Improving Diagnostic Radiography Efficiency in Rural Ireland: A Case Study

Thesis Abstract

In rural Ireland, diagnostic radiography departments face protracted wait times, variable image quality, and uneven access to radiographers, collectively impacting timely diagnosis and patient outcomes. The study addresses the efficiency gap in diagnostic radiography within rural healthcare networks by examining workflow processes, resource allocation, and inter-professional coordination. The aim is to enhance operational efficiency while preserving image quality and patient safety. Specific objectives are (1) to map current radiography workflows across three rural hospitals and identify bottlenecks; (2) to quantify the relationship between staffing levels, equipment utilization, and radiographic turnaround times; (3) to evaluate image quality outcomes against throughput measures and patient satisfaction; (4) to develop a contextually grounded model of efficiency incorporating the perspectives of radiographers, radiologists, and managers; and (5) to formulate evidence-based, implementable recommendations tailored to rural Irish settings. The study adopts a mixed-methods sequential explanatory design. The quantitative phase will survey radiography departments in three rural Health Service Executive (HSE) regions, targeting a population of 120 radiographers and radiology managers, with a sample size of 90 respondents to achieve a 95% confidence level and a 7% margin of error. Data will be collected on throughput, staffing ratios, equipment uptime, and turnaround times from hospital information systems, complemented by image quality scores from routine QA audits. Regression analysis and time-series methods will examine relationships between staffing, equipment utilization, and turnaround times, while ANOVA will test differences across hospital sites. The qualitative phase will involve semi-structured interviews with 24 participants (radiographers, radiologists, and department managers) to explore perceived efficiency drivers, barriers, and contextual factors. Thematic analysis, guided by the Normalisation Process Theory (NPT) and the Resource-Based View (RBV) of the firm, will interpret how practices become routinely embedded and how resources contribute to sustainable efficiency. Data integration will occur at the interpretation stage to triangulate findings. Ethical approval will be obtained from the relevant institutional review boards, with informed consent secured from all participants. Data will be anonymised and stored securely in compliance with GDPR. Validity and reliability will be ensured through pilot-testing of instruments, cross-checking quantitative data against hospital dashboards, and investigator triangulation in qualitative coding. The study will also apply a robust model specification that integrates process efficiency indicators (cycle time, batch size, and imaging modality mix) with quality indicators (contrast resolution, exposure index consistency, and repeat rates). Expected findings indicate that modest but targeted improvements in scheduling robustness, cross-site staffing flexibility, and proactive preventive maintenance predict meaningful reductions in radiographic turnaround times without compromising image quality. It is anticipated that higher proactive maintenance uptime and standardized imaging protocols will correlate with lower repeat rates and improved radiologist downstream interpretation times. The study also expects to reveal contextual nuances, such as geographic isolation amplifying transport and scheduling challenges, and the pivotal role of inter-professional communication in sustaining improvements. Theoretical contributions include the operationalization of NPT within rural radiography workflows and an RBV-informed framework for efficiency that foregrounds intangible organizational capabilities alongside tangible assets. The study contributes to knowledge by providing a transferable, evidence-based model of diagnostic radiography efficiency tailored to rural health systems, combining quantitative metrics with qualitative insights to generate implementable recommendations. Practical implications encompass a staged intervention plan encompassing workflow redesign, staffing contingency strategies, and a minimally disruptive equipment maintenance schedule, designed for replication in comparable rural health contexts. The final recommendations advocate for (a) the establishment of regional radiography hubs with flexible cross-site staffing, (b) investment in predictive maintenance and real-time utilization dashboards, (c) standardized imaging protocols and continuous radiographer professional development focused on efficiency and QA, and (d) governance structures that enable rapid, data-driven decision-making. The study concludes that targeted, theory-informed, context-aware interventions can achieve substantial efficiency gains in rural diagnostic radiography without sacrificing diagnostic accuracy or patient safety.