Effects of Including Gender Pronoun Questions in Surveys.

This research examines whether the mere presence of asking about gender pronouns (e.g., she/her, he/him, they/them, and ze/zir) in a survey enhances participants' attitudes and satisfaction of answering the questions. A large sample (N = 1,511) of heterosexual, cisgender, and LGBTQIA+ participants across the United States (US) were surveyed an online "personality test" (as a deception), with the real purpose of examining whether asking a pronoun question enhanced their perceptions of the survey. Three demographic groups were included: (i) heterosexual-cisgender (n = 503), (ii) gay-cisgender (n = 509), and (iii) genderqueer (trans, non-conforming, other, n = 499). Half of each group were randomly given either a survey that included a gender pronoun question (test) or not (control), and then all rated their perceptions of the survey questions. For participants who identified as heterosexual or gay, no major differences were found between survey conditions. However, participants who identified as genderqueer experienced significant increases of satisfaction, comfort level, and perceived relevance of the questions when given a survey that asked their gender pronouns versus the survey that did not. These findings have implications for any surveys that ask about personal demographics, and suggest that any form of written communication should include clarity about gender pronouns.

A Free Open-Source Bayesian Vancomycin Dosing App for Adults: Design and Evaluation Study.

BACKGROUND: It has been suggested that Bayesian dosing apps can assist in the therapeutic drug monitoring of patients receiving vancomycin. Unfortunately, Bayesian dosing tools are often unaffordable to resource-limited hospitals. Our aim was to improve vancomycin dosing in adults. We created a free and open-source dose adjustment app, VancoCalc, which uses Bayesian inference to aid clinicians in dosing and monitoring of vancomycin. OBJECTIVE: The aim of this paper is to describe the design, development, usability, and evaluation of a free open-source Bayesian vancomycin dosing app, VancoCalc. METHODS: The app build and model fitting process were described. Previously published pharmacokinetic models were used as priors. The ability of the app to predict vancomycin concentrations was performed using a small data set comprising of 52 patients, aged 18 years and over, who received at least 1 dose of intravenous vancomycin and had at least 2 vancomycin concentrations drawn between July 2018 and January 2021 at Lakeridge Health Corporation Ontario, Canada. With these estimated and actual concentrations, median prediction error (bias), median absolute error (accuracy), and root mean square error (precision) were calculated to evaluate the accuracy of the Bayesian estimated pharmacokinetic parameters. RESULTS: A total of 52 unique patients' initial vancomycin concentrations were used to predict subsequent concentration; 104 total vancomycin concentrations were assessed. The median prediction error was -0.600 ug/mL (IQR -3.06, 2.95), the median absolute error was 3.05 ug/mL (IQR 1.44, 4.50), and the root mean square error was 5.34. CONCLUSIONS: We described a free, open-source Bayesian vancomycin dosing calculator based on revisions of currently available calculators. Based on this small retrospective preliminary sample of patients, the app offers reasonable accuracy and bias, which may be used in everyday practice. By offering this free, open-source app, further prospective validation could be implemented in the near future.

Initial Mix-and-Match COVID-19 Vaccination Perceptions, Concerns, and Side Effects across Canadians.

Research indicates that mixing the first two doses of COVID-19 vaccine types (i.e., adenoviral vector and mRNA) produces potent immune responses against the coronavirus, but it is unclear how individuals may perceive these benefits, or whether there are different concerns compared to individuals who received two doses of the same vaccine. This research examines the demographic characteristics, psychological perceptions, and vaccination-related opinions and experiences of a large Canadian sample (N = 1002) who had received two initial doses of any COVID-19 vaccine combination. Participants included 791 (78.9%) who received two doses of the exact same brand and type of vaccine, 164 (16.4%) who received two doses of the same type of vaccine (i.e., either mRNA or adenoviral vector) but from different brands (e.g., Pfizer-BioNTech + Moderna), and 47 (4.7%) who received two doses from different types and brands of vaccine (e.g., Oxford-AstraZeneca + Pfizer-BioNTech). Results showed that, after the first vaccine dose, participants who received an adenoviral vector vaccine (e.g., Oxford-AstraZeneca) experienced the highest number of common side effects, and more severe levels of each side effect compared to those who received an mRNA vaccine (e.g., Pfizer-BioNTech or Moderna). After the second dose, participants who received Moderna as their second vaccine experienced the highest number of and most severe side effects, regardless of whether they received Moderna, Pfizer-BioNTech, or Oxford-AstraZeneca as their first dose. Real-world implications of these findings are discussed.

