Decision support systems (DSSs) have been historically classified into two categories: those based on probabilistic formalisms and those based on artificial intelligence (AI) formalisms. In our lab, we override this dichotomy by building systems that use both types of formalisms, according to the specific decision task. To make a concrete example, consider a clinical practice guideline that is made by a set of recommendations. A recommendation can in general be represented as a production rule, a very well-known AI formalism. But some of these recommendations may also point to the need of sharing the decision with patients and their families, or to take into account other values than health outcomes, e.g. costs. In these cases, a probabilistic formalism such as an influence diagram or a decision tree is appropriate. These probabilistic models may also embeds Markov models, that describe the transitions of a patient, or of a patients cohort, among a set of possible health states, starting from an initial state. This allow, for example, to estimate life expectancy, quality-adjusted life years, costs and other values that are considered crucial for the decision at hand. Given the model results, patients, their relatives and healthcare professionals may reason and take a more informed decision.
Importantly, the decision may concern a specific patient, or a population. In the first case, questions must be done to the patient in order to elicit his own preferences, and this is per-se another research area, since different methods exist and must be patient-tailored: some methods are based on direct questions, some other are based on questionnaires for self-administration. Together with our medical partners, we make experimental research on this topic.
Another important topic is the analysis of the compliance of physicians and patients to the suggestions provided by a DSS. Of course the DSS can suggest something, but the final decision is up to the human subject. Thus, it is very interesting to measure the compliance, in order to individuate weaknesses of the DSS, and take the opportune corrective actions, or to detect incorrect human behaviours, that need educational interventions.
As a methodological approach to DSS, ontologies are used to represent the entities of the decision problem domain and their relationships.
Stroke- Guidelines for stroke management (prevention, treatment and rehabilitation) have been represented, implemented, and results analysed. Non compliance have been studied and correlated to health and cost outcomes.
Atrial Fibrillation- Guidelines for atrial fibrillation are currently studied, taking into particular account the set of parallel guidelines that drug prescriptions generate for the patient at home, for guiding him to the most correct drug administration. Decision trees are linked to the guideline, e.g. for the choice of undergoing or not to oral anticoagulant treatment.
Amyloidosis- Guidelines for diagnosis and protocols for treatment are represented, with the final goal of linking them to the electronic medical record.
Tools: NEWGUIDE, Protégé, TreeAge
Please find further information searching in our publication repository or by contacting the Responsible of each area.
People working on this topic:
Supervisors: Silvana Quaglini
Prof. Mor Peleg, Haifa University, Israel
Prof. Yuval Shahar, Ben Gurion University, Beer Sheva, Israel.
Prof. Werner Ceusters, University at Buffalo, Buffalo, NY, USA
Giuseppe Micieli, Anna Cavallini, IRCCS C. Mondino, Pavia, Italy.
Giampaolo Merlini, Giovanni Palladini, IRCCS Policlinico San Matteo, Pavia, Italy
Silvia Priori, Carlo Napolitano, Andrea Mazzanti,IRCCS S. Maugeri, Pavia, Italy
Most of the work on this topic is currently carried out within the EU project Mobiguide.
 P Ciccarese, E Caffi, S Quaglini, M Stefanelli. Architectures and tools for innovative health information systems: the Guide Project. International journal of medical informatics 74 (7-8), 553-562