Background With increased specialization of health care services and high levels of patient mobility, accessing health care services across multiple hospitals or clinics has become very common for diagnosis and treatment, particularly for patients with chronic diseases such as cancer. With informed knowledge of a patient’s history, physicians can make prompt clinical decisions for smarter, safer, and more efficient care. However, due to the privacy and high sensitivity of electronic health records (EHR), most EHR data sharing still happens through fax or mail due to the lack of systematic infrastructure support for secure, trustable health data sharing, which can also cause major delays in patient care. Objective Our goal was to develop a system that will facilitate secure, trustable management, sharing, and aggregation of EHR data. Our patient-centric system allows patients to manage their own health records across multiple hospitals. The system will ensure patient privacy protection and guarantee security with respect to the requirements for health care data management, including the access control policy specified by the patient. Methods We propose a permissioned blockchain-based system for EHR data sharing and integration. Each hospital will provide a blockchain node integrated with its own EHR system to form the blockchain network. A web-based interface will be used for patients and doctors to initiate EHR sharing transactions. We take a hybrid data management approach, where only management metadata will be stored on the chain. Actual EHR data, on the other hand, will be encrypted and stored off-chain in Health Insurance Portability and Accountability Act–compliant cloud-based storage. The system uses public key infrastructure–based asymmetric encryption and digital signatures to secure shared EHR data. Results In collaboration with Stony Brook University Hospital, we developed ACTION-EHR, a system for patient-centric, blockchain-based EHR data sharing and management for patient care, in particular radiation treatment for cancer. The prototype was built on Hyperledger Fabric, an open-source, permissioned blockchain framework. Data sharing transactions were implemented using chaincode and exposed as representational state transfer application programming interfaces used for the web portal for patients and users. The HL7 Fast Healthcare Interoperability Resources standard was adopted to represent shared EHR data, making it easy to interface with hospital EHR systems and integrate a patient’s EHR data. We tested the system in a distributed environment at Stony Brook University using deidentified patient data. Conclusions We studied and developed the critical technology components to enable patient-centric, blockchain-based EHR sharing to support cancer care. The prototype demonstrated the feasibility of our approach as well as some of the major challenges. The next step will be a pilot study with health care providers in both the United States and Switzerland. Our work provides an exemplar testbed to build next-generation EHR sharing infrastructures.

Purpose: Gestational Diabetes Mellitus (GDM) is a condition affecting 3-4% of pregnant women due to increased resistance to insulin caused by the growth of the fetus. Such a condition disappears just after delivery, but it is an indicator of the insurgence of diabetes type 2 (DT2) later in life: about 40% of the women affected by GDM also develop DT2 [22]. GDM brings several complications during pregnancy to both the mother and the fetus. We aim here at presenting our Personal Health System for monitoring GDM and we also present the results of outpatient monitoring and management by utilizing a personal health system (PHS) for GDM. Methods: The Personal Health System (PHS) was deployed in a feasibility study, modelled as a single-center, parallel group, open randomized controlled trial conducted in Lausanne University Hospital. Patients (n=24) were assigned to 2 different groups: standard protocol group (SP) and telemedicine group (TM). SP patients were managed by regular clinic visits. TM patients were managed with our EPHS system. The targeted feasibility outcome was whole trial feasibility, functioning of the PHS and its appropriateness for patient use. Results: Mean age was 32±5 years and patients were pregnant for 29.1±1.9 weeks at study inclusion. Patients came from 16 different countries. The follow-up rate was 100%. Acceptability in the TM-group was high, as 100% were satisfied with the care provided and equally 100% were at ease with the technology. Overall median[IQR] glucose control was 5.4 mmol/l [4.7-6.4] in the TM-group and 5.7mmol/l [4.9-6.7] in the SP-group (p<0.001). Four out of 6 daily plasma glucose values were significantly better controlled with telemedicine compared to standard care. Conclusion: The feasibility study that we conducted shows that PHSs have a great potential to improve the life of the patient by allowing a better communication of their physiological values to the caregivers. With respect to the particular case of GDM, the study suggests that use of PHS technology may improve glycaemic control in GDM, but to confirm this trend, a main trial is needed.

Asynchronous messaging is leading human-machine interaction due to the boom of mobile devices and social networks. The recent release of dedicated APIs from messaging platforms boosted the development of computer programs able to conduct conversations, (i.e., chatbots), which have been adopted in several domain-specific contexts. This paper proposes SMAG: a chatbot framework supporting a smoking cessation program (JDF) deployed on a social network. In particular, it details the single-agent implementation, the campaign results, a multi-agent design for SMAG enabling the modelization of personalized behavior and user profiling, and highlighting of coupling chatbot technology with and multi-agent systems.

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This book compiles four years of research work that I carried out as a member of the Parallelism and Artificial Intelligence group of the Computer Science Department at the University of Fribourg in Switzerland, working on a PhD thesis funded by the Swiss National Science Foundation. I benefited enormously from the knowledge, the expertise, and the support of many people. My special thanks go to: Béat Hirsbrunner who gave me the opportunity to work in a highly qualified research group and who has guided my research activity during the past years; Rolf Ingold, Andrea Omicini, and George Papadopoulos, for accepting the invitation to act as jury members; Fabrice Chantemargue and Oliver Krone for the numerous discussions, ideas, criticisms, and also for their constant help and enthusiasm; Antony Robert, Valerio Scarani, Michele Schumacher, and Robert Van Kommer for reviewing previous versions of this document; Christian Wettstein for implementing the first version of STL++; Ivan Doitchinov for implementing Agent&Co; all colleagues in the PAI group for creating a stimulating research environment; and last but not least, my family and my friends for their constant support and encouragement.