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

Abstract

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 e-health platforms by their doctors. Unfortunately, such text is the outcome of a highly labor-intensive process if it is done by healthcare professionals. It is also prone to incompleteness and 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.