Yan Fossat

According to medical guidelines, the distinction between “healthy” and “unhealthy” patients is commonly based on single, discrete values taken at an isolated point in time (e.g., blood pressure or core temperature). Perhaps a more robust and insightful diagnosis can be obtained by studying the functional interdependence of such indicators and the homeostasis that controls them. This requires quasi-continuous measurements and a procedure to map the data onto a parsimonious control model with a… Show more

According to medical guidelines, the distinction between “healthy” and “unhealthy” patients is commonly based on single, discrete values taken at an isolated point in time (e.g., blood pressure or core temperature). Perhaps a more robust and insightful diagnosis can be obtained by studying the functional interdependence of such indicators and the homeostasis that controls them. This requires quasi-continuous measurements and a procedure to map the data onto a parsimonious control model with a degree of universality. The current research illustrates this approach using glucose homeostasis as a target. Data were obtained from 41 healthy subjects wearing over-the-counter glucose monitors, and projected onto a simple proportional–integral (PI) controller, widely used in engineering applications. The indicators quantifying the control function are clustered for the great majority of subjects, while a few outliers exhibit less responsive homeostasis. Practical implications for healthcare and education are further discussed. Show less

Digital therapeutics is a newly described concept in healthcare which is proposed to change patient behavior and treat medical conditions using a variety of digital technologies. However, the term is rarely defined with criteria that make it distinct from simply digitizedversions of traditional therapeutics. Our objective is to describe a more valuable characteristic of digital therapeutics, which is distinct from traditional medicine or therapy: that is, the utilization of artificial… Show more

Digital therapeutics is a newly described concept in healthcare which is proposed to change patient behavior and treat medical conditions using a variety of digital technologies. However, the term is rarely defined with criteria that make it distinct from simply digitizedversions of traditional therapeutics. Our objective is to describe a more valuable characteristic of digital therapeutics, which is distinct from traditional medicine or therapy: that is, the utilization of artificial intelligence and machine learning systems to monitor and predict individual patient symptom data in an adaptive clinical feedback loop via digital biomarkers to provide a precision medicine approach to healthcare. Artificial intelligence platforms can learn and predict effective interventions for individuals using a multitude of personal variables to provide a customized and more tailored therapy regimen. Digital therapeutics coupled with artificial intelligence and machine learning also allows more effective clinical observations and management at the population level for various health conditions and cohorts. This vital differentiation of digital therapeutics compared to other forms of therapeutics enables a more personalized form of healthcare that actively adapts to patients’ individual clinical needs, goals, and lifestyles. Importantly, these characteristics are what needs to be emphasized to patients, physicians, and policy makers to advance the entire field of digital healthcare. Show less

This study investigated the speech recognition abilities of popular voice assistants when being verbally asked about commonly dispensed medications by a variety of participants. Voice recordings of 46 participants (12 of which had a foreign accent in English)were played back to Amazon’s Alexa, Google Assistant, and Apple’s Siri for the brand- and generic names of the top 50 most dispensed medications in the United States. A repeated measures ANOVA indicated that Google Assistant achieved the… Show more

This study investigated the speech recognition abilities of popular voice assistants when being verbally asked about commonly dispensed medications by a variety of participants. Voice recordings of 46 participants (12 of which had a foreign accent in English)were played back to Amazon’s Alexa, Google Assistant, and Apple’s Siri for the brand- and generic names of the top 50 most dispensed medications in the United States. A repeated measures ANOVA indicated that Google Assistant achieved the highest comprehension accuracy for both brand medication names (M=91.8%, SD=4.2) and generic medication names (M=84.3%,SD=11.2), followed by Siri (brand names M=58.5%, SD=11.2; generic names M=51.2%, SD=16.0), and the lowest accuracy by Alexa (brand names M=54.6%, SD=10.8; generic names M=45.5%, SD=15.4). An interaction between voice assistant and participant accent was also found, demonstrating lower comprehension performance overall for those with a foreign accent using Siri (M=48.8%, SD=11.8) and Alexa (M=41.7%, SD=12.7), compared to participants without a foreign accent (SiriM=57.0%, SD=11.7; AlexaM=53.0%, SD=10.9). No significant difference between participant accents were found for Google Assistant. These findings show a substantial performance lead for Google Assistant compared to its voice assistant competitors when comprehending medication names, but there is still room for improvement. Show less

Highlights
2D and 3D nature videos elicited higher creativity compared to urban videos.
Real-life nature and urban environments elicited equally high creativity.
Urban stimuli watched indoors diminished creativity performance versus nature stimuli.

Patients are typically debriefed by their healthcare provider after any medical procedure or surgery to discuss their findings and any next steps involving medication or treatment instructions. However, without any medical or scientific background knowledge, it can feel overwhelming and esoteric for a patient to listen to a physician describe a complex operation. Instead, providing patients with engaging visuals and a virtual reality (VR) simulation of their individual clinical findings could… Show more

Patients are typically debriefed by their healthcare provider after any medical procedure or surgery to discuss their findings and any next steps involving medication or treatment instructions. However, without any medical or scientific background knowledge, it can feel overwhelming and esoteric for a patient to listen to a physician describe a complex operation. Instead, providing patients with engaging visuals and a virtual reality (VR) simulation of their individual clinical findings could lead to more effective transfer of medical knowledge and comprehension of treatment information. A newly developed VR technology is described, called HealthVoyager, which is designed to help facilitate this knowledge transfer between physicians and patients. The platform represents a customizable, VR software system utilizing a smartphone or tablet computer to portray personalized surgical or procedural findings as well as representations of normal anatomy. The use of such technology for eliciting medical understanding and patient satisfaction can have many practical and clinical applications for a variety of disease states and patient populations. Show less

Many potential benefits have been theorized on the uses for chatbots within the context of healthcare, such as improved patient education and treatment compliance. However, little is known about the perspectives of practicing medical physicians on the use of chatbots in healthcare, even though these individuals are the traditional benchmark of proper patient care.

The present study examined whether any differences existed in verbal comprehension performance when information was communicated through either a human or a robot speaker, and whether the nonverbal communication of either a “high social” robot (i.e., frequent gestures, direct head gaze) or a “low social” robot (i.e., no gestures, indirect head gaze) influenced comprehension. This study also assessed whether question difficulty moderated comprehension performance.