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Chapter 1. Role of Community Model in Networked Healthcare Organizations -- Chapter 2. Blockchain Architecture for the Healthcare Ecosystem -- Chapter 3. Blockchain-Based Dynamic Consent for Healthcare and Research -- Chapter 4. "Pay for Value": Blockchain for Drug Pricing in Canada -- Chapter 5. A Blockchain-Centric Data Sharing Framework for Building Trust in Healthcare Insurance -- Chapter 6. Learning to Trust: Exploring the Relationship between Trust and User Experience in Blockchain Systems -- Chapter 7. Design and Implementation Considerations for Blockchain for Health Records -- Chapter 8. Blockchain Implementation for Decentralized Real-World Research -- Chapter 9. The inter-organizational environment of blockchain in healthcare: The state of blockchain healthcare consortia.

ABSTRACTOne of the major issues in the development of large, rule‐based expert systems is related to improving their performance efficiency. One way to address this issue is by reducing the number of unsuccessful tries a system goes through before executing a rule to establish a goal or an intermediary fact. On the average, the number of unsuccessful tries can be reduced if the rules that are tried first are those that are expected to execute most frequently, and this can be established by extracting information on the probability distributions of the input parameters. In this paper, a rule base is modeled as a network and simulated to investigate potential performance improvements by changing the order used to test the rules. The model of the rule base is also used to investigate performance gains achieved by parameter factorization and premise clause reordering.

ABSTRACTOnline health infomediaries are emerging as a critical element in the healthcare sector to support and influence individuals' health and wellness decisions. The business success and effectiveness of health infomediaries depend on the active and sustained engagement of patients. Although the growth in the number of participants in an infomediary is expected to add value by increasing the diversity of information that is potentially exchanged, the infomediary cannot survive without the sustained engagement of existing users. The challenge is to understand the underlying processes at the operational workflow level of an infomediary that can lead to sustained engagement of patients. For an infomediary to increase engagement, it needs to know not only what motivates participants to join an infomediary but also what keeps them engaged in various stages of participation or transitions. In this study, we employ a Markov Chain modeling approach, along with an analysis of the user activities data, to understand the underlying mechanism of patient engagement along with several transition states in an online health infomediary. We tracked 127,610 members, with more than 1 million activities involved in an online health infomediary that supports cosmetic and reconstructive surgery patients over one year. Patients' decisions for cosmetic and reconstructive surgery are health and well‐being choices that rely not only on patients' current situation but also on the knowledge and experience of others. This relevance of the health infomediary context is explored in this study. We sampled the activities of 32,505 active users' activities with data on more than 500,000 activities. We analyzed the dynamics of user behaviors by modeling longitudinal transition probabilities across different states of participation. Additional analyses and robustness checks, using text‐mined data from the users' activities, are introduced to gain nuanced insights into user engagement. Our study provides several practical implications for the design and management of an online health infomediary.