ReviewThe EPMA journal2019
Artificial intelligence supported patient self-care in chronic heart failure: a paradigm shift from reactive to predictive, preventive and personalised care.
Review in The EPMA journal, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 78 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
78 citing papers in PubMed, 2 syntheses or guidelines pooled it, 189 citations in OpenAlex.
- Artificial Intelligence in Cardiovascular Medicine: Focus on Hypertension.Hypertension (Dallas, Tex. : 1979) · 2026Pooled it
- Artificial Intelligence in the Heart of Medicine: A Systematic Approach to Transforming Arrhythmia Care with Intelligent Systems.Current cardiology reviews · 2025Pooled it
- Perceived Algorithmic Care Intensity and Older Adults' Autonomy in Community Smart Care: Mixed Methods Study.JMIR aging · 2026Article
- Application of Q-methodology in chronic disease research: a scoping review.BMC medical research methodology · 2026Article
- A Preventive Social Media Intervention for Perinatal Depression and Anxiety in Regional, Rural, and Remote Communities: Participatory Co-Design Study.Journal of medical Internet research · 2026Article
- Enhanced Prediction of Cardiovascular Disease Through Integrated Machine Learning Models Combining Clinical and Demographic Characteristics.Diagnostics (Basel, Switzerland) · 2026Article
- Estimating long-term care needs in data-scarce settings: a diagnostic model with evidence from MENA.Population health metrics · 2026Article
- Revolutionizing Multimorbidity Care: A Narrative Review on Artificial Intelligence Applications.Health science reports · 2026Article
- Artificial intelligence in heart failure.The Egyptian heart journal : (EHJ) : official bulletin of the Egyptian Society of Cardiology · 2026Review
- Digital Solutions in HF Education: What Can Patients and Clinicians Gain?Current heart failure reports · 2026Review
- Unveiling Heart Failure: Gender-Specific Insights and Innovations in Women's Cardiac Health.Current pharmaceutical design · 2026Review
- Artificial intelligence technology for the ethical issues research from a Marxist perspective under deep learning.Scientific reports · 2025Article
- A comparative analysis of echocardiographic data for heart transplant eligibility in brain death cases.The Egyptian heart journal : (EHJ) : official bulletin of the Egyptian Society of Cardiology · 2025Article
- Responsible artificial intelligence (AI) in healthcare: a paradigm shift in leadership and strategic management.Leadership in health services (Bradford, England) · 2025Review
- Developing Requirements for a Digital Self-Care Intervention for Adults With Heart Failure: Qualitative Workshop Study.Journal of medical Internet research · 2025Article
- Multimodal AI in Biomedicine: Pioneering the Future of Biomaterials, Diagnostics, and Personalized Healthcare.Nanomaterials (Basel, Switzerland) · 2025Review
- The present and future of cardiological telemonitoring in Europe: a statement from seven European countries.Herzschrittmachertherapie & Elektrophysiologie · 2025Review
- Revolutionizing hematological disorder diagnosis: unraveling the role of artificial intelligence.Annals of medicine and surgery (2012) · 2025Review
- Development and psychometric evaluation of the artificial intelligence attitude scale for nurses.BMC nursing · 2025Article
- AI Applications for Chronic Condition Self-Management: Scoping Review.Journal of medical Internet research · 2025Article
18 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
33 authors at 12 institutions in 7 countries.
Funding
No grant is acknowledged in the PubMed record.
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.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.