In the latest months, the outbreak of SARS-CoV-2 has forced worldwide healthcare systems to rethink their organisation. In this landscape, particular attention has been devoted to discharged patients. Remote monitoring on patients’ health status is used, through dedicated web platforms and apps, to check home rehabilitation progress and, at the same time, promptly notify the arise of anomalies. Nevertheless, the variety of patients and the large volume of collected data call for models, tools and methods for data representation and exploration, in order to focus on relevant groups of patients only. Given our previous research efforts in the Big Data exploration field, we designed a Risk Monitoring Services ecosystem, devoted to support doctors (e.g., medical researchers, clinicians, analysts) in the analysis of data collected through app by: (i) identifying groups of SARS-CoV-2 discharged patients, built according to features such as sex, age, co-morbidities, prior therapies; (ii) monitoring the health status of patients, by extracting snapshots of patients’ health parameters measurements, evolving over time, and comparing them with baseline or reference values within the same patients group; (iii) promptly notifying doctors when some measurements diverge from reference values for a group of patients.
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