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Healthcare organizations are in a continuous effort to improve healthoutcomes, reduce costs and enhance patient experience of care. Data isessential to measure and help achieving these improvements in healthcaredelivery. Consequently, a data influx from various clinical, financial andoperational sources is now overtaking healthcare organizations and theirpatients. The effective use of this data, however, is a major challenge.Clearly, text is an important medium to make data accessible. Financial reportsare produced to assess healthcare organizations on some key performanceindicators to steer their healthcare delivery. Similarly, at a clinical level,data on patient status is conveyed by means of textual descriptions tofacilitate patient review, shift handover and care transitions. Likewise,patients are informed about data on their health status and treatments viatext, in the form of reports or via ehealth platforms by their doctors.Unfortunately, such text is the outcome of a highly labour-intensive process ifit is done by healthcare professionals. It is also prone to incompleteness,subjectivity and hard to scale up to different domains, wider audiences andvarying communication purposes. Data-to-text is a recent breakthroughtechnology in artificial intelligence which automatically generates naturallanguage in the form of text or speech from data. This chapter provides asurvey of data-to-text technology, with a focus on how it can be deployed in ahealthcare setting. It will (1) give an up-to-date synthesis of data-to-textapproaches, (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 ahealthcare setting, and (4) highlight recent research challenges.

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