processes in clinical thinking and decision making...processes in clinical thinking and decision...
TRANSCRIPT
Arthur Felice, MD, MSc, FRCS Ed, FEBSUniversity of Malta
Processes in clinical thinking and decision
making
Training clinicians
Is training in clinical thinking and decision making ‘pie in the sky’?
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A. Knowledge
B. Experience
C. Application of the rules of Logic (including avoidance of fallacies of reasoning and biases)
Clinical Reasoning Involves :
Do surgical trainees receive coaching in the latter requirement?
There is hardly any such training!
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Knowledge Basic sciences
Clinical science
Appraised research evidence
Mastery of rules of logic, fallacies and biases
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Experience: Philosophical ConsiderationConcepts arise in our mind - Descartes
Concepts arise from experience of natural categories – Hume and Locke
Concepts arise from the interaction between our mind and the external reality – Kant
Concepts evolve and change as a result of interactions with external reality - Hegel
Reality exists independently of the human mind and is different from human experiences and concepts. - Popper
The Muller Lyer Illusion:
The Zulu Round House:
Correct Interpretation of Auscultation Findings:
General Physicians 41%
Cardiologists 79%
(Scott Butterworth J, Reppert E H)
Definitions: Boolean logic – an algebraic system
Deduction – from general to particular
Induction - from particular to general
Inference – includes deduction, induction and abduction (Practical reasoning)
Abduction – generating a hypothesis 10
Forms of Reasoning Employed in the Clinical Practice:
A. Logical Reasoning (Discursive)Theoretical Reasoning Inductive
DeductiveHypothetico-deductive
Practical Reasoning
B. Intuitive Reasoning
Logic in Computer ScienceComputer application Mathematical
Symbolic logicPhilosophical logic
Digital circuits Boolean logic Deduction
Logic programming Automated theorem-proving Deduction
Databases Relational algebra Deduction
Artificial intelligence Automated theorem-proving DeductionInference
Algorithms Complexity and responsiveness Aristotelian practical reasoning
Artificial Neural Networks
Non-linear statistical analysisProbabilistic reasoning
Inference from observationPattern recognitionInductive reasoning
Definition of Diagnosis:
Opinion revision from imperfect information
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Mental Processes Used in Diagnosis:• Pattern recognition (or categorisation)
• Inductive, deductive and hypothetico-deductive reasoning
• Practical reasoning
• Intuitive thought
• Hypothesis testing (generating competing hypothesis)
• Algorithms
Anyone can recognise this!(Pattern recognition)
Clinical thinking 16
The Compound Eye
Traditional ways of reaching a diagnosis
A. Organizing data: Taxonomy / systems and organs / pathophysiology
B. Differential Diagnosis – inductive reasoning
C. Goal–seeking (Heuristic) attitude
Practicalreasoning
Hypothetico-deductive reasoning
Aristotle in “The School of Athens”
Raffaello Sanzio 1483 – 1520
Reasoning:
A. Theoretical
B. Practical Retrospective
Prospective
Theoretical Reasoning
More Precise
Logically more robust
Leads to truthful conclusion
Practical Reasoning
Less Precise
Logically less robust
Leads to action – ‘Opearcy’(attaining an end)
Practical Reasoning
F A
E
B
Optimal decisions –
application of statistical decision - rule to data (e.g. mathematics)
Decision making:
Suboptimal decisions –
Based on probabilities, close-calls and balancing trade-offs
(e.g. clinical decision making)
Decision making requires:
Data collection
Data analysis
Decision on patient management
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Clinical management decisions depend on: What is the problem?
What are the available options?
Selecting the best option.
Assessing the patient’s preference
Actions that need taking (urgently or routinely)
Ascertaining compliance
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Influences on decision making
Evidence
Values
Circumstances
Bias
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Aids to clinical decision making Multivariate equations and analysis Decision analysis Clinical problem analysis Mechanistic case diagramming Clinical algorithms Guidelines Computerised guidelines Scoring systems Information technology Artificial intelligence
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Multivariate Equations
Allows simultaneous considerations of multiple factors where each relevant variable is weighted differently.
