Advancing compassionate practices in virtual long COVID care
‘Long COVID’ (LC) is an episodic condition that impacts up to 35% of COVID patients. Many people with LC (PWLC) have failed to receive compassionate care from healthcare professionals (HCPs) who did not acknowledge their condition and/or respond to their suffering. A recent systematic review demonstrates that virtual care is the second most common LC…
Read MoreBuilding compassionate practitioners for the future: Leveraging simulation-based learning in cardiac sonography education
Cardiac Sonographer & Professor, Mohawk College Cardiac sonographers work in busy environments where their patient interactions are short and focused on acquiring diagnostic images. For students entering clinicals, navigating the stressors and limited time combined with their lack of confidence in cardiac anatomy imaging creates barriers in delivering compassionate care. Babitha’s study will assess if…
Read MoreSynthesizing health information systems and matters of care: A meta-ethnography on the effects of healthinformation systems on carer identities
Wilson Centre Scientist at Medical Education, Temerty Faculty of Medicine,University of Toronto Scientist, The Institute of Education Research at University Health Network Paula proposes to synthesize what is known about impacts of health information systems on healthcare worker identities. Shewill do this through a specific type of literature review called a meta-ethnography. More than a summary…
Read MoreEmpowering family-centered and culturally compassionate digital healthcare for patients and families living with heart failure
Scientific Director and Scientist, University Health Network (Centre for Digital Therapeautics) Current digital healthcare for heart failure (HF) neglects the needs of an aging and increasingly diverse Canadian population. Quynh will co-design and test a family-centered and culturally compassionate digital health module (i.e., Medly Caretown) within a standard of care HF digital therapeutic (i.e., Medly).…
Read MoreIntegrating automated medication dispensers,compassion fatigue andcompassion satisfaction: A pilot pre-post mixed methods study
Clinical Associate Professor and Co-Director, Master of Advanced Pharmacy Practice Program at the University of Waterloo Informal caregivers, such as spouses and children, are the primary sources of support for medication management among older adults. However, caregivers are not adequately supported for the time-consuming nature and complexity of medication management. This can lead to caregiver burnout and compassion…
Read MoreUsing machine learning-based techniques to explore predictability of progressive hearing loss in a pediatric population
Research Associate, Chiildren’s Hospital of Eastern Ontario Research Institute Inc. Permanent hearing loss is a disorder affecting about 1500 Canadian children each year and can have an impact on developmental outcomes. Despite well-established screening programs, surveillance is necessary since many children eventually experience a decrease in hearing levels. Using machine-learning techniques, Flora’s study will explore…
Read MoreUsing digital care and communications platform in perioperative settings to improve Quadruple health outcomes: Analytics and artificial intelligence (AI) based Informed decisions, risks prediction, and wearable monitoring devices.
Assistant Professor, Western University When patients are discharged from the hospital after surgery, certain complications can result in emergency department visits and hospital readmissions. Surgical recovery after discharge from the hospital can also pose a challenge to patients and their caregivers. Continuity of care after discharge has been shown to reduce emergency department visits and…
Read MoreDeveloping a compassionate digital health system to support patients with dementia and their caregivers in the community, with a focus on inclusion and caregiver support in home deployment.
Scientist, University Health Network A large portion of people living with dementia (PlwD) in the community exhibit responsive behaviors that could lead to injuries and distress to them and their caregivers. In this project, Shehrozwill co-design his cloud-based digital health platform with PlwD and their caregivers to deploy it in homes while keeping their dignity…
Read MoreUsing machine learning approaches to predict tumour recurrence and progression to provide personalized and compassionate care for non-muscle invasive bladder cancer patients
Surgical Oncologist, Division of Urology at Trillium Health Partners Bladder cancer is the most expensive cancer to treat due to high recurrence rates and a need for lifelong disease surveillance. Current monitoring strategies are costly, patient unfriendly, and lack supporting evidence. Existing predictive models perform poorly and do not capture changes in patients’ cancer course…
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