Abstract— the motivation for the implementation of a decision support system in maternity care came from the fact that people are constantly making quick decisions based on incomplete information. There is a significant impact on the patient health, as well as in increasing medical errors. To implement this system, it was resorted to the technologies of Business Intelligence, which involved the construction of two data warehouses with a dimensional structure in a star shape, for two distinct modules, in Gynecology and Obstetrics cares. The feasibility of an evidence-based practice and medical decision making in real time with universal and interoperable features are some of the benefits resulting from the implementation of decision support system in maternity care. In this paper we present the architecture of BI solution, some clinical outcomes and some benefits of the BI solution in a real world context. I. INTRODUCTION The Information and Communication technologies have a high potential to facilitate information sharing, communication and collaboration between health professionals, increasing the quality and efficiency of the health system as well as the use of Electronic Health Records (EHR) [1], [2],[3]. In recent times, the technologies of Business Intelligence (BI) have been the target of interest to health professionals and to the professionals of Information Technology (IT), due to its applicability in EHR [4]. BI is a process that encompasses several methodologies, applications and technologies for collecting, storing, manipulating, analyzing and providing access to data to help enterprise users make better and faster business decisions. Thus BI has the ability to operationalize the repository content of EHR in supporting evidence-based practice and improving the quality of healthcare delivery [3], [4],[5]. In the case of healthcare organizations, the majority of clinical data that document their daily activities are stored in Relational Database Management System (RDBMS). Because of the extensive amount of information, this information is stored in different ways and therefore highly heterogeneous with each other. On the other hand, a decision-making process, where it is necessary to integrate multiple data provided by clinical, medical, financial and administrative systems, where the sources are quite Research supported by Foundation for Science and Technology (FCT). Andreia Brandão and Eliana Pereira are students of the Department of Informatics, University of Minho, Braga, Portugal. António Abelha and José Machado are with the Computer Science and Technology Centre (CCTC), University of Minho, Braga, Portugal (e-mail: abelha@ di.uminho.pt, jmac@di.uminho.pt). Filipe Portela and Manuel Santos are with the ALGORITMI research centre, University of Minho, Guimarães, Portugal (e-mail: cfp@ dsi.uminho.pt, mfs@dsi.uminho.pt). José Machado is the corresponding author. heterogeneous, large and complex becomes extremely important to meet the data quality that directly interferes in the success of the Knowledge Discovery Database (KDD) process [5][6]. So, with this increasing amount of information there is also a corresponding need to apply Data Mining (DM) technologies to extract data quality from information stored in databases to provide real-time decisions [4]. Most clinical data are not structured and the techniques of DM work well with structured data. It is inferred another advantage to using BI as a decision support technology since it allows the combination of structured and unstructured data [4]. In the new Centro Materno Infantil do Norte (CMIN), formerly known as Maternidade Júlio Dinis (MJD), the EHR platform is supported by the Agency for Integration, Archive and Diffusion of Medical Information (AIDA) [7]. This platform is considered a good source for the application of BI technologies, since it is stored many records of patients [3], [4]. However, the large amount of data stored hinders the health professional to have a sense of what type of patients are received and their characteristics. Thus, the implementation of a BI platform in CMIN aims to help and support the decision making process, providing access to key perform indicators (KPIs) for the care of patients in gynecology and obstetrics. As referred, this article is focused on presenting the architecture of a global BI platform that can be used in Maternity Care Hospitals, taking as a case of study the CMIN. It was planned apply BI and Data Mining (DM) techniques in order to develop a platform where it is possible generate useful KPIs (clinical and management) for healthcare professionals in the context of the Obstetric and Gynecological (GO) module and in the Voluntary Interruption of Pregnancy (VIP) module. For the construction of this platform, two Data Warehouse (DW) were built using the star schema, one for each module of CMIN (GO and VIP). For better accuracy of the solution and validity of information generated throughout the work has been pleading with clinicians and specialists. The solution in development is a pervasive real-time web- based BI application and due to their characteristics allows it to be accessed anywhere and anytime. Beyond the introduction, the article includes six sections. The first one is related to the background and relative work done in the CMIN. Topics such as interoperability, the work done on the GO module and work done on VIP module are addressed. The second section discusses in general terms the BI process used in this project. The third section discusses the process of DM. The fourth section outlines the BI system that is being implemented in CMIN and some of the indicators to get at the end. Finally, some discussions and Real-Time Business Intelligence Platform to Maternity Care Andreia Brandão, Eliana Pereira, Filipe Portela, Manuel Santos, António Abelha and José Machado