Committ...
12
Information is Everywhere in Healthcare
Shortliffe EH. Biomedical informatics in the education of
physicians. JAMA. 201...
13
“Information” in Medicine
Shortliffe EH. Biomedical informatics in the education of physicians. JAMA. 2010 Sep 15;304(1...
14
Outline
“Information” in Healthcare
• Health IT & eHealth
• Some Health IT Applications
• A Dream for Healthcare
• Q&A
15
(IOM, 2001)(IOM, 2000) (IOM, 2011)
Landmark IOM Reports
16
IOM Reports Summary
• Humans are not perfect and are bound to
make errors
• Highlight problems in U.S. health care
syst...
17
Image Source: (Left) http://docwhisperer.wordpress.com/2007/05/31/sleepy-heads/
(Right) http://graphics8.nytimes.com/im...
18Image Source: Suthan Srisangkaew, Department of Pathology, Facutly of Medicine Ramathibodi Hospital, Mahidol University
...
19
To Err is Human 3: Cognition
• Cognitive Errors - Example: Decoy Pricing
The Economist Purchase Options
• Economist.com...
20
• It already happens....
(Mamede et al., 2010; Croskerry, 2003;
Klein, 2005; Croskerry, 2013)
What If This Happens in H...
21
Cognitive Biases in Healthcare
Mamede S, van Gog T, van den Berge K, Rikers RM, van Saase JL, van Guldener C, Schmidt H...
22
Cognitive Biases in Healthcare
Croskerry P. The importance of cognitive errors in diagnosis and strategies to minimize ...
23
Cognitive Biases in Healthcare
Klein JG. Five pitfalls in decisions about diagnosis and prescribing. BMJ. 2005 Apr 2;33...
24
• Medication Errors
– Drug Allergies
– Drug Interactions
• Ineffective or inappropriate treatment
• Redundant orders
• ...
25
Use of information and communications
technology (ICT) in health & healthcare
settings
Source: The Health Resources and...
26
Health
Information
Technology
Goal
Value-Add
Tools
Health IT: What’s in a Word?
27
Hospital Information System (HIS) Computerized Provider Order Entry (CPOE)
Electronic
Health
Records
(EHRs)
Picture Arc...
28
mHealth
Biosurveillance
Telemedicine &
Telehealth
Images from Apple Inc., Geekzone.co.nz, Google, HealthVault.com and A...
29
• Guideline adherence
• Better documentation
• Practitioner decision making or
process of care
• Medication safety
• Pa...
30
• Master Patient Index (MPI)
• Admit-Discharge-Transfer (ADT)
• Electronic Health Records (EHRs)
• Computerized Physici...
31
• Pharmacy applications
• Laboratory Information System (LIS)
• Radiology Information System (RIS)
• Specialized applic...
32
Computerized Provider Order Entry (CPOE)
33
Values
• No handwriting!!!
• Structured data entry: Completeness, clarity,
fewer mistakes (?)
• No transcription errors...
34
• The real place where most of the
values of health IT can be achieved
– Expert systems
• Based on artificial intellige...
35
– Alerts & reminders
• Based on specified logical conditions
• Examples:
– Drug-allergy checks
– Drug-drug interaction ...
36
Example of “Reminders”
37
• Reference information or evidence-
based knowledge sources
– Drug reference databases
– Textbooks & journals
– Online...
38Image Source: https://webcis.nyp.org/webcisdocs/what-are-infobuttons.html
Infobuttons
39
• Pre-defined documents
– Order sets, personalized “favorites”
– Templates for clinical notes
– Checklists
– Forms
• Ca...
40Image Source: http://www.hospitalmedicine.org/ResourceRoomRedesign/CSSSIS/html/06Reliable/SSI/Order.cfm
Order Sets
41
• Simple UI designed to help clinical
decision making
– Abnormal lab highlights
– Graphs/visualizations for lab results...
42Image Source: http://geekdoctor.blogspot.com/2008/04/designing-ideal-electronic-health.html
Abnormal Lab Highlights
43
External Memory
Knowledge Data
Long Term Memory
Knowledge Data
Inference
DECISION
PATIENT
Perception
Attention
Working
...
44
Abnormal lab
highlights
Clinical Decision Making
External Memory
Knowledge Data
Long Term Memory
Knowledge Data
Inferen...
45
Clinical Decision Making
External Memory
Knowledge Data
Long Term Memory
Knowledge Data
Inference
DECISION
PATIENT
Perc...
46
External Memory
Knowledge Data
Long Term Memory
Knowledge Data
Inference
DECISION
PATIENT
Perception
Attention
Working
...
