Counterfactual reasoning using causal Bayesian networks as a healthcare governance tool.

Background: Healthcare governance (HG) is a quality assurance processes that aims to maintain and improve clinical practice. Clinical decisions are routinely reviewed after the outcome is known to learn lessons for the future. When the outcome is positive, then practice is praised, but when practice is suboptimal, the area for improvement is highlighted. This process requires counterfactual reasoning, where we predict what would have happened given both what happened and the possible different decisions. Causal models that capture the mechanisms that generate events can support counterfactual reasoning.

Objective: This study is an initial attempt to show how counterfactual reasoning with causal Bayesian networks (CBNs) can be used as a HG tool to assess what would have happened if treatments other than those occurred had been selected.

Methods: Motivated by the Defence Medical Services (DMS) mortality and morbidity (M&M) review meeting, in this paper we (1) extended the use of counterfactual reasoning in CBNs to review decisions, where the alternative treatment strategies and its effect belong to different stages of care, (2) placed counterfactual reasoning in a specific clinical context to examine how it can be used as a HG tool.

Results: Using three realistic examples, we demonstrated how the proposed counterfactual reasoning can be used to assist the DMS M&M review meetings.

Conclusions: Useful lessons can be learned by assessing decisions after they are made. M&M review meetings are fruitful ground for counterfactual reasoning. The use of a clinical decision support tool that can assist clinicians in assessing counterfactual probabilities will be beneficial.