Signature
TI = L_bar * (1 - o) / o
| Inputs | Definition | Unit |
|---|---|---|
L_bar | Average length of stay on the same counts as o | days |
o | Average bed occupancy, occupied bed-days divided by available bed-days, above zero | proportion, for example 0.85 |
TI | Average number of days an available bed stands empty between successive patients | days |
|---|
Function
Bed turnover rate and turnover interval function
Maps the discharges, available beds and occupied bed-days of a period to the two measures reported as bed turnover: the turnover rate, the number of patients each bed served, and the turnover interval, the average time a bed stands empty between one patient and the next. It also expresses both through bed occupancy and average length of stay, and spreads the annual cost of a staffed bed over the patients it served. The records follow the notation of the Bed Turnover article. The throughput identity linking annual discharges to beds, occupancy and average length of stay is held on the Average Length of Stay page (HE-FM-ALOS-002) and is referenced here, not repeated.
Try this function
Implementations
Excel
Bed turnover interval from occupancy and stay in one cell
Excel multiplies the named average length of stay cell by one minus occupancy and divides by occupancy, entered as a proportion.
=ALOS*(1-Occupancy)/Occupancy
Assumptions
Same counts for occupancy, stay and the turnover interval
o and L_bar come from the same bed-day and discharge counts. The result then equals the interval computed from bed-days with HE-FM-BTO-002.
Average occupancy conceals daily variation in bed demand
The formula uses period averages. Capacity planning models such as discrete event simulation represent the day-to-day variation in emergency admissions that an average interval conceals.
Worked examples
Bed turnover interval at 85% occupancy and a 5-day stay
At an average stay of 5 days, 85% occupancy leaves a turnover interval of about 0.88 days, the baseline value from bed-days.
L_bar = 5; o = 0.85; TI = 0.8824
Bed turnover interval at 95% occupancy and a 5-day stay
Raising occupancy to 95% at the same stay cuts the interval to about 0.26 days, roughly six hours, leaving little empty-bed time to absorb peaks in emergency demand.
L_bar = 5; o = 0.95; TI = 0.2632
Route A bed turnover interval at a 4.25-day stay
In route A, a 4.25-day stay at 85% occupancy gives a turnover interval of 0.75 days.
L_bar = 4.25; o = 0.85; TI = 0.75
Common errors
Occupancy entered as a percentage in the turnover interval
Entering occupancy as 85 instead of 0.85 at a 5-day stay returns about minus 4.94 days, an impossible negative interval, instead of about 0.88 days.
Judging the bed turnover interval against a fixed benchmark
Aloh and colleagues cite a suggested ideal turnover interval of 1 to 3 days, but the buffer a hospital needs depends on how variable its demand is. In Bagust and colleagues' simulation, risks of having no bed for an emergency admission became discernible above about 85% average occupancy, which at a 5-day stay is an interval of about 0.88 days.
Sources
Turnover interval as 365 over turnover rate minus stay
Aloh HE, Onwujekwe OE, Aloh OG, Nweke CJ. Is bed turnover rate a good metric for hospital scale efficiency? A measure of resource utilization rate for hospitals in Southeast Nigeria. Cost Effectiveness and Resource Allocation. 2020;18:21. Methods: turnover interval computed as 365 divided by the bed turnover rate minus average length of stay. Discussion: the interval as the average time beds are unoccupied between successive inpatients, and the suggested ideal of 1 to 3 days.
Barber-Johnson diagram relating stay, interval, turnover and occupancy
Morera Salas M. Diagrama de Barber y Johnson para el análisis de la gestión de la cama hospitalaria en Costa Rica. Revista Costarricense de Salud Pública. 2013;22(1). Introduction and methods: average stay on the vertical axis, turnover interval on the horizontal axis, turnover on the diagonals and occupancy on lines from the origin, the mathematical relations letting each point represent four values.
Bed occupancy and emergency admission risk simulation
Bagust A, Place M, Posnett JW. Dynamics of bed use in accommodating emergency admissions: stochastic simulation model. BMJ. 1999;319(7203):155-158. Abstract: risks are discernible when average bed occupancy exceeds about 85%, with regular shortages and periodic bed crises at 90% or more.
Canonical Identity
Stable URI · Machine-readable · Resolvable · CC BY 4.0