During the global COVID-19 pandemic, healthcare systems across many regions faced unprecedented pressure due to rapidly increasing patient volumes, limited diagnostic resources, workforce shortages, and evolving clinical understanding of the disease.
In response to these challenges, Teknostra's R&D team conducted an exploratory research initiative focused on the development of AI-assisted clinical triage frameworks that could potentially support healthcare professionals in resource-constrained environments.
The project investigated whether established clinical scoring and diagnostic prioritization methodologies could be adapted into intelligent decision-support systems capable of assisting frontline healthcare workers in patient assessment, risk stratification, and resource allocation during large-scale public health emergencies.
Pandemic situations often create several operational challenges:
While Artificial Intelligence was widely discussed as a potential solution during the pandemic, many AI systems faced limitations due to insufficient training data, evolving disease characteristics, and lack of validated clinical datasets.
The project therefore explored an alternative approach that combined established clinical decision-making methodologies with modern data analytics and AI technologies.