More than just friends: in-home use and design recommendations for sensing socially assistive robots (SARs) by older adults with depression

As healthcare turns its focus to preventative community-based interventions, there is increasing interest in using in-home technology to support this goal. This study evaluates the design and use of socially assistive robots (SARs) and sensors as in-home therapeutic support for older adults with depression. The seal-like SAR Paro, along with onboard and wearable sensors, was placed in the homes of 10 older adults diagnosed with clinical depression for one month. Design workshops were conducted before and after the in-home implementation with participating older adults and clinical care staff members. Workshops showed older adults and clinicians saw several potential uses for robots and sensors to support in-home depression care. Long-term in-home use of the robot allowed researchers and participants to situate desired robot features in specific practices and experiences of daily life, and some user requests for functionality changed due to extended use. Sensor data showed that participants’ attitudes toward and intention to use the robot were strongly correlated with particular Circadian patterns (afternoon and evening) of robot use. Sensor data also showed that those without pets interacted with Paro significantly more than those with pets, and survey data showed they had more positive attitudes toward the SAR. Companionship, while a desired capability, emerged as insufficient to engage many older adults in long-term use of SARs in their home.

The full article can be downloaded below.  

What’s Powering Artificial Intelligence?

To scale artificial intelligence (AI) and machine learning (ML), hardware and software developers must enable AI/ML performance across a vast array of devices. This requires balancing the need for functionality alongside security, affordability, complexity and general compute needs. Fortunately, there’s a solution hiding in plain sight.

The full Arm whitepaper can be downloaded below.  

Comparison of healthcare professionals’ motivations for using different online learning materials

Online learning is increasingly prevalent throughout all stages of medical education. There is little published literature exploring what motivates healthcare professionals to engage with different types of e-learning content. Learner motivations must be understood in order to design effective educational solutions and to optimize the overall online learning experience.

This article aims to examine engagement, satisfaction, and motivations of healthcare professionals using OPENPediatrics, an open-access medical e-learning platform.

We use a retrospective analysis of online survey data. Users were asked to report engagement and satisfaction with the platform, as well as to select motivations for using different types of content on the site: Courses, Simulators, and World Shared Practice Forum videos.

The majority of respondents were physicians and nurses in North America and Europe. Overall satisfaction with the platform was high. Most frequently cited motivations for using Courses and Simulators were: learn basic and in-depth information around topics, and learn how to deliver safer or more effective patient care. For World Shared Practice Forum videos, most commonly cited motivations were: learn in-depth information about a topic, learn the latest advances or developments in an area, and learn how to deliver safer or more effective patient care.

We appreciated both commonalities and differences in learning motivations among clinicians accessing different kinds of medical e-learning content. Respondents were consistently motivated to learn in order to deliver safer or more effective patient care, but they reported using different types of educational content depending on whether they were learning basic information versus updating or changing their knowledge.

The full article can be downloaded below.  

‘Engagement’ of patients and healthcare professionals in regulatory pharmacovigilance: establishing a conceptual and methodological framework

Engagement of patients and healthcare professionals is increasingly considered as fundamental to pharmacovigilance and risk minimisation activities. Few empirical studies of engagement exist and a lack of explicit conceptualisations impedes effective measurement, research and the development of evidence-based engagement interventions.

This article (1) develops a widely applicable conceptualisation, (2) considers various methodological challenges to researching engagement, proposing some solutions, and (3) outlines a basis for converting the conceptualisation into specific measures and indicators of engagement among stakeholders. 

We synthesise social science work on risk governance and public understandings of science with insights from studies in the pharmacovigilance field.

This leads us to define engagement as an ongoing process of knowledge exchange among stakeholders, with the adoption of this knowledge as the outcome which may feed back into engagement processes over time. We conceptualise this process via three dimensions; breadth, depth and texture. In addressing challenges to capturing each dimension, we emphasise the importance of combining survey approaches with qualitative studies and secondary data on medicines use, prescribing, adverse reaction reporting and health outcomes. A framework for evaluating engagement intervention processes and outcomes is proposed. Alongside measuring engagement via breadth and depth, we highlight the need to research the engagement process through attentiveness to texture— what engagement feels like, what it means to people, and how this shapes motivations based on values, emotions, trust and rationales.

 Capturing all three dimensions of engagement is vital to develop valid understandings of what works and why, thus informing engagement interventions of patients and healthcare professionals to given regulatory pharmacovigilance scenarios.

The full article can be downloaded below.  

