Application of staff rostering algorithms for emergency departments: protocol for a systematic review

Emergency departments (EDs) operate under conditions of high demand, uncertainty and resource constraints, making efficient personnel rostering a critical component of healthcare delivery. Poorly designed staff schedules may contribute to increased waiting times, staff burnout, reduced quality of care and inefficient use of resources. Operations research (OR) methods, including mathematical programming, heuristics, metaheuristics and simulation-based optimisation, have been widely applied to workforce scheduling problems in healthcare. However, the specific application of these methods to rostering and shift scheduling of ED personnel (eg, nurses and physicians) has not yet been systematically synthesised. This systematic review aims to identify, categorise and analyse OR algorithms used for rostering ED personnel, and to examine their modelling approaches, optimisation techniques and reported outcomes.

This systematic review will follow the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A comprehensive search will be conducted from database inception to the final search date in Google Scholar, Web of Science, MEDLINE, Embase, Scopus and ABI/Inform, with no start-date restriction. A provisional search was run on 20 March 2026; the final search will be updated before completion of study selection. Results will be restricted to articles published in English, French, Spanish or Italian. The search strategy will combine terms related to EDs, staff rostering or scheduling and OR or optimisation methods. Eligible studies will be peer-reviewed articles that apply OR or optimisation techniques, such as integer programming, constraint programming, exact algorithms, heuristics, metaheuristics, matheuristics or simulation-based approaches, to rostering or shift scheduling of ED personnel (eg, nurses and physicians); studies addressing workforce scheduling outside EDs, or not using OR or optimisation methods, will be excluded. Two reviewers will independently screen titles, abstracts and full texts according to predefined inclusion and exclusion criteria. Data will be extracted on study characteristics, modelling techniques, algorithmic approaches, decision variables, constraints, objectives and reported performance outcomes. The methodological characteristics of the models and algorithms will be synthesised using a narrative and descriptive analytical approach.

Ethical approval is not required as the study will analyse data from previously published studies. Results will be disseminated through publication in a peer-reviewed journal and presentations at relevant academic conferences.

CRD420261337311.