Sylvia G. Elkhuizen

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.

Applying the variety reduction principle to management of ancillary services

BACKGROUND As central diagnostic facilities, computer tomography (CT) scans appear to be bottlenecks in many patient-care processes. This study describes a case study concerning redesign of a CT scan department in the Academic Medical Center in Amsterdam, the Netherlands. PURPOSES The aim was to decrease access time for the CT-scan and simultaneously increase utilization level. METHODOLOGY/APPROACH An important cause of relatively low-capacity utilization is variability in the time needed for the scanning process. We performed a qualitative and quantitative analysis of current processes; identified bottlenecks and selected interventions with the greatest expected reduction of variability in flow time. FINDINGS The most promising and most feasible opportunity appeared to be to reallocate the insertion of intravenous access lines to a preparation room. The time needed for this activity was very hard to predict and needed a lot of slack in the lead time for appointments. By removing it from the CT room, lead time could be reduced by 5 minutes. The intervention resulted in a decrease of access time from 21 days to less than 5 days, and an increase of the utilization rate from 44% to 51%. This contributed directly to patient service and indirectly to cost reduction. PRACTICE IMPLICATIONS Our strategy is applicable in every appointment-based hospital facility with variation in the length of time of the process. It allows to simultaneously reduce costs and improve service for the patient.

Capacity management of nursing staff as a vehicle for organizational improvement

BackgroundCapacity management systems create insight into required resources like staff and equipment. For inpatient hospital care, capacity management requires information on beds and nursing staff capacity, on a daily as well as annual basis. This paper presents a comprehensive capacity model that gives insight into required nursing staff capacity and opportunities to improve capacity utilization on a ward level.MethodsA capacity model was developed to calculate required nursing staff capacity. The model used historical bed utilization, nurse-patient ratios, and parameters concerning contract hours to calculate beds and nursing staff needed per shift and the number of nurses needed on an annual basis in a ward. The model was applied to three different capacity management problems on three separate groups of hospital wards. The problems entailed operational, tactical, and strategic management issues: optimizing working processes on pediatric wards, predicting the consequences of reducing length of stay on nursing staff required on a cardiology ward, and calculating the nursing staff consequences of merging two internal medicine wards.ResultsIt was possible to build a model based on easily available data that calculate the nursing staff capacity needed daily and annually and that accommodate organizational improvements. Organizational improvement processes were initiated in three different groups of wards. For two pediatric wards, the most important improvements were found to be improving working processes so that the agreed nurse-patient ratios could be attained. In the second case, for a cardiology ward, what-if analyses with the model showed that workload could be substantially lowered by reducing length of stay. The third case demonstrated the possible savings in capacity that could be achieved by merging two small internal medicine wards.ConclusionA comprehensive capacity model was developed and successfully applied to support capacity decisions on operational, tactical, and strategic levels. It appeared to be a useful tool for supporting discussions between wards and hospital management by giving objective and quantitative insight into staff and bed requirements. Moreover, the model was applied to initiate organizational improvements, which resulted in more efficient capacity utilization.

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 Optimization of Hospital Resource Calendars

As demand for health care increases, a high efficiency on limited resources is necessary for affordable high patient service levels. Here, we present an adaptive approach to efficient resource usage by automatic optimization of resource calendars. We describe a precise model based on a case study at the radiology department of the Academic Medical Center Amsterdam (AMC). We model the properties of the different groups of patients, with additional differentiating urgency levels. Based on this model, we develop a detailed simulation that is able to replicate the known scheduling problems. In particular, the simulation shows that due to fluctuations in demand, the allocations in the resource calendar must be flexible in order to make efficient use of the resources. We develop adaptive algorithms to automate iterative adjustments to the resource calendar. To test the effectiveness of our approach, we evaluate the algorithms using the simulation. Our adaptive optimization approach is able to maintain overall target performance levels while the resource is used at high efficiency.

Using Business Process Redesign to Reduce Wait Times at a University Hospital in the Netherlands

BACKGROUND Business process redesign (BPR) has been applied to implement more customer-focused and cost-effective care. In 2002, two pilot projects to improve patient care processes for two specific patient groups were conducted at the Academic Medical Center, a 1,000-bed university hospital in Amsterdam. METHODS The BPR consisted of process analysis, identification of bottlenecks and goals for redesign, selection of interventions, and evaluation of effects. After identifying and selecting interventions with the greatest expected benefits, changes were implemented and effects were evaluated. RESULTS For gynecologic oncology patients, access time (from telephone call to first visit) was reduced from 14 days to < 7 days, and the proportion of patients who completed all diagnostic examinations within 14 days increased from 49% to 83%. For dyspnea patients, access time was reduced to < 6 days, and the number of visits was halved. DISCUSSION Despite the fact that we applied the same approach in these two projects, the interventions turned out to be quite different. Whereas changes in communication and planning were sufficient to eliminate bottlenecks in the gynecologic oncology project, the dyspnea project required a radical redesign of processes. Experience since these projects suggests that process redesign may have only marginal impact when the greatest bottleneck occurs, as was the case for the two BPR projects, at the point of access to central diagnostic facilities.