The GENIE fellowship: Gauging the Effectiveness of Novel computer-support technologies for Implementing Evidence in healthcare

Healthcare professionals must keep pace with a rapidly growing knowledge base. By 2020, the quantity of medical evidence is expected to double every 73 days. Typically, healthcare professionals leverage 'systematic review' articles to tackle this issue. A systematic review is a consolidated summary of evidence that healthcare professionals can use to keep up-to-date with advancements in their field of practice. However, with seventy-five new experimental trials being published every day, the process of systematic review is ill-suited to keep-up; a typical systematic review requires approximately 952 working hours to complete and 70 weeks to publish, all while incurring significant financial cost in support of the review team. Numerous studies in the past two decades have examined the importance of evidence-based practice and its implications for the quality of healthcare. Increased use of evidence in clinical practice fulfils healthcare professionals' information-needs, thus reducing patients' lengths of stay and decreasing the number of complications they experience. Unfortunately, healthcare professionals have significant difficulty implementing new evidence in practice due to the time constraints associated with finding, accessing, downloading and reading consolidated summaries of research (i.e. systematic reviews). Recent developments in machine learning and natural-language processing techniques have potentiated the capacity to perform automated systematic reviews in real-time that can consolidate summaries of the evidence and deliver them at the point-of-care via 'smart' devices. The relative youth of these technologies has meant that it remains unclear how best to embed them in the research and clinical settings. Toward this end, I will initiate an intersectoral collaboration between two healthcare organisations and a data-science centre in the host institution to evaluate the practical utility of a selection of computer-support tools for expediting evidence consolidation and fulfilling healthcare professionals' information-needs. This fellowship will advance my training in expediated evidence synthesis and implementation technologies.

Award Date
27 September 2018
Award Value
Principal Investigator
Professor Cailbhe Doherty
Host Institution
University College Dublin
Applying Research into Policy & Practice Postdoctoral Fellowships