title: Emergency Department Visit Forecasting and Dynamic Nursing Staff Allocation Using Machine Learning Techniques With Readily Available Open-Source Software.
abstract: Although emergency department visit forecasting can be of use for nurse staff planning, previous research has focused on models that lacked sufficient resolution and realistic error metrics for these predictions to be applied in practice. Using data from a 1100-bed specialized care hospital with 553,000 patients assigned to its healthcare area, forecasts with different prediction horizons, from 2 to 24 weeks ahead, with an 8-hour granularity, using support vector regression, M5P, and stratified average time-series models were generated with an open-source software package. As overstaffing and understaffing errors have different implications, error metrics and potential personnel monetary savings were calculated with a custom validation scheme, which simulated subsequent generation of predictions during a 4-year period. Results were then compared with a generalized estimating equation regression. Support vector regression and M5P models were found to be superior to the stratified average model with a 95% confidence interval. Our findings suggest that medium and severe understaffing situations could be reduced in more than an order of magnitude and average yearly savings of up to €683,500 could be achieved if dynamic nursing staff allocation was performed with support vector regression instead of the static staffing levels currently in use.
authors.affiliation: Author Affiliations: GTH, Department of Electronic Engineering, Technical University of Madrid, ETSI Telecomunicación (Mr Zlotnik and Dr Martínez); and Ramón y Cajal University Hospital (Mr Zlotnik, Dr Alfaro, and Ms Pérez), Madrid; and Department of Signal Theory and Communications, Carlos III University de Madrid, Getafe (Dr Gallardo-Antolín), Spain.
authors.forename: Alexander
authors.initials: A
authors.lastname: Zlotnik
authors.forename: Ascensión
authors.initials: A
authors.lastname: Gallardo-Antolín
authors.forename: Miguel
authors.initials: M
authors.lastname: Cuchí Alfaro
authors.forename: María Carmen
authors.initials: MC
authors.lastname: Pérez Pérez
authors.forename: Juan Manuel
authors.initials: JM
authors.lastname: Montero Martínez
mesh_terms: D004636:Emergency Service, Hospital; D005544:Forecasting; D006801:Humans; D000069550:Machine Learning; D008962:Models, Theoretical; D048108:Nursing Informatics; D009740:Nursing Staff / Q000191:economics / Q000706:statistics & numerical data*; D010561:Personnel Staffing and Scheduling / Q000191:economics / Q000706:statistics & numerical data*; D012984:Software; D000078329:Workforce