The approach involves making comprehensive use of data generated during treatment – that is usually fragmented into separate systems - and complementing it by continuing to monitor how the patient’s heart is functioning after discharge from hospital. The mass of data thus gathered is analysed using AI and machine-learning methods, which have been developed using data from former heart attack patients
What the study means for patients in practice is that a small electrocardiogram recorder is attached to their chest when they leave hospital. It can be linked to the internet for monitoring purposes.
“The results can be used to enhance patient safety and to target the monitoring more accurately at the patients who would benefit the most from it. In the Tampere University Heart Hospital area alone, up to a thousand patients a year can benefit from the study,” said Kari Antila, Senior Scientist at the Technical Research Centre of Finland (VTT), where the system was developed.
High risk patients are identified during treatment. “In this way, the sensitivity and specificity of the method can be maximised in those situations where the projected probability is high,” said Niku Oksala, associate professor of surgery, who is leading the project.
“Combining the existing fragmented information into meaningful refined data that supports decision-making is probably the most significant measure for improving cost-efficiency in the context of the current debate about Finland’s social and healthcare reform. In addition, it is an important step aimed at enhancing patient safety,” said Jussi Hernesniemi, cardiology specialist and lead researcher on the project.
The MADDEC (Mass Data in the Detection and Prevention of Serious Adverse Events Leading to Complications in Cardiovascular diseases) clinical study was launched in May and the research was presented at the international European Medical and Biological Engineering conference in Tampere last week.
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