Lucia Sacchi1, Stefania Rubrichi2, Carla Rognoni3, Silvia Panzarasa2, Enea Parimbelli2, Andrea Mazzanti4, Carlo Napolitano4, Silvia G Priori5, Silvana Quaglini2. 1. Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Via Ferrata 5, 27100 Pavia, Italy. Electronic address: lucia.sacchi@unipv.it. 2. Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Via Ferrata 5, 27100 Pavia, Italy. 3. Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Via Ferrata 5, 27100 Pavia, Italy; Centre for Research on Health and Social Care Management, Bocconi University, Via Roentgen 1, 20136 Milano, Italy. 4. Molecular Cardiology Laboratories, IRCCS Fondazione Salvatore Maugeri, Via Salvatore Maugeri, 8-10, 27100 Pavia, Italy. 5. Molecular Cardiology Laboratories, IRCCS Fondazione Salvatore Maugeri, Via Salvatore Maugeri, 8-10, 27100 Pavia, Italy; Department of Molecular Medicine, University of Pavia, Via Forlanini, 6, 27100 Pavia, Italy.
Abstract
OBJECTIVE: Taking into account patients' preferences has become an essential requirement in health decision-making. Even in evidence-based settings where directions are summarized into clinical practice guidelines, there might exist situations where it is important for the care provider to involve the patient in the decision. In this paper we propose a unified framework to promote the shift from a traditional, physician-centered, clinical decision process to a more personalized, patient-oriented shared decision-making (SDM) environment. METHODS: We present the theoretical, technological and architectural aspects of a framework that encapsulates decision models and instruments to elicit patients' preferences into a single tool, thus enabling physicians to exploit evidence-based medicine and shared decision-making in the same encounter. RESULTS: We show the implementation of the framework in a specific case study related to the prevention and management of the risk of thromboembolism in atrial fibrillation. We describe the underlying decision model and how this can be personalized according to patients' preferences. The application of the framework is tested through a pilot clinical evaluation study carried out on 20 patients at the Rehabilitation Cardiology Unit at the IRCCS Fondazione Salvatore Maugeri hospital (Pavia, Italy). The results point out the importance of running personalized decision models, which can substantially differ from models quantified with population coefficients. CONCLUSIONS: This study shows that the tool is potentially able to overcome some of the main barriers perceived by physicians in the adoption of SDM. In parallel, the development of the framework increases the involvement of patients in the process of care focusing on the centrality of individual patients.
OBJECTIVE: Taking into account patients' preferences has become an essential requirement in health decision-making. Even in evidence-based settings where directions are summarized into clinical practice guidelines, there might exist situations where it is important for the care provider to involve the patient in the decision. In this paper we propose a unified framework to promote the shift from a traditional, physician-centered, clinical decision process to a more personalized, patient-oriented shared decision-making (SDM) environment. METHODS: We present the theoretical, technological and architectural aspects of a framework that encapsulates decision models and instruments to elicit patients' preferences into a single tool, thus enabling physicians to exploit evidence-based medicine and shared decision-making in the same encounter. RESULTS: We show the implementation of the framework in a specific case study related to the prevention and management of the risk of thromboembolism in atrial fibrillation. We describe the underlying decision model and how this can be personalized according to patients' preferences. The application of the framework is tested through a pilot clinical evaluation study carried out on 20 patients at the Rehabilitation Cardiology Unit at the IRCCS Fondazione Salvatore Maugeri hospital (Pavia, Italy). The results point out the importance of running personalized decision models, which can substantially differ from models quantified with population coefficients. CONCLUSIONS: This study shows that the tool is potentially able to overcome some of the main barriers perceived by physicians in the adoption of SDM. In parallel, the development of the framework increases the involvement of patients in the process of care focusing on the centrality of individual patients.