Complex Mathematics and CT
Mathematical model/equation
Decision on diagnostic or therapeutic methodology
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Multivariate Analysis
Advantages: 1. Can evaluate information consistently and quantitatively2. May lead to better use of resources
Limitations:
1. Pat ient may be different from the ones used for the model
2. The variables may be poorly defined or selected
3. The weighting is subjective
Decision Analysis:
Cholecystectomy
CBD exploration No CBD exploration
Stone Present No Stone Stone present(missed) No Stone Present
This is usually applied to a decision tree, e.g.: The problem of exploring or otherwise the CBD during cholecystectomy
Expe
cted
util
ities
Probability of stone
No Common Bile Duct Exploration
Common Bile Duct Exploration
Clinical Problem Analysis
A systematic way of resolving a clinical problem in its entirety tackling it in terms of pathophysiology and anatomy.
Mechanistic Case Diagramminge.g.: Mechanistic case diagram in a case of intestinal obstruction
Intestinal secretions; ingested food and saliva ; swallowed air
No Passage
No ReabsorptionIntestinal Distention
Profuse bacterial growth
Pain, vomiting, (digested food)
Distention (in distal intestinal obstruction)
Constipation
Increased Intraluminal pressure
Intestinal venous congestion
Bowel wall oedema
Microcirculation impairment in bowel wall
Increased intestinal secretion
Intestinal succession splash, Distended bowel loops + fluid levels on X-ray
Necrosis of bowel wall
Perforation
Endotoxaemic and Septicaemic
shock
Abdo rigidity, Percussion tenderness, Paralytic ileus,
Free air under the diaphragm
Algorithms e.g. Management of Acute Pancreatitis
Acute Pancreatitis
Contrast Enhanced CT No NecrosisPancreatic Necrosis
Gas on imaging
Sepsis No Sepsis
CT or Ultrasound Guided FNA/Cultures
Infected Necrosis Sterile Necrosis
Conservative Management Resolution
Deterioration Laparatomy
Adopted from, Yousaf M, McCalion K and Diamond T, “Management of Severe Acute Pancreatitis” British Journal of Surgery, Vol 90, pg 407 – 420, 2003
Clinical thinking 33
Management of Transitional cell Carcinoma of the Bladder
Clinical thinking 34
The Cyclical Pattern of Learning
Unconsciously incompetent
Consciously
Incompetent
Consciously Competent
Unconsciously
Competent
New Knowledge or Technology
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ALGORITHMS
Usefulness: Effective education tools for clinicians and patients; improve patient care: reduce cost of care.
Limitations: Difficult to apply for very complex clinical problems; all variables are weighted equally; does not consider interaction between factors; are deterministic and do not indicate the level of confidence of recommendations;author dependent
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GUIDELINES
Features:
Decision points
Evidence necessary
Applicable layout
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Computerised guidelines
Advantages: Accessible reference Shows errors Improves clarity Adaptable to particular clinical state Proposes decision support Send reminders
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SCORING SYSTEMS
- Usually derived from IT or ANN
Utility: Indications e.g. Glasgow Coma Scale; Alvarado
Score Grading severity Priorities e.g. Priority criteria for surgery Audit Research
Limitations: Does not consider urgency (c.f. priority) Does not consider efficacy of the procedure
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Information Technology and Artificial Intelligence
1. I.T.
2. Mathematical Processes
3. Artificial Neural Networks
The Supercomputer vs. The Human Brain
FLOPS MIPS Memory Watts
Supercomputer 1 petaFLOP 550 - 23,000 24 gigabytes x 0.5 million times the energy used by the brain
Human brain 20 petaFLOPS 10 quadrimillion 3 – 100 terabytes
10 – 25 watts
FLOPS: Floating point operations per second.MIPS: Million computer instructions per second.
Mega- X 106
Giga-: X 109
Tera-: X !012
Peta-: X 1015
Comparing characteristics
Brain Supercomputer
1 trillion cells with 1000 trillion connections 1 million silicone neurones
Learns more easily and faster Can tackle many tasks concurrently with greater ease and faster.
Uses logic but influenced by emotion (effect of context)
Uses logic only
Better at interpreting the outside world, new ideas and imagination
Faster at logical computing
Claude Bernard (1813 – 1878):“Man can learn nothing unless he proceeds from the
known to the unknown ………… In the biological sciences, the role of method is even more important than in other sciences, because of the complexity of the phenomena and the countless sources of error.”
…an extended detective story
Any Questions?
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Thank you!
Woman holding a balance by Johannes Vermeer1622 – 1665An allegory of judgment