Online simulation for the operational management of inpatient beds
Creators: name: Oakley, David
Publisher: name: Lancaster University
Year: 2019
Subjects: discrete event simulation
online simulation
symbiotic simulation
validation
transition matrix
dynamic transition matrix
patient information
clinical opinion
emergency departments
Expert Judgment
hospital length of stay
Hospital Operations
hospitals
inpatient admission
Inpatient
Inpatient services
hospital administration
elective
emergency planning
emergency care
Emergency management

description: In many modern hospitals, resources such as beds, theatre time, medical
      equipment and staff are shared between patients who require immediate care
      and must be dealt with as they arrive (emergency patients), and those
      whose care requirements are known to the hospital some time in advance
      (elective patients). Caring for these two types of patients poses a
      logistical challenge, 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. Failing to strike this balance can result in
      negative outcomes, such as patient-stays on non-ideal wards, or increased
      waiting time for elective procedures (in the case of public health
      services).
      
      The potential benefits of using discrete event simulation (DES) models in
      healthcare are well established, and they are often preferred to other
      modelling approaches because of their ability to emulate the randomness
      seen in real systems, at a level of detail which is necessary for models
      to be convincing. However, their use is often limited to strategic or
      tactical decision making, and few have attempted to produce models which
      can help hospitals with short-term (operational) decision making. This is
      where Online Discrete Event Simulation (ODES) can help.
      
      An ODES (also known as symbiotic simulation) takes all the components of a
      DES model, and adds the ability to load the state of the real system at
      run-time to make predictions about how the real system might evolve in the
      short-term. This thesis reports the development of a whole-hospital,
      proof-of-concept ODES to assess the impact of elective admissions
      decisions, on wards which are shared with emergency patients. The model is
      parameterised by analysing 18 months of patient administrative data from
      an Australian General Hospital.
      
      Since ODES is a relatively new method, this research focuses on
      formalising the model development process, resulting in a new “black-box”
      validation method for handling conditionally distributed simulation
      outputs. Additionally, a new probabilistic routing method is developed to
      better represent inter-ward dependencies during peaks in bed demand. A
      statistical analysis of the relationship between ward transfers and ward
      occupancy is conducted on real hospital data to parameterise so-called
      “Dynamic Transition Matrices” for this purpose. Finally, the ODES is used
      to demonstrate how additional patient-level information (which might only
      become available after admission) can affect the predicted bed census.
      Clinicians’ discharge date estimates fit this criterion, and the case is
      made for more scientific use of this type of information, as part of an
      operational ODES model.