The Maieutic-H Model: Integrating Machine Learning-Based Prioritisation, Interaction Analysis, and Motivational Strategies for Scalable Healthcare Waste Management

Healthcare waste mismanagement persists because the organisational and communicative dynamics driving erroneous practices remain largely unaddressed, particularly in high-turnover clinical units that concentrate the highest volumes of hazardous waste and operational pressure. Existing training interventions typically address motivational, analytical, or operational dimensions in isolation, lacking a systemic and adaptive framework. This study designs and theoretically validates Maieutic-H, a multilevel training ecosystem integrating narrative and gamified motivational strategies, Video-Based Interaction Analysis of communicative practices, and an AI-driven support system employing an interpretable Random Forest classifier to prioritise corrective actions, embedded within a six-phase adaptive cycle with longitudinal monitoring at one, six, and twelve months. Theoretical validation through comparison with eleven programmes from the literature identifies three recurring, sub-optimal configurations—single-component, parallel-component, and quasi-integrated interventions—none of which combines data-driven prioritisation with interactional analysis to surface operational blind spots. Preliminary qualitative validation against expert interviews showed 93% concordance between operator-perceived priorities and model-generated relevance scores, informing an adaptive recalibration (𝛼=0.70 ) that weights field-derived evidence over the simulated training baseline. The Maieutic-H model offers a scalable, theoretically grounded framework for sustainable behavioural change and regulatory compliance in complex clinical environments, aligning interpretable machine learning with healthcare process engineering. This manuscript presents a model development and theoretical validation study. Evidence on effectiveness will require empirical testing of the full Maieutic-H pathway in hospital pilot studies.