Developing an AI-decision support tool for ED visits in oncology to augment compassion-centered care
Clinician Scientist, Sunnybrook Research Institute Many cancer patients access the emergency department (ED). However, 60% of ED visits are preventable with limited tools to stratify risk groups and personalize care plans. Ultimately, this would avoid anxiety and distress. Here, William proposes to develop an artificial intelligence (AI)-guided decision support tool to improve management and follow-up…
Read MoreRecognizing warnings in adults at risk of life-threatening diagnoses using AI
Researcher, Unity Health Toronto & Clinician Scientist, Sinai Health Delays, and often omissions, of life-threatening diagnoses are common, especially with hospitalized patients. To render the correct diagnosis, healthcare providers need sufficient uninterrupted time to review a patient’s medical record and speak with the patient directly, but the chaotic inpatient environment presents many challenges: interruptions, task-switching,…
Read MoreIdentifying patient‐validated compassion and equity concepts in community care clinical notes through natural language processing
Assistant Professor, The University of British Columbia – Okanagan In Charlene’s project, she aims to develop and test compassion and equity-grounded natural language processing (NLP) application to leverage otherwise unused community nursing clinical notes. She will develop a dictionary that merges understandings of equity and compassionate care from the perspectives of community nurses and patients…
Read MoreOptimizing interactions between humans and artificial intelligence when administering remote cognitive assessments
Minnie’s team is collaborating with people with disabilities to pilot remote assessments to evaluate how the design of human-computer-environment interactions can be optimized for compassionate care.
Read MoreExploring the impact of genomics and AI on health equity and compassionate care in oncology
Ben is examining potential disparities arising from the application of genomics and machine learning to predict clinical outcomes.
Read MoreDeveloping an AI enabled program that codes parent-infant interaction quality
Lyndsay will develop an AI enabled program to rapidly code interactions so clinicians can provide feedback aimed at improving parent-child interactions.
Read MoreEnhancing paediatric pain research and compassionate pain care through the integration of AI methods
Jennifer’s work focuses on enhancing youth engagement in digital pain self-management solutions.
Read MoreUsing AI to reduce falls in long-term care
Building up on several years of transdisciplinary research, Babak’s work will clinically validate an intelligent fall risk assessment system.
Read MoreEvaluating AI prediction tools in enhancing compassionate care in hospital
Amol is implementing and evaluating an AI prediction tool to understand if it enhances compassionate care by enabling clinicians to treat patients earlier and improve communication.
Read MoreCreating an AI tool to predict survival of long-term care residents with Covid-19 infection
Martin is creating a machine-learning based mortality risk prediction tool for COVID-19 positive residents in all Ontario LTC homes.
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