Identifying and Explaining the Components Influencing Artificial Intelligence–Based Patient Relationship Management in Advanced Therapy Medicinal Products (ATMPs)

Abstract

This study aimed to identify and explain the components influencing artificial intelligence (AI)-based patient relationship management in the field of Advanced Therapy Medicinal Products (ATMPs) and to develop an integrated conceptual structure appropriate to the specific characteristics of advanced therapies. This applied qualitative exploratory study used a deductive–inductive analytical approach. Initially, a targeted review of the literature was conducted to identify preliminary concepts related to AI capabilities, patient interaction and experience, organizational and infrastructural requirements, governance, and expected outcomes. These concepts informed the development of a semi-structured interview guide. Eleven experts with professional experience in ATMP-related clinical, technical, research and development, cell and gene therapy, quality assurance, management, commercialization, and data-related domains participated in the study. Interview data were analyzed through a deductive–inductive thematic process in which meaning units were coded and progressively organized into concepts, subcategories, and main categories. Analysis of the interviews resulted in ten main categories: AI capabilities across the patient journey; patient interaction and experience; trust and acceptance; governance, ethics, and accountability; infrastructure and organizational readiness; implementation barriers; expected outcomes and value; organizational transformation and digital maturity; model evaluation; and success and sustainability factors. AI capabilities included patient screening, monitoring, adverse-event identification, decision support, data management, and personalized communication. Trust was associated with usefulness, safety, transparency, explainability, and human oversight. Data quality, interoperability, organizational readiness, managerial support, privacy, security, accountability, and regulatory requirements emerged as essential implementation conditions. Expected outcomes included improved clinical performance, service quality, efficiency, patient experience, and continuity of care. AI-based patient relationship management in ATMPs should be understood as a multidimensional, patient-centered, and organizationally embedded system in which technological capabilities create value only when supported by reliable data, interoperable infrastructure, human oversight, organizational readiness, responsible governance, and continuous evaluation.