A Proposed Architecture for Implementing a Knowledge Management System in the Brazilian National Cancer Institute

Because their services are based decisively on the collection, analysis and exchange of clinical information or knowledge, within and across organizational boundaries, knowledge management has exceptional application and importance to health care organizations. This article proposes a conceptual framework for a knowledge management system, which is expected to support both hospitals and the oncology network in Brazil. Under this holistic single-case study, triangulation of multiple sources of data collection was used by means of archival records, documents and participant observation, as two of the authors were serving as INCA staff members, thus gaining access to the event and its documentation and being able to perceive reality from an insider point of view. The benefits derived from the present status of the ongoing implementation, so far, are: (i) speediness of cancer diagnosis and enhanced quality of both diagnosis and data used in epidemiological studies; (ii) reduction in treatment costs; (iii) relief of INCA’S labor shortage; (iii) improved management performance; (iv) better use of installed capacity; (v) easiness of massive (explicit) knowledge transference among the members of the network; and (vi) increase in organizational capacity of knowledge retention (institutionalization of procedures)

Developing a distributed electronic health-record store for India

The DIGHT project is addressing the problem of building a scalable and highly available information store for the Electronic Health Records (EHRs) of the over one billion citizens of India

Data science, analytics and artificial intelligence in e-health : trends, applications and challenges

Acknowledgments. This work has been partially supported by the Divina Pastora Seguros company.More than ever, healthcare systems can use data, predictive models, and intelligent algorithms to optimize their operations and the service they provide. This paper reviews the existing literature regarding the use of data science/analytics methods and artificial intelligence algorithms in healthcare. The paper also discusses how healthcare organizations can benefit from these tools to efficiently deal with a myriad of new possibilities and strategies. Examples of real applications are discussed to illustrate the potential of these methods. Finally, the paper highlights the main challenges regarding the use of these methods in healthcare, as well as some open research lines