Grant: Team Grant: Embracing Diversity to Achieve Precision & Health Equity - Aging
University of Toronto
$1,996,903.00
Transforming Sepsis Care: Robust prediction and decision making leveraging the Social Determinants of Health
EF 2024-25
Résumé
Sepsis is a life-threatening condition that happens when the body's response to an infection harms its own tissues and organs. It affects millions of people worldwide each year and is a leading cause of death in hospitals, especially among vulnerable groups like the elderly, people with weakened immune systems, and those with chronic health conditions. Early detection and prompt treatment are crucial for improving survival rates, but current methods often fail to identify sepsis quickly enough, leading to delays in care.
Our project aims to improve sepsis care by developing artificial intelligence (AI) tools that help healthcare professionals identify sepsis early and select the most effective treatments. To ensure these tools work fairly for all patients, regardless of their background or experiences, we have three key goals. First, we will collect data on social determinants of health-factors like income, education, housing stability, and race-that can influence a person's risk of developing sepsis and their recovery. This information, often missing from medical records, will be gathered through patient surveys and by analyzing doctors' notes using specialized software. Second, we will create advanced AI models that combine medical data with social factors to predict the onset of sepsis earlier than current methods and assist doctors in choosing the best antibiotic treatments for each patient. Third, we will prospectively validate these AI tools in hospitals across Ontario within the GEMINI Research Network. The AI system will run silently in the background to evaluate its performance without affecting patient care. By integrating AI into sepsis management, our project has the potential to improve patient outcomes, promote health equity, enhance clinical decision-making, and reduce healthcare costs by optimizing resources.
Programme
Team Grant: Embracing Diversity to Achieve Precision & Health Equity - Aging
Bénéficiaire
Emplacement
Ontario, Canada
Type de bénéficiaire
Academia
Type
Grant
Source
CIHR Funding Decisions Database
Tel que publié
Id
525566
Name
Gopalkrishnan Rahul
Region
Ontario
Country
Canada
Orgtype
Institution Paid; Research Institution
Instname2
Population and Public Health
Theme2
Clinical
Prcname2
Team Grant :Embracing Diversity to Achieve Precision & Health Equity
Projecttitle
Transforming Sepsis Care: Robust prediction and decision making leveraging the Social Determinants of Health
Programtype2
Operating Grants
Orgnameinp2
University of Toronto
Cihramount2
$1,996,903
Cihrequipment2
$0
Programname2
Team Grant: Embracing Diversity to Achieve Precision & Health Equity - Aging
Keyworddelim
Equity In Predictive Models; Machine Learning/Artificial Intelligence In Healthcare; Sepsis; Social Determinants Of Health
Abstract
Sepsis is a life-threatening condition that happens when the body's response to an infection harms its own tissues and organs. It affects millions of people worldwide each year and is a leading cause of death in hospitals, especially among vulnerable groups like the elderly, people with weakened immune systems, and those with chronic health conditions. Early detection and prompt treatment are crucial for improving survival rates, but current methods often fail to identify sepsis quickly enough, leading to delays in care.
Our project aims to improve sepsis care by developing artificial intelligence (AI) tools that help healthcare professionals identify sepsis early and select the most effective treatments. To ensure these tools work fairly for all patients, regardless of their background or experiences, we have three key goals. First, we will collect data on social determinants of health-factors like income, education, housing stability, and race-that can influence a person's risk of developing sepsis and their recovery. This information, often missing from medical records, will be gathered through patient surveys and by analyzing doctors' notes using specialized software. Second, we will create advanced AI models that combine medical data with social factors to predict the onset of sepsis earlier than current methods and assist doctors in choosing the best antibiotic treatments for each patient. Third, we will prospectively validate these AI tools in hospitals across Ontario within the GEMINI Research Network. The AI system will run silently in the background to evaluate its performance without affecting patient care. By integrating AI into sepsis management, our project has the potential to improve patient outcomes, promote health equity, enhance clinical decision-making, and reduce healthcare costs by optimizing resources.
Competitiondate
202410
Approvedterm2
5 yrs 0 mth
Pinamesdelim
Gopalkrishnan, Rahul; Razak, Fahad; Richard, Lucie; Siddiqi, Arjumand; Verma, Amol
Approvedterm
5 ans 0 mois
Cihrcontribution2
~ $1,996,903~ $0~