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

Caballero Ruiz, Estefanía and García Sáez, Gema and Rigla Cros, Mercedes and Villaplana, María and Pons, Belén and Hernando Pérez, Maria Elena (2017). A web-based clinical decision support system for gestational diabetes: Automatic diet prescription and detection of insulin needs. "International Journal of Medical Informatics" (n. 102); pp. 35-49. ISSN 1386-5056. https://doi.org/10.1016/j.ijmedinf.2017.02.014.

Description

Title: A web-based clinical decision support system for gestational diabetes: Automatic diet prescription and detection of insulin needs
Author/s:
  • Caballero Ruiz, Estefanía
  • García Sáez, Gema
  • Rigla Cros, Mercedes
  • Villaplana, María
  • Pons, Belén
  • Hernando Pérez, Maria Elena
Item Type: Article
Título de Revista/Publicación: International Journal of Medical Informatics
Date: 2017
ISSN: 1386-5056
Subjects:
Freetext Keywords: Computer assisted therapy, Personalized decision support,E-health, Gestational diabetes, Telemedicine
Faculty: E.T.S.I. Telecomunicación (UPM)
Department: Tecnología Fotónica y Bioingeniería
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

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 of 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 “Parc 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 good metabolic control. Additionally, Sinedie saves patients unnecessary displacements which contributes to medical centres’ waiting list reduction

Funding Projects

TypeCodeAcronymLeaderTitle
Government of SpainPII0/01125UnspecifiedUnspecifiedUnspecified
Government of SpainPI10/01150UnspecifiedUnspecifiedUnspecified

More information

Item ID: 49767
DC Identifier: http://oa.upm.es/49767/
OAI Identifier: oai:oa.upm.es:49767
DOI: 10.1016/j.ijmedinf.2017.02.014
Official URL: https://www.sciencedirect.com/science/article/pii/S1386505617300539?via%3Dihub
Deposited by: Memoria Investigacion
Deposited on: 20 Mar 2018 17:36
Last Modified: 19 Mar 2019 15:23
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