IntroductionGenerative artificial intelligence (GenAI) is becoming an important tool in medical product development. A main component of this development includes annotating, summarizing, and extracting key insights from expert interviews to identify clinical pain points and curate device requirements. These tasks are time- and labor-intensive, resulting in increased administrative burden and reduced efficiency. As a result, researchers have developed large language models (LLMs) that can disseminate research and interview findings with reduced workload and improved productivity. This study explores the use of GenAI, specifically GPT-4o, to extract user functional and design requirements from medical professional interviews for the iterative development of an infant heart rate detector for neonatal resuscitation.MethodsA total of 29 healthcare practitioners were interviewed using a semistructured interview format. The interviews were recorded and transcribed. GPT-4o was used to extract user insights from the transcripts, and the results were compared with manual interviewer notes.ResultsA total of 26 h of interview data were collected. All interviewees validated the clinical need for a modality that enables quick and accurate heart rate (HR) measurement during neonatal resuscitation. A set of user requirements was extracted from the interviews and curated under the themes of ease of use, fast and accurate HR measurement, reusability, display, battery life, start-up time, and cost. Also, quantitative analyses of the interviewee’s years of experience, clinical settings, and specialties were conducted.DiscussionThese analyses were conducted using GPT-4o and compared with ground-truth manual annotations to determine the accuracy and reliability of GenAI in content extraction and summarization.ConclusionOverall, this study explored the user requirements identified through in-depth interviews for the development of a pediatric medical device. It also aimed to demonstrate the potential of GenAI in curating these design requirements, offering a framework for researchers and product designers to explore the use of LLMs in curating user requirements and design specifications for medical devices.
Real-world outcomes from 2,905 episodes of hospital at home care: a propensity-matched cohort study
BackgroundHospital at home (HAH) services within the UK have expanded rapidly over the last 5 years, but there is comparatively little evidence demonstrating their clinical