Revolutionizing Insulin Initiation: Integrating Digital Health and Clinical Insight in Diabetes Care

Abstract

Insulin therapy initiation in diabetes treatment is often delayed, which leads to poor blood sugar control and a higher chance of developing medical complications. Digital health technologies that use artificial intelligence through their decision-support systems show potential to decrease treatment delays while enhancing patient health outcomes. The research used mixed methods to analyze electronic medical records from 1,000 diabetes patients who received treatment at three tertiary-care hospitals while conducting a structured survey among 250 endocrinologists and diabetologists, and diabetes nurses. The AI-assisted clinical decision tool determined optimal insulin initiation timing through evaluation of HbA1c level, fasting plasma glucose, and unsatisfactory oral therapy outcomes. The researchers used SPSS version 28 for statistical analysis, which established p < 0.05 as the threshold for statistical significance. The AI decision support system implementation reduced insulin initiation time by 3.5 months, which resulted in a decrease from 9.2 months to 5.7 months (p = 0.001). The percentage of patients following their treatment plan increased from 62% to 88% while the average HbA1c level decreased from 1.4% to 2.2% during six months. The clinicians experienced better treatment initiation certainty while they could more effectively incorporate their work processes. The combination of AI decision support systems with clinician knowledge results in better insulin initiation speed, patient treatment compliance, and blood sugar control. Digital-clinical collaboration represents a promising pathway to reduce therapeutic inertia. The practice enhances patient-centered diabetes care delivery.