Explainable Machine Learning Model to Predict COVID-19 Severity Among Older Adults in the Province of Quebec.

Context: Patients over the age of 65 years are more likely to experience higher severity and mortality rates than other populations from COVID-19. Clinicians need assistance in supporting their decisions regarding the management of these patients. Artificial Intelligence (AI) can help with this regard. However, the lack of explainability-defined as "the ability to understand and evaluate the internal mechanism of the algorithm/computational process in human terms"-of AI is one of the major challenges to its application in health care. We know little about application of explainable AI (XAI) in health care. Objective: In this study, we aimed to evaluate the feasibility of the development of explainable machine learning models to predict COVID-19 severity among older adults. Design: Quantitative machine learning methods. Setting: Long-term care facilities within the province of Quebec. Participants: Patients 65 years and older presented to the hospitals who had a positive polymerase chain reaction test for COVID-19. Intervention: We used XAI-specific methods (e.g., EBM), machine learning methods (i.e., random forest, deep forest, and XGBoost), as well as explainable approaches such as LIME, SHAP, PIMP, and anchor with the mentioned machine learning methods. Outcome measures: Classification accuracy and area under the receiver operating characteristic curve (AUC). Results: The age distribution of the patients (n=986, 54.6% male) was 84.5□19.5 years. The best-performing models (and their performance) were as follows. Deep forest using XAI agnostic methods LIME (97.36% AUC, 91.65 ACC), Anchor (97.36% AUC, 91.65 ACC), and PIMP (96.93% AUC, 91.65 ACC). We found alignment with the identified reasoning of our models' predictions and clinical studies' findings-about the correlation of different variables such as diabetes and dementia, and the severity of COVID-19 in this population. Conclusions: The use of explainable machine learning models, to predict the severity of COVID-19 among older adults is feasible. We obtained a high-performance level as well as explainability in the prediction of COVID-19 severity in this population. Further studies are required to integrate these models into a decision support system to facilitate the management of diseases such as COVID-19 for (primary) health care providers and evaluate their usability among them.

A cross-sectional study of the preventive health care activities of western Canadian rural-living patients unattached to primary care providers.

Prevention services, such as screening tests and vaccination, are underutilized, especially by rural populations and patients without a usual primary care provider. Little is known about the compounding impacts on preventive care of being unattached and living in a rural area and there has been no comprehensive exploration of this highly vulnerable population's prevention activities. The twofold purpose of this research was to examine rural unattached patients' prevention activity self-efficacy and completion and to explore their experiences accessing healthcare, including COVID-19 impacts. Two thirds of patients had been unattached for over one year, and over 20 % had been unattached for over 5 years; males experienced longer unattachment compared to females. Completion rates of prevention activities were relatively low, ranging from 5.9 % (alcohol screening) to 59 % (vision test). Most participants did not complete their prevention care activities in line with the Lifetime Prevention Schedule timeline: 65 % of participants had less than half of their activities up-to-date and only 6.7 % of participants were up to date on 75 % or more of their prevention activities. Participants with higher prevention self-efficacy scores were more likely to be up-to-date on associated prevention activities but the longer patients had been unattached, the fewer their up-to-date prevention activities. Patients expressed negative impacts of COVID-19 including walk-in clinics shutting down limiting access to care. These results suggest serious gaps in rural unattached patients' preventive care and highlight the need for support when they are without a usual primary care provider, which can be lengthy.

Technology Recommendations to Support Person-Centered Care in Long-Term Care Homes during the COVID-19 Pandemic and Beyond.

The COVID-19 pandemic has exposed persistent inequities in the long-term care sector and brought strict social/physical distancing distancing and public health quarantine guidelines that inadvertently put long-term care residents at risk for social isolation and loneliness. Virtual communication and technologies have come to the forefront as the primary mode for residents to maintain connections with their loved ones and the outside world; yet, many long-term care homes do not have the technological capabilities to support modern day technologies. There is an urgent need to replace antiquated technological infrastructures to enable person-centered care and prevent potentially irreversible cognitive and psychological declines by ensuring residents are able to maintain important relationships with their family and friends. To this end, we provide five technological recommendations to support the ethos of person-centered care in residential long-term care homes during the pandemic and  in a post-COVID-19 pandemic world.

Emergency Remote Learning in Nursing Education During the COVID-19 Pandemic.

