1. A common ground theory of medical decision-making 1: The CREDO stack
John Fox
Department of Engineering Science
University of Oxford and OpenClinical

2. Thanks to … Clinicians Psychologists, Informatics/CS/AI
Alyssa Alabassi
John Bury
Robert Dunlop
John Emery
Marc Gutenstein
Andrzej Glowinski
Mike O’Neil
Vicky Monaghan
Vivek Patkar
Jean-Louis Renaud-Salis
Robert Walton
Matt Williams
Guy Wood-Gush
Psychologists, Informatics/CS/AI
Andrew Coulson
Ioannis Chronakis
Subrata Das
David Glasspool
Omar Khan
Paul Krause
Simon Parsons
Mor Peleg
Ali Rahmanzadeh
Matt South
Rory Steele
Paul Taylor
Richard Thomson

3. Summary
Medicine is a rich and challenging domain for decision science and decision engineering
It raises major challenges and curiously neglected questions at many levels
theory, technology, applications and more …
The common ground theory aims to provide a general framework in which to
Promote discussion across disciplines
Clarify research questions and
Develop practical solutions
The CREDO stack is a particular instance, but there are many others

4. The borders of the common ground
“Prescriptive” (axiomatic, rational) theories
Lindley “there is only one correct way to take a decision”
EUT, Multicriteria DT, game theory, … and many ad hoc variants
“Descriptive” (empirical, explanatory) theories
Cognitive (Nobel Laureates - Herbert Simon, Daniel Kahneman)
Neuroscience (neuroanatomy, neuropsychology, “hot cognition”)
Ecological (e.g. Gary Klein “naturalistic” theories)
“Practical” (engineering, design) theories
Decisions are often framed and made with respect to standard practice
Decision systems may need to engage with accepted practice

5. Medical motivation: Quality and safety of patient care
UK National health service
Vincent data on medical error in Acute Hospitals
>10% acute hospital admissions in NHS lead to avoidable medical error
US Institute of Medicine
IOM: “To err is human”; “Crossing the quality chasm”
McGlynn: Quality of Health Care Delivered to Adults in the USA

6. Quality of Health in the USA McGlynn NEJM 2003

7. Classical definition of DM
Decision-making [is] a cognitive process resulting in the selection of a belief or a course of action from among several alternative possibilities.

8. The CREDO stack

9. Diversity of medical decisions
Screening for and classification of hazards;
Risk stratification and management;
Selection of tests and investigations;
Diagnosing the cause(s) of clinical complaints;
Selecting treatments and other interventions;
Prescribing drugs (routes, dosages, polypharmacy etc.);
Referring patient to a colleague
Deciding whether a decision is needed;
Initiating, adjusting and stopping treatments;
Deciding whether earlier decisions are correct or not; if not why not; adjust; reverse, reframe, retake;

10. Diversity of medical decisions
Screening for and classification of hazards;
Risk stratification and management;
Selection of tests and investigations;
Diagnosing the cause(s) of clinical complaints;
Selecting treatments and other interventions;
Prescribing drugs (routes, dosages, polypharmacy etc.);
Referring patient to a colleague
Deciding whether a decision is needed;
Initiating, adjusting and stopping treatments;
Deciding whether earlier decisions are correct or not; if not why not; adjust; reverse, reframe, retake;

11. MDM is reason based Refer to specialist colleague if …
There is a possible life threatening condition
I don’t know what to do or lack sufficient knowledge
The NICE clinical guideline says I should
Patient is eligible for a research trial
Difficult patient, and I can’t resolve issue by myself
Patient has asked to be referred
Colleague or mentor has suggested I should …

12. MDM is dynamic
Decision-makers must deal with changing and often unpredictable circumstances
Decisions are not just choices, they are points in an evolving narrative (patient and professional)
Common ground theory should address the whole cycle of decision-making:
When is a decision needed? what is the goal of the decision?
What knowledge and strategies are relevant?
When is it appropriate and safe to commit?
When is it necessary to revisit and revise commitments as the situation evolves?

