Estefanía Caballero-Ruiz1, Gema García-Sáez2, Mercedes Rigla3, María Villaplana4, Belen Pons5, M Elena Hernando6. 1. Bioengineering and Telemedicine Group, Centro de Tecnología Biomédica, ETSI de Telecomunicación, Universidad Politécnica de Madrid, Avd. Complutense n°30, 28040, Madrid, Spain. Electronic address: ecaballero@gbt.tfo.upm.es. 2. Bioengineering and Telemedicine Group, Centro de Tecnología Biomédica, ETSI de Telecomunicación, Universidad Politécnica de Madrid, Avd. Complutense n°30, 28040, Madrid, Spain. Electronic address: ggarcia@gbt.tfo.upm.es. 3. Endocrinology and Nutrition Department, Parc Tauli University Hospital, Institut Universitari Parc Taulí - Universitat Autònoma de Barcelona, Parc Taulí 1, 08208 Sabadell, Spain. Electronic address: mrigla@tauli.cat. 4. Endocrinology and Nutrition Department, Parc Tauli University Hospital, Institut Universitari Parc Taulí - Universitat Autònoma de Barcelona, Parc Taulí 1, 08208 Sabadell, Spain. Electronic address: mvillaplana@tauli.cat. 5. Endocrinology and Nutrition Department, Parc Tauli University Hospital, Institut Universitari Parc Taulí - Universitat Autònoma de Barcelona, Parc Taulí 1, 08208 Sabadell, Spain. Electronic address: bpons@tauli.cat. 6. Bioengineering and Telemedicine Group, Centro de Tecnología Biomédica, ETSI de Telecomunicación, Universidad Politécnica de Madrid, Avd. Complutense n°30, 28040, Madrid, Spain; CIBER-BBN: Networking Research Centre for Bioengineering, Biomaterials and Nanomedicine, Madrid, Spain. Electronic address: elena@gbt.tfo.upm.es.
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 3.477days. 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.
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 3.477days. 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.
Authors: Kaveh G Shojania; Alison Jennings; Alain Mayhew; Craig R Ramsay; Martin P Eccles; Jeremy Grimshaw Journal: Cochrane Database Syst Rev Date: 2009-07-08
Authors: Trisha Shang; Jennifer Y Zhang; B Wayne Bequette; Jennifer K Raymond; Gerard Coté; Jennifer L Sherr; Jessica Castle; John Pickup; Yarmela Pavlovic; Juan Espinoza; Laurel H Messer; Tim Heise; Carlos E Mendez; Sarah Kim; Barry H Ginsberg; Umesh Masharani; Rodolfo J Galindo; David C Klonoff Journal: J Diabetes Sci Technol Date: 2021-07