Write Like a Pro or an Amateur? The Effect of Medical Language Formality in Senior Care: A Multi-Method Approach

Abstract

As the world’s senior population grows rapidly, complex senior care problems pose significant challenges for family caregivers, partially because of their limited medical literacy and information asymmetry. To alleviate such challenges, family caregivers participate in senior care forums to share problems and experiences. Although studies indicate that this caregiver-generated online health information benefits patients, how such information can be perceived easily and correctly remain unclear. This study aims to fill this gap by exploring mechanisms to improve the helpfulness of online health information. Specifically, we investigate the effect of medical language formality. We propose a multi-method framework, including a novel Medical-Enriched DEep Learning (MEDEL) feature extraction method, econometric analyses, and a randomized experiment. The MEDEL model extracts independent variables for the econometric analyses. The econometric analyses and randomized experiment provide consistent evidence that when the medical language of health information is informal, the senior care information is more helpful. This study contributes to IS theory, methodology, and practice. We bridge the gap between medical language formality and information helpfulness in senior care. Our findings provide a theoretical foundation to understand the influence of language formality on many other business communications. Our proposed multi-method approach can also be generalized to investigate research questions involving complex textual features. Forum sites could leverage our proposed approach to improve the helpfulness of online health information and user satisfaction.

Keywords: medical language formality, multi-method, deep learning, randomized experiment, text mining, health IT