Beyond Protocol Automation: A Sociotechnical Framework for Understanding and Measuring CT and MRI Acquisition Protocol Adherence in Resource-Constrained Healthcare Settings
Year: 2026
Type: article
Abstract: Background Adherence to computed tomography (CT) and magnetic resonance imaging (MRI) acquisition protocols is essential for image quality, diagnostic reliability, and patient safety. However, adherence is influenced by technological, infrastructural, professional, and organisational factors, particularly in resource-constrained healthcare settings. Artificial intelligence (AI) may support protocol standardisation and quality assurance, but its effectiveness depends on implementation context. Objective To examine how technological, infrastructural, human, and organisational determinants influence CT and MRI acquisition protocol adherence and develop a framework linking failure mechanisms to measurable outcomes and corrective strategies. Review approach: This narrative review synthesises literature on AI-enabled imaging technologies, technical infrastructure, professional competence, workflow demands, and organisational governance across protocol selection, acquisition, and post-acquisition quality assurance. Synthesis and contribution The review conceptualises protocol adherence as a sociotechnical process and proposes a determinant-to-failure-to-measurement framework linking contextual constraints to protocol-selection errors, acquisition deviations, and quality-assurance failures. It identifies potential indicators, including protocol deviations, omitted sequences, repeat examinations attributable to acquisition errors, and image adequacy, alongside context-appropriate interventions such as staff training, standardised checklists, protocol audits, and AI-enabled monitoring. Conclusion Sustainable improvement requires alignment between technology, infrastructure, professional competence, and organisational governance. The proposed framework may guide quality improvement and future research, although its relationships require empirical validation.
Source: F1000Research
Institution: Kampala International University
Language: English
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $1,350
OpenAlex ID: W7225730766