Digital Health and Real-World Evidence in Onco-Hematological Patients

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Background

Clinical practice, particularly in onco-hematological settings, often relies on data from randomized controlled trials (RCTs) as they typically provide the highest level of scientific evidence; however, translating trial results to everyday clinical practice is not straightforward due to low overall trial accrual (<5% of all newly diagnosed cancer patients) and under-representation of patient frailties, such as older age, advanced disease stage, comorbidities and lower socio-economic status, in RCTs. RCTs provide evidence concerning the best achievable responses under controlled experimental conditions and are essential for clinical research, particularly in the pharmaco-epidemiological field. Real-world (RW) studies generate evidence concerning the actual advantage of treatment approaches, as achieved in a real-life setting, which is indispensable for public health research. In addition, RW studies can provide information on patterns and quality of care, as well as access to adequate health care, providing relevant information for health care organizations.

Digital health, as defined by the World Health Organization (WHO), is a term encompassing e-health and, in general, the use of advanced computer sciences, such as big data, genomics and artificial intelligence (AI). Digital health plays a crucial role in boosting health systems and public health, increasing equity in health services access, and moving toward universal health coverage. In this context, digital health can enhance our capability in studying and treating onco-hematological patients in the real-life setting by collecting and sharing RW data (RWD) from electronic health records with the final goal of supporting clinical and translational research. In addition, digital health provides solutions that capture patient-reported outcomes (PROs), allows symptom monitoring and enhanced patient management, and digital health RW studies can collate data from many cases - an added value when analyzing rare hematologic malignancies.

Digital health solutions address many unmet needs, including access to care and reassurance, increasing adherence and treatment efficacy, decreasing hospitalizations and optimizing healthcare resource utilization. Within these, AI can support the remote monitoring of cancer patients, thus helping healthcare providers with advanced warnings and allowing timely interventions. Several initiatives have arisen in this field, such as the WHO’s ‘Setting Up a Cancer Centre’ or the Union for International Cancer Control and Europe’s ‘Beating Cancer Plan’.

Current challenges for digital health studies include data sharing and interoperability, with open community data standards and initiatives such as Fast Healthcare Interoperability Resources (FHIR), the Observational Medical Outcomes Partnership (OMOP), or the Common Data Model (CDM) helping to overcome obstacles.

This Research Topic is devoted to studies evaluating onco-hematological patients’ characteristics, patterns and quality of care, and the impact of treatments in the RW setting while using digital health inside or outside of the clinic. We will consider observational and interventional digital health registry-based studies, studies using or assessing novel administrative flows, and other data-driven or AI-driven studies. Original Research articles, Review articles and short communications are invited. Studies applying innovative methods are encouraged. Articles reporting partial or preliminary results will not be accepted.

Please note: manuscripts consisting solely of bioinformatics or computational analysis of public genomic or transcriptomic databases which are not accompanied by validation (independent cohort or biological validation in vitro or in vivo) are out of scope for this section and will not be accepted via Frontiers in Oncology. Please submit manuscripts of this type via Frontiers in Digital Health.

Keywords: Hematologic Malignancies, E-Health, Digital Health, Clinical Practice, Real-World Evidence, Artificial Intelligence, Big Data, Real-World Data, Real-World Setting

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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