A powerful and convenient model for any
process workflows, including clinical pathways in
medical practice, is the Partially Observable Markov
Decision Process model (POMDP). However, the
algorithms for computing the optimal policy in such
models are approximate and require a significant
number of data samples for convergence. In addition,
there aren’t easy-to-use software products that can be
used by the medical staff. This article presents a software
that can be used as a digital second opinion assistant for
optimization of the clinical pathways using only data
from the patient’s medical history and basic statistics.
Based on an original methodology that combines
deterministic modeling of the workflows with stochastic
optimization, it implements an efficient algorithm for
assessing the risks of recurring conditions and developing
secondary complications in clinical practice. It combines
an intuitive and convenient interactive model editor with
completely automatic calculation of the optimal
treatment. Initially developed for security by design of
cyber systems, it can be used for optimization of any
sequential processes in other areas, such as diagnostics,
monitoring, and optimization.
Keywords—Clinical Pathways, Complications, Second
Opinion, Partially Observable Markov Decision Process
model, Clinical Risk Assessment, Optimal Treatment