Ontology-Driven, Adaptive, Medical Questionnaires for Patients with Mild Learning Disabilities

Abstract

Patients with Learning Disabilities (LD) have substantial and unmet healthcare needs, and previous studies have highlighted that they face both health inequalities and worse outcomes than the general population. Primary care practitioners are often the first port-of-call for medical consultations, and one issue faced by LD patients in this context is the very limited time available during consultations-typically less than ten minutes. In order to alleviate this issue, we propose a digital communication aid in the form of an ontology-based medical questionnaire that can adapt to a patient's medical context as well as their accessibility needs (physical and cognitive). The application is intended to be used in advance of a consultation so that a primary care practitioner may have prior access to their LD patients' self-reported symptoms. This work builds upon and extends previous research carried out in the development of adaptive medical questionnaires to include interactive and interface functionalities designed specifically to cater for patients with potentially complex accessibility needs. A patient's current health status and accessibility profile (relating to their impairments) is used to dynamically adjust the structure and content of the medical questionnaire. As such, the system is able to significantly limit and focus questions to immediately relevant concerns while discarding irrelevant questions. We propose that our ontology-based design not only improves the relevance and accessibility of medical questionnaires for patients with LDs, but also provides important benefits in terms of medical knowledge-base modularity, as well as for software extension and maintenance.

Designing Clinical AAC Tablet Applications with Adults who have Mild Intellectual Disabilities

“…The app would then move on to displaying sub-symptoms in an accessible format (see "Interaction Modalities") or if the person was not in pain display the primary symptoms described previously. A potential model to achieve this questionnaire structure is discussed in [38].…”

“…This should be facilitated by an accessible questionnaire that first extracts the primary symptom experienced by the user, before exploring further symptoms related to that condition. Such a structure enables the questionnaire to incorporate a wide range of conditions, whilst remaining succinct enough to accommodate for short attention spans [38]. In addition, the questions embedded within should be based on the health demographics of the ID population, meaning the app has the potential to identify conditions commonly overlooked by GPs.…”