Medication Name Comprehension of Intelligent Virtual Assistants: A Comparison of Amazon Alexa, Google Assistant, and Apple Siri Between 2019 and 2021.

The current study was a replication and comparison of our previous research which examined the comprehension accuracy of popular intelligent virtual assistants, including Amazon Alexa, Google Assistant, and Apple Siri for recognizing the generic and brand names of the top 50 most dispensed medications in the United States. Using the exact same voice recordings from 2019, audio clips of 46 participants were played back to each device in 2021. Google Assistant achieved the highest comprehension accuracy for both brand medication names (86.0%) and generic medication names (84.3%), followed by Apple Siri (brand names = 78.4%, generic names = 75.0%), and the lowest accuracy by Amazon Alexa (brand names 64.2%, generic names = 66.7%). These findings represent the same trend of results as our previous research, but reveal significant increases of ~10-24% in performance for Amazon Alexa and Apple Siri over the past 2 years. This indicates that the artificial intelligence software algorithms have improved to better recognize the speech characteristics of complex medication names, which has important implications for telemedicine and digital healthcare services.

Identification of Risk Factors and Symptoms of COVID-19: Analysis of Biomedical Literature and Social Media Data.

BACKGROUND: In December 2019, the COVID-19 outbreak started in China and rapidly spread around the world. Lack of a vaccine or optimized intervention raised the importance of characterizing risk factors and symptoms for the early identification and successful treatment of patients with COVID-19. OBJECTIVE: This study aims to investigate and analyze biomedical literature and public social media data to understand the association of risk factors and symptoms with the various outcomes observed in patients with COVID-19. METHODS: Through semantic analysis, we collected 45 retrospective cohort studies, which evaluated 303 clinical and demographic variables across 13 different outcomes of patients with COVID-19, and 84,140 Twitter posts from 1036 COVID-19-positive users. Machine learning tools to extract biomedical information were introduced to identify mentions of uncommon or novel symptoms in tweets. We then examined and compared two data sets to expand our landscape of risk factors and symptoms related to COVID-19. RESULTS: From the biomedical literature, approximately 90% of clinical and demographic variables showed inconsistent associations with COVID-19 outcomes. Consensus analysis identified 72 risk factors that were specifically associated with individual outcomes. From the social media data, 51 symptoms were characterized and analyzed. By comparing social media data with biomedical literature, we identified 25 novel symptoms that were specifically mentioned in tweets but have been not previously well characterized. Furthermore, there were certain combinations of symptoms that were frequently mentioned together in social media. CONCLUSIONS: Identified outcome-specific risk factors, symptoms, and combinations of symptoms may serve as surrogate indicators to identify patients with COVID-19 and predict their clinical outcomes in order to provide appropriate treatments.

COVID-19 has inspired global healthcare innovation.

Under normal circumstances, healthcare innovation is costly and time-consuming. However, the COVID-19 pandemic has produced the silver lining of inspiring healthcare innovation around the world, with collaboration across multiple disciplines all working toward the same goal of saving lives. Healthcare innovation can develop at unprecedented speed when individuals focus on solving real-world problems, and collaborate with cross-functional teams. Anyone can innovate, from anywhere, at any age, and this open-minded perspective allows innovation to occur at its finest when motivated to find solutions toward a well-defined problem.

Homeostasis as a proportional-integral control system.

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.

The Need for Artificial Intelligence in Digital Therapeutics.

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.

Enhancing the Patient Experience for Individuals Undergoing Endoscopic Procedures Using Virtual Reality.

