Enhancing Clinical Reasoning Using Probabilistically Generated

Virtual Patient Cases

Jeroen Donkers, Bep Boode, Danëlle Verstegen, Bas de Leng, Jean van Berlo

Maastricht University

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

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

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)

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?

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

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)

Example: Diabetes Mellitus

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

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

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

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