. For patients suffering from two or more chronic conditions, known as multimorbidity, several guidelines have to be applied simultaneously, which may lead to severe adverse effects when the combined recommendations and prescribed medications are inconsistent or incomplete. This paper presents an automated formal framework to detect, highlight and resolve conflicts in the treatments used for patients with multimorbidities focusing on medications. The presented extended framework has a front-end which takes guidelines captured in a standard modelling language and returns the visualisation of the detected conflicts as well as suggested alternative treatments. Internally, the guidelines are transformed into formal models capturing the possible unfoldings of the guidelines. The back-end takes the formal models associated with multiple guidelines and checks their correctness with a theorem prover, and inherent inconsistencies with a constraint solver. Key to our approach is the use of an optimising constraint solver which enables us to search for the best solution that resolves/minimises conflicts according to medication efficacy and the degree of severity in case of harmful combinations, also taking into account their temporal overlapping. The approach is illustrated throughout with a real medical example.

Common chronic conditions are routinely treated following standardised procedures known as clinical pathways. For patients suffering from two or more chronic conditions, referred to as multimorbidities, several pathways have to be applied simultaneously. However, since pathways rarely consider the presence of comorbidities, applying several pathways may lead to potentially harmful (medication) conflicts. This paper proposes an automated framework to detect, highlight and resolve conflicts in the treatments used for patients with multimorbidites. We use BPMN as a modelling language for capturing care guidelines. A BPMN model is transformed into an intermediate formal model capturing the possible unfoldings of the pathway. Putting together the constraint solver Z3 and the theorem prover Isabelle, we combine treatment plans and check the correctness of the approach. We illustrate the approach with an example from the medical domain and discuss future work.

In complex system design, it is important to construct several design models focusing on different aspects of a system to gain a better understanding of individual component structure and behaviour. Scenarios of execution are commonly used to specify partial behaviour and interactions between a group of system objects or components. However, partial specifications may hide inconsistencies or an otherwise unintentionally incomplete or underspecified behavioural model. This paper proposes a new powerful technique combining constraint solvers and theorem provers to complete partial specifications and determine overall model inconsistencies. We use a true-concurrent model, namely labelled event structures, which can be used as the underlying semantics of widely used workflow or scenario-based languages. We show how an interplay between the theorem prover Isabelle and constraint solver Z3 can be used for detecting and solving partial specifications and inconsistencies over event structures.

We introduce `formal methods' of mechanized reasoning from computer science to address two problems in auction design and practice: is a given auction design soundly specified, possessing its intended properties; and, is the design faithfully implemented when actually run? Failure on either front can be hugely costly in large auctions. In the familiar setting of the combinatorial Vickrey auction, we use a mechanized reasoner, Isabelle, to first ensure that the auction has a set of desired properties (e.g. allocating all items at non-negative prices), and to then generate verified executable code directly from the specified design. Having established the expected results in a known context, we intend next to use formal methods to verify new auction designs.