13. MDM is reflective Peter Pritchard, a now retired GP (2004):
I am committed to putting the patient first
I respect the patient’s identity, dignity, beliefs, and values
I am open to self-criticism and self-audit
I am open to peer criticism and peer audit
I try to provide care of high quality and apply evidence-based medicine where appropriate
I am dedicated to lifelong reflection and learning.

14. Example: cancer care

15. Example: cancer care

16. Example: decisions in context

17. The CREDO stack

18. Agent theory
Cognitive agents engage with their environment in perceiving, acting and communicating (with the clinical team and patients).
From these engagements, cognitive agents form and modify beliefs about a current situation, leading to goals that guide their behavior over time.
Cognitive agents draw upon substantial (sometimes prodigious) bodies of knowledge, both general and specialist (e.g. medicine)
Other key cognitive functions include abilities to reason, frame and make decisions, formulate plans and schedule tasks.
All these processes are subject to uncertainty, requiring different kinds of cognitive control, including ‘reactive’ (situation-driven) and ‘deliberative’ (goal-driven).

19. Metacognition and decision-making
Key features of humans in general (and medical professionals in particular) are that we
Autonomously recognise the need for decisions, frame them and make them as circumstances evolve;
Can reflect upon the rationales for our beliefs, goals, decisions and plans
Can review and modify our commitments and intentions as circumstances change)
None of this is addressed in classical decision science or decision engineering

20. Dynamic decision-making A synoptic view
Detect a problem
Frame decision (specify goal and decision type)
Assemble options which might resolve the problem
Identify relevant data and criteria
Construct reasons for/against options
Engage with uncertainty, values, preferences
Aggregate reasons to assess relative merit of options
Commit to a decision
Implement the decision (actions, plans etc).
Monitor outcomes against goals and respond
Cycle

21. A common ground theory
Goals
Beliefs
Actions
Options
Commitments
Plans
From decision science to decision engineering: the CREDO stack
ResearchGate 2014

22. Example: risk assessment
Worried,
well
Moderate
risk
Options
Commitments
Beliefs
Plans
Goals
Actions
Genetic, statistical &
other lines of reasoning
Population or
moderate or
high risk

23. Example: test selection
Investigate for possible cancer
Pain,
nodule
Order
Mammogram &
ultrasound tr
Options
Commitments
Beliefs
Plans
Goals
Actions
Mammogram, ultrasound
Age, symptoms, …
Family history
Ultrasound
Mammogram
CT etc.

24. Reasons and decisions Argument construction Argument aggregation
Knowledge U Data LA (Claim, Reason, Qualifier)
Argument aggregation
{(Claim, Reason, Qualifier)} Agg (Claim, Modality)
Fox et al ECAI, 1992; UAI 1994; Fox and Das, 2000
Krause et al Computational Intelligence 1995

25. Uncertainty and arguments
Quantitative
[0,1] degree of belief (e.g. probability, possibility)
[-1,+1] bipolar measures (e.g. belief functions)
{1,2,3,…n} ad hoc weighting of arguments
Qualitative
+ “supporting” arguments
{+,-} “supporting” and “opposing” arguments
{++,--, +, -} … plus “confirming” and “excluding”
Modal
Linguistic (perhaps, possible, probable, plausible …)

26. Ten features of argumentation in decision-making
Argumentation is a process of constructing reasons for or against competing claims.
The background knowledge (theories) which is used in constructing an argument may be specific to a particular domain such as medicine or law, or embody general principles that are applicable in all domains.
Arguments increase or reduce confidence in a claim, though we may not be able to be precise about its quantitative impact.
The more independent and valid lines of argument we may construct in support of a claim the greater the confidence that is warranted in the claim
the more independent lines of argument against the greater the doubt
In some cases a single argument can be conclusive – it confirms or refutes a claim absolutely.
Furthermore, one argument may appear to conclusively support a claim, while another conclusively supports a logically contradictory claim.
Tolerance of contradictions makes sense because arguments can be based on different background assumptions; a formal treatment should be similarly tolerant.
Arguments and theories can themselves be objects of reasoning
e.g. “I do not accept your argument that my theory necessarily predicts climate change because you are making unreasonable assumptions about the physics of the greenhouse effect”.

