1. Clinical Decision
Support Systems
Nawanan Theera-Ampornpunt, M.D., Ph.D.
June 14, 2014
http://www.SlideShare.net/Nawanan

2. 2
Outline
• What is a Decision?
• Clinical Decision Making
• Roles of IT in Decision Making
• Clinical Decision Support Systems
– Definitions
– Types & examples
– Architecture
• Issues Related to CDS Implementation
• Summary

3. 3
WHAT IS A DECISION?

4. 4
Wisdom
Knowledge
Information
Data
Data-Information-Knowledge-
Wisdom (DIKW) Pyramid

5. 5
Wisdom
Knowledge
Information
Data
Contextualization/
Interpretation
Processing/
Synthesis/
Organization
Judgment
Data-Information-Knowledge-
Wisdom (DIKW) Pyramid

6. 6
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

7. 7
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

8. 8
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!!!

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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)

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LET’S TAKE A LOOK AT
PATIENT CARE PROCESS

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Patient Care
Image Sources: (Left) Faculty of Medicine Ramathibodi Hospital (Right) /en.wikipedia.org/wiki/File:Newborn_Examination_1967.jpg (Nevit Dilmen)

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EXERCISE 1
Provide some examples of
“decisions” health care
providers make

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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?

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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?

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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?

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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?

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CLINICAL
DECISION MAKING

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

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PROBLEMS WITH
HUMAN’S
DECISION MAKING

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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

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Image Source: Suthan Srisangkaew, Department of Pathology, Facutly of Medicine Ramathibodi Hospital, Mahidol University
To Err is Human 2: Memory

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

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• Medical Errors
–Drug allergies
–Drug interactions
• Missing Abnormal Lab Findings
• Clinical Practice Guidelines
• Bias in Judgment & Decision-Making
Common Errors in Healthcare

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ROLES OF
INFORMATION TECHNOLOGY
IN DECISION MAKING

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

26. 26
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

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CLINICAL DECISION
SUPPORT SYSTEMS
(CDS)

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• 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?

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• 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?

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• 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)

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• Expert systems
–Based on artificial
intelligence, machine
learning, rules, or
statistics
–Examples: differential
diagnoses, treatment
options
CDS Examples
Shortliffe (1976)

32. 32
• 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

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Example of “Reminders”

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• 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

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

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

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

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

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

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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

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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

42. 42
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

43. 43
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

44. 44
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

45. 45
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

46. 46
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)

47. 47
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

48. 48
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

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ISSUES RELATED TO
CDS IMPLEMENTATION

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• 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

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IBM’s Watson
Image Source: socialmediab2b.com

52. 52
Image Source: englishmoviez.com
Rise of the Machines?

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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

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• 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

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Issues
• Alert sensitivity & alert fatigue
Alert Fatigue

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• Liabilities
– Clinicians as “learned intermediaries”
• Prohibition of certain transactions vs.
Professional autonomy
(see Strom et al., 2010)
Ethical-Legal Issues of CDS

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Workarounds

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• “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

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

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• 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

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• 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

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• 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

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• 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

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• 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

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• 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

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• 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

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• 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
Key Points

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Intelligent &
helpful
machines
Machines with a
human touch
Machines that
replace humans
HAL 9000 Data David NS-5
Dangerous
killer machines
What Will The Future Be
for Health Care?

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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.

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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.

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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.