A data-centered approach is seen as the future of diabetes diagnosis and treatment.

Akansh Khurana filed this report in BioSpectrum:

Once prediabetes progresses to diabetes, it leads to serious complications and becomes much harder to control. Screening and awareness campaigns hence become a priority for those at risk or affected.

THB India, leading clinical intelligence company, is currently working on algorithm-based risk assessment tools that can identify those at high risk of diabetes progression. These patients, who are positive for risk factors, can then be advised and guided towards lifestyle management strategies and prevention methods through personalized engagements.

Integrating these risk assessment tools with clinical decisions support (CDS) systems enable doctors to make data-driven decisions at point of care. Doctors can also optimize the treatment by identifying in real-time which treatment options are working in favor of their patients and where the gaps are to be addressed.

For instance, doctors can identify treatment interventions that can prevent progression to diabetes, short and long-term effects/side effects of the current treatment. Doctors can also identify signs and similarities in different patient groups (similar comorbidities or similar risk factors), and construct related treatment regimens accordingly.

With more robust data, predictive analytics can be utilized for other chronic disorders as well. Implementation of big data analytics in healthcare represents a paradigm shift in how we approach healthcare and how decisions are made. It will ultimately lead to optimized care, reduced disease burden, and improved population health outcomes.