Using Real-Time, Partially Automated Interactive System to Interpret Patient’s Data; Helping The Patient To Achieve Diabetic Self-Management: A Rapid Literature Review

Abstract

Background: Diabetes is an endocrine chronic condition with a high prevalence rate among the population that needs a complex management process. However, many advanced health care technologies were evolving to help patients achieve their centered care and self-management using real-time proactive techniques through interactive systems to detect early complications and prevent them. The purpose of the current review is to assess the findings of literature reviews of the main interventions that used a real-time partially automated interactive systems to interpret patient’s data including biological information, exercise, and dietary content calculated from a message sent by the patient and respond with actionable findings, helping patients to achieve diabetes self-management. Methods: PubMed\ MEDLINE, CINAHL, Google Scholar, and Research Gate were used to search the literature for studies published between the periods 2015 to 2021. Results: Eleven articles were included in the literature review. The retrieved studies approved the significant effect of achieving diabetic self-management by utilizing Information Technology (IT) with the Natural Language Processing (NLP) methods by sending a real-time, partially automated interactive system to interpret patient's biological information, physical activity, and dietary content calculated using a message sent by patients to achieve their self-management. Conclusion: Improved blood glucose levels, glycemic control, better readings of blood pressure, and lifestyle improvement including dietary intake and physical activity were offered using continuous real-time messages to improve their health outcomes.