Impact of a Telemedicine System with Automated Reminders on Outcomes in Women with Gestational Diabetes Mellitus

Homko, Carol J.; Deeb, Larry C.; Rohrbacher, Kimberly; Mulla, Wadia; Mastrogiannis, Dimtrios; Gaughan, John; Santamore, William P.; Bové, Alfred A.

doi:10.1089/dia.2012.0010

Cited by 101 publications

(164 citation statements)

References 19 publications

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“…Some studies conclude that telemedicine, through a more accurate and frequent monitoring, improves glycaemic control and reduces GDM complications [16,17]. But there is still some controversy in the area as other studies [13,[18][19][20] obtained that, although not inferior, telemedicine does not improve glycaemia values or neonatal outcomes compared to traditional care.…”

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

120

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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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

120

143

View full text Add to dashboard Cite

show abstract

“…In 2012, a U.S.-based RCT (n = 80) demonstrated that adding interactive voice response to a telemonitoring system for pregnant women with multiple medical conditions ''increased system utilization and contact between women with GDM and their healthcare providers.'' 97 However, it had no impact on blood glucose values or infant birth weight. 97 GDM and Intermediate Outcomes (Weight, Diet, Exercise)…”

Section: Feasibility Of Gdm Telemonitoringmentioning

confidence: 96%

“…97 However, it had no impact on blood glucose values or infant birth weight. 97 GDM and Intermediate Outcomes (Weight, Diet, Exercise)…”

Section: Feasibility Of Gdm Telemonitoringmentioning

confidence: 96%

The Empirical Evidence for the Telemedicine Intervention in Diabetes Management

Bashshur

Shannon

Smith

et al. 2015

Telemedicine and e-Health

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Objective: The research presented here assesses the scientific evidence for the telemedicine intervention in the management of diabetes (telediabetes), gestational diabetes, and diabetic retinopathy. The impetus derives from the confluence of high prevalence of these diseases, increasing incidence, and rising costs, while telemedicine promises to ameliorate, if not prevent, type 2 diabetes and its complications. Materials and Methods: A purposeful review of the literature identified relevant publications from January 2005 to December 2013. These were culled to retain only credible research articles for detailed review and analysis. The search yielded approximately 17,000 articles with no date constraints. Of these, 770 appeared to be research articles within our time frame. A review of the abstracts yielded 73 articles that met the criteria for inclusion in the final analysis. Evidence is organized by research findings regarding feasibility/acceptance, intermediate outcomes (e.g., use of service, and screening compliance), and health outcomes (control of glycemic level, lipids, body weight, and physical activity.) Results: Definitions of telediabetes varied from study to study vis-à-vis diabetes subtype, setting, technology, staffing, duration, frequency, and target population. Outcome measures also varied. Despite these vagaries, sufficient evidence was obtained from a wide variety of research studies, consistently pointing to positive effects of telemonitoring and telescreening in terms of glycemic control, reduced body weight, and increased physical exercise. The major contributions point to telemedicine's potential for changing behaviors important to diabetes control and prevention, especially type 2 and gestational diabetes. Similarly, screening and monitoring for retinopathy can detect symptoms early that may be controlled or treated. Conclusions: Overall, there is strong and consistent evidence of improved glycemic control among persons with type 2 and gestational diabetes as well as effective screening and monitoring of diabetic retinopathy.

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“…In total, 5 studies described an app for pregnant women with diabetes mellitus (type 1 or 2) or GDM [36,[39][40][41][42]. All studies in this subgroup evaluated an internet-based telemedicine system to monitor and transmit results to a health care professional.…”

Section: Effectiveness Of Medical Appsmentioning

confidence: 99%

The Usability and Effectiveness of Mobile Health Technology�Based Lifestyle and Medical Intervention Apps Supporting Health Care During Pregnancy: Systematic Review

Overdijkink¹,

Velu²,

Rosman³

et al. 2017

Preprint

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Impact of a Telemedicine System with Automated Reminders on Outcomes in Women with Gestational Diabetes Mellitus

Abstract: Our enhanced telemedicine monitoring system increased system utilization and contact between women with GDM and their healthcare providers but did not impact upon pregnancy outcomes.

Cited by 101 publications

References 19 publications

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

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

The Empirical Evidence for the Telemedicine Intervention in Diabetes Management

The Usability and Effectiveness of Mobile Health Technology�Based Lifestyle and Medical Intervention Apps Supporting Health Care During Pregnancy: Systematic Review

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