Electronic medical records (EMRs) are stored in relational databases. It can be challenging to access the required information if the user is unfamiliar with the database schema or general database fundamentals. Hence, researchers have explored text-to-SQL generation methods that provide healthcare professionals direct access to EMR data without needing a database expert. However, currently available datasets have been essentially "solved" with state-of-the-art models achieving accuracy greater than or near 90%. In this paper, we show that there is still a long way to go before solving text-to-SQL generation in the medical domain. To show this, we create new splits of the existing medical text-to-SQL dataset MIMICSQL that better measure the generalizability of the resulting models. We evaluate state-of-the-art language models on our new split showing substantial drops in performance with accuracy dropping from up to 92% to 28%, thus showing substantial room for improvement. Moreover, we introduce a novel data augmentation approach to improve the generalizability of the language models. Overall, this paper is the first step towards developing more robust text-to-SQL models in the medical domain.\footnote{The dataset and code will be released upon acceptance.

翻译：电子医疗记录（EMR）存储在关系数据库中。若用户不熟悉数据库模式或基本数据库原理，获取所需信息可能面临挑战。因此，研究人员探索了文本到SQL生成方法，使医疗专业人员无需依赖数据库专家即可直接访问EMR数据。然而，现有数据集已基本被"攻克"——最先进的模型实现了超过或接近90%的准确率。本文表明，在解决医疗领域的文本到SQL生成问题上仍任重道远。为此，我们对现有医疗文本到SQL数据集MIMICSQL进行新划分，以更好衡量所得模型的泛化能力。我们在新划分上评估了最先进的语言模型，发现性能大幅下降——准确率从高达92%骤降至28%，这表明存在显著的改进空间。此外，我们提出一种新颖的数据增强方法来提升语言模型的泛化能力。总体而言，本文是迈向在医疗领域开发更稳健的文本到SQL模型的第一步。