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

arXiv: https://arxiv.org/abs/1808.03507 · HTML: https://mlsanity.com/paper/1808.03507/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Aug 2018.

## Built on by (most cited first, 9 of 9)

- [Natural Language Processing for Smart Healthcare](https://mlsanity.com/paper/2110.15803/) (2021)
- [Innovations in Neural Data-to-text Generation: A Survey](https://mlsanity.com/paper/2207.12571/) (2022)
- [Toward Relieving Clinician Burden by Automatically Generating Progress Notes using Interim Hospital Data](https://mlsanity.com/paper/2410.12845/) (2024)
- [Improving Factual Accuracy of Neural Table-to-Text Output by Addressing Input Problems in ToTTo](https://mlsanity.com/paper/2404.04103/) (2024)
- [Medical Scientific Table-to-Text Generation with Human-in-the-Loop under the Data Sparsity Constraint](https://mlsanity.com/paper/2205.12368/) (2022)
- [Comparing informativeness of an NLG chatbot vs graphical app in diet-information domain](https://mlsanity.com/paper/2206.13435/) (2022)
- [Impact of Model Size on Fine-tuned LLM Performance in Data-to-Text Generation: A State-of-the-Art Investigation](https://mlsanity.com/paper/2407.14088/) (2024)
- [Mapping Process for the Task: Wikidata Statements to Text as Wikipedia Sentences](https://mlsanity.com/paper/2210.12659/) (2022)
- [Automated Clinical Report Generation for Remote Cognitive Remediation: Comparing Knowledge-Engineered Templates and LLMs in Low-Resource Settings](https://mlsanity.com/paper/2605.06594/) (2026)

## Abstract (arXiv)

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.