Learning Health Systems and Rapid Learning in Radiotherapy: Current Status and Future Directions – A Critical Analysis

Abstract

Introduction: Learning Health Systems (LHS) and rapid learning frameworks offer a promising path for continuous quality improvement in Radiotherapy by leveraging routine clinical real-world data. Objectives: To critically analyze the conceptual, operational, and regulatory barriers to implementing LHS and rapid learning in Radiotherapy, identifying workflow changes, practical recommendations, and future research priorities. Material and Methods: A narrative review and documentary analysis guided by a core research question and formal eligibility criteria. Searches covered PubMed/MEDLINE and Scopus (2019-2024), along with institutional repositories (IAEA, OECD, IARC, European Union), synthesizing 12 key sources via thematic analysis. Results: Differentiating digital infrastructure (LHS) from iterative operational cycles (rapid learning) is pivotal. Relevant obstacles include electronic health record heterogeneity, workforce analytical deficits, and regulatory constraints under the European Medical Device Regulation (MDR 2017/745). In daily practice, implementation supports evidence-based planning from local cohorts, predictive adaptive quality assurance, and automated tracking of patient-reported clinical outcomes. Conclusions: Sustainable integration requires oncological data standardization, digital literacy training for multidisciplinary teams, and agile regulatory compliance. Future research must focus on prospective, multicenter studies evaluating the measurable clinical benefit and cost-effectiveness of these systems.