Summary table
What was already known on the topic?
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Little research on the effects of a clinical
Diabetes prevalence is increasing all over the world due to sociocultural changes. It is becoming a public health problem which is originating an increment in the demand for health services [1]. The most common type of diabetes is type 2 (T2DM), whose risk factors are a poor diet, physical inactivity, advanced age or high blood glucose (BG) during pregnancy, affecting both mother and child [2]. The incidence of Gestational diabetes (GDM) is also increasing, as women tend to delay childbearing to older ages, and if new diagnosis criteria, that has recently proven to be cost-effective [3] are adopted, the prevalence could be doubled. Besides causing perinatal complications such as foetal macrosomia, shoulder dystocia or caesarean section [4], women affected by GDM have a 7-fold increased risk of developing T2DM compared with those who had a normoglycaemic pregnancy [5]. Clinical guidelines for GDM [6], [7], [8] establish that pregnant women with GDM should register self-monitoring data (glycaemia, diet, ketonuria) and attend periodic visits to the medical centre every 1 or 2 weeks. It is recommended that patients measure their BG levels at least four times a day: before breakfast (breakfast preprandial) and after the three main meals (breakfast postprandial, lunch postprandial and dinner postprandial). Patients also have to monitor their fasting urine ketones every day using urine strips. During medical visits, clinicians assess if a treatment adjustment is needed based on the patients’ monitoring data.
Telemedicine systems, widely applied to diabetes care, have shown that they are able to improve clinical outcomes and self-care 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]. A recent study [15], reported that telematics reviews require less time than face-to-face visits (4.6 min vs. 11.7 min). 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.
Limitations and research gaps identified in telemedicine systems for diabetes include usability, real-time feedback, and decision support capabilities [21]. Although telemedicine saves the patients unnecessary displacements and improves the access to specialized assistance, it might raise the clinicians’ workload to unacceptable levels, as it generates a greater amount of patient data that needs to be evaluated. The use of decision support tools (DSTs) integrated in telemedicine systems, are pointed out as a feasible solution to limited human resources in order to analyse the information generated [22]. Expert systems are computer systems designed to imitate the decision-making ability of a person who has expert knowledge and experience and they usually incorporate a knowledge base containing accumulated experience and an inference or rules engine [23]. An increasing number of expert systems has been proposed to manage diabetes care and to build DSTs, including diabetes diagnosis, therapy adjustment and support to patient self-management [24], [25], [26], [27]. The complexity of diabetes care which is affected by multiple variables and the lack of a gold standard affect the formalization of knowledge when building expert systems in diabetes. DSTs are able to prevent clinicians from undertaking the time consuming task of row data assessment helping to automatically inspect self-monitoring data. Clinicians can interpret the information obtained by the automatic analysis of DSTs, such as recognized glycaemic patterns or weekly statistics, to make a more accurate high level evaluation. DSTs based on expert systems can also suggest treatment changes using protocol-based reasoning [28], automating some of them and asking for medical verification only when a more thorough decision needs to be taken. The validation of advice generated by expert systems and DSTs is a time-consuming task which requires clinical co-operation.
Clinical decision support systems (CDSS) contain DSTs to assist in decision-making tasks. CDSS have been developed in different clinical domains to generate several modes of decision support, including alerts when critical values are detected, reminders and advice for therapy prescription [29]. CDSS can help to optimize the clinicians’ time indicating those patients which are more critical and therefore need a deeper examination. CDSS have shown to improve the practitioners’ performance, but the effects on patient outcomes in acute care have been inconsistent [30]. In GDM, the use of CDSS has been very limited. The DIABNET system was designed to be used by physicians during face-to-face visits and proposed qualitative diet modifications and quantitative changes in insulin therapy based on the offline analysis of self-monitoring data [31] and therefore did not aim to reduce them. The MobiGuide system, which is based on computerized clinical guidelines, proposes personalized decision support for GDM patients and their caregivers and is adapted to a mobile environment [32].
This paper presents Sinedie (Smart and educational system for gestational diabetes), a telemedicine and educational health platform for GDM care enhanced by decision support capabilities. The following sections describe the system functionalities and design and present the evaluation results related to the usage of the system during a clinical trial at “Parc Tauli University Hospital” (PTUH) in Spain.
Sinedie is a web based CDSS that aims to provide a safe and effective platform to manage GDM patients’ care. The main objective is to enhance healthcare processes, by improving the access to specialized healthcare assistance, reducing face-to-face visits to prevent patients from unnecessary displacements and optimizing the clinicians’ time by reducing the clinical evaluation time required per patient.
We uploaded to Sinedie the monitoring data from 25 patients who were followed up at the hospital (since diagnostic until either insulinization or delivery) during an average period of 63.761 ± 37.073 (median = 69.000; percentile5 = 17.600; percentile25 = 29.000; percentile75 = 87.000; percentile95 = 125.000) days per patient. The data set contains a total of 6025 BG measurements, 124 ketonuria values, and 226 diet non-compliance. Patients attended a total of 75 face-to-face visits where physicians
Our first design goal, of face-to-face visits reduction (88.556%), was successfully achieved as patients treated with Sinedie only have to go to consultation when they are required by the physician to evaluate a therapy adjustment (mainly due to the need to start insulin). The average duration per visit did not increase with the use of the system (6.752 ± 3.319 min in conventional care vs. 15.000 ± 0.000 with Sinedie), even though patients in the intervention group attended to less visits than
The use of Sinedie reduced face-to-face visits as well as the time clinicians devote to patients’ evaluation, which enhances clinicians’ efficiency to overcome their growing workload. The system detected all cases that required a therapy adjustment, including all patients that needed an insulin therapy, obtaining no false negatives.
The CDSS presented (Sinedie) improves the access to specialized assistance, allowing patients to send their monitoring data from home, which prevents unnecessary
None of the authors of this article have competing financial interests or personal relationships with other people or organizations that could inappropriately influence their work.
E. C-R., G. G-S., M.E. H. and M. R contributed to the conception and design of the study. E. C-R., G. G-S., B. P., M. V. and M. R. acquired the data. E. C-R., G. G-S., M.E. H., M. V. and M. R analysed and interpreted the data. E. C-R., G. G-S. and M.E. H. drafted the article. All authors critically revised the manuscript for important intellectual content and approved the final version to be submitted. Summary table What was already known on the topic? Little research on the effects of a clinical
This work has been funded by the Spanish grant Sinedie (PII0/01125 and PI10/01150), co-funded by FEDER. We would like to thank the patients and professionals from the “Parc Tauli University Hospital” for their collaboration and support in this research.
After excluding titles and abstracts not meeting the inclusion criteria, we reviewed the full text of 206 studies. Of these, 35 articles with 5112 patients identified a PRO as a primary or secondary outcome and were included in the study [31–65] (shown in PRISMA flowchart Figure S1). One hundred and seventy-one studies did not include a PRO in their primary or secondary outcomes, however 27 studies included PROs as exploratory outcome (Table S1).