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Wendy Chapman

Publications and source records attributed to Wendy Chapman.

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From Framework to Practice: Designing a Real-World Telehealth Application for Palliative Care

As digital health solutions continue to reshape healthcare delivery, telehealth software applications have become vital for improving accessibility, continuity of care, and patient outcomes. This paper presents an analysis of designing a software application focused on Enhanced Telehealth Capabilities (ETHC) for palliative care, integrating across three socio-technical dimensions: quality, human values, and real-world. Designing for quality attributes -- such as performance, maintainability, safety, and security -- ensured that the system is technically robust and compliant with clinical standards. Designing for human values -- empathy, inclusivity, accessibility, and transparency -- helped enhance patient experience, trust, and ethical alignment. Designing for real-world -- through a multidisciplinary, experience-based co-design approach involving clinicians, patients, and carers that guided iterative cycles of prototyping, usability testing, and real-world evaluation -- ensured continuous refinement of features and alignment with clinical practice. The resulting telehealth software solution demonstrated that our socio-technical design framework was successful in producing a secure, equitable, and resilient digital health application. Our design approach can assist others designing software in health and other domains.

cs.HC

An Interactive Tool for Natural Language Processing on Clinical Text

Natural Language Processing (NLP) systems often make use of machine learning techniques that are unfamiliar to end-users who are interested in analyzing clinical records. Although NLP has been widely used in extracting information from clinical text, current systems generally do not support model revision based on feedback from domain experts. We present a prototype tool that allows end users to visualize and review the outputs of an NLP system that extracts binary variables from clinical text. Our tool combines multiple visualizations to help the users understand these results and make any necessary corrections, thus forming a feedback loop and helping improve the accuracy of the NLP models. We have tested our prototype in a formative think-aloud user study with clinicians and researchers involved in colonoscopy research. Results from semi-structured interviews and a System Usability Scale (SUS) analysis show that the users are able to quickly start refining NLP models, despite having very little or no experience with machine learning. Observations from these sessions suggest revisions to the interface to better support review workflow and interpretation of results.

cs.HC