Social and juristic challenges of artificial intelligence

Artificial intelligence is becoming seamlessly integrated into our everyday lives, augmenting our knowledge and capabilities in driving, avoiding traffic, finding friends, choosing the perfect movie, and even cooking a healthier meal. It also has a significant impact on many aspects of society and industry, ranging from scientific discovery, healthcare and medical diagnostics to smart cities, transport and sustainability. Within this 21st century ‘man meets machine’ reality unfolding, several social and juristic challenges emerge for which we are poorly prepared. We here review social dilemmas where individual interests are at odds with the interests of others, and where artificial intelligence might have a particularly hard time making the right decision. An example thereof is the well-known social dilemma of autonomous vehicles. We also review juristic challenges, with a focus on torts that are at least partly or seemingly due to artificial intelligence, resulting in the claimant suffering a loss or harm. Here the challenge is to determine who is legally liable, and to what extent. We conclude with an outlook and with a short set of guidelines for constructively mitigating described challenges.

The full article can be downloaded below.  

How Conversational Artificial Intelligence Is Providing Companionship To The Elderly

Looking to address the challenges of elderly loneliness, Accenture has developed a solution that uses conversational AI to let people capture memorable stories for future generations while providing companionship. Powered by Google Voice Assistant, Memory Lane initiates conversations by asking someone who is lonely to tell their life story. This is then turned into a physical book and podcast.

The full Forbes article can be viewed at this link.  

The Present And Future Of Computer Vision

Computer vision, or the ability of artificially intelligent systems to “see” like humans, has been a subject of increasing interest and rigorous research for decades now. As a way of emulating the human visual system, the research in the field of computer vision purports to develop machines that can automate tasks that require visual cognition. However, the process of deciphering images, due to the significantly greater amount of multi-dimensional data that needs analysis, is much more complex than understanding other forms of binary information. This makes developing AI systems that can recognize visual data more complicated.

But, the use of deep learning and artificial neural networks is making computer vision more capable of replicating human vision. In fact, computer vision is becoming more adept at identifying patterns from images than the human visual cognitive system. For instance, in the field of healthcare, computer vision-based technology has said to have exceeded the pattern recognition capabilities of human physicians. Researchers have tested an AI that can detect neurological illnesses by reading CT scan images faster than radiologists.

The full Forbes article can be read at this link.  

How This Founder Is Using Technology To Reduce Racial Disparities In Healthcare

The inequalities in the healthcare industry are widely documented, with the Center for Disease Control and Prevention reporting earlier this year, for example, that there are massive racial disparities in pregnancy-related deaths. There is a lack of diversity in the medical field with only about 4% of U.S. doctors being black/African American and 5% being Hispanic; Latinos are currently one of the fastest growing ethnic groups in the U.S., which makes the latter statistic particularly problematic. With these health disparities in mind, a new platform, Hued, was developed. Hued is a platform that “diversifies the patient/doctor connection by connecting patients (of color) with health and medical professionals (of color) that specifically understand their cultural, physical and mental needs.” Hued founder Kimberly Wilson sat down to discuss the platform, why it was started and how it will impact diversity and inclusion in the field of medicine.

The full Forbes article can be found at this link.  

This App Listens In And Fills Out Paperwork So Doctors Can Focus On Patients

Since the shift to electronic health records (EHRs) in the United States accelerated a decade ago, the day-to-day impact on doctors is staggering. To keep records current and useful, physicians have to ensure all records of patient interactions get recorded, meaning that a large chunk of their day is spent staring at a screen rather than taking care of their patients. Recent studies show that doctors may spend as much as half of their work day filling out the records of their interactions with patients.

“In an ideal world, physicians would just interact with patients,” Saykara founder and CEO Harjinder Sandhu told Forbes. “That’s what they want to do. They don’t want to type notes.”

His Seattle-based company’s solution is an app that records a doctor’s interactions with patients, using AI and machine learning to hone in on key points of the doctor’s side of the conversation and appropriately document on the EHR, leaving the doctor free to focus on the patient. A new update to Saykara software passively records in the office without requiring activation, making operation even easier for doctors, who previously had to start recordings manually. 

The full Forbes article can be viewed at this link.  

DigiVoice: Voice Biomarker Featurization and Analysis Pipeline

In recent years, data-driven models have enabled significant advances in medicine. Simultaneously, voice has shown potential for analysis in precision medicine as a biomarker for screening illnesses. There has been a growing trend to pursue voice data to understand neuropsychiatric diseases. In this paper, we present DigiVoice, a comprehensive feature extraction and analysis pipeline for voice data. DigiVoice supports raw .WAV files and text transcriptions in order to analyze the entire content of voice. DigiVoice supports feature extraction including acoustic, natural language, linguistic complexity, and semantic coherence features. DigiVoice also supports machine learning capabilities including data visualization, feature selection, feature transformation, and modeling. To our knowledge, DigiVoice provides the most comprehensive voice feature set for data analysis to date. With DigiVoice, we plan to accelerate research to correlate voice biomarkers with illness to enable data-driven treatment. We have worked closely with our industry partner, NeuroLex Laboratories, to make voice computing open source and accessible. DigiVoice enables researchers to leverage our technology across the domains of voice computing and precision medicine without domain-specific expertise. Our work allows any researchers to use voice as a biomarker in their past, current, or future studies.

The full article can be downloaded below.