Evaluating the Impact of Digital Radiography on Diagnostic Accuracy in Emergency Settings

Thesis Abstract

The increasing adoption of digital radiography (DR) in emergency medical settings has been driven by its potential to enhance diagnostic accuracy, expedite patient management, and improve overall clinical outcomes. Despite these advantages, empirical evidence quantifying the extent to which DR influences diagnostic precision, especially in high-pressure emergency environments, remains limited and contested. This study aims to evaluate the impact of digital radiography on diagnostic accuracy in emergency settings, by systematically comparing DR with conventional film-based radiography (FR) in real-world clinical practice. The specific objectives include determining the diagnostic accuracy rates of DR versus FR, exploring radiographers' and radiologists' perceptions of image quality and diagnostic confidence, and identifying factors influencing the accuracy of DR in emergency workflows. The research adopts a cross-sectional, comparative design, integrating quantitative analysis with qualitative insights. The theoretical framework is anchored in the Technology Acceptance Model (TAM) and the Cognitive Load Theory, which elucidate factors influencing technology integration and decision-making under cognitive stress. The study population comprises radiographers, radiologists, and emergency physicians working in tertiary hospitals with active radiography units. A stratified random sampling technique is employed to select 150 radiographers and 50 radiologists from five hospitals, ensuring proportional representation. Data collection involves structured questionnaires to assess perceptions of image quality and diagnostic confidence, combined with retrospective analysis of image reports, clinical outcomes, and diagnostic error rates over a six-month period. The accuracy of diagnoses is validated through comparison with follow-up clinical evidence and established gold standard diagnoses. Additionally, semi-structured interviews with 20 key personnel provide qualitative insights into workflow challenges and perceived benefits of DR. Data analysis involves statistical techniques such as paired t-tests and chi-square tests to compare diagnostic accuracy rates between DR and FR, with logistic regression modeling used to identify predictors of diagnostic success. Thematic analysis is applied to qualitative interview data to elucidate contextual factors affecting DR implementation. Validity and reliability of questionnaires are ensured through pilot testing, expert review, and internal consistency checks via Cronbach's alpha coefficients exceeding 0.8. Expected findings indicate that digital radiography significantly improves diagnostic accuracy in emergency settings, reducing false negatives and false positives compared to conventional film-based imaging. The study also anticipates high levels of perceived image quality and diagnostic confidence among radiologists and radiographers, which correlate positively with accuracy rates. Workflow efficiencies, such as faster image acquisition, processing, and sharing, are expected to be identified as facilitators, while challenges such as equipment costs and technical training may limit full integration. This research contributes novel empirical evidence to the growing body of knowledge on imaging technology's role in emergency diagnostics, providing specific data on accuracy metrics within real-world clinical workflows. It advances understanding of factors that influence the successful implementation of DR systems and informs policy development aimed at optimizing emergency radiography services. The findings support the hypothesis that digital radiography enhances diagnostic performance, thereby advocating for increased resource allocation and training in rapid-response settings. The study concludes with recommendations for best practices in adopting DR in emergency departments, emphasizing the importance of continuous staff training, infrastructure investment, and quality assurance protocols. It also proposes areas for further research, including longitudinal studies to assess the long-term impact of DR on patient outcomes and cost-effectiveness analyses to support broader adoption strategies. Ultimately, the findings aim to guide clinicians, administrators, and policymakers in making evidence-based decisions that enhance emergency diagnostic capabilities through technological integration.