TY - JOUR

T1 - Symbiotic simulation for the operational management of inpatient beds

T2 - model development and validation using Δ-method

AU - Oakley, D.

AU - Onggo, B.S.

AU - Worthington, D.

PY - 2019/6/3

Y1 - 2019/6/3

N2 - In many modern hospitals, resources are shared between patients who require immediate care, and must be dealt with as they arrive (emergency patients), and those whose care requirements are partly known to the hospital some time in advance (elective patients). Catering for these two types of patients is a challenging short-term operational decision-making problem, since some portion of each resource must be set aside for emergency patients when planning for the number and type of elective patients to admit. This paper shows how symbiotic simulation can help hospitals with important short-term operational decision making. We demonstrate how a symbiotic simulation model can be developed from an existing simulation model by adding the ability to load the state of the physical system at run-time and by making use of conditional length-of-stay distributions. The model is parameterised using 18 months of patient administrative data from an Anonymised General Hospital. Further, we propose a new Δ-Method that is suitable for validating a stochastic symbiotic simulation model. We demonstrate the benefit of our symbiotic simulation by showing how it can be used as an early warning system, and how additional patient-level information which might only become available after admission, can affect the predicted bed census. © 2019, The Author(s).

AB - In many modern hospitals, resources are shared between patients who require immediate care, and must be dealt with as they arrive (emergency patients), and those whose care requirements are partly known to the hospital some time in advance (elective patients). Catering for these two types of patients is a challenging short-term operational decision-making problem, since some portion of each resource must be set aside for emergency patients when planning for the number and type of elective patients to admit. This paper shows how symbiotic simulation can help hospitals with important short-term operational decision making. We demonstrate how a symbiotic simulation model can be developed from an existing simulation model by adding the ability to load the state of the physical system at run-time and by making use of conditional length-of-stay distributions. The model is parameterised using 18 months of patient administrative data from an Anonymised General Hospital. Further, we propose a new Δ-Method that is suitable for validating a stochastic symbiotic simulation model. We demonstrate the benefit of our symbiotic simulation by showing how it can be used as an early warning system, and how additional patient-level information which might only become available after admission, can affect the predicted bed census. © 2019, The Author(s).

KW - Bed management

KW - OR in health services

KW - Symbiotic simulation

KW - Validation

KW - adult

KW - article

KW - case report

KW - clinical article

KW - decision making

KW - female

KW - general hospital

KW - hospital patient

KW - human

KW - length of stay

KW - male

KW - simulation

KW - stochastic model

KW - validation process

U2 - 10.1007/s10729-019-09485-1

DO - 10.1007/s10729-019-09485-1

M3 - Journal article

JO - Health Care Management Science

JF - Health Care Management Science

SN - 1386-9620

ER -