Clinical decision support systems use information technology to reduce errors and improve clinical decision making by providing clinicians with patient data, knowledge resources, and clinical guidance at the point of care through tools such as diagnostic decision trees, drug databases, alerts, and reminders. However, implementing clinical decision support systems poses challenges around usability, workflow integration, and clarifying the relationship between machine recommendations and human clinical judgment.

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

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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

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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

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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

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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)
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)

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• 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

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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
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

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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
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

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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.