Evaluation and implementation of a Just-In-Time bed-assignment strategy to reduce wait times for surgical inpatients.

Early bed assignments of elective surgical patients can be a useful planning tool for hospital staff; they provide certainty in patient placement and allow nursing staff to prepare for patients' arrivals to the unit. However, given the variability in the surgical schedule, they can also result in timing mismatches-beds remain empty while their assigned patients are still in surgery, while other ready-to-move patients are waiting for their beds to become available. In this study, we used data from four surgical units in a large academic medical center to build a discrete-event simulation with which we show how a Just-In-Time (JIT) bed assignment, in which ready-to-move patients are assigned to ready-beds, would decrease bed idle time and increase access to general care beds for all surgical patients. Additionally, our simulation demonstrates the potential synergistic effects of combining the JIT assignment policy with a strategy that co-locates short-stay surgical patients out of inpatient beds, increasing the bed supply. The simulation results motivated hospital leadership to implement both strategies across these four surgical inpatient units in early 2017. In the several months post-implementation, the average patient wait time decreased 25.0% overall, driven by decreases of 32.9% for ED-to-floor transfers (from 3.66 to 2.45 hours on average) and 37.4% for PACU-to-floor transfers (from 2.36 to 1.48 hours), the two major sources of admissions to the surgical floors, without adding additional capacity.

Dynamic fair balancing of COVID-19 patients over hospitals based on forecasts of bed occupancy.

This paper introduces mathematical models that support dynamic fair balancing of COVID-19 patients over hospitals in a region and across regions. Patient flow is captured in an infinite server queueing network. The dynamic fair balancing model within a region is a load balancing model incorporating a forecast of the bed occupancy, while across regions, it is a stochastic program taking into account scenarios of the future bed surpluses or shortages. Our dynamic fair balancing models yield decision rules for patient allocation to hospitals within the region and reallocation across regions based on safety levels and forecast bed surplus or bed shortage for each hospital or region. Input for the model is an accurate real-time forecast of the number of COVID-19 patients hospitalised in the ward and the Intensive Care Unit (ICU) of the hospitals based on the predicted inflow of patients, their Length of Stay and patient transfer probabilities among ward and ICU. The required data is obtained from the hospitals' data warehouses and regional infection data as recorded in the Netherlands. The algorithm is evaluated in Dutch regions for allocation of COVID-19 patients to hospitals within the region and reallocation across regions using data from the second COVID-19 peak.

Real-time forecasting of COVID-19 bed occupancy in wards and Intensive Care Units.

This paper presents a mathematical model that provides a real-time forecast of the number of COVID-19 patients admitted to the ward and the Intensive Care Unit (ICU) of a hospital based on the predicted inflow of patients, their Length of Stay (LoS) in both the ward and the ICU as well as transfer of patients between the ward and the ICU. The data required for this forecast is obtained directly from the hospital's data warehouse. The resulting algorithm is tested on data from the first COVID-19 peak in the Netherlands, showing that the forecast is very accurate. The forecast may be visualised in real-time in the hospital's control centre and is used in several Dutch hospitals during the second COVID-19 peak.

Developing and testing a computerized decision support system for nurse-to-patient assignment: a multimethod study.

Nurse-to-patient assignment is a frequently recurring, time-consuming, and complex process owing to the many considerations involved. Creating well-balanced, high-quality assignments is crucial to ensuring patient safety, quality of care, and job satisfaction for nurses. A computerized decision support system can assist (charge) nurses in the nurse-to-patient assignment process. In this two-phase multimethod study, a computerized decision support system was developed and evaluated. Three nursing wards in a 1000-bed Dutch university hospital participated. In the first phase of this study, considerations relevant to the assignment process--and their relative importance--were investigated in a literature review, focus group sessions with nurses, and a survey among nurses. Using information from the first phase, the computerized decision support system was developed based on an integer linear program. In the second phase, a before-and-after study was conducted to test and evaluate the computerized decision support system both quantitatively (duration of the assignment process) and qualitatively (survey on workload). Thirty-six measurements were performed to test the computerized decision support system. After implementation, a 30% time reduction was achieved in the nurse-to-patient assignments, and nurses (N = 138) experienced a lower workload. Therefore, the implementation of computerized decision support system would increase both the quality and safety of care as well as the nurses' job satisfaction and should be investigated rigorously in the coming years.