Due to the corona (COVID-19) pandemic, several countries are currently conducting non-face-to-face education. Therefore, teachers of nursing colleges have been carrying out emergency remote education. This study developed a questionnaire to understand the status of Emergency Remote Learning (ERL) in nursing education internationally, translated it into 7 languages, and distributed it to 18 countries. A total of 328 nursing educators responded, and the most often used online methods were Social networking technology such as Facebook, Google+ and Video sharing platform such as YouTube. The ERL applied to nursing education was positively evaluated as 3.59 out of 5. The results of the study show that during the two semesters nursing college professors have well adapted to this unprecedent crisis of teaching. The world after COVID-19 has become a completely different place, and nursing education should be prepared for 'untact' education.

Evidence-Based Health Informatics as the Foundation for the COVID-19 Response: A Joint Call for Action.

BACKGROUND: As a major public health crisis, the novel coronavirus disease 2019 (COVID-19) pandemic demonstrates the urgent need for safe, effective, and evidence-based implementations of digital health. The urgency stems from the frequent tendency to focus attention on seemingly high promising digital health interventions despite being poorly validated in times of crisis. AIM: In this paper, we describe a joint call for action to use and leverage evidence-based health informatics as the foundation for the COVID-19 response and public health interventions. Tangible examples are provided for how the working groups and special interest groups of the International Medical Informatics Association (IMIA) are helping to build an evidence-based response to this crisis. METHODS: Leaders of working and special interest groups of the IMIA, a total of 26 groups, were contacted via e-mail to provide a summary of the scientific-based efforts taken to combat COVID-19 pandemic and participate in the discussion toward the creation of this manuscript. A total of 13 groups participated in this manuscript. RESULTS: Various efforts were exerted by members of IMIA including (1) developing evidence-based guidelines for the design and deployment of digital health solutions during COVID-19; (2) surveying clinical informaticians internationally about key digital solutions deployed to combat COVID-19 and the challenges faced when implementing and using them; and (3) offering necessary resources for clinicians about the use of digital tools in clinical practice, education, and research during COVID-19. DISCUSSION: Rigor and evidence need to be taken into consideration when designing, implementing, and using digital tools to combat COVID-19 to avoid delays and unforeseen negative consequences. It is paramount to employ a multidisciplinary approach for the development and implementation of digital health tools that have been rapidly deployed in response to the pandemic bearing in mind human factors, ethics, data privacy, and the diversity of context at the local, national, and international levels. The training and capacity building of front-line workers is crucial and must be linked to a clear strategy for evaluation of ongoing experiences.

How to Improve Information Technology to Support Healthcare to Address the COVID-19 Pandemic: an International Survey with Health Informatics Experts.

OBJECTIVES: To identify the ways in which healthcare information and communication technologies can be improved to address the challenges raised by the COVID-19 pandemic. METHODS: The study population included health informatics experts who had been involved with the planning, development and deployment of healthcare information and communication technologies in healthcare settings in response to the challenges presented by the COVID-19 pandemic. Data were collected via an online survey. A non-probability convenience sampling strategy was employed. Data were analyzed with content analysis. RESULTS: A total of 65 participants from 16 countries responded to the conducted survey. The four major themes regarding recommended improvements identified from the content analysis included: improved technology availability, improved interoperability, intuitive user interfaces and adoption of standards of care. Respondents also identified several key healthcare information and communication technologies that can help to provide better healthcare to patients during the COVID-19 pandemic, including telehealth, advanced software, electronic health records, remote work technologies (e.g., remote desktop computer access), and clinical decision support tools. CONCLUSIONS: Our results help to identify several important healthcare information and communication technologies, recommended by health informatics experts, which can help to provide better care to patients during the COVID-19 pandemic. The results also highlight the need for improved interoperability, intuitive user interfaces and advocating the adoption of standards of care.

COVID-19 restrictions: experiences of immigrant parents in Toronto.

Parenting is a demanding undertaking, requiring continuous vigilance to ensure children's emotional, physical, and spiritual well-being. It has become even more challenging in the context of COVID-19 restrictions that have led to drastic changes in family life. Based on the results of a qualitative interpretive descriptive study that aimed to understand the experiences of immigrants living in apartment buildings in the Greater Toronto Area, Ontario, Canada, this paper reports the experiences of 50 immigrant parents. During the summer and fall of 2020, semi-structured interviews were conducted by phone or virtually, audio-recorded, then translated and transcribed. The transcripts were analyzed using thematic analysis. Results revealed that parenting experiences during the pandemic entailed dealing with changing relationships, coping with added burdens and pressures, living in persistent fear and anxiety, and rethinking lifestyles and habits. Amid these changes and challenges, some parents managed to create opportunities for their children to improve their diet, take a break from their rushed lives, get in touch with their cultural and linguistic backgrounds, and spend more quality time with their family. While immigrant parents exhibit remarkable resilience in dealing with the pandemic-related meso and macro-levels restrictions, funding and programs are urgently needed to support them in addressing the impact of these at the micro level.