ABSTRACT
Introduction: Out-of-office blood pressure (BP) monitoring is increasingly valuable in the diagnosis and management of hypertension. With advances in wearable BP technologies, the ability to gain insight into BP outside of traditional centers of care has expanded greatly. Methods: Here we explore the usability of a novel, wrist-worn BP cuff monitor for out-of-office data collection with participants following digital cues rather than in-person instruction. Transmitted measurements were used to evaluate BP variation with the time of day and day of week, BP variation with mood, and orthostatic measurements. Results: Fifty participants, with a mean age of 44.5 years, were enrolled and received the BP monitor. 82% of the participants transmitted data via the smartphone application, and the median wear time of the device during the 4-week study was 11 days (IQR 8-17). Discussion: This prospective digital pilot study illustrates the usability of wearable oscillometric BP technology combined with digital cues via a smartphone application to obtain complex out-of-office BP measurements, including orthostatic vital signs and BP associated with emotion. 25 out of 32 participants who attempted orthostatic vital signs based on in-app instruction were able to do so correctly, while 24 participants transmitted BP readings associated with emotion, with a significant difference in BP noted between calm and stressed emotional states.
ABSTRACT
The application of artificial intelligence (AI) to healthcare has garnered significant enthusiasm in recent years. Despite the adoption of new analytic approaches, medical education on AI is lacking. We aim to create a usable AI primer for medical education. We discuss how to generate a clinical question involving AI, what data are suitable for AI research, how to prepare a dataset for training and how to determine if the output has clinical utility. To illustrate this process, we focused on an example of how medical imaging is employed in designing a machine learning model. Our proposed medical education curriculum addresses AI's potential and limitations for enhancing clinicians' skills in research, applied statistics and care delivery.
The application of artificial intelligence (AI) to healthcare has generated increasing interest in recent years; however, medical education on AI is lacking. With this primer, we provide an overview on how to understand AI, gain exposure to machine learning (ML) and how to develop research questions utilizing ML. Using an example of a ML application in imaging, we provide a practical approach to understanding and executing a ML analysis. Our proposed medical education curriculum provides a framework for healthcare education which we hope will propel healthcare institutions to implement ML laboratories and training environments and improve access to this transformative paradigm.
