processes in clinical thinking and decision making...processes in clinical thinking and decision...

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