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Improving breast cancer care coordination and symptom management by using AI driven predictive toolkits
بهبود هماهنگی مراقبت از سرطان پستان و مدیریت علائم با استفاده از ابزارهای پیش بینی کننده هوش مصنوعی-2020 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
Keywords: Care coordination | Symptom management | Predictive tools | Care allocation | Nurse navigator | Multidisciplinary discussion |
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