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Enhancing Clinical Reasoning Using Probabilistically Generated Virtual Patient Cases

This study explores the use of probabilistically generated virtual patient cases to enhance clinical reasoning and mitigate biases in subjective probabilities. The study demonstrates the use of Bayesian belief networks to generate populations of virtual patients and assesses the effectiveness of this approach in improving subjective probabilities.

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Enhancing Clinical Reasoning Using Probabilistically Generated Virtual Patient Cases

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  1. Enhancing Clinical Reasoning Using Probabilistically Generated Virtual Patient Cases Jeroen Donkers, Bep Boode, Danëlle Verstegen, Bas de Leng, Jean van Berlo Maastricht University

  2. Contents • Clinical reasoning and uncertainty • Bayesian belief networks • From belief networks to virtual patients • Example: Diabetes • Conclusions

  3. Clinical Reasoning and Uncertainty • During clinical reasoning, a doctor has to deal with: • Indirect observations and measurements • E.g. Vague complaints, measurement errors, etc. • Partly understood processes • Influence of drinking red wine on diabetes? • Genetical, epigenetical (and metagenetical) variations • How will this particular patient react to that medication? • Incomplete and contradictory medical literature • E.g., only data available for men >80 year and obese children. • Population characteristics • Maastricht versus London • All these factors lead to stochastic variables in the reasoning process

  4. Clinical Reasoning and Uncertainty • Still, a doctor has to make quick decisions: • ordering the items in a differential diagnosis • Requesting the most informative lab test • Choosing the most effective therapy • Doctors (and all other humans) use subjective probabilities • Unconsiously • Mostly experience-based • Biased by many factors – availability, representativeness, etc. • Only partly supported by scientific and epidemiological data (evidence based) • Errors in subjective probabilities may lead to wrong decisions (Elstein & Schwarz, 2002, BMJ)

  5. Improving Subjective Probabilities • Problem-based learning and evidence-based medicine are important educational means to improve clinical reasoning – increasing the use of objective data (Elstein & Schwarz) • However, the set of patient cases presented is often biased • Both can be supported by virtual patients • Support theory suggests that extensive case descriptions (e.g. VPs) can bias subjective probabilities (Bergus et al.) • Can we improve subjective probabilities by populations of VPs?

  6. Populations of VPs • Could: • Lead to formation of better subjective probabilities by preventing some biases in probability estimation • Should: • Be large enough to enable learning • Contain small cases to prevent support bias • Represent realistic patient population • How to generate? • Using a mathematical simulation model (e.g. Archimedes) • Using probabilistic models

  7. Bayesian Belief Networks (BBN) • Judea Pearl, 1988 • Compact graphical model of a joint probability distribution • Structure: models conditional independencies • Basis: P(A|BC) = P(A|B)  A is independent from C, given B • Parameters: conditional probabilities • Graphic representation enables knowledge elicitation • Supports prognosis as well as diagnostic reasoning • P(disease | symptoms) and P(effects | disease) • Allows fast computations (querying) • Robust for small errors in probabilities • Can generate populations (by sampling)

  8. Example: Diabetes Mellitus

  9. From BBNs to VPs • Step 1: generate a sample, using proper population settings • Step 2: instantiate sampled data by selecting realistic values for qualitative categories

  10. From BBNs to VPs • These case descriptions can be transformed into VPs: • Transform into MVP compliant package • Directly import into VP system • However, a more preferable option is to let the VP system generate a new instance on demand • Option a: integrate the BBN into the VP system • Option b: compile the BNN into a VP with stochastical nodes

  11. Pilot study • To measure the effect of VP populations we have planned the following controlled experiment: • 3 groups students: A B C • 2 VP populations: one sampled and one hand-picked • Group A (control): no intervention • Group B: hand-picked VP population • Group C: BBN-generated VP population • VP populations are pre-generated to enforce comparability • VP cases are presented using SurveyMonkey • MCQ to stimulate and test probability formation

  12. Conclusions • Subjective probabilities play a major role in clinical reasoning, but can easily be biased • VP populations could lead to improved subjective probabilities • BNNs can generate VP populations and are less difficult to build than simulation models • However, it is still delicate and laborious • Connections to VP system have yet to be developed • Pilot will reveal the effectiveness of the approach

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