0
ICT Applications for Healthcare
MUICT Seminar
Nawanan Theera-Ampornpunt, M.D., Ph.D.
Faculty of Medicine Ramathibodi Hospi...
2
A Bit About Myself...
2003 M.D. (First-Class Honors) (Ramathibodi)
2009 M.S. in Health Informatics (U of MN)
2011 Ph.D. ...
3
Outline
• Healthcare & Information
• Why We Need ICT in Healthcare
• Health IT & eHealth
• Some ICT Applications
• A Dre...
4
Let’s take a look at
these pictures...
5Image Source: Guardian.co.uk
Manufacturing
6Image Source: http://www.oknation.net/blog/phuketpost/2013/10/19/entry-3
Banking
7ER - Image Source: nj.com
Healthcare (on TV)
8
Healthcare
(At an undisclosed nearby hospital)
9
• Life-or-Death
• Difficult to automate human decisions
– Nature of business
– Many & varied stakeholders
– Evolving sta...
10
Back to
something simple...
11
What Clinicians Want?
To treat & to
care for their
patients to their
best abilities,
given limited
time &
resources
Ima...
12
High Quality Care
• Safe
• Timely
• Effective
• Patient-Centered
• Efficient
• Equitable
Institute of Medicine, Committ...
13
Information is Everywhere in Healthcare
Shortliffe EH. Biomedical informatics in the education of
physicians. JAMA. 201...
14
“Information” in Medicine
Shortliffe EH. Biomedical informatics in the education of physicians. JAMA. 2010 Sep 15;304(1...
15
Outline
“Information” in Healthcare
• Why We Need ICT in Healthcare
• Health IT & eHealth
• Some ICT Applications
• A ...
16
Why We Need ICT
in Healthcare?
#1: Because information is
everywhere in healthcare
17
(IOM, 2001)(IOM, 2000) (IOM, 2011)
Landmark IOM Reports
18
Patient Safety
• To Err is Human (IOM, 2000) reported
that:
– 44,000 to 98,000 people die in U.S.
hospitals each year a...
19
IOM Reports Summary
• Humans are not perfect and are bound to
make errors
• Highlight problems in U.S. health care
syst...
20
Image Source: (Left) http://docwhisperer.wordpress.com/2007/05/31/sleepy-heads/
(Right) http://graphics8.nytimes.com/im...
21Image Source: Suthan Srisangkaew, Department of Pathology, Facutly of Medicine Ramathibodi Hospital, Mahidol University
...
22
To Err is Human 3: Cognition
• Cognitive Errors - Example: Decoy Pricing
The Economist Purchase Options
• Economist.com...
23
• It already happens....
(Mamede et al., 2010; Croskerry, 2003;
Klein, 2005; Croskerry, 2013)
What If This Happens in H...
24
Cognitive Biases in Healthcare
Mamede S, van Gog T, van den Berge K, Rikers RM, van Saase JL, van Guldener C, Schmidt H...
25
Cognitive Biases in Healthcare
Croskerry P. The importance of cognitive errors in diagnosis and strategies to minimize ...
26
Cognitive Biases in Healthcare
Klein JG. Five pitfalls in decisions about diagnosis and prescribing. BMJ. 2005 Apr 2;33...
27
• Medication Errors
– Drug Allergies
– Drug Interactions
• Ineffective or inappropriate treatment
• Redundant orders
• ...
28
Why We Need ICT
in Healthcare?
#2: Because healthcare is
error-prone and technology
can help
29
Fragmented Healthcare
http://www.dplindbenchmark.com/wp-content/uploads/2013/02/HHRI-Our-Health-Care-River.pdf
30
Why We Need ICT
in Healthcare?
#3: Because access to
high-quality patient
information improves care
31
Why We Need ICT
in Healthcare?
#4: Because healthcare at
all levels is fragmented &
in need of process
improvement
32
Outline
“Information” in Healthcare
Why We Need ICT in Healthcare
• Health IT & eHealth
• Some ICT Applications
• A D...
33
Use of information and communications
technology (ICT) in health & healthcare
settings
Source: The Health Resources and...
34
Use of information and communications
technology (ICT) for health; Including
• Treating patients
• Conducting research
...
35
eHealth  Health IT
Slide adapted from: Boonchai Kijsanayotin
eHealth & Health IT
36
HIS
All information about health
eHealth
HMIS
mHealth
Tele-
medicine
Slide adapted from: Karl Brown (Rockefeller Founda...
