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Determining diagnostic radiographer staffing requirements: A workload-based approach

Authors :
Louzanne Bam
I.H. de Kock
C. Cloete
Source :
Radiography. 28:276-282
Publication Year :
2022
Publisher :
Elsevier BV, 2022.

Abstract

Introduction The topic of healthcare human resource planning for diagnostic radiographers has received limited research attention to date. This research is concerned with developing a framework that can be used to determine diagnostic radiographer staffing requirements at a unit- or department level (i.e. at the micro-level). Methods An inductive approach is applied to formulate requirement specifications that inform the development of the framework. A number of verification and validation activities are performed, including theoretical verification and a case study application. Results The diagnostic radiographer staffing framework consists of seven steps that comprise a workload-based approach to determining the number of full time equivalent diagnostic radiographers that are required for each modality, or group of modalities. Both clinical and non-clinical activities are considered, and guidance is provided on calculating staffing requirements to cover leave allowances. A number of potential approaches to determining activity times are also discussed. Conclusion The framework represents a holistic approach to determining the required number of diagnostic radiographers at a practice-level, that is designed to remain relevant as technological advances are made in the field of diagnostic radiography. Implications for practice By providing a practical guideline, with accompanying examples, the framework is expected to hold value for individuals involved in the management of diagnostic radiography practices. The framework proposes an approach to a topic that affects every radiography practice in operation yet has received limited attention in literature to date.

Details

ISSN :
10788174
Volume :
28
Database :
OpenAIRE
Journal :
Radiography
Accession number :
edsair.doi.dedup.....93fa5c2bd37ff9c55192ffea781330e7