EM, Borycki, Kushniruk AW, Kletke R, Vimarlund V, Sentharijah Y, and Y. Quintana. 2021. “Enhancing Safety During a Pandemic Using Virtual Care Remote Monitoring Technologies”. IMIA Yearbook of Medical Informatics - International Medical Informatics Association.

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 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:

L, Zoe, and Quintana Y. 2021. “Challenges to Global Standardization of Outcome Measures”. In AMIA 2021 Virtual Informatics Summit. American Medical Informatics Association.
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.
BR, South, Chapman WW, Dankwa-Mullan I, Matheny M, and Quintana Y. 2021. “Challenges and Opportunities for Implementing Artificial Intelligence at the Speed of Technology Innovation During the COVID-19 Era”. In AMIA 2021 Virtual Informatics Summit. American Medical Informatics Association Conference.
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.
BZ, Fite, Hinostroza V, States L, Hicks-Nelson A, Baratto L, Kallianos K, Codari M, et al. 2021. “Increasing Diversity in Radiology and Molecular Imaging: Current Challenges”. Molecular Imaging and Biology, no. Apr 26, 2021: 1–14.
This paper summarizes the 2020 Diversity in Radiology and Molecular Imaging: What We Need to Know Conference, a three-day virtual conference held September 9-11, 2020. The World Molecular Imaging Society (WMIS) and Stanford University jointly organized this event to provide a forum for WMIS members and affiliates worldwide to openly discuss diversity in science, technology, engineering, and mathematics (STEM). The participants discussed three main conference themes, “Racial Diversity in STEM,” “Women in STEM,” and “Global Health,” which were discussed through seven plenary lectures, twelve scientific presentations, and nine roundtable discussions. Breakout sessions were designed to flip the classroom and seek input from attendees on important topics such as increasing the representation of underrepresented minority (URM) members and women in STEM, generating pipeline programs in the fields of molecular imaging, supporting existing URM and women members in their career pursuits, developing mechanisms to effectively address microaggressions, providing leadership opportunities for URM and women STEM members, improving global health research, and developing strategies to advance culturally competent healthcare.
I, Dankwa-Mullan, Scheufele E, Matheny ME, Quintana Y, Chapman WW, Jackson G, and South BR. 2021. “A Proposed Strategy on Integrating Health Equity and Racial Justice into the Artificial Intelligence Development Lifecycle”. Journal of Health Care for the Poor and Underserved 32: 300–317.

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.


Y, Quintana, and Torous J. 2020. “A Framework for Evaluation of Mobile Apps for Youth Mental Health”. Guelph, Ontario, Canada: Homewood Research Institute.
This report represents a key step toward that goal. It presents a Framework that a) identifies critical issues that must be addressed in designing and evaluating apps, and b) outlines criteria and protocols for rigorously evaluating these issues to generate the evidence needed by those looking for credible apps. This type of Framework is novel and groundbreaking.
M, Nasr, Karray F, and Quintana Y. 2020. “Human-Machine Interaction Platform for Home Care Support System”. In 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 4210-15. Toronto, ON, Canada, October 14, 2020: IEEE.

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

C, Tassone, Keshavjee K, Paglialonga A, Moreira N, Pinto J, and Quintana Y. 2020. “Evaluation of Mobile Apps for Treatment of Patients at Risk of Developing Gestational Diabetes”. Health Informatics J 26 (3): 1983-94.
Keywords: app evaluation, diabetes, gestational diabetes, health apps, mHealth, patient empowerment, prediabetes
M, Harlemon, Ajayi O, Kachambwa P, Kim MS, Simonti CN, Quiver MH, Petersen DC, et al. 2020. “A Custom Genotyping Array Reveals Population-Level Heterogeneity for the Genetic Risks of Prostate Cancer and Other Cancers in Africa”. Cancer Research.

Although prostate cancer is the leading cause of cancer mortality for African men, the vast majority of known disease associations have been detected in European study cohorts. Furthermore, most genome-wide association studies have used genotyping arrays that are hindered by SNP ascertainment bias. To overcome these disparities in genomic medicine, the Men of African Descent and Carcinoma of the Prostate (MADCaP) Network has developed a genotyping array that is optimized for African populations. The MADCaP Array contains more than 1.5 million markers and an imputation backbone that successfully tags over 94% of common genetic variants in African populations. This array also has a high density of markers in genomic regions associated with cancer susceptibility, including 8q24. We assessed the effectiveness of the MADCaP Array by genotyping 399 prostate cancer cases and 403 controls from seven urban study sites in sub-Saharan Africa. Samples from Ghana and Nigeria clustered together, whereas samples from Senegal and South Africa yielded distinct ancestry clusters. Using the MADCaP array, we identified cancer-associated loci that have large allele frequency differences across African populations. Polygenic risk scores for prostate cancer were higher in Nigeria than in Senegal. In summary, individual and population-level differences in prostate cancer risk were revealed using a novel genotyping array.

Significance: This study presents an Africa-specific genotyping array, which enables investigators to identify novel disease associations and to fine-map genetic loci that are associated with prostate and other cancers.

Y, Anacak, Zubizarreta E, Zaghloul M, Laskar S, Alert J, Gondhowiardjo S, Giselvania A, et al. 2020. “The Practice of Paediatric Radiation Oncology in Low- and Middle-Income Countries: Outcomes of an International Atomic Energy Agency Study”. Clinical Oncology Epub 2020 Nov 26. .

Aims: Childhood cancer survival is suboptimal in most low- and middle-income countries (LMICs). Radiotherapy plays a significant role in the standard care of many patients. To assess the current status of paediatric radiotherapy, the International Atomic Energy Agency (IAEA) undertook a global survey and a review of practice in eight leading treatment centres in middle-income countries (MICs) under Coordinated Research Project E3.30.31; 'Paediatric radiation oncology practice in low and middle income countries: a patterns-of-care study by the International Atomic Energy Agency.'

Materials and methods: A survey of paediatric radiotherapy practices was distributed to 189 centres worldwide. Eight leading radiotherapy centres in MICs treating a significant number of children were selected and developed a database of individual patients treated in their centres comprising 46 variables related to radiotherapy technique.

Results: Data were received from 134 radiotherapy centres in 42 countries. The percentage of children treated with curative intent fell sequentially from high-income countries (HICs; 82%) to low-income countries (53%). Increasing deficiencies were identified in diagnostic imaging, radiation staff numbers, radiotherapy technology and supportive care. More than 92.3% of centres in HICs practice multidisciplinary tumour board decision making, whereas only 65.5% of centres in LMICs use this process. Clinical guidelines were used in most centres. Practice in the eight specialist centres in MICs approximated more closely to that in HICs, but only 52% of patients were treated according to national/international protocols whereas institution-based protocols were used in 41%.

Conclusions: Quality levels in paediatric radiotherapy differ among countries but also between centres within countries. In many LMICs, resources are scarce, coordination with paediatric oncology is poor or non-existent and access to supportive care is limited. Multidisciplinary treatment planning enhances care and development may represent an area where external partners can help. Commitment to the use of protocols is evident, but current international guidelines may lack relevance; the development of resources that reflect the capacity and needs of LMICs is required. In some LMICs, there are already leading centres experienced in paediatric radiotherapy where patient care approximates to that in HICs. These centres have the potential to drive improvements in service, training, mentorship and research in their regions and ultimately to improve the care and outcomes for paediatric cancer patients.