Mor Peleg, Ph.D.
Associate Professor 
Department of Information Systems
University of Haifa

Realizing the proven capabilities of computers in better processing of complex knowledge and data, medicine is heading toward computer-supported decision making. Following this vision, several research groups have developed languages that allow the representation of CPGs as computer-interpretable guidelines (CIGs). CIG engines match a patient’s data to the medical knowledge contained in the CIG in order to automatically deliver patient-specific recommendations at the point of care. However, existing CIG formalisms have not yet demonstrated in practice effective mechanisms for integrating CIGs to handle multi morbidities. The aim of this research is to develop and evaluate a new methodology for integrating the knowledge of CIGs for different chronic diseases to create non-conflicting management plans for patients with chronic comorbidities.

In the talk, professor Peleg will present their methodology, which is a work in progress. Their methodology takes a goal-based approach that is modular and reusable. They envision a system of single-disease guidelines acting as agents whose invocation and conclusions are coordinated b a “Controller” agent that uses design patterns to recognize conflicts between different CPG goals and actions. The Controller then suggests ways in which these conflicts may be mitigated. It relies on CPG knowledge supplemented with general medical knowledge about the physiological effects of drugs and about drug hierarchies. Their approach is being implemented using existing commercial tools and standards. They use the PROforma CIG formalism and tools and the NDF-RT drug ontology for representing guideline and general medical knowledge, and the HL7 virtual medical record standard for structuring, storing, and retrieving patients’ data. The talk will focus on demonstrating how they have been developing their approach by applying information system analysis methods (Sequence Diagrams) to use-case patient scenarios of multi-morbidity patients.