The evolution of cancer registration

Natural History, Trial Readiness and Gene Discovery: Advances in Patient Registries for Neuromuscular Disease

Inherited neuromuscular diseases (NMDs) are genetic disorders that affect the skeletal muscles or the nerves controlling muscle function. With a new generation of diagnostic options and recent advances in translational research improving the opportunities for therapy development for these rare conditions, capturing patient information in databases collecting a range of clinical and genetic data together with contact details has assumed an increasingly important role in trial planning and recruitment as well as natural history data collection. Here we provide an overview of a decade of patient registration activities in the NMD field, with a particular focus on patient registries set up with trial readiness in mind. A summary is provided of databases collecting precise genetic information focused on confirming the causative mutation and their evolution into registries that combine genetic data with additional clinical information useful for trial feasibility and recruitment. Use of these systems for a range of purposes beyond trial recruitment, including natural history assessment, care standards monitoring, genotype-phenotype correlation and disease burden evaluation is also described within the context of research networks (TREAT-NMD) and European Reference Networks (ERN-EURO-NMD). New initiatives including registries using controlled vocabularies for computational accessibility that focus on phenotypic data capture for gene discovery are analysed, and examples of the lessons learned at every stage are provided in order to allow new patient registration initiatives to benefit from the extensive experience gained.

The Need for a Definition of Big Data for Nursing Science: A Case Study of Disaster Preparedness

The rapid development of technology has made enormous volumes of data available and achievable anytime and anywhere around the world. Data scientists call this change a data era and have introduced the term "Big Data", which has drawn the attention of nursing scholars. Nevertheless, the concept of Big Data is quite fuzzy and there is no agreement on its definition among researchers of different disciplines. Without a clear consensus on this issue, nursing scholars who are relatively new to the concept may consider Big Data to be merely a dataset of a bigger size. Having a suitable definition for nurse researchers in their context of research and practice is essential for the advancement of nursing research. In view of the need for a better understanding on what Big Data is, the aim in this paper is to explore and discuss the concept. Furthermore, an example of a Big Data research study on disaster nursing preparedness involving six million patient records is used for discussion. The example demonstrates that a Big Data analysis can be conducted from many more perspectives than would be possible in traditional sampling, and is superior to traditional sampling. Experience gained from the process of using Big Data in this study will shed light on future opportunities for conducting evidence-based nursing research to achieve competence in disaster nursing.

Big Data and Comparative Effectiveness Research in Radiation Oncology: Synergy and Accelerated Discovery

Several advances in large data set collection and processing have the potential to provide a wave of new insights and improvements in the use of radiation therapy for cancer treatment. The era of electronic health records, genomics, and improving information technology resources creates the opportunity to leverage these developments to create a learning healthcare system that can rapidly deliver informative clinical evidence. By merging concepts from comparative effectiveness research with the tools and analytic approaches of “big data,” it is hoped that this union will accelerate discovery, improve evidence for decision making, and increase the availability of highly relevant, personalized information. This combination offers the potential to provide data and analysis that can be leveraged for ultra-personalized medicine and high-quality, cutting-edge radiation therapy.