IT & Decision Support Systems
in Hospital Supply Chains
EGIE 512 Hospital Logistics and Supply Chain Management
Nawanan Th...
2
2003 M.D. (First-Class Honors) (Ramathibodi)
2009 M.S. in Health Informatics (U of MN)
2011 Ph.D. in Health Informatics ...
3
Outline
• Healthcare & Information
• Health Information Technology
• Clinical Decision Making
• Clinical Decision Suppor...
4
Let’s take a look at
these pictures...
5
Image Source: Guardian.co.uk
Manufacturing
6
Image Source: http://www.oknation.net/blog/phuketpost/2013/10/19/entry-3
Banking
7
ER - Image Source: nj.com
Healthcare (on TV)
8
Healthcare
(at an undisclosed hospital)
9
• Life-or-Death
• Difficult to automate human decisions
– Nature of business
– Many & varied stakeholders
– Evolving sta...
10
• Large variations & contextual dependence
Input Process Output
Patient
Presentation
Decision-
Making
Biological
Respon...
11
Input Process Output
Transfer
Banking
Value-Add
- Security
- Convenience
- Customer Service
Location A Location B
But.....
12
Input Process Output
Assembling
Manufacturing
Raw
Materials
Finished
Goods
Value-Add
- Innovation
- Design
- QC
But...A...
13
Input Process Output
Patient Care
Health care
Sick Patient Well Patient
Value-Add
- Technology & medications
- Clinical...
14
Engineer’s Perspectives
• Logistics & Supply Chain
(Administrative)
• Focus on Processes
• Analytical, Systematic Mind
...
15
Back to
something simple...
16
To treat & to
care for their
patients to their
best abilities,
given limited
time &
resources
Image Source: http://en.w...
17
• Safe
• Timely
• Effective
• Patient-Centered
• Efficient
• Equitable
Institute of Medicine, Committee on Quality of H...
18
Shortliffe EH. Biomedical informatics in the education of
physicians. JAMA. 2010 Sep 15;304(11):1227-8.
Information Is ...
19
Shortliffe EH. Biomedical informatics in the education of physicians. JAMA. 2010 Sep 15;304(11):1227-8.
“Information” i...
20
Why We Need ICT
in Healthcare?
#1: Because information is
everywhere in healthcare
21
(IOM, 2001)(IOM, 2000) (IOM, 2011)
Landmark IOM Reports
22
• 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
p...
23
• Humans are not perfect and are bound to
make errors
• Highlight problems in U.S. health care
system that systematical...
24
• Perception errors
Image Source: interaction-dynamics.com
To Err Is Human 1: Perception
25
Image Source: (Left) http://docwhisperer.wordpress.com/2007/05/31/sleepy-heads/
(Right) http://graphics8.nytimes.com/im...
26
Image Source: Suthan Srisangkaew, Department of Pathology, Facutly of Medicine Ramathibodi Hospital, Mahidol University...
27
• Cognitive Errors - Example: Decoy Pricing
The Economist Purchase Options
• Economist.com subscription $59
• Print sub...
28Klein JG. Five pitfalls in decisions about diagnosis and prescribing. BMJ. 2005 Apr 2;330(7494):781-3.
“Everyone makes m...
29
Mamede S, van Gog T, van den Berge K, Rikers RM, van Saase JL, van Guldener C, Schmidt HG. Effect of
availability bias ...
30
Croskerry P. The importance of cognitive errors in diagnosis and strategies to minimize them.
Acad Med. 2003 Aug;78(8):...
31
• Medication Errors
–Drug Allergies
–Drug Interactions
• Ineffective or inappropriate treatment
• Redundant orders
• Fa...
32
Why We Need ICT
in Healthcare?
#2: Because healthcare is
error-prone and technology
can help
33
Why We Need ICT
in Healthcare?
#3: Because access to
high-quality patient
information improves care
34
Use of information and communications
technology (ICT) in health & healthcare
settings
Source: The Health Resources and...
35
Health
Information
Technology
Goal
Value-Add
Tools
Health IT: What’s in a Word?
36
• Patient’s Health
• Population’s Health
• Organization’s Health
(Quality, Reputation & Finance)
“Health” in Health IT
37
Hospital Information System (HIS) Computerized Provider Order Entry (CPOE)
Electronic
Health
Records
(EHRs)
Picture Arc...
38
mHealth
Biosurveillance
Telemedicine &
Telehealth
Images from Apple Inc., Geekzone.co.nz, Google, HealthVault.com and A...
