Context:

Our healthcare client wanted to forecast future HbA1c levels using patient data and identify individuals at risk of poor glycemic control, enabling early intervention.

Resolution:

We provided a data science team to solve this; a smart system was developed that used a predictive model for HbA1c levels—a key marker of long-term blood glucose control. They created time-lag variables for HbA1c trends, encoded medication adherence and physical activity levels. Finally, the team integrated into a clinical dashboard to provide risk scores and personalized alerts for clinicians.

Result:

The model forecast accuracy for 80% of patients, it enabled proactive medication adjustments and lifestyle counselling and most important it reduced follow-up delays and improved patient engagement.