A Telemedicine system based on Internet and short message service as a new approach in the follow-up of patients with gestational diabetes

Pérez-Ferre, Natalia; Galindo, Mercedes; Fernández, Ma Dolores; Velasco, Victoria; Cruz, Ma José de la; Martín, Patricia; Valle, Laura del; Calle-Pascual, Alfonso

doi:10.1016/j.diabres.2009.12.002

Cited by 75 publications

(74 citation statements)

References 10 publications

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“…However, it will require more personal and material sources, which cannot always be assumed in clinical practice. The introduction of alternative ways of follow-up in this postpartum period, such as telemedicine, has been demonstrated to be feasible in the followup of GDM [30] and may achieve a reduction in the number of face-to-face visits, reducing the costs and time for patients and health professionals.…”

Section: Tablementioning

confidence: 99%

Diabetes mellitus and abnormal glucose tolerance development after gestational diabetes: A three-year, prospective, randomized, clinical-based, Mediterranean lifestyle interventional study with parallel groups

et al. 2015

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Section: Tablementioning

confidence: 99%

Diabetes mellitus and abnormal glucose tolerance development after gestational diabetes: A three-year, prospective, randomized, clinical-based, Mediterranean lifestyle interventional study with parallel groups

et al. 2015

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“…management [4][5][6]. Several studies, including our own, have shown that adopting a u-healthcare system in diabetes management helps patients improve their glucose control and reduce side effects such as hypoglycemia or weight gain [7,8].…”

Section: Introductionmentioning

confidence: 99%

Multifactorial intervention in diabetes care using real-time monitoring and tailored feedback in type 2 diabetes

et al. 2015

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“…In GDM, telemedicine has proven to reduce face-to-face visits as it allows remote patient monitoring so visits are only scheduled if a change in the therapy is required [13,14]. A recent study [15], reported that telematics reviews require less time than face-to-face visits (4.6 min vs. 11.7min).…”

Section: Introductionmentioning

confidence: 99%

A web-based clinical decision support system for gestational diabetes: Automatic diet prescription and detection of insulin needs

Caballero-Ruiz

García-Sáez

Rigla

et al. 2017

International Journal of Medical Informatics

108

140

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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...

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A Telemedicine system based on Internet and short message service as a new approach in the follow-up of patients with gestational diabetes

Cited by 75 publications

References 10 publications

Diabetes mellitus and abnormal glucose tolerance development after gestational diabetes: A three-year, prospective, randomized, clinical-based, Mediterranean lifestyle interventional study with parallel groups

Diabetes mellitus and abnormal glucose tolerance development after gestational diabetes: A three-year, prospective, randomized, clinical-based, Mediterranean lifestyle interventional study with parallel groups

Multifactorial intervention in diabetes care using real-time monitoring and tailored feedback in type 2 diabetes

A web-based clinical decision support system for gestational diabetes: Automatic diet prescription and detection of insulin needs

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