39
• Guideline adherence
• Better documentation
• Practitioner decision making or
process of care
• Medication safety
• Pa...
40
• Master Patient Index (MPI)
• Admit-Discharge-Transfer (ADT)
• Electronic Health Records (EHRs)
• Computerized Physici...
41
• Pharmacy applications
• Laboratory Information System (LIS)
• Radiology Information System (RIS)
• Specialized applic...
42
• Business
Intelligence
• Data Mining/
Utilization
• MIS
• Research
Informatics
• E-learning
• CDSS
• HIE
• CPOE
• PACS...
43
The Challenge - Knowing What It Means
Electronic Medical
Records (EMRs)
Computer-Based
Patient Records
(CPRs)
Electroni...
44
Computerized Physician Order Entry
(CPOE)
45
Values
• No handwriting!!!
• Structured data entry: Completeness, clarity,
fewer mistakes (?)
• No transcription errors...
46
Ordering Transcription Dispensing Administration
CPOE
Automatic
Medication
Dispensing
Electronic
Medication
Administrat...
47
CLINICAL DECISION MAKING
48
WHAT IS A DECISION?
49
Wisdom
Knowledge
Information
Data
Data-Information-Knowledge-
Wisdom (DIKW) Pyramid
50
Wisdom
Knowledge
Information
Data
Contextualization/
Interpretation
Processing/
Synthesis/
Organization
Judgment
Data-I...
51
Wisdom
Knowledge
Information
Data
Contextualization/
Interpretation
Processing/
Synthesis/
Organization
Judgment
100,00...
52
Example: Problem A
• Patient A has a blood pressure reading of
170/100 mmHg
• Data: 170/100
• Information: BP of Patien...
53
Example: Problem B
• Patient B is allergic to penicillin. He was recently
prescribed amoxicillin for his sore throat.
•...
54
Decision & Decision Making
• Decision
– “A choice that you make about something
after thinking about it : the result of...
55
LET’S TAKE A LOOK AT
PATIENT CARE PROCESS
56
Patient Care
Image Sources: (Left) Faculty of Medicine Ramathibodi Hospital (Right) /en.wikipedia.org/wiki/File:Newborn...
57
EXERCISE 1
Provide some examples of
“decisions” health care
providers make
58
Clinical Decisions
• Patient Care
– What patient history to ask?
– What physical examinations to do?
– What investigati...
59
Clinical Decisions
• Patient Care
– What treatment to order/perform?
• Medications
• Surgery/Procedures/Nursing Interve...
60
Clinical Decisions
• Management
– How to improve quality of care and clinical
operations?
– How to allocate limited bud...
61
Clinical Decisions
• Public Health
– How to improve health of population?
– How to investigate/control/prevent disease
...
62
External Memory
Knowledge Data
Long Term Memory
Knowledge Data
Inference
DECISION
PATIENT
Perception
Attention
Working
...
63
IOM (2000)
“To Err Is Human”
64
ROLES OF
INFORMATION TECHNOLOGY
IN DECISION MAKING
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
CLINICAL DECISION
SUPPORT SYSTEMS
(CDS)
68
• Clinical Decision Support (CDS) “is a
process for enhancing health-related
decisions and actions with pertinent,
orga...
69
• Computer-based clinical decision support
(CDS): “Use of the computer [ICT] to bring
relevant knowledge to bear on the...
70
• The real place where most of the values
of health IT can be achieved
• There are a variety of forms and nature
of CDS...
71
• Expert systems
–Based on artificial
intelligence, machine
learning, rules, or
statistics
–Examples: differential
diag...
72
• Alerts & reminders
–Based on specified logical conditions
• Drug-allergy checks
• Drug-drug interaction checks
• Drug...
73
Example of “Reminders”
74
• Reference information or evidence-
based knowledge sources
–Drug reference databases
–Textbooks & journals
–Online li...
75
Infobuttons
Image Source: https://webcis.nyp.org/webcisdocs/what-are-infobuttons.html
76
• Pre-defined documents
–Order sets, personalized “favorites”
–Templates for clinical notes
–Checklists
–Forms
• Can be...
77
Order Sets
Image Source: http://www.hospitalmedicine.org/ResourceRoomRedesign/CSSSIS/html/06Reliable/SSI/Order.cfm
78
• Simple UI designed to help clinical
decision making
–Abnormal lab highlights
–Graphs/visualizations for lab results
–...