37
Health
Information
Technology
Goal
Value-Add
Tools
Health IT: What’s in a Word?
38
 All components are essential
 All components should be balanced
Slide adapted from: Boonchai Kijsanayotin
eHealth Co...
39
eHealth in Thailand: The current status. Stud Health Technol Inform
2010;160:376–80, Presented at MedInfo2010 South Afr...
40Slide adapted from: Boonchai Kijsanayotin
Thailand: Unbalanced Development
41
eHealth Applications
Enabling Policies &
Strategies
Foundation Policies
& Strategies
• Services
• Applications
• Softwa...
42Slide adapted from: Boonchai Kijsanayotin
Thailand’s eHealth Development
43
 Silo-type systems
 Little integration and interoperability
 Mostly aim for administration and management
 40% of w...
44
Outline
“Information” in Healthcare
Why We Need ICT in Healthcare
Health IT & eHealth
• Some ICT Applications
• A Dr...
45
Hospital Information System (HIS) Computerized Provider Order Entry (CPOE)
Electronic
Health
Records
(EHRs)
Picture Arc...
46
mHealth
Biosurveillance
Telemedicine &
Telehealth
Images from Apple Inc., Geekzone.co.nz, Google, HealthVault.com and A...
47
• Guideline adherence
• Better documentation
• Practitioner decision making or
process of care
• Medication safety
• Pa...
48
• Master Patient Index (MPI)
• Admit-Discharge-Transfer (ADT)
• Electronic Health Records (EHRs)
• Computerized Physici...
49
• Pharmacy applications
• Laboratory Information System (LIS)
• Radiology Information System (RIS)
• Specialized applic...
50
The Challenge - Knowing What It Means
Electronic Medical
Records (EMRs)
Computer-Based
Patient Records
(CPRs)
Electroni...
51
Computerized Provider Order Entry (CPOE)
52
Values
• No handwriting!!!
• Structured data entry: Completeness, clarity,
fewer mistakes (?)
• No transcription errors...
53
• The real place where most of the
values of health IT can be achieved
– Expert systems
• Based on artificial intellige...
54
– Alerts & reminders
• Based on specified logical conditions
• Examples:
– Drug-allergy checks
– Drug-drug interaction ...
55
Example of “Reminders”
56
• Reference information or evidence-
based knowledge sources
– Drug reference databases
– Textbooks & journals
– Online...
57Image Source: https://webcis.nyp.org/webcisdocs/what-are-infobuttons.html
Infobuttons
58
• Pre-defined documents
– Order sets, personalized “favorites”
– Templates for clinical notes
– Checklists
– Forms
• Ca...
59Image Source: http://www.hospitalmedicine.org/ResourceRoomRedesign/CSSSIS/html/06Reliable/SSI/Order.cfm
Order Sets
60
• Simple UI designed to help clinical
decision making
– Abnormal lab highlights
– Graphs/visualizations for lab results...
61Image Source: http://geekdoctor.blogspot.com/2008/04/designing-ideal-electronic-health.html
Abnormal Lab Highlights
62
External Memory
Knowledge Data
Long Term Memory
Knowledge Data
Inference
DECISION
PATIENT
Perception
Attention
Working
...
63
Abnormal lab
highlights
Clinical Decision Making
External Memory
Knowledge Data
Long Term Memory
Knowledge Data
Inferen...
64
Clinical Decision Making
External Memory
Knowledge Data
Long Term Memory
Knowledge Data
Inference
DECISION
PATIENT
Perc...
65
External Memory
Knowledge Data
Long Term Memory
Knowledge Data
Inference
DECISION
PATIENT
Perception
Attention
Working
...
66
External Memory
Knowledge Data
Long Term Memory
Knowledge Data
Inference
DECISION
PATIENT
Perception
Attention
Working
...
67
External Memory
Knowledge Data
Long Term Memory
Knowledge Data
Inference
DECISION
PATIENT
Perception
Attention
Working
...
68Image Source: socialmediab2b.com
IBM’s Watson
69Image Source: englishmoviez.com
Rise of the Machines?
70
• CDSS as a replacement or supplement of
clinicians?
– The demise of the “Greek Oracle” model (Miller & Masarie, 1990)
...