47
External Memory
Knowledge Data
Long Term Memory
Knowledge Data
Inference
DECISION
PATIENT
Perception
Attention
Working
...
48
External Memory
Knowledge Data
Long Term Memory
Knowledge Data
Inference
DECISION
PATIENT
Perception
Attention
Working
...
49
• CDSS as a replacement or supplement of
clinicians?
– The demise of the “Greek Oracle” model (Miller & Masarie, 1990)
...
50
Some risks
• Alert fatigue
Unintended Consequences of Health IT
51
Workarounds
52
Outline
“Information” in Healthcare
Health IT & eHealth
Some Health IT Applications
• A Dream for Healthcare
• Q&A
53
Hospital A Hospital B
Clinic C
Government
Lab Patient at Home
Health Information Exchange (HIE)
54
More Resources
• American Medical Informatics Association (AMIA)
www.amia.org
• International Medical Informatics Assoc...
55
Outline
“Information” in Healthcare
Health IT & eHealth
Some Health IT Applications
A Dream for Healthcare
• Q&A
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  1. 1. Overview of Health IT Nawanan Theera-Ampornpunt, M.D., Ph.D. Faculty of Medicine Ramathibodi Hospital June 26, 2014 SlideShare.net/Nawanan
  2. 2. 2 Outline • “Information” in Healthcare • Health IT & eHealth • Some Health IT Applications • A Dream for Healthcare • Q&A
  3. 3. 3 Let’s take a look at these pictures...
  4. 4. 4Image Source: Guardian.co.uk Manufacturing
  5. 5. 5Image Source: http://www.oknation.net/blog/phuketpost/2013/10/19/entry-3 Banking
  6. 6. 6ER - Image Source: nj.com Healthcare (on TV)
  7. 7. 7 Healthcare (At an undisclosed nearby hospital)
  8. 8. 8 • Life-or-Death • Difficult to automate human decisions – Nature of business – Many & varied stakeholders – Evolving standards of care • Fragmented, poorly-coordinated systems • Large, ever-growing & changing body of knowledge • High volume, low resources, little time Why Healthcare Isn’t Like Any Others
  9. 9. 9 Back to something simple...
  10. 10. 10 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)
  11. 11. 11 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.
  12. 12. 12 Information is Everywhere in Healthcare Shortliffe EH. Biomedical informatics in the education of physicians. JAMA. 2010 Sep 15;304(11):1227-8.
  13. 13. 13 “Information” in Medicine Shortliffe EH. Biomedical informatics in the education of physicians. JAMA. 2010 Sep 15;304(11):1227-8.
  14. 14. 14 Outline “Information” in Healthcare • Health IT & eHealth • Some Health IT Applications • A Dream for Healthcare • Q&A
  15. 15. 15 (IOM, 2001)(IOM, 2000) (IOM, 2011) Landmark IOM Reports
  16. 16. 16 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
  17. 17. 17 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 To Err is Human 1: Attention
  18. 18. 18Image Source: Suthan Srisangkaew, Department of Pathology, Facutly of Medicine Ramathibodi Hospital, Mahidol University To Err is Human 2: Memory
  19. 19. 19 To Err is Human 3: Cognition • Cognitive Errors - Example: Decoy Pricing The Economist Purchase Options • Economist.com subscription $59 • Print subscription $125 • Print & web subscription $125 Ariely (2008) 16 0 84 The Economist Purchase Options • Economist.com subscription $59 • Print & web subscription $125 68 32 # of People # of People
  20. 20. 20 • It already happens.... (Mamede et al., 2010; Croskerry, 2003; Klein, 2005; Croskerry, 2013) What If This Happens in Healthcare?
  21. 21. 21 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.
  22. 22. 22 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.
  23. 23. 23 Cognitive Biases in Healthcare Klein JG. Five pitfalls in decisions about diagnosis and prescribing. BMJ. 2005 Apr 2;330(7494):781-3. “Everyone makes mistakes. But our reliance on cognitive processes prone to bias makes treatment errors more likely than we think”
  24. 24. 24 • Medication Errors – Drug Allergies – Drug Interactions • Ineffective or inappropriate treatment • Redundant orders • Failure to follow clinical practice guidelines Common Errors
  25. 25. 25 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 Health IT
  26. 26. 26 Health Information Technology Goal Value-Add Tools Health IT: What’s in a Word?
