Data mining is the process of selecting, exploring, and modeling large amounts of data to discover unknown patterns or relationships useful to the data analyst. This article describes applications of data mining for the analysis of blood glucose and diabetes mellitus data. The diabetes management context is particularly well suited to a data mining approach. The availability of electronic health records and monitoring facilities, including telemedicine programs, is leading to accumulating huge data sets that are accessible to physicians, practitioners, and health care decision makers. Moreover, because diabetes is a lifelong disease, even data available for an individual patient may be massive and difficult to interpret. Finally, the capability of interpreting blood glucose readings is important not only in diabetes monitoring but also when monitoring patients in intensive care units. This article describes and illustrates work that has been carried out in our institutions in two areas in which data mining has a significant potential utility to researchers and clinical practitioners: analysis of (i) blood glucose home monitoring data of diabetes mellitus patients and (ii) blood glucose monitoring data from hospitalized intensive care unit patients.
Current computerized reminder and decision support systems intended to improve diabetes care have had a limited effect on clinical outcomes. Increasing pressures on health care networks to meet standards of diabetes care have created an environment where information technology systems for diabetes management are often created under duress, appended to existing clinical systems, and poorly integrated into the existing workflow. After defining the components of diabetes disease management, the authors present an eight-step conceptual framework to guide the development of more effective diabetes information technology systems for translating clinical information into clinical action.
Diabetes has emerged as a major public health concern in developing nations. Health systems in most developing countries are yet to integrate effective prevention and control programs for diabetes into routine health care services. Given the inadequate human resources and underfunctioning health systems, we need novel and innovative approaches to combat diabetes in developing-country settings. In this regard, the tremendous advances in telecommunication technology, particularly cell phones, can be harnessed to improve diabetes care. Cell phones could serve as a tool for collecting information on surveillance, service delivery, evidence-based care, management, and supply systems pertaining to diabetes from primary care settings in addition to providing health messages as part of diabetes education. As a screening/diagnostic tool for diabetes, cell phones can aid the health workers in undertaking screening and diagnostic and follow-up care for diabetes in the community. Cell phones are also capable of acting as a vehicle for continuing medical education; a decision support system for evidence-based management; and a tool for patient education, self-management, and compliance. However, for widespread use, we need robust evaluations of cell phone applications in existing practices and appropriate interventions in diabetes.
Latinos have higher rates of diabetes and diabetes-related complications compared to non-Latinos. Clinical diabetes self-management tools that rely on innovative health information technology (HIT) may not be widely used by Latinos, particularly those that have low literacy or numeracy, low income, and/or limited English proficiency. Prior work has shown that tailored diabetes self-management educational interventions are feasible and effective in improving diabetes knowledge and physiological measures among Latinos, especially those interventions that utilize tailored coaching and navigator programs. In this article, we discuss the role of HIT for diabetes management in Latinos and describe a novel “eNavigator” role that we are developing to increase HIT adoption and thereby reduce health care disparities.