71
Some risks
• Alert fatigue
Unintended Consequences of Health IT
72
Workarounds
73
Outline
“Information” in Healthcare
Why We Need ICT in Healthcare
Health IT & eHealth
Some ICT Applications
• A Dre...
74
Hospital A Hospital B
Clinic C
Government
Lab Patient at Home
Health Information Exchange (HIE)
75
Standards & Interoperability in HIE
Technical Standards
(TCP/IP, encryption,
security)
Exchange Standards (HL7 v.2,
HL7...
76
Hospital A Hospital B
Clinic C
Government
Lab Patient at Home
Message
Message
Message
Message
Message
Message Exchange
77
• As the second formally-trained M.D., Ph.D.
in Health Informatics in Thailand, I am
driven and socially obligated...
•...
78http://www.ega.or.th/Content.aspx?m_id=94
Cloud: To Go or Not To Go?
79WHO mHealth Report: http://www.who.int/goe/publications/goe_mhealth_web.pdf
Roles of mHealth in Future Healthcare
80
Outline
“Information” in Healthcare
Why We Need ICT in Healthcare
Health IT & eHealth
Some ICT Applications
A Drea...
81
• What will the future be for healthcare?
• Where’s the roles of ICT professionals in
future healthcare?
• How to lever...
82
Patients Are Counting on Us...
Image Source: http://www.flickr.com/photos/childrensalliance/3191862260/
83
Intelligent &
helpful
robots
Intelligent
humanistic
robots in a
human world
Machines that
replace humans
for a “better”...
84
More Resources
• American Medical Informatics Association (AMIA)
www.amia.org
• International Medical Informatics Assoc...
85
Outline
“Information” in Healthcare
Why We Need ICT in Healthcare
Health IT & eHealth
Some ICT Applications
A Drea...
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Transcript of "ICT Applications for Healthcare"

  1. 1. ICT Applications for Healthcare MUICT Seminar Nawanan Theera-Ampornpunt, M.D., Ph.D. Faculty of Medicine Ramathibodi Hospital May 28, 2014 SlideShare.net/Nawanan
  2. 2. 2 A Bit About Myself... 2003 M.D. (First-Class Honors) (Ramathibodi) 2009 M.S. in Health Informatics (U of MN) 2011 Ph.D. in Health Informatics (U of MN) 2012 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
  3. 3. 3 Outline • Healthcare & Information • Why We Need ICT in Healthcare • Health IT & eHealth • Some ICT Applications • A Dream for Healthcare • Food for Thought for ICT Folks • Q&A
  4. 4. 4 Let’s take a look at these pictures...
  5. 5. 5Image Source: Guardian.co.uk Manufacturing
  6. 6. 6Image Source: http://www.oknation.net/blog/phuketpost/2013/10/19/entry-3 Banking
  7. 7. 7ER - Image Source: nj.com Healthcare (on TV)
  8. 8. 8 Healthcare (At an undisclosed nearby hospital)
  9. 9. 9 • 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
  10. 10. 10 Back to something simple...
  11. 11. 11 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)
  12. 12. 12 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.
  13. 13. 13 Information is Everywhere in Healthcare Shortliffe EH. Biomedical informatics in the education of physicians. JAMA. 2010 Sep 15;304(11):1227-8.
  14. 14. 14 “Information” in Medicine Shortliffe EH. Biomedical informatics in the education of physicians. JAMA. 2010 Sep 15;304(11):1227-8.
  15. 15. 15 Outline “Information” in Healthcare • Why We Need ICT in Healthcare • Health IT & eHealth • Some ICT Applications • A Dream for Healthcare • Food for Thought for ICT Folks • Q&A
  16. 16. 16 Why We Need ICT in Healthcare? #1: Because information is everywhere in healthcare
  17. 17. 17 (IOM, 2001)(IOM, 2000) (IOM, 2011) Landmark IOM Reports
  18. 18. 18 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
  19. 19. 19 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
  20. 20. 20 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
  21. 21. 21Image Source: Suthan Srisangkaew, Department of Pathology, Facutly of Medicine Ramathibodi Hospital, Mahidol University To Err is Human 2: Memory
  22. 22. 22 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
  23. 23. 23 • It already happens.... (Mamede et al., 2010; Croskerry, 2003; Klein, 2005; Croskerry, 2013) What If This Happens in Healthcare?
  24. 24. 24 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.
  25. 25. 25 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.
