title: Using the M/G/∞ queueing model to predict inpatient family medicine service census and resident workload.
abstract: The number and timing of unplanned admissions to inpatient teaching services vary. Recent changes to resident duty hours make it essential to maximize learning experiences and balance workload on these services. Queueing theory provides a mechanism for understanding and planning for the variations in admissions and daily census. Daily admissions, length of stay, and daily census were modeled for a teaching inpatient family medicine service over 46 months using an M/G/∞ queueing model. Q-Q plots and a Kolmogorov-Smirnov test were used to check the fit of actual data to the model. Admissions and daily census followed a Poisson distribution (λ = 3.28 and λ = 8.28, respectively), while length-of-stay followed a lognormal distribution (µ = 0.49, σ2 = 0.83). The M/G/∞ queueing model proved useful for predicting overflow admission frequency, defining expected resident workload in terms of patient-days, and determining hospital unit size requirements.
authors.affiliation: Mayo Clinic, Rochester, MN, USA garrison.gregory@mayo.edu.
authors.forename: Gregory M
authors.initials: GM
authors.lastname: Garrison
authors.affiliation: Mayo Clinic, Rochester, MN, USA.
authors.forename: Jennifer L
authors.initials: JL
authors.lastname: Pecina
mesh_terms: D019352:Censuses; D005194:Family Practice / Q000193:education*; D006801:Humans; D007297:Inpatients; D007396:Internship and Residency; D007902:Length of Stay / Q000706:statistics & numerical data; D015233:Models, Statistical; D010343:Patient Admission / Q000706:statistics & numerical data; D016526:Workload
keywords: inpatients; internship and residency; patient admission; queueing theory; systems theory