Designing a Speech-Based Decision Support Tool for Parkinson’s Disease in Integrated Care
Cwm Taf Morgannwg University Health Board, Wales (with Imperial College London)
How they used Delve
A researcher at Cwm Taf Morgannwg University Health Board and Imperial College London used Delve to run a grounded theory analysis across surveys, focus groups and interviews with 31 Welsh health and social care professionals, comparing codes across sources while designing a speech-based decision support tool for Parkinson's disease.
“Data were analysed using grounded theory via Delve software [5]. Themes were derived iteratively, triangulated across methods, and validated through document review. Thematic saturation was achieved, enhancing trustworthiness and validity. The process involved simultaneous data collection and analysis to iteratively shape codes and categories. Codes were continuously compared across sources to ensure consistent interpretation and alignment with real-world practice.”
- Field
- Health informatics and integrated care; a speech-based clinical decision support tool for Parkinson's disease
- Data
- Surveys from 31 of 50 staff in Community Integrated Health and Social Care (16 health, 15 social care), two focus groups totalling 12 participants across occupational therapy, Parkinson's nursing, physiotherapy, pharmacy, speech and language therapy, care management and telecare, 11 follow-up purposively sampled interviews, and the Welsh Digital and Data Strategy document
- Approach
- Grounded theory run in Delve with simultaneous data collection and analysis so codes and categories were shaped iteratively; codes continuously compared across sources for consistent interpretation, themes triangulated across methods and validated against a document review of the Digital and Data Strategy for Health and Social Care in Wales; thematic saturation reported
- Data types
- Focus groups, Interviews, Open-ended survey responses, Documents
Abstract
Parkinson’s Disease (PD) is a progressive neurodegenerative condition that requires timely intervention to manage symptoms and prevent deterioration. This study investigates the essential requirements for a speech-based decision support tool to monitor PD progression in a community-integrated care setting. While still at an early design stage, the envisioned tool may take the form of mobile or desktop software accessible to patients, carers, and professionals in home and clinical settings. A mixed-methods approach, including surveys (n=31), focus groups (n=12), interviews (n=11), and policy document analysis, was used to gather insights from health and social care staff in Wales. Four major themes emerged: essential technical requirements (reliability, ease of use), workforce needs (training, analytic transparency), patient considerations (preferences, privacy), and systemic integration (interoperability, funding). Findings highlight the potential of speech-based AI systems for early, objective detection of PD deterioration. However, clinician trust, digital literacy, and user-centered design remain critical for adoption. Co-design with people with PD (PwPD), carers, and staff is strongly recommended for future development and evaluation. This study contributes to the growing field of intelligent systems in digital health and decision support.
Citation
Sheiladen Aquino (2025). Designing a Speech-Based Decision Support Tool for Parkinson’s Disease in Integrated Care. Studies in Health Technology and Informatics. https://doi.org/10.3233/shti250699