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

Parsing as incremental restructuring

A prevalent trend in modeling human sentence processing has been to account for both initial attachment preferences and reanalysis behaviors with minimal extensions to a presumed set of initial parsing operations. Here, an entirely different formulation of the initial attachment and revision processes is suggested. Rather than assuming that all parsing is (as much as possible) initial attachment, the opposite approach is advocated: that all parsing---even initial attachment---is restructuring. The realization of parsing as restructuring arises from a set of independently motivated computational assumptions within the competitive attachment architecture, a hybrid connectionist model of the human sentence processor. Central to the model is a unique parallel attachment operation that simultaneously attaches the current input phrase, while reattaching previously structured phrases. Within this model, reanalysis is not a separate process or module, but rather a side effect of the primary means of forming syntactic structures. The ease of performing possible reanalyses is therefore determined by the same conditions, such as recency and lexical preferences, that affect initial attachments. Furthermore, independently motivated constraints on the network structure determine the allowable syntactic configurations that may undergo restructuring within the competitive attachment operation. The model thus also provides a computational explanation of gardenpath sentences, in which automatic reanalysis is impossible

Customization of Medical Report Data

Structured reporting offers a number of theoretical advantages, perhaps the most important of which is creation of standardized report databases. The standardized data created can in turn be used to customize data display, report content, historical data retrieval, interpretation analysis, and results communication in both a context and user-specific manner. In addition, these referenceable report databases can be used to facilitate the practice of evidence based medicine, through data-driven meta-analysis and determination of best practice guidelines. This concept will only be realized if the customized data delivery technology provides real and tangible value to end users, accentuates workflow, can be seamlessly integrated into existing information system technologies, and be shown to yield reproducibility of the evidence domain. The time is here for the medical imaging and clinical communities to embrace this vision in order to improve clinical outcomes and patient safety