Artificial intelligence supported patient self-care in chronic heart failure: a paradigm shift from reactive to predictive, preventive and personalised care

Abstract

Heart failure (HF) is one of the most complex chronic disorders with high prevalence, mainly due to the ageing population and better treatment of underlying diseases. Prevalence will continue to rise and is estimated to reach 3% of the population in Western countries by 2025. It is the most important cause of hospitalisation in subjects aged 65 years or more, resulting in high costs and major social impact. The current “one-size-fits-all” approach in the treatment of HF does not result in best outcome for all patients. These facts are an imminent threat to good quality management of patients with HF. An unorthodox approach from a new vision on care is required. We propose a novel predictive, preventive and personalised medicine approach where patients are truly leading their management, supported by an easily accessible online application that takes advantage of artificial intelligence. This strategy paper describes the needs in HF care, the needed paradigm shift and the elements that are required to achieve this shift. Through the inspiring collaboration of clinical and high-tech partners from North-West Europe combining state of the art HF care, artificial intelligence, serious gaming and patient coaching, a virtual doctor is being created. The results are expected to advance and personalise self-care, where standard care tasks are performed by the patients themselves, in principle without involvement of healthcare professionals, the latter being able to focus on complex conditions. This new vision on care will significantly reduce costs per patient while improving outcomes to enable long-term sustainability of top-level HF care.

The Value of Predictive Nursing in Perioperative Period of Allogeneic Hematopoietic Stem Cell Transplantation

“…Studies have shown that predictive care is effective in critically ill and surgical patients. However, the application and research of predictive nursing in patients with allo-HSCT during perioperative period is still lacking [4,10].…”

Differences in cohort study data affect external validation of artificial intelligence models for predictive diagnostics of dementia - lessons for translation into clinical practice

“…Methods from the field of artificial intelligence (AI), and more specifically machine learning, pose a great opportunity to drive the transition towards the PPPM paradigm [9]. These methods involve the use of biomedical data to build (i.e., "train") models which are capable of addressing a plethora of problems encountered in health research: Given a suitable data, they can assist diagnosis [10], model disease progression [11], identify patient subgroups for stratification [12], analyze survival chances [13], assist disease monitoring, and support appropriate therapies and medication [14].…”

Putting AI at the centre of heart failure care

“…The diversity of AI development teams will mandate interdisciplinary integration to achieve adoption, utility, safety, and inclusion all under the auspices of a new philosophy of care-AI-enabled care. 11 Although the landscape of HF care may be changing, the patient-doctor relationship will not be entirely replaced by AI. However, those who do not use AI will, in all probability, be replaced by those who do.…”

“…Artificial intelligence (AI), a rapidly evolving field in medicine, especially cardiology, is revolutionizing risk prediction and stratification, diagnostics, precision medicine, workflows, and efficiency 8–10 . In a strategic paper, we [PAtient Self‐care uSing eHealth In chrONic Heart Failure (PASSION‐HF) consortium] propose using digital therapeutics powered by AI as a personalized approach to HF self‐care 11 . PASSION‐HF aims to develop a virtual ‘doctor at home’ system.…”

“…PASSION‐HF aims to develop a virtual ‘doctor at home’ system. Being able to pool datasets smartly and extrapolating relevance at an individual level, our AI approach offers huge potential for reducing clinician burden, improving clinical efficacy, and enhancing patient experience and outcomes 11 …”