79
Abnormal Lab Highlights
Image Source: http://geekdoctor.blogspot.com/2008/04/designing-ideal-electronic-health.html
80
External Memory
Knowledge Data
Long Term Memory
Knowledge Data
Inference
DECISION
PATIENT
Perception
Attention
Working
...
81
External Memory
Knowledge Data
Long Term Memory
Knowledge Data
Inference
DECISION
PATIENT
Perception
Attention
Working
...
82
External Memory
Knowledge Data
Long Term Memory
Knowledge Data
Inference
DECISION
PATIENT
Perception
Attention
Working
...
83
External Memory
Knowledge Data
Long Term Memory
Knowledge Data
Inference
DECISION
PATIENT
Perception
Attention
Working
...
84
External Memory
Knowledge Data
Long Term Memory
Knowledge Data
Inference
DECISION
PATIENT
Perception
Attention
Working
...
85
External Memory
Knowledge Data
Long Term Memory
Knowledge Data
Inference
DECISION
PATIENT
Perception
Attention
Working
...
86
External Memory
Knowledge Data
Long Term Memory
Knowledge Data
Inference
DECISION
PATIENT
Perception
Attention
Working
...
87
External Memory
Knowledge Data
Long Term Memory
Knowledge Data
Inference
DECISION
PATIENT
Perception
Attention
Working
...
88
User User Interface
Patient
Data
Inference Engine
Knowledge
BaseOther Data
• Rules & Parameters
• Statistical data
• Li...
89
ISSUES RELATED TO
CDS IMPLEMENTATION
90
• How will CDS be implemented in real life?
• Will it interfere with user workflow?
• Will it be used by users? If not,...
91
IBM’s Watson
Image Source: socialmediab2b.com
92
Image Source: englishmoviez.com
Rise of the Machines?
93
Issues
• CDSS as a supplement or replacement of clinicians?
– The demise of the “Greek Oracle” model (Miller & Masarie,...
94
• Features with improved clinical practice
(Kawamoto et al., 2005)
– Automatic provision of decision support as part of...
95
Issues
• Alert sensitivity & alert fatigue
Alert Fatigue
96
• Liabilities
– Clinicians as “learned intermediaries”
• Prohibition of certain transactions vs.
Professional autonomy
...
97
Workarounds
98
• “Unanticipated and unwanted effect of
health IT implementation”
(www.ucguide.org)
• Resources
– www.ucguide.org
– Ash...
99
Ash et al. (2004)
Unintended Consequences of
CDS & Health IT
100
• Errors in the process of entering and
retrieving information
– A human-computer interface that is not
suitable for a...
101
• Errors in communication & coordination
– Misrepresenting collective, interactive work as
a linear, clearcut, and pre...
102
• Which type of CDS should be chosen?
• What algorithms should be used?
• How to “represent” knowledge in the system?
...
103
• Choosing the right CDSS strategies
• Expertise required for proper CDSS design &
implementation
• Everybody agreeing...
104
• Speed is Everything
• Anticipate Needs and Deliver in Real Time
• Fit into the User’s Workflow
• Little Things (like...
105
OTHER DECISION SUPPORT
SYSTEMS
106
• Provides information needed to manage
an organization (e.g. a hospital)
effectively and efficiently
• A broad catego...
107
• Allows for
– Data analysis
– Correlation
– Trending
– Reporting of data across multiple sources
Health IT Workforce ...
108
• Examples
– Clinical and Financial Analytics and Decision
Support
– Query and Reporting Tools
– Data Mining
– Online ...
109
Image Source: http://www.hiso.or.th/dashboard/
Data Reporting Systems
110
Image Source: http://www.inetsoft.com/business/solutions/applying_business_intelligence_to_manufacturing/
Business Int...
111
Image Source: https://www.sas.com/technologies/bi/entbiserver/
Business Dashboards
112
• There are several decisions made in a clinical
patient care process
• Data leads to information, knowledge, and
ulti...
113
• CDS comes in various forms, designs, and
architecture
• There are many issues related to design,
implementation and ...
114
• Current mindset: CDS should be used to help, not
replace, human providers
• Be attentive to workarounds, alert fatig...
115
Intelligent &
helpful
robots
Intelligent
humanistic robots
in a human world
Machines that
replace humans
for a “better...
116
References
• Ash JS, Berg M, Coiera E. Some unintended consequences of information
technology in health care: the natu...
117
References
• Greenes RA. Clinical decision support: the road ahead. Oxford (UK): Elsevier;
2007. 581 p.
• Institute of...
118
References
• Shortliffe EH. Computer-based medical consultations: MYCIN. New York (NY):
Elsevier; 1976. 264 p.
