Background:The growth of diabetes prevalence is causing an increasing demand in health care services which affects the clinicians' workload as medical resources do not grow at the same rate as the diabetic population. Decision support tools can help clinicians with the inspection of monitoring data, providing a preliminary analysis to ease their interpretation and reduce the evaluation time per patient. This paper presents Sinedie, a clinical decision support system designed to manage the treatment of patients with gestational diabetes. Sinedie aims to improve access to specialized healthcare assistance, to prevent patients from unnecessary displacements, to reduce the evaluation time per patient and to avoid gestational diabetes adverse outcomes. Methods: A web-based telemedicine platform was designed to remotely evaluate patients allowing them to upload their glycaemia data at home directly from their glucose meter, as well as report other monitoring variables like ketonuria and compliance to dietary treatment. Glycaemia values, not tagged by patients, are automatically labelled with their associated meal by a classifier based on the Expectation Maximization clustering algorithm and a C4.5 decision tree learning algorithm. Two finite automata are combined to determine the patient's metabolic condition, which is analysed by a rule-based knowledge base to generate therapy adjustment recommendations. Diet recommendations are automatically prescribed and notified to the patients, whereas recommendations about insulin requirements are notified also to the physicians, who will decide if insulin needs to be prescribed. The system provides clinicians with a view where patients are prioritized according to their metabolic condition. A randomized controlled clinical trial was designed to evaluate the effectiveness and safety oí Sinedie interventions versus standard care and its impact in the professionals' workload in terms of the clinician's time required per patient; number of face-to-face visits; frequency and duration of telematics reviews; patients' compliance to self-monitoring; and patients' satisfaction. Results: Sinedie was clinically evaluated at "Pare Tauli University Hospital" in Spain during 17 months with the participation of 90 patients with gestational diabetes. Sinedie detected all situations that required a therapy adjustment and all the generated recommendations were safe. The time devoted by clinicians to patients' evaluation was reduced by 27.389% and face-to-face visits per patient were reduced by 88.556%. Patients reported to be highly satisfied with the system, considering it useful and trusting in being well controlled. There was no monitoring loss and, in average, patients measured their glycaemia 3.890 times per day and sent their monitoring data every 3.477 days. Conclusions: Sinedie generates safe advice about therapy adjustments, reduces the clinicians' workload and helps physicians to identify which patients need a more urgent or more exhaustive examination and those who present g...
MobiGuide's feasibility was demonstrated by a working prototype for the AF and GDM domains, which is usable by patients and clinicians, achieving high compliance to self-measurement recommendations, while enhancing the satisfaction of patients and care providers.
Gestational diabetes (GDM) burden has been increasing progressively over the past years. Knowing that intrauterine exposure to maternal diabetes confers high risk for macrosomia as well as for future type 2 diabetes and obesity of the offspring, health care organizations try to provide effective control in spite of the limited resources. Artificial-intelligence-augmented telemedicine has been proposed as a helpful tool to facilitate an efficient widespread medical assistance to GDM. The aim of the study we present was to test the feasibility and acceptance of a mobile decision-support system for GDM, developed in the seventh framework program MobiGuide Project, which includes computer-interpretable clinical practice guidelines, access to data from the electronic health record as well as from glucose, blood pressure, and activity sensors. The results of this pilot study with 20 patients showed that the system is feasible. Compliance of patients with blood glucose monitoring was higher than that observed in a historical group of 247 patients, similar in clinical characteristics, who had been followed up for the 3 years prior to the pilot study. A questionnaire on the use of the telemedicine system showed a high degree of acceptance.
In the past decade diabetes management has been transformed by the addition of continuous glucose monitoring and insulin pump data. More recently, a wide variety of functions and physiologic variables, such as heart rate, hours of sleep, number of steps walked and movement, have been available through wristbands or watches. New data, hydration, geolocation, and barometric pressure, among others, will be incorporated in the future. All these parameters, when analyzed, can be helpful for patients and doctors' decision support. Similar new scenarios have appeared in most medical fields, in such a way that in recent years, there has been an increased interest in the development and application of the methods of artificial intelligence (AI) to decision support and knowledge acquisition. Multidisciplinary research teams integrated by computer engineers and doctors are more and more frequent, mirroring the need of cooperation in this new topic. AI, as a science, can be defined as the ability to make computers do things that would require intelligence if done by humans. Increasingly, diabetes-related journals have been incorporating publications focused on AI tools applied to diabetes. In summary, diabetes management scenarios have suffered a deep transformation that forces diabetologists to incorporate skills from new areas. This recently needed knowledge includes AI tools, which have become part of the diabetes health care. The aim of this article is to explain in an easy and plane way the most used AI methodologies to promote the implication of health care providers-doctors and nurses-in this field.
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