  26. 26. 26 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”
  27. 27. 27 • Medication Errors – Drug Allergies – Drug Interactions • Ineffective or inappropriate treatment • Redundant orders • Failure to follow clinical practice guidelines Common Errors
  28. 28. 28 Why We Need ICT in Healthcare? #2: Because healthcare is error-prone and technology can help
  29. 29. 29 Fragmented Healthcare http://www.dplindbenchmark.com/wp-content/uploads/2013/02/HHRI-Our-Health-Care-River.pdf
  30. 30. 30 Why We Need ICT in Healthcare? #3: Because access to high-quality patient information improves care
  31. 31. 31 Why We Need ICT in Healthcare? #4: Because healthcare at all levels is fragmented & in need of process improvement
  32. 32. 32 Outline “Information” in Healthcare Why We Need ICT in Healthcare • Health IT & eHealth • Some ICT Applications • A Dream for Healthcare • Food for Thought for ICT Folks • Q&A
  33. 33. 33 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
  34. 34. 34 Use of information and communications technology (ICT) for health; Including • Treating patients • Conducting research • Educating the health workforce • Tracking diseases • Monitoring public health. Sources: 1) WHO Global Observatory of eHealth (GOe) (www.who.int/goe) 2) World Health Assembly, 2005. Resolution WHA58.28 Slide adapted from: Mark Landry, WHO WPRO & Boonchai Kijsanayotin eHealth
  35. 35. 35 eHealth  Health IT Slide adapted from: Boonchai Kijsanayotin eHealth & Health IT
  36. 36. 36 HIS All information about health eHealth HMIS mHealth Tele- medicine Slide adapted from: Karl Brown (Rockefeller Foundation), via Boonchai Kijsanayotin More Terms
  37. 37. 37 Health Information Technology Goal Value-Add Tools Health IT: What’s in a Word?
  38. 38. 38  All components are essential  All components should be balanced Slide adapted from: Boonchai Kijsanayotin eHealth Components (WHO-ITU Model)
  39. 39. 39 eHealth in Thailand: The current status. Stud Health Technol Inform 2010;160:376–80, Presented at MedInfo2010 South Africa 39 Thailand’s eHealth: 2010
  40. 40. 40Slide adapted from: Boonchai Kijsanayotin Thailand: Unbalanced Development
  41. 41. 41 eHealth Applications Enabling Policies & Strategies Foundation Policies & Strategies • Services • Applications • Software • Standards & Interoperability • Capability Building • Leadership & Governance • Legislation & Policy • Strategy & Investment • Infrastructure Slide adapted from: Boonchai Kijsanayotin eHealth Development Model
  42. 42. 42Slide adapted from: Boonchai Kijsanayotin Thailand’s eHealth Development
  43. 43. 43  Silo-type systems  Little integration and interoperability  Mostly aim for administration and management  40% of work-hours spent on managing reports and documents  Lack of national leadership and governance body  Inadequate HIS foundations development Slide adapted from: Boonchai Kijsanayotin Thailand’s eHealth Situation
  44. 44. 44 Outline “Information” in Healthcare Why We Need ICT in Healthcare Health IT & eHealth • Some ICT Applications • A Dream for Healthcare • Food for Thought for ICT Folks • Q&A
  45. 45. 45 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
  46. 46. 46 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
  47. 47. 47 • Guideline adherence • Better documentation • Practitioner decision making or process of care • Medication safety • Patient surveillance & monitoring • Patient education/reminder Values of Health IT
  48. 48. 48 • 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
  49. 49. 49 • 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
  50. 50. 50 The Challenge - Knowing What It Means Electronic Medical Records (EMRs) Computer-Based Patient Records (CPRs) Electronic Patient Records (EPRs) Electronic Health Records (EHRs) Personal Health Records (PHRs) Hospital Information System (HIS) Clinical Information System (CIS) EHRs & HIS
  51. 51. 51 Computerized Provider Order Entry (CPOE)
  52. 52. 52 Values • No handwriting!!! • Structured data entry: Completeness, clarity, fewer mistakes (?) • No transcription errors! • Streamlines workflow, increases efficiency Computerized Provider Order Entry (CPOE)
  53. 53. 53 • 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)
  54. 54. 54 – 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)
  55. 55. 55 Example of “Reminders”
  56. 56. 56 • 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