• Strom...
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IT & Decision Support Systems in Hospital Supply Chains

  1. 1. IT & Decision Support Systems in Hospital Supply Chains EGIE 512 Hospital Logistics and Supply Chain Management Nawanan Theera-Ampornpunt, M.D., Ph.D. March 20, 2014 http://www.SlideShare.net/Nawanan
  2. 2. 2 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 SlideShare.net/Nawanan http://groups.google.com/group/ThaiHealthIT Introduction
  3. 3. 3 Outline • Healthcare & Information • Health Information Technology • Clinical Decision Making • Clinical Decision Support Systems – Definitions – Types & examples • Issues Related to CDS Implementation • Other Decision Support Systems • Summary
  4. 4. 4 Let’s take a look at these pictures...
  5. 5. 5 Image Source: Guardian.co.uk Manufacturing
  6. 6. 6 Image Source: http://www.oknation.net/blog/phuketpost/2013/10/19/entry-3 Banking
  7. 7. 7 ER - Image Source: nj.com Healthcare (on TV)
  8. 8. 8 Healthcare (at an undisclosed 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 • Large variations & contextual dependence Input Process Output Patient Presentation Decision- Making Biological Responses Why Healthcare Isn’t Like Any Others
  11. 11. 11 Input Process Output Transfer Banking Value-Add - Security - Convenience - Customer Service Location A Location B But...Are We That Different?
  12. 12. 12 Input Process Output Assembling Manufacturing Raw Materials Finished Goods Value-Add - Innovation - Design - QC But...Are We That Different?
  13. 13. 13 Input Process Output Patient Care Health care Sick Patient Well Patient Value-Add - Technology & medications - Clinical knowledge & skills - Quality of care; process improvement - Information But...Are We That Different?
  14. 14. 14 Engineer’s Perspectives • Logistics & Supply Chain (Administrative) • Focus on Processes • Analytical, Systematic Mind • Tracking & Improving – Patient Flow – Materials Flow (Drugs, Documents, Equipments) – Information Flow • Main Objectives – Efficiency – Variability – Traceability Clinician’s Perspectives • Patient Care (Clinical) • Focus on Outcomes • Specialized Clinical Mind • Improving – Patient Care Process – Healthcare Delivery • Main Objectives – Quality • Effectiveness • Safety • Timeliness Engineers & Clinicians
  15. 15. 15 Back to something simple...
  16. 16. 16 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) What Clinicians Want?
  17. 17. 17 • 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. High-Quality Care
  18. 18. 18 Shortliffe EH. Biomedical informatics in the education of physicians. JAMA. 2010 Sep 15;304(11):1227-8. Information Is Everywhere in Healthcare
  19. 19. 19 Shortliffe EH. Biomedical informatics in the education of physicians. JAMA. 2010 Sep 15;304(11):1227-8. “Information” in Medicine
  20. 20. 20 Why We Need ICT in Healthcare? #1: Because information is everywhere in healthcare
  21. 21. 21 (IOM, 2001)(IOM, 2000) (IOM, 2011) Landmark IOM Reports
  22. 22. 22 • 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 Patient Safety
  23. 23. 23 • 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 IOM Reports Summary
  24. 24. 24 • Perception errors Image Source: interaction-dynamics.com To Err Is Human 1: Perception
  25. 25. 25 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 2: Attention
  26. 26. 26 Image Source: Suthan Srisangkaew, Department of Pathology, Facutly of Medicine Ramathibodi Hospital, Mahidol University To Err Is Human 3: Memory
  27. 27. 27 • 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 To Err Is Human 4: Cognition
  28. 28. 28Klein 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” Cognitive Biases in Healthcare
  29. 29. 29 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. Cognitive Biases in Healthcare
  30. 30. 30 Croskerry P. The importance of cognitive errors in diagnosis and strategies to minimize them. Acad Med. 2003 Aug;78(8):775-80. Cognitive Biases in Healthcare
  31. 31. 31 • Medication Errors –Drug Allergies –Drug Interactions • Ineffective or inappropriate treatment • Redundant orders • Failure to follow clinical practice guidelines Common Errors
  32. 32. 32 Why We Need ICT in Healthcare? #2: Because healthcare is error-prone and technology can help
  33. 33. 33 Why We Need ICT in Healthcare? #3: Because access to high-quality patient information improves care
  34. 34. 34 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
  35. 35. 35 Health Information Technology Goal Value-Add Tools Health IT: What’s in a Word?
