The SARS-CoV2 pandemic has impacted risk management globally. Blockchain has been increasingly applied to healthcare management, as a strategic tool to strengthen operative protocols and to create the proper basis for an efficient and effective evidence-based decisional process. We aim to validate blockchain in healthcare, and to suggest a trace-route for a COVID19-safe clinical practice. The use of blockchain in combination with artificial intelligence systems allows the creation of a generalizable predictive system that could contribute to the containment of pandemic risk on national territory. A SWOT analysis of the adoption of a blockchain-based prediction model in healthcare and SARS-CoV-2 infection has been carried out to underline opportunities and limits to its adoption. Blockchain could play a strategic role in future digital healthcare: specifically, it may work to improve COVID19-safe clinical practice. The main concepts, and particularly those related to clinical workflow, obtainable from different blockchain-based models have been reported here and critically discussed.

EAM using a specific parameter setting proved highly effective at identifying the MDAI in MAT, even in patients with previous surgery and multiple re-entrant loops. Ablation of the MDAI yielded acute arrhythmia suppression with low rate of recurrence during follow-up.

RM of patients with cardiac implantable electronic devices (CIEDs) is cost saving from the perspectives of the HCS, patients, and caregivers. Introducing appropriate reimbursements will make RM sustainable even for the provider, i.e. the hospitals which provide the service and encourage widespread adoption of RM.

Intraprocedural measuring of paced RV-LV interlead electrical delay obtained during RV and LV pacing predicts mid-term CRT response.

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