Potential Technologies Review: A Hybrid Information Retrieval Framework to Accelerate Demand-Pull Innovation in Biomedical Engineering

Launching biomedical innovations based on clinical demands instead of translating basic research findings to practice reduces the risk that the results will not fit the clinical routine. To realize this type of innovation, a meta-analysis of the body of research is necessary to reveal demand-matching concepts. However, both the data deluge and the narrow time constraints for innovation make it impossible to perform such reviews manually. Thus, this paper proposes a specifically adapted "Potential Technologies Review" approach focusing on automated text mining and information retrieval techniques. The novel framework combines features from both systematic and scoping reviews. It aims at high coverage and reproducibility while mapping technologies--even with a fuzzy initial scope. To achieve these goals for search and triage, a set of closely interrelated methods has been developed: (a) automated query optimization, (b) screening prioritization, and (c) recall estimation. To determine appropriate parameters, a variety of published literature corpora were used and compared with an evaluation on a real-world dataset. Our results show that it is feasible to automate the identification of relevant works using this newly introduced framework. It achieved a workload reduction of up to 91% "Work-saved-over Sampling (WSS)" with a 76% overall recall compared with manually screening search results. Reducing the workload is a prerequisite for a rapid Potential Technologies Review when conducting demand-pull innovations. Moreover, it facilitates the updating and closer monitoring of latest findings. Studying the robustness of the framework and expanding it to patent documents are future tasks.