ArticleBreast (Edinburgh, Scotland)2020
Improving breast cancer care coordination and symptom management by using AI driven predictive toolkits.
Article in Breast (Edinburgh, Scotland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07474584 (The OpRESTORE AI-Patient Navigator Study), which is not on this map. Cited by 17 papers, 1 of them a synthesis that pooled it.
What it found
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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.
The OpRESTORE AI-Patient Navigator Study: Developing and Testing an AI-enhanced Patient Navigator for UK Veterans With Service-related Physical Health Problems
Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Global research trends in the application of artificial intelligence in oncology care: a bibliometric study.Frontiers in oncology · 2024Pooled it
- Symptom management in cancer care: An evolutionary concept analysis for a digitally enabled collaborative framework.Asia-Pacific journal of oncology nursing · 2026Review
- AI digital-twin ecosystem translating gut-microbiome-neuroimmune signals into precision sleep-mood interventions.Frontiers in psychiatry · 2026Review
- Artificial Intelligence in Outpatient Primary Care: A Scoping Review on Applications, Challenges, and Future Directions.Journal of general internal medicine · 2026Article
- Continuous nursing symptom management in cancer chemotherapy patients using deep learning.Scientific reports · 2025Article
- Enhancing High Reliability in Oncology Care: The Critical Role of Nurses-A Systematic Review and Thematic Analysis.Healthcare (Basel, Switzerland) · 2025Review
- Implementing Explainable Machine Learning Models for Practical Prediction of Early Neonatal Hypoglycemia.Diagnostics (Basel, Switzerland) · 2024Article
- An exploration of facilitators and barriers to patient navigator core functions with breast cancer patients: Implications for the development of a human-centered mHealth app.PEC innovation · 2023Article
- Facilitating family communication of familial hypercholesterolemia genetic risk: Assessing engagement with innovative chatbot technology from the IMPACT-FH study.PEC innovation · 2023Article
- The Approach of Artificial Intelligence in Neuroendocrine Carcinomas of the Breast: A Next Step towards Precision Pathology?-A Case Report and Review of the Literature.Medicina (Kaunas, Lithuania) · 2023Review
- Qualitative analysis of cancer care experiences among rural cancer survivors and caregivers.The Journal of rural health : official journal of the American Rural Health Association and the National Rural Health Care Association · 2022Article
- Machine Learning Approaches for Predicting Acute Respiratory Failure, Ventilator Dependence, and Mortality in Chronic Obstructive Pulmonary Disease.Diagnostics (Basel, Switzerland) · 2021Article
- Nurse navigators' telemonitoring for cancer patients with COVID-19: a French case study.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2021Article
- Artificial Intelligence for Hospital Health Care: Application Cases and Answers to Challenges in European Hospitals.Healthcare (Basel, Switzerland) · 2021Article
- Survivorship Care of Older Adults With Cancer: Priority Areas for Clinical Practice, Training, Research, and Policy.Journal of clinical oncology : official journal of the American Society of Clinical Oncology · 2021Review
- Artificial intelligence (AI) in breast cancer care - Leveraging multidisciplinary skills to improve care.Breast (Edinburgh, Scotland) · 2021Article
- Restructured society and environment: A review on potential technological strategies to control the COVID-19 pandemic.The Science of the total environment · 2020Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Integrated breast cancer care is complex, marked by multiple hand-offs between primary care and specialists over an extensive period of time. Communication is essential for treatment compliance, lowering error and complication risk, as well as handling co-morbidity. The director role of care, however, becomes often unclear, and patients remain lost across departments. Digital tools can add significant value to care communication but need clarity about the directives to perform in the care team. In effective breast cancer care, multidisciplinary team meetings can drive care planning, create directives and structured data collection. Subsequently, nurse navigators can take the director's role and become a pivotal determinant for patient care continuity. In the complexity of care, automated AI driven planning can facilitate their tasks, however, human intervention stays needed for psychosocial support and tackling unexpected urgency. Care allocation of patients across centres, is often still done by hand and phone demanding time due to overbooked agenda's and discontinuous system solutions limited by privacy rules and moreover, competition among providers. Collection of complete outcome information is limited to specific collaborative networks today. With data continuity over time, AI tools can facilitate both care allocation and risk prediction which may unveil non-compliance due to local scarce resources, distance and costs. Applied research is needed to bring AI modelling into clinical practice and drive well-coordinated, patient-centric cancer care in the complex web of modern healthcare today.
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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.