How prescriptive analytics influences decision making in precision medicine

Failure of the old model of medical decision making, “one-size-fits-all”, has encouraged the healthcare/medicine landscape to take advantage of big data and analytics for tailoring the treatments[1], based on individual patient’s differences in gen, environment, and lifestyle [2]. Whereas literature has demonstrated a strong contribution to the adoption of healthcare analytics over patient’s data, for better decision making [3], understanding the level and the degree that each type of analytics influences decision making, is crucial for addressing the type of problems [4]. While descriptive, diagnostic, and predictive analytics generate knowledge for decision support systems, prescriptive analytics recommends a proactive decision[5]. This study aims to highlight the influential and effective role of prescriptive analytics for fulfilling precision medicine which is defined as an emerging approach in medical decision making .FCT – Fundação para a Ciência e Tecnologia within the Projects Scope: DSAIPA/DS/0084/201

Intelligent decision support System for precision medicine (IDSS 4 PM)

Availability of healthcare big data and limited human cognitive to decide timely, from one hand and unsuccessful business model of traditional Medical Decision Making(MDM), on the other hand, have challenged the healthcare/medicine landscape to pioneer Precision Medicine (PM). This study aims to propose the conceptual framework of the Intelligent Decision Support system for Precision Medicine (IDSS4 PM), by highlighting the fundamental role of key technologies.FCT – Fundação para a Ciência e Tecnologia within the Projects Scope: DSAIPA/DS/0084/201

Characteristics of the intelligent decision support system for precision medicine (IDSS4PM)

Reducing medical errors, increasing the performance of treatments, cutting the cost of overtreatment, meeting the patient’s expectations, and finally save more lives, are some of the major benefits of Intelligent Decision Support System for Precision Medicine(IDSS4PM). This paradigm intends to explore them in the healthcare domain. This study aims to introduce the architecture and remarkable features of such a framework where Simon’s model of decision-making supports the rationality of the proposed paradigm.FCT – Fundação para a Ciência e Tecnologia within the Projects Scope: DSAIPA/DS/0084/201

Implementation considerations for the applied business intelligence in healthcare

This paper won the "Best Paper Award" certified by ICDSM 2021-ChinaWhereas it is argued that, implementation of Business Intelligence (BI) systems in healthcare is costly, complex, resource incentive and has been remained as a challenge to undertake [2]. It is important to take into account that implementation of such a system in a knowledge-driven and complex domain like healthcare, not only needs technology infrastructure but also, requires a strategic plan, decision flow, individual interaction, and knowledge management [5]. This study aims to identify and present the most critical factors that should be considered in the BI project management-implementation phase. Besides, theoretical foundations support the finding and discussion.FCT – Fundação para a Ciência e Tecnologia within the Projects Scope: DSAIPA/DS/0084/201

Adoption of precision medicine: limitations and considerations

Research is ongoing all over the world for identifying the barriers and finding effective solutions to accelerate the projection of Precision Medicine (PM) in the healthcare industry. Yet there has not been a valid and practical model to tackle the several challenges that have slowed down the widespread of this clinical practice. This study aimed to highlight the major limitations and considerations for implementing Precision Medicine. The two theories Diffusion of Innovation and Socio-Technical are employed to discuss the success indicators of PM adoption. Throughout the theoretical assessment, two key theoretical gaps are identified and related findings are discussed.FCT – Fundação para a Ciência e Tecnologia within the Projects Scope: DSAIPA/DS/0084/201