  36. 36. 36 • Patient’s Health • Population’s Health • Organization’s Health (Quality, Reputation & Finance) “Health” in Health IT
  37. 37. 37 Hospital Information System (HIS) Computerized Provider Order Entry (CPOE) Electronic Health Records (EHRs) Picture Archiving and Communication System (PACS) Screenshot Images from Faculty of Medicine Ramathibodi Hospital, Mahidol University Various Forms of Health IT
  38. 38. 38 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
  39. 39. 39 • Guideline adherence • Better documentation • Practitioner decision making or process of care • Medication safety • Patient surveillance & monitoring • Patient education/reminder Values of Health IT
  40. 40. 40 • 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 Enterprise-Wide Hospital IT
  41. 41. 41 • 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
  42. 42. 42 • Business Intelligence • Data Mining/ Utilization • MIS • Research Informatics • E-learning • CDSS • HIE • CPOE • PACS • EHRs Enterprise Resource Planning (ERP) • Finance • Materials • HR • ADT • HIS • LIS • RIS Strategic Operational ClinicalAdministrative Position may vary based on local context 4 Ways IT Can Support Hospitals
  43. 43. 43 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
  44. 44. 44 Computerized Physician Order Entry (CPOE)
  45. 45. 45 Values • No handwriting!!! • Structured data entry: Completeness, clarity, fewer mistakes (?) • No transcription errors! • Streamlines workflow, increases efficiency Computerized Physician Order Entry (CPOE)
  46. 46. 46 Ordering Transcription Dispensing Administration CPOE Automatic Medication Dispensing Electronic Medication Administration Records (e-MAR) Barcoded Medication Administration Barcoded Medication Dispensing Stages of Medication Process
  47. 47. 47 CLINICAL DECISION MAKING
  48. 48. 48 WHAT IS A DECISION?
  49. 49. 49 Wisdom Knowledge Information Data Data-Information-Knowledge- Wisdom (DIKW) Pyramid
  50. 50. 50 Wisdom Knowledge Information Data Contextualization/ Interpretation Processing/ Synthesis/ Organization Judgment Data-Information-Knowledge- Wisdom (DIKW) Pyramid
  51. 51. 51 Wisdom Knowledge Information Data Contextualization/ Interpretation Processing/ Synthesis/ Organization Judgment 100,000,000 I have 100,000,000 baht in my bank account I am rich!!!!! I should buy a luxury car (and a BIG house)! Example
  52. 52. 52 Example: Problem A • Patient A has a blood pressure reading of 170/100 mmHg • Data: 170/100 • Information: BP of Patient A = 170/100 mmHg • Knowledge: Patient A has high blood pressure • Wisdom (or Decision): – Patient A needs to be investigated for cause of HT – Patient A needs to be treated with anti-hypertensives – Patient A needs to be referred to a cardiologist
  53. 53. 53 Example: Problem B • Patient B is allergic to penicillin. He was recently prescribed amoxicillin for his sore throat. • Data: Penicillin, amoxicillin, sore throat • Information: – Patient B has penicillin allergy – Patient B was prescribed amoxicillin for his sore throat • Knowledge: – Patient B may have allergic reaction to his prescription • Wisdom (or Decision): – Patient B should not take amoxicillin!!!
  54. 54. 54 Decision & Decision Making • Decision – “A choice that you make about something after thinking about it : the result of deciding” (Merriam-Webster Dictionary) • Decision making – “The cognitive process resulting in the selection of a course of action among several alternative scenarios.” (Wikipedia)
  55. 55. 55 LET’S TAKE A LOOK AT PATIENT CARE PROCESS
  56. 56. 56 Patient Care Image Sources: (Left) Faculty of Medicine Ramathibodi Hospital (Right) /en.wikipedia.org/wiki/File:Newborn_Examination_1967.jpg (Nevit Dilmen)
  57. 57. 57 EXERCISE 1 Provide some examples of “decisions” health care providers make
  58. 58. 58 Clinical Decisions • Patient Care – What patient history to ask? – What physical examinations to do? – What investigations to order? • Lab tests • Radiologic studies (X-rays, CTs, MRIs, etc.) • Other special investigations (EKG, etc.) – What diagnosis (or possible diagnosis) to make?
  59. 59. 59 Clinical Decisions • Patient Care – What treatment to order/perform? • Medications • Surgery/Procedures/Nursing Interventions • Patient Education/Advice for Self-Care • Admission – How should patient be followed-up? – With good or poor response to treatment, what to do next? – With new information, what to do next?