27. Ten features of argumentation in decision-making
Some arguments may be stronger and take precedence over others, leading to the rebuttal of one argument by another
Similarly some arguments may corroborate or buttress others, thereby strengthening the claim.
In the absence of information about relative strength contradictory arguments can still play an important part in analysing evidence and making decisions.
Natural language provides an expressive vocabulary for discussing evidence.
It would be desirable to develop techniques which use sound formal and mathematical languages for argumentation tasks but which can be translated to and from intuitive, natural language forms.
If a rational agent is forced to choose between two or more competing hypotheses or actions it should choose the one in which it has the greatest overall confidence that it is the most credible (hypothesis) or the most beneficial (action), unless there are grounds to suspect that the current order of preference is not to be relied upon.
A rational agent that is not forced to choose may defer a decision on the grounds that the arguments are
inconclusive,
unreliable
otherwise unwarranted

28. Formalising the common ground theory

29. Common ground theory (1): DM
Decision making
Planning
John Fox1,2*, Richard P. Cooper3 and David W. Glasspool4
A canonical theory of dynamic decision-making
Front. Psychol., 02 April 2013

30. The CREDO stack

31. The knowledge ladder Agents Expert systems, Personal care agents Plans
Care pathways, workflows
Decisions
Reasons (arguments, evidence, preferences)
Rules
Alerts, reminders, interpretations
Descriptions
Medical facts, Clinical notes
Concepts
Class hierarchies, semantic networks
Diseases, Symptoms, Findings, Drugs
Terminologies, coding systems
Symbols

32. The CREDO stack

33. Decision engineering (See wikipedia article “decision engineering”)
… it is possible to design decisions using proven engineering methods used for designing other “objects” like bridges, buildings …
A shared language of standard components … readily understood by all stakeholders
Software tools for design, development and deployment of apps and agents
Populate generic decision models (Dx, Tx, Rx …) with domain-specific (medical) knowledge

34. PROforma: Reification into “tasks”
Enquiries
Actions
Candidates
Commitments
Beliefs
Plans
Goals
Actions
Decision
Plan
Fox et al, MIE 1996;
Fox and Das, AI in hazardous applications, MIT Press, 2000

35. Decision engineering

36. The CREDO stack

37. Applications Care pathways in cardiology UPMC (USA), NHS (NZ) , NHS UK
Diagnosis and treatment in endocrine conditions (thyroid, diabetes)
AACE (USA)
Decision support for general practitioners
BPAC (NZ)
Triage for common conditions
NHS Choices (UK)
Supporting the breast MDT- Royal Free Hospital
BASO 2008, ASCO 2009, BMJ Open, 2012
Triple assessment of suspected breast cancer
Brit J Cancer 2006
Chemotherapy for children with acute lymphoblastic leukaemia
Brit J Haematology 2005
Planning care for women at risk of breast/ovarian cancer
Methods of Information in Medicine 2004
GP referrals for common cancers
MEDINFO 2003
Genotype of HIV+ patients interpretation and selection of anti-retrovirals (InferMed, Hoffman la Roche)
AIDS 2002
Genetic risk assessment
BMJ 1999, 2000
Support for mammographic screening
Medical Imaging 1999
Prescribing in general practice
BMJ 1997 37

38. The CREDO stack

39. Decision support: human interaction

40. Where next for decision science and engineering?
Embedded cognition
Time, space, objects
Planning and acting, safely
Symbolic cognition
Perception
Language
Learning
Multi-agent collaboration

41. Summary Medicine is a challenging domain for
Understanding human error and expertise
Developing decision theory, empirical science and engineering methods
It raises many important questions and some strangely neglected ones
This will require contributions from many disciplines but there is a high level of fragmentation in decision science
The “domino” is a first draft of a common ground theory, to promote interdisciplinary discussion
The CREDO stack validates the theory to a first approximation demonstrates its practical value