Flexible nurse staffing based on hourly bed census predictions

Workload on nursing wards depends highly on patient arrivals and patient lengths of stay, which are both inherently variable. Predicting this workload and staffing nurses accordingly is essential for guaranteeing quality of care in a cost effective manner. This paper introduces a stochastic method that uses hourly census predictions to derive efficient nurse staffing policies. The generic analytic approach minimizes staffing levels while satisfying so-called nurse-to-patient ratios. In particular, we explore the potential of flexible staffing policies which allow hospitals to dynamically respond to their fluctuating patient population by employing float nurses. The method is applied to a case study of the surgical inpatient clinic of the Academic Medical Center (AMC) Amsterdam. This case study demonstrates the method's potential to study the complex interaction between staffing requirements and several interrelated planning issues such as case mix, care unit partitioning and size, and surgical block planning. Inspired by the numerical results, the AMC decided that this flexible nurse staffing methodology will be incorporated in the redesign of the inpatient care operations during the upcoming years

Integral resource capacity planning for inpatient care services based on hourly bed census predictions

The design and operations of inpatient care facilities are typically largely historically shaped. A better match with the changing environment is often possible, and even inevitable due to the pressure on hospital budgets. Effectively organizing inpatient care requires simultaneous consideration of several interrelated planning issues. Also, coordination with upstream departments like the operating theater and the emergency department is much-needed. We present a generic analytical approach to predict bed census on nursing wards by hour, as a function of the Master Surgical Schedule (MSS) and arrival patterns of emergency patients. Along these predictions, insight is gained on the impact of strategic (i.e., case mix, care unit size, care unit partitioning), tactical (i.e., allocation of operating room time, misplacement rules), and operational decisions (i.e., time of admission/discharge). The method is used in the Academic Medical Center Amsterdam as a decision support tool in a complete redesign of the inpatient care operations

Reduce fluctuations in capacity to improve the accessibility of radiotherapy treatment cost-effectively

This paper is motivated by a case study to reduce the throughput times for radiotherapy treatment. The goal is to find a cost-effective way to meet future throughput targets. A combination of queuing theory and computer simulation was used. First, computer simulation to detect the bottleneck(s) in a multi-step radiotherapy process. Despite, the investment in an additional linear accelerator, the main bottleneck turned out to be the outpatient department (OPD). Next, based on queuing theory, waiting times were improved by reducing the fluctuations in the OPD capacity. Computer simulation was used again to quantify the effect on the total throughput time of a radiotherapy patient. The results showed a reduction in both access times as well as waiting times prior to the consecutive steps: the preparation phase and actual treatment. The paper concludes with practical suggestions on how to reduce the fluctuations in capacity, and seems of interest for other radiotherapy departments or other multi-step situations in a hospital

Optimization of online patient scheduling with urgencies and preferences

We consider the online problem of scheduling patients with urgencies and preferences on hospital resources with limited capacity. To solve this complex scheduling problem effectively we have to address the following sub problems: determining the allocation of capacity to patient groups, setting dynamic rules for exceptions to the allocation, ordering timeslots based on scheduling efficiency, and incorporating patient preferences over appointment times in the scheduling process. We present a scheduling approach with optimized parameter values that solves these issues simultaneously. In our experiments, we show how our approach outperforms standard scheduling benchmarks for a wide range of scenarios, and how we can efficiently trade-off scheduling performance and fulfilling patient preferences

Adaptive resource allocation for efficient patient scheduling

Objective Efficient scheduling of patient appointments on expensive resources is a complex and dynamic task. A resource is typically used by several patient groups. To service these groups, resource capacity is often allocated per group, explicitly or implicitly. Importantly, due to fluctuations in demand, for the most efficient use of resources this allocation must be flexible. Methods We present an adaptive approach to automatic optimization of resource calendars. In our approach, the allocation of capacity to different patient groups is flexible and adaptive to the current and expected future situation. We additionally present an approach to determine optimal resource openings hours on a larger time frame. Our model and its parameter values are based on extensive case analysis at the Academic Medical Hospital Amsterdam. Results and conclusion We have implemented a comprehensive computer simulation of the application case. Simulation experiments show that our approach of adaptive capacity allocation improves the performance of scheduling patients groups with different attributes and makes efficient use of resource capacity

The usefulness of CA15.3, mucin-like carcinoma-associated antigen and carcinoembryonic antigen in determining the clinical course in patients with metastatic breast cancer

Levels of mucin-like carcinoma-associated antigen (MCA), CA15.3 and carcinoembryonic antigen (CEA) were measured in consecutive serum samples of 40 women with metastatic breast cancer. A change in antigen level of more than 25%, either an increase or a decrease, was considered to predict progressive or responsive disease respectively. A change of less than 25% was considered to predict stable disease. MCA, CA15.3 and CEA were elevated in the serum of 68%, 76% and 48% of the patients respectively (P<0.05). The overall prediction of clinical course was similar for all three markers. A more than 25% increase of MCA, CA15.3, and CEA was observed in 61%, 54% and 36% respectively. The predictive value of a more than 25% increase was high for all three markers: 94%, 94%, 83%. Changes in marker levels were correlated with each other. Logistic regression analysis showed that combining MCA and CA15.3 did not improve the prediction further. In conclusion, these tumour markers may help in evaluating the disease course and there is no advantage in combining MCA and CA15.3