  60. 60. 60 Clinical Decisions • Management – How to improve quality of care and clinical operations? – How to allocate limited budget & resources? – What strategies should the hospital pursue & what actions/projects should be done?
  61. 61. 61 Clinical Decisions • Public Health – How to improve health of population? – How to investigate/control/prevent disease outbreak? – How to allocate limited budget & resources? – What areas of the country’s public health need attention & what to do with it?
  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 IOM (2000) “To Err Is Human”
  64. 64. 64 ROLES OF INFORMATION TECHNOLOGY IN DECISION MAKING
  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
  66. 66. 66 External Memory Knowledge Data Long Term Memory Knowledge Data Inference DECISION PATIENT Perception Attention Working Memory CLINICIAN Elson, Faughnan & Connelly (1997) Possible Human Errors Possibility of Human Errors
  67. 67. 67 CLINICAL DECISION SUPPORT SYSTEMS (CDS)
  68. 68. 68 • Clinical Decision Support (CDS) “is a process for enhancing health-related decisions and actions with pertinent, organized clinical knowledge and patient information to improve health and healthcare delivery” (Including both computer-based & non-computer-based CDS) (Osheroff et al., 2012) What Is A CDS?
  69. 69. 69 • Computer-based clinical decision support (CDS): “Use of the computer [ICT] to bring relevant knowledge to bear on the health care and well being of a patient.” (Greenes, 2007) What Is A CDS?
  70. 70. 70 • The real place where most of the values of health IT can be achieved • There are a variety of forms and nature of CDS Clinical Decision Support Systems (CDS)
  71. 71. 71 • Expert systems –Based on artificial intelligence, machine learning, rules, or statistics –Examples: differential diagnoses, treatment options CDS Examples Shortliffe (1976)
  72. 72. 72 • Alerts & reminders –Based on specified logical conditions • Drug-allergy checks • Drug-drug interaction checks • Drug-lab interaction checks • Drug-formulary checks • Reminders for preventive services or certain actions (e.g. smoking cessation) • Clinical practice guideline integration (e.g. best practices for chronic disease patients) CDS Examples
  73. 73. 73 Example of “Reminders”
  74. 74. 74 • 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) CDS Examples
  75. 75. 75 Infobuttons Image Source: https://webcis.nyp.org/webcisdocs/what-are-infobuttons.html
  76. 76. 76 • Pre-defined documents –Order sets, personalized “favorites” –Templates for clinical notes –Checklists –Forms • Can be either computer-based or paper-based CDS Examples
  77. 77. 77 Order Sets Image Source: http://www.hospitalmedicine.org/ResourceRoomRedesign/CSSSIS/html/06Reliable/SSI/Order.cfm
  78. 78. 78 • Simple UI designed to help clinical decision making –Abnormal lab highlights –Graphs/visualizations for lab results –Filters & sorting functions CDS Examples
  79. 79. 79 Abnormal Lab Highlights Image Source: http://geekdoctor.blogspot.com/2008/04/designing-ideal-electronic-health.html
  80. 80. 80 External Memory Knowledge Data Long Term Memory Knowledge Data Inference DECISION PATIENT Perception Attention Working Memory CLINICIAN Elson, Faughnan & Connelly (1997) How CDS Supports Decision Making Abnormal lab highlights
  81. 81. 81 External Memory Knowledge Data Long Term Memory Knowledge Data Inference DECISION PATIENT Perception Attention Working Memory CLINICIAN Elson, Faughnan & Connelly (1997) How CDS Supports Decision Making Order Sets
  82. 82. 82 External Memory Knowledge Data Long Term Memory Knowledge Data Inference DECISION PATIENT Perception Attention Working Memory CLINICIAN Elson, Faughnan & Connelly (1997) How CDS Supports Decision Making Drug-Allergy Checks
  83. 83. 83 External Memory Knowledge Data Long Term Memory Knowledge Data Inference DECISION PATIENT Perception Attention Working Memory CLINICIAN Elson, Faughnan & Connelly (1997) How CDS Supports Decision Making Drug-Drug Interaction Checks
  84. 84. 84 External Memory Knowledge Data Long Term Memory Knowledge Data Inference DECISION PATIENT Perception Attention Working Memory CLINICIAN Elson, Faughnan & Connelly (1997) How CDS Supports Decision Making Drug-Drug Interaction Checks
  85. 85. 85 External Memory Knowledge Data Long Term Memory Knowledge Data Inference DECISION PATIENT Perception Attention Working Memory CLINICIAN Elson, Faughnan & Connelly (1997) How CDS Supports Decision Making Clinical Practice Guideline Alerts/Reminders
