The Empirical Evidence for the Telemedicine Intervention in Diabetes Management

Bashshur, Rashid L.; Shannon, Gary W.; Smith, Brian R.; Woodward, Maria A.

doi:10.1089/tmj.2015.0029

Cited by 81 publications

(56 citation statements)

References 119 publications

(182 reference statements)

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“…There is limited evidence that fewer outpatient visits may be needed for women with GDM using telemedicine systems [38]. We did not identify any formal health economic evaluations of telemedicine systems for gestational diabetes.…”

Section: Discussionmentioning

confidence: 99%

“…With 1 in 3 people on the planet predicted to own a mobile phone by the end of 2016 [9], there is great enthusiasm among both patients and health care professionals to harness digital technologies to improve human health. In line with this, the number and sophistication of apps developed specifically for women with diabetes in pregnancy has increased [10,11]. Digital technologies in this patient group have most commonly been used to record and transmit blood glucose readings to the clinical care team between outpatient visits.…”

Section: Introductionmentioning

confidence: 99%

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Telemedicine Technologies for Diabetes in Pregnancy: A Systematic Review and Meta-Analysis

et al. 2016

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BackgroundDiabetes in pregnancy is a global problem. Technological innovations present exciting opportunities for novel approaches to improve clinical care delivery for gestational and other forms of diabetes in pregnancy.ObjectiveTo perform an updated and comprehensive systematic review and meta-analysis of the literature to determine whether telemedicine solutions offer any advantages compared with the standard care for women with diabetes in pregnancy.MethodsThe review was developed using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework. Randomized controlled trials (RCT) in women with diabetes in pregnancy that compared telemedicine blood glucose monitoring with the standard care were identified. Searches were performed in SCOPUS and PubMed, limited to English language publications between January 2000 and January 2016. Trials that met the eligibility criteria were scored for risk of bias using the Cochrane Collaborations Risk of Bias Tool. A meta-analysis was performed using Review Manager software version 5.3 (Nordic Cochrane Centre, Cochrane Collaboration).ResultsA total of 7 trials were identified. Meta-analysis demonstrated a modest but statistically significant improvement in HbA1c associated with the use of a telemedicine technology. The mean HbA1c of women using telemedicine was 5.33% (SD 0.70) compared with 5.45% (SD 0.58) in the standard care group, representing a mean difference of −0.12% (95% CI −0.23% to −0.02%). When this comparison was limited to women with gestational diabetes mellitus (GDM) only, the mean HbA1c of women using telemedicine was 5.22% (SD 0.70) compared with 5.37% (SD 0.61) in the standard care group, mean difference −0.14% (95% CI −0.25% to −0.04%). There were no differences in other maternal and neonatal outcomes reported.ConclusionsThere is currently insufficient evidence that telemedicine technology is superior to standard care for women with diabetes in pregnancy; however, there was no evidence of harm. No trials were identified that assessed patient satisfaction or cost of care delivery, and it may be in these areas where these technologies may be found most valuable.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Telemedicine Technologies for Diabetes in Pregnancy: A Systematic Review and Meta-Analysis

et al. 2016

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“…Telemedicine systems, widely applied to diabetes care, have shown that they are able to improve clinical outcomes and selfcare by registering electronically diabetes monitoring data [9][10][11], and are especially useful in rural areas where, due to a lack of health care professionals, patients have to travel tens of kilometres to reach the specialized assistance [12]. 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].…”

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

116

143

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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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“…23 Telemedicine for DR screening has the potential to grow rapidly as a result of these successes. [24][25][26] Despite the promise of telemedicine for DR, we do not understand patients' perceptions of telemedicine for eye disease management. Patient satisfaction with DR telemedicine varies widely from 35% to 99%.…”

Section: Introductionmentioning

confidence: 99%

Patient Attitudes Toward Telemedicine for Diabetic Retinopathy

Valikodath

Leveque

Wang

et al. 2017

Telemedicine and e-Health

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It is critical to understand the attitudes of people with DM where telemedicine shows promise for disease management and end-organ damage prevention. Patients' attitudes are influenced by their health and perceptions, but not by their demographics. Receptive patients focus on convenience, whereas unreceptive patients strongly value their patient-physician relationships or have long-standing DM. Telemedicine monitoring should be designed for people who are in need and receptive to telemedicine.

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The Empirical Evidence for the Telemedicine Intervention in Diabetes Management

Cited by 81 publications

References 119 publications

Telemedicine Technologies for Diabetes in Pregnancy: A Systematic Review and Meta-Analysis

Telemedicine Technologies for Diabetes in Pregnancy: A Systematic Review and Meta-Analysis

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

Patient Attitudes Toward Telemedicine for Diabetic Retinopathy

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