The integration of human factors engineering methods within the medical device design and development process has been highlighted by international standards organizations. Such methods are contributing to the development of safer medical devices, more suitable to users’ needs. Errors during device operation might hamper effective patient diagnosis and treatment, or eventually lead to injury or death. Thus, the designing process of a medical device is indeed crucial to user experience and safety operation. This paper presents a human-centred design analysis of a novel IoT-based screening prototype (iLoF) based on Artificial Intelligence algorithms built-in in a patented-photonics system developed by a deep tech startup. The influence of the design process during the development of the prototype was addressed, based on a human-centred design methodology and considering the device’s application environment. iLoF’s prototype on-field applicability was evaluated considering a single case-study carried out at one of the main hospitals in Portugal through interviews to ten healthcare professionals with high experience in laboratorial testing. A benchmark assessment and a comparison matrix along with the market products are also presented to fully understand the technology state and to find new solutions that can influence iLoF’s product development.
Medico-scientific concepts are not easily understood by laypeople that frequently use lay synonyms. For this reason, strategies that help users formulate health queries are essential. Health Suggestions is an existing extension for Google Chrome that provides suggestions in lay and medico-scientific terminologies, both in English and Portuguese. This work proposes, evaluates, and compares further strategies for generating suggestions based on the initial consumer query, using multi-concept recognition and the Unified Medical Language System (UMLS). The evaluation was done with an English and a Portuguese test collection, considering as baseline the suggestions initially provided by Health Suggestions. Given the importance of understandability, we used measures that combine relevance and understandability, namely, uRBP and uRBPgr. Our best method merges the Consumer Health Vocabulary (CHV)-preferred expression for each concept identified in the initial query for lay suggestions and the UMLS-preferred expressions for medico-scientific suggestions. Multi-concept recognition was critical for this improvement.
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