The results of medical procedures can often be difficult to translate into comprehensible and engaging information for patients. This randomized controlled trial evaluated the satisfaction and perceived value of a technology, called HealthVoyager, which creates a personalized virtual reality (VR) experience of a patient's endoscopy or colonoscopy findings in comparison to the standard practice (ie, reviewing printed reports). The platform allows gastroenterologists to create a customized VR patient report to help translate medical knowledge and procedural information to the patient. Forty-one patients (17 HealthVoyager [test]; 24 standard practice [control]) completed a self-report survey assessing their experience for receiving medical information. Results demonstrated that patients were significantly more satisfied in learning about their gastrointestinal condition and procedural results using HealthVoyager rather than with the standard of care. These results have implications for improving the knowledge translation of medical findings between healthcare providers and patients in various disease states and patient populations.

Do you understand the words that are comin outta my mouth? Voice assistant comprehension of medication names.

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 (Siri M = 57.0%, SD = 11.7; Alexa M = 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.

Physicians' Perceptions of Chatbots in Health Care: Cross-Sectional Web-Based Survey.

BACKGROUND: Many potential benefits for the uses of chatbots within the context of health care have been theorized, 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 health care, even though these individuals are the traditional benchmark of proper patient care. OBJECTIVE: This study aimed to investigate the perceptions of physicians regarding the use of health care chatbots, including their benefits, challenges, and risks to patients. METHODS: A total of 100 practicing physicians across the United States completed a Web-based, self-report survey to examine their opinions of chatbot technology in health care. Descriptive statistics and frequencies were used to examine the characteristics of participants. RESULTS: A wide variety of positive and negative perspectives were reported on the use of health care chatbots, including the importance to patients for managing their own health and the benefits on physical, psychological, and behavioral health outcomes. More consistent agreement occurred with regard to administrative benefits associated with chatbots; many physicians believed that chatbots would be most beneficial for scheduling doctor appointments (78%, 78/100), locating health clinics (76%, 76/100), or providing medication information (71%, 71/100). Conversely, many physicians believed that chatbots cannot effectively care for all of the patients' needs (76%, 76/100), cannot display human emotion (72%, 72/100), and cannot provide detailed diagnosis and treatment because of not knowing all of the personal factors associated with the patient (71%, 71/100). Many physicians also stated that health care chatbots could be a risk to patients if they self-diagnose too often (714%, 74/100) and do not accurately understand the diagnoses (74%, 74/100). CONCLUSIONS: Physicians believed in both costs and benefits associated with chatbots, depending on the logistics and specific roles of the technology. Chatbots may have a beneficial role to play in health care to support, motivate, and coach patients as well as for streamlining organizational tasks; in essence, chatbots could become a surrogate for nonmedical caregivers. However, concerns remain on the inability of chatbots to comprehend the emotional state of humans as well as in areas where expert medical knowledge and intelligence is required.

Using mobile virtual reality to enhance medical comprehension and satisfaction in patients and their families.

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.

Asymmetry in Gaze Direction Discrimination Between the Upper and Lower Visual Fields.

Previous research has shown that gaze direction can only be accurately discriminated within parafoveal limits (∼5° eccentricity) along the horizontal visual field. Beyond this eccentricity, head orientation seems to influence gaze discrimination more than iris cues. The present study examined gaze discrimination performance in the upper visual field (UVF) and lower visual field (LVF), and whether head orientation affects gaze judgments beyond parafoveal vision. Direct and averted gaze faces, in frontal and deviated head orientations, were presented for 150 ms along the vertical meridian while participants maintained central fixation during gaze discrimination judgments. Gaze discrimination was above chance level at all but one eccentricity for the two gaze-head congruent conditions. In contrast, for the incongruent conditions, gaze was discriminated above chance only from -1.5° to +3°, with an asymmetry between the UVF and LVF. Beyond foveal vision, response rates were biased toward head orientation rather than iris eccentricity, occurring in the LVF for both head orientations, and in the UVF for frontal head views. These findings suggest that covert processing of gaze direction involves the integration of eyes and head cues, with congruency of these two social cues driving response differences between the LVF and the UVF.

