iManageMyHealth and iSupportMyPatients: mobile decision support and health management apps for cancer patients and their doctors

Clinical decision support systems can play a crucial role in healthcare delivery as they promise to improve health outcomes and patient safety, reduce medical errors and costs and contribute to patient satisfaction. Used in an optimal way, they increase the quality of healthcare by proposing the right information and intervention to the right person at the right time in the healthcare delivery process. This paper reports on a specific approach to integrated clinical decision support and patient guidance in the cancer domain as proposed by the H2020 iManageCancer project. This project aims at facilitating efficient self-management and management of cancer according to the latest available clinical knowledge and the local healthcare delivery model, supporting patients and their healthcare providers in making informed decisions on treatment choices and in managing the side effects of their therapy. The iManageCancer platform is a comprehensive platform of interconnected mobile tools to empower cancer patients and to support them in the management of their disease in collaboration with their doctors. The backbone of the iManageCancer platform comprises a personal health record and the central decision support unit (CDSU). The latter offers dedicated services to the end users in combination with the apps iManageMyHealth and iSupportMyPatients. The CDSU itself is composed of the so-called Care Flow Engine (CFE) and the model repository framework (MRF). The CFE executes personalised and workflow oriented formal disease management diagrams (Care Flows). In decision points of such a Care Flow, rules that operate on actual health information of the patient decide on the treatment path that the system follows. Alternatively, the system can also invoke a predictive model of the MRF to proceed with the best treatment path in the diagram. Care Flow diagrams are designed by clinical experts with a specific graphical tool that also deploys these diagrams as executable workflows in the CFE following the Business Process Model and Notation (BPMN) standard. They are exposed as services that patients or their doctors can use in their apps in order to manage certain aspects of the cancer disease like pain, fatigue or the monitoring of chemotherapies at home. The mHealth platform for cancer patients is currently being assessed in clinical pilots in Italy and Germany and in several end-user workshops

Framework of Big Data Analytics in Real Time for Healthcare Enterprise Performance Measurements

Healthcare organizations (HCOs) currently have many information records about their patients. Yet, they cannot make proper, faster, and more thoughtful conclusions in many cases with their information. Much of the information is structured data such as medical records, historical data, and non-clinical information. This data is stored in a central repository called the Data Warehouse (DW). DW provides querying and reporting to different groups within the healthcare organization to support their future strategic initiatives. The generated reports create metrics to measure the organization\u27s performance for post-action plans, not for real-time decisions. Additionally, healthcare organizations seek to benefit from the semi-structured and unstructured data by adopting emerging technology such as big data to aggregate all collected data from different sources obtained from Electronic Medical Record (EMR), scheduling, registration, billing systems, and wearable devices into one volume for better data analytic. For data completeness, big data is an essential element to improve healthcare systems. It is expected to revamp the outlook of the healthcare industry by reducing costs and improving quality. In this research, a framework is developed to utilize big data that interconnects all aspects of healthcare for real-time analytics and performance measurements. It is a comprehensive framework that integrates 41 integrated components in 6 layers: Organization, People, Process, Data, Technology, and Outcomes to ensure successful implementation. Each component in the framework and its linkage with other components are explained to show the coherency. Moreover, the research highlights how data completeness leads to better healthcare quality outcomes, and it is essential for healthcare organization survival. Additionally, the framework offers guidelines for selecting the appropriate technology with the flexibility of implementing the solution on a small or large scale, considering the benefits vs. investment. A case study has been used to validate the framework, and interviews with Subject Matter Experts (SMEs) have been conducted to provide another valuable perspective for a complete picture. The findings revealed that focusing only on big data technology could cause failing implementation without accomplishing the desired value of the data analytics outcomes. It is only applied for one-dimensional, not at the enterprise level. In addition, the framework proposes another 40 components that need to be considered for a successful implementation. Healthcare organizations can design the future of healthcare utilizing big data and analytics toward the fourth revolution in healthcare known as Healthcare 4.0 (H 4.0). This research is a contribution to this effort and a response to the needs