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Since the last decade, most of our daily activities have become digital. Digital health takes into account the ever-increasing synergy between advanced medical technologies, innovation, and digital communication. Thanks to machine learning, we are not limited anymore to a descriptive analysis of the data, as we can obtain greater value by identifying and predicting patterns resulting from inductive reasoning. Machine learning software programs that disclose the reasoning behind a prediction allow for “what-if” models by which it is possible to understand if and how, by changing certain factors, one may improve the outcomes, thereby identifying the optimal behavior. Currently, diabetes care is facing several challenges: the decreasing number of diabetologists, the increasing number of patients, the reduced time allowed for medical visits, the growing complexity of the disease both from the standpoints of clinical and patient care, the difficulty of achieving the relevant clinical targets, the growing burden of disease management for both the health care professional and the patient, and the health care accessibility and sustainability. In this context, new digital technologies and the use of artificial intelligence are certainly a great opportunity. Herein, we report the results of a careful analysis of the current literature and represent the vision of the Italian Association of Medical Diabetologists (AMD) on this controversial topic that, if well used, may be the key for a great scientific innovation. AMD believes that the use of artificial intelligence will enable the conversion of data (descriptive) into knowledge of the factors that “affect” the behavior and correlations (predictive), thereby identifying the key aspects that may establish an improvement of the expected results (prescriptive). Artificial intelligence can therefore become a tool of great technical support to help diabetologists become fully responsible of the individual patient, thereby assuring customized and precise medicine. This, in turn, will allow for comprehensive therapies to be built in accordance with the evidence criteria that should always be the ground for any therapeutic choice.

Background: International guidelines for diabetes care emphasize the urgency of promptly achieving and sustaining adequate glycemic control to reduce the occurrence of micro/macrovascular complications in patients with type 2 diabetes mellitus (T2DM). However, data from the Italian Association of Medical Diabetologists (AMD) Annals reveal that only 47% of T2DM patients reach appropriate glycemic targets, with approximately 30% relying on insulin therapy, either solely or in combination. This artificial intelligence analysis seeks to assess the potential impact of timely insulin initiation in all eligible patients via a "what-if" scenario simulation, leveraging real-world data. Methods: This retrospective cohort study utilized the AMD Annals database, comprising 1,186,247 T2DM patients from 2005 to 2019. Employing the Logic Learning Machine (LLM), we simulated timely insulin use for all eligible patients, estimating its effect on glycemic control after 12 months within a cohort of 85,239 patients. Of these, 20,015 were employed for the machine learning phase and 65,224 for simulation. Results: Within the simulated scenario, the introduction of appropriate insulin therapy led to a noteworthy projected 17% increase in patients meeting the metabolic target after 12 months from therapy initiation within the cohort of 65,224 individuals. The LLM's projection envisages 32,851 potential patients achieving the target (hemoglobin glycated < 7.5%) after 12 months, compared to 21,453 patients observed in real-world cases. The receiver operating characteristic (ROC) curve analysis for this model demonstrated modest performance, with an area under the curve (AUC) value of 70.4%. Conclusions: This study reaffirms the significance of combatting therapeutic inertia in managing T2DM patients. Early insulinization, when clinically appropriate, markedly enhances patients' metabolic goals at the 12-month follow-up. [ABSTRACT FROM AUTHOR]

Background and Aims: Hypoglycemia represents a relevant burden in people with diabetes. Consequences of hypoglycemia/fear of hypoglycemia on quality of life (QoL) and behaviors of patients with T1DM and T2DM were assessed.Methods and Results: HYPOS-1 was an observational retrospective study. Fear of hypoglycemia (Fear of Hypoglycemia Questionnaire, FHQ), general health status (visual analog scale of EuroQol questionnaire, EQ5D-VAS) psychological well-being (WHO-5 well being index, WHO-5), diabetes related distress (Problem Areas in Diabetes 5, PAID-5), and corrective/preventive behaviors following hypoglycemia were compared between people with and without previous experience of severe and symptomatic hypoglycemia and by tertiles of FHQ scores. A multivariate analysis was performed to identify factors associated with the likelihood of being in the third tertile of FHQ score. Overall, 2229 patients were involved. Severe hypoglycemia had statistically significant and clinically relevant (measured as effect sizes) negative impact on EQ5D-VAS, WHO-5, PAID-5, and FHQ both in T1DM and T2DM. In T2DM, symptomatic episodes had similar impact of severe hypoglycemia. Moving from the first to the third FHQ tertile, lower scores of EQ-5D VAS and WHO-5, and higher levels of PAID-5 were found. Patients in the third tertile performed more frequently corrective/preventive actions that negatively impact on metabolic control. Previous hypoglycemia, insulin treatment, female gender, age, and school education were the independent factors associated with increased likelihood to be in the third tertile.Conclusion: Not only severe but also symptomatic hypoglycemia negatively affect patient QoL, especially in T2DM. Addressing fear of hypoglycemia should be a goal of diabetes education. [ABSTRACT FROM AUTHOR]

Background: Self-monitoring of blood glucose (SMBG) is a proven tool to improve glycemic control, even if it might increase direct costs for diabetes management. In Italy, the purchase, prescription rules and responsibilities, and distribution of testing strips per type of patient are managed differently in each of the 20 Italian regions. The Italian scientific societies for diabetes (Società Italiana Diabetologia [SID] and Associazione Medici Diabetologi [AMD]) have issued validated guidelines for SMBG, but not all regions apply them. We investigated whether following SID-AMD guidelines would help decreasing SMBG and diabetes healthcare costs in Italy., Materials and Methods: We compared the regions applying and not applying SMBG guidelines for the mean number of testing strips used, number of hospitalizations (with the principal diagnosis of diabetes, excluding diabetes complications), and duration of hospitalization, as indirect measures of SMBG cost., Results: Regions applying the guidelines recorded higher SMBG testing strip utilization than regions not applying guidelines, but they recorded fewer hospitalizations for diabetes (36.2 ± 11.3 vs. 79.9 ± 27.8 hospitalizations per 100,000 inhabitants, P<0.002) and fewer days in the hospital (363 ± 106 vs. 685 ± 194 days of hospitalization for diabetes per 100,000 inhabitants, P<0.002)., Conclusions: Our data suggest that application of guidelines for SMBG prescription and a strict cooperation between health providers and regional health economic deciders were associated with greater utilization of SMBG testing strips. They were also associated with significantly reduced number of hospitalizations and reduced overall duration of hospitalization for patients with diabetes, potentially saving healthcare costs.