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Background: The determination of prognosis in heart failure (HF) has focused primarily on the identification of potential biological and physiological markers and not on communication. High morbidity and mortality rates suggest that patients require prognostic information to assist in life planning. This study examined the preferences of both patients with HF and cardiologists for prognosis communication in the outpatient clinical setting, with the aim of guiding practitioners in undertaking prognosis conversations. Methods: Using qualitative descriptive techniques informed by a grounded theory approach, 32 patients with HF and 9 cardiologists from outpatient settings in Ontario, Canada were interviewed to identify convergent preferences for prognosis communication. Strategies to enhance methodological rigor were employed. Results: Individualized, context-specific prognosis communication between patients and cardiologists was preferred. Two main themes and ten related attributes were identified to describe convergent preferences; 1) Set the Stage for Prognosis Communication, and 2) Map the HF route. Attributes reflected the complex, dynamic, interactive and iterative nature of prognosis communication preferences. Conclusions: Prognosis communication occurs within a complex, adaptive healthcare system. While specific preferences exist, changing contextual elements within and outside of the system create conditions that require cardiologists to adjust their approach to individual patients. Patients with HF and cardiologists each have preferences that affect their willingness and ability to engage in dyadic HF-specific prognosis communication. Findings have relevance for the implementation of any efforts, including HF guidelines, aimed at improving prognosis communication. Our findings, informed by a complexity science approach, offer an innovative and robust alternative to traditional linear approaches to prognosis communication.
Research of automatic integration of structured and semi-structured data has not resulted in success over the past fifty years. No theory of data integration exists. It is unknown what the theoretical necessary requirements are, to fully support automatic data integration from autonomous heterogeneous data sources. Therefore, it is not possible to objectively evaluate if and how much new algorithms, techniques, and specifically Data Definition Languages, move towards meeting such theoretical requirements. To overcome the serious reverse salient the field and industry are in, it will be helpful if a data integration theory would be developed. This article proposes a new look at data integration by using complex adaptive systems principles to analyze current shortcomings and propose a direction that may lead to a data integration theory.