Making effective use of healthcare data using data-to-text technology

Healthcare organizations are in a continuous effort to improve health outcomes, reduce costs and enhance patient experience of care. Data is essential to measure and help achieving these improvements in healthcare delivery. Consequently, a data influx from various clinical, financial and operational sources is now overtaking healthcare organizations and their patients. The effective use of this data, however, is a major challenge. Clearly, text is an important medium to make data accessible. Financial reports are produced to assess healthcare organizations on some key performance indicators to steer their healthcare delivery. Similarly, at a clinical level, data on patient status is conveyed by means of textual descriptions to facilitate patient review, shift handover and care transitions. Likewise, patients are informed about data on their health status and treatments via text, in the form of reports or via ehealth platforms by their doctors. Unfortunately, such text is the outcome of a highly labour-intensive process if it is done by healthcare professionals. It is also prone to incompleteness, subjectivity and hard to scale up to different domains, wider audiences and varying communication purposes. Data-to-text is a recent breakthrough technology in artificial intelligence which automatically generates natural language in the form of text or speech from data. This chapter provides a survey of data-to-text technology, with a focus on how it can be deployed in a healthcare setting. It will (1) give an up-to-date synthesis of data-to-text approaches, (2) give a categorized overview of use cases in healthcare, (3) seek to make a strong case for evaluating and implementing data-to-text in a healthcare setting, and (4) highlight recent research challenges.Comment: 27 pages, 2 figures, book chapte

From Discourse Plans to User-Adapted Hypermedia

. This summary describes how user-adapted hypermedia can be generated from discourse plans; the application domain is that of instruction manuals. 1 Introduction Various methods can be employed to build user-adapted hypermedia (Brusilovsky, 1996). One can start from a maximally connected graph, in which several pages are associated to each node, by defining context-related pages and link selection heuristics. Alternatively, one can employ a knowledge base to generate what is needed in each context. In this paper, we discuss the potential of the discourse plan as a knowledge source in this building process. Discourse planning has proved to be an efficient method for generating user-adapted multimedia. Its application to hypermedia generation is suited to those cases in which knowledge underlying the discourse structure can contribute to making the hyperdocument understandable to the user. We claim that this is the case for educational hypermedia, and in particular for instruction manua..