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 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 datato-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.

Building a Persuasive Virtual Dietitian

“…Thus, we could classify the MADiMan NLG module as a data-to-text system, which can be seen as a sort of transformation channel that converts numeric information into textual information [30]. Reference [31] is a recent survey of data-to-text technology with a focus on the healthcare setting. The authors considered several kinds of data sources and a categorized overview of NLG applications for transforming these data sources to text.…”

“…. informing patients adequately about their health status and treatment options for building up trust between patient and doctor" [31].…”

“…We use a commonly adopted architecture for NLG in data-to-text systems, especially in application contexts (the well-known problem of hallucinations in neural networks deter their use in real-world NLG [37], especially in the domain of health and wellness), which is a pipeline composed of four distinct modules: data analyser, document planner, sentence planner and surface realizer [30,31]. Each module tackles a specific issue, i.e.…”

Set to Ordered Text: Generating Discharge Instructions from Medical Billing Codes

“…For the medical domain, interest in neural text generation has been springing up, with a primary focus on document summarization (Moradi and Ghadiri, 2018). For interested readers, Pauws et al (2019) provides a recent review on how data-to-text technology can be applied in healthcare settings.…”

A Hierarchical Model for Data-to-Text Generation

“…One emerging research field consists in transcribing data-structures into natural language in order to ease their understandablity and their usablity. This field is referred to as "data-to-text" [8] and has its place in several application domains (such as journalism [22] or medical diagnosis [25]) or wide-audience applications (such as financial [26] and weather reports [30], or sport broadcasting [4,39]). As an example, Figure 1 shows a data-structure containing statistics on NBA basketball games, paired with its corresponding journalistic description.…”