  57. 57. 57Image Source: https://webcis.nyp.org/webcisdocs/what-are-infobuttons.html Infobuttons
  58. 58. 58 • Pre-defined documents – Order sets, personalized “favorites” – Templates for clinical notes – Checklists – Forms • Can be either computer-based or paper-based Other CDS Examples
  59. 59. 59Image Source: http://www.hospitalmedicine.org/ResourceRoomRedesign/CSSSIS/html/06Reliable/SSI/Order.cfm Order Sets
  60. 60. 60 • Simple UI designed to help clinical decision making – Abnormal lab highlights – Graphs/visualizations for lab results – Filters & sorting functions Other CDS Examples
  61. 61. 61Image Source: http://geekdoctor.blogspot.com/2008/04/designing-ideal-electronic-health.html Abnormal Lab Highlights
  62. 62. 62 External Memory Knowledge Data Long Term Memory Knowledge Data Inference DECISION PATIENT Perception Attention Working Memory CLINICIAN Elson, Faughnan & Connelly (1997) Clinical Decision Making
  63. 63. 63 Abnormal lab highlights Clinical Decision Making External Memory Knowledge Data Long Term Memory Knowledge Data Inference DECISION PATIENT Perception Attention Working Memory CLINICIAN
  64. 64. 64 Clinical Decision Making External Memory Knowledge Data Long Term Memory Knowledge Data Inference DECISION PATIENT Perception Attention Working Memory CLINICIAN Drug-Allergy Checks
  65. 65. 65 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
  66. 66. 66 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
  67. 67. 67 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
  68. 68. 68Image Source: socialmediab2b.com IBM’s Watson
  69. 69. 69Image Source: englishmoviez.com Rise of the Machines?
  70. 70. 70 • 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
  71. 71. 71 Some risks • Alert fatigue Unintended Consequences of Health IT
  72. 72. 72 Workarounds
  73. 73. 73 Outline “Information” in Healthcare Why We Need ICT in Healthcare Health IT & eHealth Some ICT Applications • A Dream for Healthcare • Food for Thought for ICT Folks • Q&A
  74. 74. 74 Hospital A Hospital B Clinic C Government Lab Patient at Home Health Information Exchange (HIE)
  75. 75. 75 Standards & Interoperability in HIE Technical Standards (TCP/IP, encryption, security) Exchange Standards (HL7 v.2, HL7 v.3 Messaging, HL7 CDA, DICOM) Vocabularies, Terminologies, Coding Systems (ICD-10, ICD-9, CPT, SNOMED CT, LOINC) Information Models (HL7 v.3 RIM, ASTM CCR, HL7 CCD) Standard Data Sets Functional Standards (HL7 EHR Functional Specifications) Some may be hybrid: e.g. HL7 v.3, HL7 CCD Unique ID
  76. 76. 76 Hospital A Hospital B Clinic C Government Lab Patient at Home Message Message Message Message Message Message Exchange
  77. 77. 77 • As the second formally-trained M.D., Ph.D. in Health Informatics in Thailand, I am driven and socially obligated... • To promote personal & population health through establishment of sustainable foundations for eHealth and strengthening of the field of Biomedical and Health Informatics in Thailand before my end of life. • HIE is at the heart of my life-long dream My “Mission in Life”
  78. 78. 78http://www.ega.or.th/Content.aspx?m_id=94 Cloud: To Go or Not To Go?
  79. 79. 79WHO mHealth Report: http://www.who.int/goe/publications/goe_mhealth_web.pdf Roles of mHealth in Future Healthcare
  80. 80. 80 Outline “Information” in Healthcare Why We Need ICT in Healthcare Health IT & eHealth Some ICT Applications A Dream for Healthcare • Food for Thought for ICT Folks • Q&A
  81. 81. 81 • What will the future be for healthcare? • Where’s the roles of ICT professionals in future healthcare? • How to leverage different perspectives & strengths to achieve common goals? • How will we shape future healthcare together? Some Food for Thought
  82. 82. 82 Patients Are Counting on Us... Image Source: http://www.flickr.com/photos/childrensalliance/3191862260/
  83. 83. 83 Intelligent & helpful robots Intelligent humanistic robots in a human world Machines that replace humans for a “better” world HAL 9000 Data David NS-5 Dangerous killer machines What ICT Will It Be?
  84. 84. 84 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
  85. 85. 85 Outline “Information” in Healthcare Why We Need ICT in Healthcare Health IT & eHealth Some ICT Applications A Dream for Healthcare Food for Thought for ICT Folks • Q&A
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