Medical treatment decisions are based on randomised clinical trials that rely on average group-level measurements. To determine the most suitable form of therapy that meets patients’ wishes and expectations with respect to their lifestyle and disease progression, physicians need to take patient characteristics into account. Advancements in the area of Artificial Intelligence (AI), Machine Learning (ML) and the establishment of national databases over the last decades now enable such personalised medicine approaches.
We combine our industry knowledge in pharma and healthcare, brand independence, and a unique set of data analytics capabilities to develop personalised healthcare solutions with prognostic and predictive accuracy for patients with neurological disorders.
Personalised healthcare solutions that use AI algorithms and are based on the authentic data of thousands of patients make choosing a suitable treatment for physicians both safer and more transparent.
In a project with NeuroTransData (NTD), a network of neurologists and psychiatrists in the area of multiple sclerosis (MS), we co-designed the first predictive healthcare solution for MS patients. PHREND offers both, physicians and patients an effective comparison of different MS therapies. Powered by an algorithm that comprises the data of different forms of therapy of 25’000 patients over the course of more than ten years, PHREND gives patients and doctors a second opinion on what drugs and therapies are likely to be most effective and a match with a patient’s personal characteristics and needs.
Philip van Hövell
Health Analytics Leader, Data and Analytics, PwC Switzerland
Tel: +41 58 792 10 76
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