We report experience in requirements elicitation of domain knowledge from experts in clinical and cognitive neurosciences. The elicitation target was a causal model for early signs of dementia indicated by changes in user behaviour and errors apparent in logs of computer activity. A Delphi-style process consisting of workshops with experts followed by a questionnaire was adopted. The paper describes how the elicitation process had to be adapted to deal with problems encountered in terminology and limited consensus among the experts. In spite of the difficulties encountered, a partial causal model of user behavioural pathologies and errors was elicited. This informed requirements for configuring data- and text-mining tools to search for the specific data patterns. Lessons learned for elicitation from experts are presented, and the implications for requirements are discussed as “unknown unknowns”, as well as configuration requirements for directing data-/text-mining tools towards refining awareness requirements in healthcare applications.

We report the process used to create artefacts for self-reporting Parkinson's Disease symptoms. Our premise was that a technology-based approach would provide participants with an effective, flexible, and resilient technique. After testing four prototypes using Bluetooth, NFC, and a microcontroller we accomplished almost full compliance and high acceptance using a paper diary to track day-to-day fluctuations over 49 days. This diary is tailored to each patient's condition, does not require any handwriting, allows for implicit reminders, provides recording flexibility, and its answers can be encoded automatically. We share five design implications for future Parkinson's self-reporting artefacts: reduce participant completion demand, design to offset the effect of tremor on input, enable implicit reminders, design for positive and negative consequences of increased awareness of symptoms, and consider the effects of handwritten notes in compliance, encoding burden, and data quality.