  86. 86. 86 External Memory Knowledge Data Long Term Memory Knowledge Data Inference DECISION PATIENT Perception Attention Working Memory CLINICIAN Elson, Faughnan & Connelly (1997) How CDS Supports Decision Making Integration of Evidence-Based Resources (e.g. drug databases, literature)
  87. 87. 87 External Memory Knowledge Data Long Term Memory Knowledge Data Inference DECISION PATIENT Perception Attention Working Memory CLINICIAN Elson, Faughnan & Connelly (1997) How CDS Supports Decision Making Diagnostic/Treatment Expert Systems
  88. 88. 88 User User Interface Patient Data Inference Engine Knowledge BaseOther Data • Rules & Parameters • Statistical data • Literature • Etc. • System states • Epidemiological/ surveillance data • Etc. Example of CDS Architecture Other Systems
  89. 89. 89 ISSUES RELATED TO CDS IMPLEMENTATION
  90. 90. 90 • How will CDS be implemented in real life? • Will it interfere with user workflow? • Will it be used by users? If not, why? • What user interface design is best? • What are most common user complaints? • Who is responsible if something bad happens? • How to balance reliance on machines & humans Human Factor Issues of CDS
  91. 91. 91 IBM’s Watson Image Source: socialmediab2b.com
  92. 92. 92 Image Source: englishmoviez.com Rise of the Machines?
  93. 93. 93 Issues • 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) Human Factor Issues of CDS Wrong Assumption Correct Assumption
  94. 94. 94 • Features with improved clinical practice (Kawamoto et al., 2005) – Automatic provision of decision support as part of clinician workflow – Provision of recommendations rather than just assessments – Provision of decision support at the time and location of decision making – Computer based decision support • Usability & impact on productivity Human Factor Issues of CDS
  95. 95. 95 Issues • Alert sensitivity & alert fatigue Alert Fatigue
  96. 96. 96 • Liabilities – Clinicians as “learned intermediaries” • Prohibition of certain transactions vs. Professional autonomy (see Strom et al., 2010) Ethical-Legal Issues of CDS
  97. 97. 97 Workarounds
  98. 98. 98 • “Unanticipated and unwanted effect of health IT implementation” (www.ucguide.org) • Resources – www.ucguide.org – Ash et al. (2004) – Campbell et al. (2006) – Koppel et al. (2005) Unintended Consequences of CDS & Health IT
  99. 99. 99 Ash et al. (2004) Unintended Consequences of CDS & Health IT
  100. 100. 100 • 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) Unintended Consequences of CDS & Health IT
  101. 101. 101 • Errors in communication & coordination – 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) Unintended Consequences of CDS & Health IT
  102. 102. 102 • Which type of CDS should be chosen? • What algorithms should be used? • How to “represent” knowledge in the system? • How to update/maintain knowledge base in the system? • How to standardize data/knowledge? • How to implement CDS with good system performance? Technical Issues of CDS
  103. 103. 103 • Choosing the right CDSS strategies • Expertise required for proper CDSS design & implementation • Everybody agreeing on the “rules” to be enforced • Evaluation of effectiveness Other Issues
  104. 104. 104 • Speed is Everything • Anticipate Needs and Deliver in Real Time • Fit into the User’s Workflow • Little Things (like Usability) Can Make a Big Difference • Recognize that Physicians Will Strongly Resist Stopping • Changing Direction Is Easier than Stopping • Simple Interventions Work Best • Ask for Additional Information Only When You Really Need It • Monitor Impact, Get Feedback, and Respond • Manage and Maintain Your Knowledge-based Systems Bates et al. (2003) “Ten Commandments” for Effective CDS
  105. 105. 105 OTHER DECISION SUPPORT SYSTEMS
  106. 106. 106 • Provides information needed to manage an organization (e.g. a hospital) effectively and efficiently • A broad category of information systems – Administrative reports – Enterprise resource planning (ERP) – Supply Chain Management (SCM) – Customer Relationship Management (CRM) – Project management tools – Knowledge management tools – Business intelligence (BI) Management Information Systems (MIS)
  107. 107. 107 • Allows for – Data analysis – Correlation – Trending – Reporting of data across multiple sources Health IT Workforce Curriculum Version 2.0/Spring 2011 Business Intelligence (BI)
