This can empower the patient, reduce delays in the healthcare system, and potentially off-load some of the health system burden
Whilst the professor points out that ML will never replace the clinician, it will provide better tools for personalised medicine – an evolving trend for over a decade. ‘MRI and CT scans have ML tools to help the clinician interpret them, or that flag potential abnormalities,’ he pointed out. ‘Even ECG machines have forms of ML to provide the automated diagnoses. This will enable the clinician to make better management decisions and be more efficient.’
Patients also win where ML automates tools, e.g. focused analysis of continuous large data from an Apple watch, activity monitors or smart devices in the bathroom, elsewhere at home or even in a care home. ‘This can empower the patient, reduce delays in the healthcare system, and potentially off-load some of the health system burden,’ Narayan pointed out. ML can provide simple automated tips; patients can benefit from better and safer drug prescribing and, he believes, many other benefits have yet to be realised.
Other presentations in the session will examine digital heart rate analysis and question whether we can predict sudden cardiac death as well as prevent sudden cardiac death in heart failure.
*ESC 2021, Sudden cardiac death: can we move from prediction to prevention? August 27, 8.30am (CEST).
Sanjiv Narayan is Professor of Medicine at Stanford University, Director of the Computational Arrhythmia Laboratory, and Co-Director of the Stanford Arrhythmia Center. He oversees and directs several NIH-funded studies to develop machine learning and computer models for arrhythmias, to bring them directly to the care of patients.
Source: Healthcare in Europe