Objectives: This paper describes a methodology for gathering requirements and early design of remote monitoring technology (RMT) for enhancing patient safety during pandemics using
virtual care technologies. As pandemics such as COrona VIrus Disease (COVID-19) progress there is an increasing need for effective virtual care and RMT to support patient care while they are at home.

Methods: The authors describe their work in conducting literature reviews by searching PubMed.gov and the grey literature for articles, and government websites with guidelines describing the signs and symptoms of COVID-19, as well as the progression of the disease. The reviews focused on identifying gaps where RMT could be applied in novel ways and formed the basis for the subsequent modelling of use cases for applying RMT described in this paper.

Results: The work was conducted in the context of a new Home of the Future laboratory which has been set up at the University of Victoria. The literature review led to the development of
a number of object-oriented models for deploying RMT. This modeling is being used for a number of purposes, including for education of students in health infomatics as well as testing of new use cases for RMT with industrial collaborators and projects within the smart home of the future laboratory.

Conclusions: Object-oriented modeling, based on analysis of gaps in the literature, was found to be a useful approach for describing, communicating and teaching about potential new
uses of RMT.

Keywords: Remote monitoring technology, assistive living, COVID-19, pandemics, user requirements, safety, public health informatics, health informatics

Yearb Med Inform 2021: http://dx.doi.org/10.1055/s-0041-1726485

Global standardization of outcome measures for disease states can help researchers and healthcare providers compare healthcare institutions’ and populations' health outcomes. Despite the creation of standardized outcome sets, clinical institutions' adoption of these sets is not common. A literature review shows that among the challenges to standardizing outcome measures include the difficulties of achieving consensus in the working groups creating these outcome sets, the tradeoffs made when selecting outcome measurement tools, and the high costs of implementing a new or different set of outcome measures. The duplication of effort to create these standard sets can also limit standardization, which could be minimized through increased transparency of how these standard sets are developed. We propose some approaches to improve how to create and implement standard sets to broaden their usability across institutions.

The COVID-19 pandemic has created multiple opportunities to implement Artificial Intelligence (AI) technologies in new ways that address the initial infectious curve (e.g., triaging patients and disseminating information during disease outbreaks), as well as the subsequent curves of pandemic sequelae (managing gaps in care of chronic conditions, addressing new and exacerbated mental health needs, and rectifying worsening health disparities. However,
numerous challenges limit scaling development and application of AI technologies in healthcare settings, especially in the context of a rapidly evolving public health emergency. Data representing diverse patient cohorts are necessary both to train and to test systems but often are labor intensive to create and deidentify. The need for new codes and concepts can delay data availability. Biases in data must be identified, evaluated, and managed to mitigate
downstream effects. System performance must be continuously monitored and validated as clinical information, such as disease transmission characteristics, become available. This panel will discuss these challenges and propose solutions that include ensuring adequate, equitable, and unbiased data sources are used for AI development, validation of AI in clinical settings, with the context of the rapidly evolving COVID-19 public health crisis as a discussion focus.

The COVID-19 pandemic has created multiple opportunities to deploy artificial intelligence (AI)-driven tools and applied interventions to understand, mitigate, and manage the pandemic and its consequences. The disproportionate impact of COVID-19 on racial-ethnic and socially disadvantaged populations underscores the need to anticipate and address social inequalities and health disparities in AI development and application. Before the pandemic, there was growing optimism about AI's role in addressing inequities and enhancing personalized care. Unfortunately, ethical and social issues that are encountered in scaling, developing, and applying advanced technologies in healthcare settings have intensified during the rapidly evolving public health crisis. Critical voices concerned with the 'disruptive' potentials and risk for 'engineered inequities' have called for reexamining ethical guidelines in the development and application of AI. This paper proposes a framework to incorporate ethical AI principles into the development process in ways that intentionally promote racial health equity and social justice.  Without centering on equity, justice and ethical AI, these tools may exacerbate structural inequities that can lead to disparate health outcomes.

Abstract—There has been a tremendous increase in the costs of caring for older adults owing to the fact that societies are aging around around the world. This has led to a decrease in the number of caregivers who are able to assist. Investigative studies indicate that older adults require social as well as physical support for their well-being which prompted researchers to use social and cognitive robots and advanced human machine interaction devices. However, most of these studies have shortcomings when it comes to providing means of a natural interaction with the machine. With speech being the most natural way for human communication and the huge developments in the Internet of Things and smart homes, equipping a robotic system with powerful natural speech interaction capabilities to maintain a conversation with an elderly while being linked to other smart home devices shows a promising direction. This paper describes a scalable and expandable system with main goal of designing a natural speech-enabled system for older adults that is capable of linking to multiple active agents with minimal integration efforts. The system makes use of the power of commercially available digital assistant systems, integrated with an intelligent conversational agent, robotics, and smart wearables. The main advantage of the system is that it could provide a portion of the population, namely older adults and the disabled, the flexibility of interacting naturally with powerful social robots in smart home environments, hence providing them with much needed independence.

Index Terms—Social robots, Human-Robot Interaction, Elderly care, Artificial Intelligence, Speech Interaction, Ambient Assisted Living