  108. 108. 108 • Examples – Clinical and Financial Analytics and Decision Support – Query and Reporting Tools – Data Mining – Online Scoreboards and Dashboards Business Intelligence & Data Warehousing for Healthcare. Clinical Informatics Wiki. 2008. Available from: http://www.informatics- review.com/wiki/index.php/Business_Intelligence_&_Data_Warehousing_for_Healthcare Health IT Workforce Curriculum Version 2.0/Spring 2011 Business Intelligence (BI)
  109. 109. 109 Image Source: http://www.hiso.or.th/dashboard/ Data Reporting Systems
  110. 110. 110 Image Source: http://www.inetsoft.com/business/solutions/applying_business_intelligence_to_manufacturing/ Business Intelligence (BI)
  111. 111. 111 Image Source: https://www.sas.com/technologies/bi/entbiserver/ Business Dashboards
  112. 112. 112 • There are several decisions made in a clinical patient care process • Data leads to information, knowledge, and ultimately, decision & actions • Human clinicians are not perfect and can make mistakes • A clinical decision support systems (CDS) provides support for clinical decision making (to prevent mistakes & provide best patient care) • A CDS can be computer-based or paper-based Key Points
  113. 113. 113 • CDS comes in various forms, designs, and architecture • There are many issues related to design, implementation and use of CDS – Technical Issues – Human Factor Issues – Ethical-Legal Issues Key Points
  114. 114. 114 • Current mindset: CDS should be used to help, not replace, human providers • Be attentive to workarounds, alert fatigues, and other unintended consequences of CDS – They can cause more danger to patients!! – They may lead users to abandon using CDS (a failure) • There are recommendations on how to best design & implement CDS • There are other administrative (non-clinical) decision support systems as well Key Points
  115. 115. 115 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 Will The Future Be for Health Care?
  116. 116. 116 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. • Ariely D. Predictably irrational: the hidden forces that shape our decisions. New York City (NY): HarperCollins; 2008. 304 p. • Bates DW, Kuperman GJ, Wang S, Gandhi T, Kittler A, Volk L, Spurr C, Khorasani R, Tanasijevic M, Middleton B. Ten commandments for effective clinical decision support: making the practice of evidence-based medicine a reality. J Am Med Inform Assoc. 2003 Nov-Dec;10(6):523-30. • Campbell EM, Sittig DF, Ash JS, Guappone KP, Dykstra RH. Types of unintended consequences related to computerized provider order entry. J Am Med Inform Assoc. 2006 Sep-Oct;13(5):547-56. • Elson RB, Faughnan JG, Connelly DP. An industrial process view of information delivery to support clinical decision making: implications for systems design and process measures. J Am Med Inform Assoc. 1997 Jul-Aug;4(4):266-78. • Friedman CP. A "fundamental theorem" of biomedical informatics. J Am Med Inform Assoc. 2009 Apr;16(2):169-170.
  117. 117. 117 References • Greenes RA. Clinical decision support: the road ahead. Oxford (UK): Elsevier; 2007. 581 p. • Institute of Medicine, Committee on Quality of Health Care in America. To err is human: building a safer health system. Kohn LT, Corrigan JM, Donaldson MS, editors. Washington, DC: National Academy Press; 2000. 287 p. • Kawamoto K, Houlihan CA, Balas EA, Lobach DF. Improving clinical practice using clinical decision support systems: a systematic review of trials to identify features critical to success. BMJ. 2005 Apr 2;330(7494):765. • Koppel R, Metlay JP, Cohen A, Abaluck B, Localio AR, Kimmel SE, et al. Role of computerized physician order entry systems in facilitating medication errors. JAMA. 2005 Mar 9;293(10):1197-1203. • Miller RA, Masarie FE. The demise of the "Greek Oracle" model for medical diagnostic systems. Methods Inf Med. 1990 Jan;29(1):1-2. • Osheroff JA, Teich JM, Levick D, Saldana L, Velasco FT, Sittig DF, Rogers KM, Jenders RA. Improving outcomes with clinical decision support: an implementer’s guide. 2nd ed. Chicago (IL): Healthcare Information and Management Systems Society; 2012. 323 p.
  118. 118. 118 References • Shortliffe EH. Computer-based medical consultations: MYCIN. New York (NY): Elsevier; 1976. 264 p. • Strom BL, Schinnar R, Aberra F, Bilker W, Hennessy S, Leonard CE, Pifer E. Unintended effects of a computerized physician order entry nearly hard-stop alert to prevent a drug interaction: a randomized controlled trial. Arch Intern Med. 2010 Sep 27;170(17):1578-83.
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