Development of a Radiographic Image Quality Framework for Lean Diagnostic Pathways

Thesis Abstract

In contemporary radiology practice, variability in image quality directly affects diagnostic accuracy and patient throughput, thereby impeding the efficiency gains targeted by lean diagnostic pathways. This study addresses the persistent mismatch between radiographic image quality and workflow efficiency by proposing and validating a Radiographic Image Quality Framework (RIQF) that aligns image quality determinants with lean process metrics, standardizes quality assessment, and supports continuous improvement within radiography departments. The aim is to develop, validate, and pilot a theory-driven framework that integrally links imaging parameters, technologist competencies, and workflow factors to Lean performance indicators such as cycle time, wait times, and defect rates. Specific objectives are (1) to identify core image quality determinants most influential for diagnostic confidence in chest, abdominal, and extremity radiographs under lean workflows; (2) to develop a conceptual model that maps these determinants to Lean diagnostic pathways using established radiography quality criteria and process improvement theories; (3) to empirically test the model’s predictive capacity for throughput and error rates across multiple radiology units; (4) to establish reliability and validity of an operationalized Radiographic Image Quality Instrument (RIQI) aligned with lean metrics; and (5) to formulate evidence-based recommendations for policy and practice to optimize image quality without compromising pace. A mixed-methods design guides the study, integrating a sequential explanatory approach. Quantitative data will be collected from 12 radiography units across three metropolitan hospitals, involving a target sample of 1,200 radiographs (400 per modality chest, abdomen, extremity) and 60 technologists. The RIQI will be constructed from predefined quality criteria rooted in European and American radiographic standards, harmonized with Lean tools (value stream mapping, 5S, and Kanban). Quantitative analyses will include multiple regression to identify the relative impact of image quality variables on throughput measures, structural equation modeling to test the proposed theoretical relationships, and ANOVA to explore inter-site variability. Qualitative data will be gathered from 30 in-depth interviews with radiographers and 6 focus groups with departmental managers to elucidate contextual influences and validate the instrument’s content. Thematic analysis will be employed to extract emergent patterns related to competence, equipment, and workflow integration, with triangulation against quantitative findings to refine the framework. The study will be grounded in the Theory of Planned Behavior and the Donabedian framework of structure-process-outcome, augmented by Lean management theory and the Image Quality assessment domain of the ICRP (International Commission on Radiological Protection) guidelines. The expected findings include (a) a parsimonious set of image quality determinants that consistently predict diagnostic confidence and downstream decision accuracy, (b) a validated RIQI with high internal consistency (Cronbach’s alpha ? 0.85) and adequate construct validity, and (c) empirical evidence that integrating RIQI within lean pathways reduces non-value-added time by at least 12% and lowers repeat exposure rates by 8% over a 6-month pilot. Contributions to knowledge encompass (i) a theoretically grounded, practically implementable framework that links radiographic image quality to Lean diagnostic performance, (ii) a robust measurement instrument for cross-institutional benchmarking in radiography quality, and (iii) actionable guidelines for training, equipment maintenance, and workflow redesign that optimize both image quality and throughput. The study will conclude that harmonizing image quality with lean principles enhances diagnostic reliability while preserving efficiency, and will recommend adoption of the RIQI in radiology information systems, periodic calibration of imaging protocols, ongoing competency development, and routine dashboards integrating quality and Lean metrics for continuous improvement.