Eye gaze and head orientation modulate the inhibition of return for faces.

The present study used an inhibition of return (IOR) spatial cueing paradigm to examine how gaze direction and head orientation modulate attention capture for human faces. Target response time (RT) was measured after the presentation of a peripheral cue, which was either a face (with front-facing or averted gaze, in either frontal head view or averted head view) or a house (control). Participants fixated on a centered cross at all times and responded via button press to a peripheral target after a variable stimulus onset asynchrony (SOA) from the stimulus cue. At the shortest SOA (150 ms), RTs were shorter for faces than houses, independent of an IOR response, suggesting a cue-based RT advantage elicited by faces. At the longest SOA (2,400 ms), a larger IOR magnitude was found for faces compared to houses. Both the cue-based RT advantage and later IOR responses were modulated by gaze-head congruency; these effects were strongest for frontal gaze faces in frontal head view, and for averted gaze faces in averted head view. Importantly, participants were not given any specific information regarding the stimuli, nor were they told the true purpose of the study. These findings indicate that the congruent combination of head and gaze direction influence the exogenous attention capture of faces during inhibition of return.

Effects of Peripheral Eccentricity and Head Orientation on Gaze Discrimination.

Visual search tasks support a special role for direct gaze in human cognition, while classic gaze judgment tasks suggest the congruency between head orientation and gaze direction plays a central role in gaze perception. Moreover, whether gaze direction can be accurately discriminated in the periphery using covert attention is unknown. In the present study, individual faces in frontal and in deviated head orientations with a direct or an averted gaze were flashed for 150 ms across the visual field; participants focused on a centred fixation while judging the gaze direction. Gaze discrimination speed and accuracy varied with head orientation and eccentricity. The limit of accurate gaze discrimination was less than ±6° eccentricity. Response times suggested a processing facilitation for direct gaze in fovea, irrespective of head orientation, however, by ±3° eccentricity, head orientation started biasing gaze judgments, and this bias increased with eccentricity. Results also suggested a special processing of frontal heads with direct gaze in central vision, rather than a general congruency effect between eye and head cues. Thus, while both head and eye cues contribute to gaze discrimination, their role differs with eccentricity.

Attention Capture by Direct Gaze is Robust to Context and Task Demands.

Eye-tracking was used to investigate whether gaze direction would influence the visual scanning of faces, when presented in the context of a full character, in different social settings, and with different task demands. Participants viewed individual computer agents against either a blank background or a bar scene setting, during both a free-viewing task and an attractiveness rating task for each character. Faces with a direct gaze were viewed longer than faces with an averted gaze regardless of body context, social settings, and task demands. Additionally, participants evaluated characters with a direct gaze as more attractive than characters with an averted gaze. These results, obtained with pictures of computer agents rather than real people, suggest that direct gaze is a powerful attention grabbing stimulus that is robust to background context or task demands.

Searching for a perceived gaze direction using eye tracking.

The purpose of the current study was to use eye tracking to better understand the "stare-in-the-crowd effect"-the notion that direct gaze is more easily detected than averted gaze in a crowd of opposite-gaze distractors. Stimuli were displays of four full characters aligned across the monitor (one target and three distractors). Participants completed a visual search task in which they were asked to detect the location of either a direct gaze or an averted gaze target. Reaction time (RT) results indicated faster responses to direct than averted gaze only for characters situated in the far peripheral visual fields. Eye movements confirmed a serial search strategy (definitely ruling out any pop-out effects) and revealed different exploration patterns between hemifields. The latency before the first fixation on target strongly correlated with response RTs. In the LVF, that latency was also faster for direct than averted gaze targets, suggesting that the response asymmetry in favor of direct gaze stemmed from faster direct gaze target detection. In the RVF, however, the response bias to direct gaze seemed not due to a faster visual detection but rather to a different cognitive mechanism. Direct gaze targets were also responded to even faster when their position was congruent with the direction of gaze of distractors. These findings suggest that the detection asymmetry for direct gaze is highly dependent on target position and influenced by social contexts.