Healthcare operations generates large volumes of data. Big data analytics methods are needed to derive actionable and decision-quality 'intelligence' from 'big' healthcare data in order to improve patient care. Given the technical challenges to big health data analytics, in this paper we present a specialized health analytics platform-H-DRIVE (Health Data Reconciliation Inferencing and Visualization Environment). H-DRIVE is an integrated, end-to-end health data analytics service-oriented workbench designed to empower data analysts and researchers to design analytical experiments and then perform complex analytics on their health data. We present the high-level functional and technical architecture of H-DRIVE. As a case study, we demonstrate the application of H-DRIVE in the context of optimizing the operations of a provincial pathology lab, where we analyze province-wide lab orders to prepare scorecards outlining physician lab testing performance and offer an operational dashboard to provide an overview of lab utilization.

We present a semantic interoperability framework to represent Clinical Pathways (CP) as business process workflows represented using Business Process Modeling Notation (BPMN). We take a knowledge management approach whereby we represent CP as a CP ontology. To represent a CP as a process workflow we have developed a high-level semantic mapping between the CP ontology and the BPMN ontology. The ontology mapping allows the alignment of semantic relations between two ontologies and thus ensures that a clinical process defined in the CP ontology is mapped to a standard BPMN workflow element.