Diabetic patients in the Community of Madrid who use glucose sensors will have remote monitoring of your state of health, without the need to travel in person for a consultation. In addition to measuring blood sugar, other constants such as weight or heart rate will be monitored to prevent complications or derived diseases. The system, which will be integrated in the pilot phase, will allow the patient and doctor to see the data collected in real time. Additionally, these will be recorded in the medical history. This model will gradually be used for other pathologies such as hypertension or chronic obstructive disease (COPD).
Around 38,000 Madrid patients with type 1 and 2 diabetes They will benefit from this continuous monitoring system. Until now, users of glucose sensors could view their health data from an app. However, this information was not automatically included in their medical records, preventing remote monitoring. With the introduction of IoT (Internet of Things), information from devices will be automatically sent to the servers of the Madrid Health Service (Sermas). In this way, “the data will be recorded every few minutes and will be saved in an analysis system that detects risk patterns, such as sudden drops, high glycemic variability or out-of-range moments”, explains the Department of Digitalization.
Since March, the application of this technology will make it possible to “establish digitalized care plans, designed by the health professional, adapted to the characteristics and needs”. Thus, depending on the needs of the patient, as well as blood sugar levels, Other constants will also be monitored such as weight, abdominal circumference, blood pressure, heart rate, hemoglobin A1c or lipid profile.
All this information that will be collected can be consulted in real time by the doctor and the patient. This will allow doctors to remotely analyze the patient’s progress and prevent risky situations. Expanding the set of variables monitored at home and integrating the data into the clinical history will make it possible to eliminate periodic visits that had to be scheduled with the specialist. “With this program will avoid traveling to the consultation to follow up”, stand out from the Community of Madrid. Additionally, alerts can be scheduled to report a change in any of the variables, such as weight gain or blood sugar spikes. To implement this model, a direct communication channel must also be activated between patients and the healthcare team.
“This pilot program will change the lives of thousands of Madrid residents with diabetes who currently require in-person follow-up at health centers in the region and who, through continuous monitoring, will prevent episodes of hypoglycemia and even future pathologies derived from their situation”, indicates the Minister of Digitalization, Miguel López-Valverde. The measure is part of the set of actions deployed in the health sector, such as the automation of processes, the introduction of artificial intelligence (AI) as an assistant or the deployment of tools to improve the home hospitalization service.
In addition to allowing this continuous control from home, this system will allow the detection of errors in the operation of glucose sensors. The program itself will send a warning in case of repeated failures so that the user can replace it as soon as possible and monitoring is not interrupted. The system that will be extended by the Community of Madrid will also be applied to other chronic patients with pathologies such as COPD, high blood pressure or heart failure.
Alongside testing this model, Ramón y Cajal plans to launch another pilot program to avoid complications in heart patients, such as heart attack or heart failure. By applying AI, possible complications can be predicted through information collected through teansiometers, pulse oximeter, scales, sleep apnea monitor or smart watches with the ability to perform electrocardiograms. These devices will be provided to the patient so that they can collect measurements periodically. So, for example, when the system detects drastic weight gain in a short time, it can deduce that this is due to possible water retention, a premature symptom of certain health problems.
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