  27. 27. 27 Hospital Information System (HIS) Computerized Provider Order Entry (CPOE) Electronic Health Records (EHRs) Picture Archiving and Communication System (PACS) Various Forms of Health IT Screenshot Images from Faculty of Medicine Ramathibodi Hospital, Mahidol University
  28. 28. 28 mHealth Biosurveillance Telemedicine & Telehealth Images from Apple Inc., Geekzone.co.nz, Google, HealthVault.com and American Telecare, Inc. Personal Health Records (PHRs) and Patient Portals Still Many Other Forms of Health IT
  29. 29. 29 • Guideline adherence • Better documentation • Practitioner decision making or process of care • Medication safety • Patient surveillance & monitoring • Patient education/reminder Values of Health IT
  30. 30. 30 • 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) Enterprise-wide Hospital IT
  31. 31. 31 • 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 Departmental IT in Hospitals
  32. 32. 32 Computerized Provider Order Entry (CPOE)
  33. 33. 33 Values • No handwriting!!! • Structured data entry: Completeness, clarity, fewer mistakes (?) • No transcription errors! • Streamlines workflow, increases efficiency Computerized Provider Order Entry (CPOE)
  34. 34. 34 • 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(Shortliffe, 1976) Clinical Decision Support Systems (CDS)
  35. 35. 35 – Alerts & reminders • Based on specified logical conditions • Examples: – Drug-allergy checks – Drug-drug interaction checks – Reminders for preventive services – Clinical practice guideline integration Clinical Decision Support Systems (CDS)
  36. 36. 36 Example of “Reminders”
  37. 37. 37 • Reference information or evidence- based knowledge sources – Drug reference databases – Textbooks & journals – Online literature (e.g. PubMed) – Tools that help users easily access references (e.g. Infobuttons) More CDS Examples
  38. 38. 38Image Source: https://webcis.nyp.org/webcisdocs/what-are-infobuttons.html Infobuttons
  39. 39. 39 • Pre-defined documents – Order sets, personalized “favorites” – Templates for clinical notes – Checklists – Forms • Can be either computer-based or paper-based Other CDS Examples
  40. 40. 40Image Source: http://www.hospitalmedicine.org/ResourceRoomRedesign/CSSSIS/html/06Reliable/SSI/Order.cfm Order Sets
  41. 41. 41 • Simple UI designed to help clinical decision making – Abnormal lab highlights – Graphs/visualizations for lab results – Filters & sorting functions Other CDS Examples
  42. 42. 42Image Source: http://geekdoctor.blogspot.com/2008/04/designing-ideal-electronic-health.html Abnormal Lab Highlights
  43. 43. 43 External Memory Knowledge Data Long Term Memory Knowledge Data Inference DECISION PATIENT Perception Attention Working Memory CLINICIAN Elson, Faughnan & Connelly (1997) Clinical Decision Making
  44. 44. 44 Abnormal lab highlights Clinical Decision Making External Memory Knowledge Data Long Term Memory Knowledge Data Inference DECISION PATIENT Perception Attention Working Memory CLINICIAN
  45. 45. 45 Clinical Decision Making External Memory Knowledge Data Long Term Memory Knowledge Data Inference DECISION PATIENT Perception Attention Working Memory CLINICIAN Drug-Allergy Checks
  46. 46. 46 External Memory Knowledge Data Long Term Memory Knowledge Data Inference DECISION PATIENT Perception Attention Working Memory CLINICIAN Elson, Faughnan & Connelly (1997) Clinical Decision Making Drug-Drug Interaction Checks
  47. 47. 47 External Memory Knowledge Data Long Term Memory Knowledge Data Inference DECISION PATIENT Perception Attention Working Memory CLINICIAN Elson, Faughnan & Connelly (1997) Clinical Decision Making Clinical Practice Guideline Reminders
  48. 48. 48 External Memory Knowledge Data Long Term Memory Knowledge Data Inference DECISION PATIENT Perception Attention Working Memory CLINICIAN Elson, Faughnan & Connelly (1997) Clinical Decision Making Diagnostic/Treatment Expert Systems
  49. 49. 49 • 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” Model Friedman (2009) Wrong Assumption Correct Assumption Proper Roles of CDS
  50. 50. 50 Some risks • Alert fatigue Unintended Consequences of Health IT
  51. 51. 51 Workarounds
  52. 52. 52 Outline “Information” in Healthcare Health IT & eHealth Some Health IT Applications • A Dream for Healthcare • Q&A
  53. 53. 53 Hospital A Hospital B Clinic C Government Lab Patient at Home Health Information Exchange (HIE)
  54. 54. 54 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
  55. 55. 55 Outline “Information” in Healthcare Health IT & eHealth Some Health IT Applications A Dream for Healthcare • Q&A
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