Vollständiger Abstract
Worum geht es in dieser Arbeit?
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.
Bibliografischer Nachweis
Publikationsdaten
- Autor:innen
- Maria Assunta Cappelli, Eva Cappelli, Francesco Cappelli, Giovanni Marmora, Gianluigi Giorgetti, Giovanni De Feo
- Quelle
- Processes
- Publikation
- 2026-01-01
- Band / Ausgabe
- Nicht angegeben
- Seiten
- Nicht angegeben
- ISSN / ISBN
- 2227-9717
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Zitierfähiger Nachweis
Maria Assunta Cappelli, Eva Cappelli, Francesco Cappelli, Giovanni Marmora, Gianluigi Giorgetti, Giovanni De Feo (2026). The Maieutic-H Model: Integrating Machine Learning-Based Prioritisation, Interaction Analysis, and Motivational Strategies for Scalable Healthcare Waste Management. Processes. https://doi.org/10.3390/pr14172765
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Themen, Förderung und Nutzung
Lizenzhinweise: Lizenz 1