Reallocating resources to focused factories: a case study in chemotherapy

This study investigates the expected service performance associated with a proposal to reallocate resources from a centralized chemotherapy department to a breast cancer focused factory. Using a slotted queueing model we show that a decrease in performance is expected and calculate the amount of additional resources required to offset these losses. The model relies solely on typical outpatient scheduling system data, making the methodology easy to replicate in other outpatient clinic settings. Finally, the paper highlights important factors to consider when assigning capacity to focused factories. These considerations are generally relevant to other resource allocation decisions

An exact approach for relating recovering surgical patient workload to the master surgical schedule

No other department influences the workload of a hospital more than the Department of Surgery and in particular, the activities in the operating room. These activities are governed by the master surgical schedule (MSS), which states which patient types receive surgery on which day. In this paper we describe an analytical approach to project the workload for downstream departments based on this MSS. Specifically the ward occupancy distributions, patient admission/discharge distributions, and the distributions for ongoing interventions/treatments is computed. Recovering after surgery requires the support of multiple departments, such as nursing, physiotherapy, rehabilitation and long term care. With our model, managers from these departments can determine their workload by aggregating tasks associated with recovering surgical patients. The model, which supported the development of a new MSS at the Netherlands Cancer Institute-Antoni van Leeuwenhoek Hospital, provides the foundation for a decision support tool to relate downstream hospital departments to the operating room

Reallocating resources to focused factories: a case study in chemotherapy

This study investigates the expected service performance associated with a proposal to reallocate resources from a centralized chemotherapy department to a breast cancer focused factory. Using a slotted queueing model we show that a decrease in performance is expected and calculate the amount of additional resources required to offset these losses. The model relies solely on typical outpatient scheduling system data, making the methodology easy to replicate in other outpatient clinic settings. Finally, the paper highlights important factors to consider when assigning capacity to focused factories. These considerations are generally relevant to other resource allocation decisions

Improving the efficiency of a chemotherapy day unit: Applying a business approach to oncology

Aim: To improve the efficiency of a hospital-based chemotherapy day unit (CDU). - \ud Methods: The CDU was benchmarked with two other CDUs to identify their attainable performance levels for efficiency, and causes for differences. Furthermore, an in-depth analysis using a business approach, called lean thinking, was performed. An integrated set of interventions was implemented, among them a new planning system. The results were evaluated using pre- and post-measurements. - \ud Results: We observed 24% growth of treatments and bed utilisation, a 12% increase of staff member productivity and an 81% reduction of overtime. - \ud Conclusions: The used method improved process design and led to increased efficiency and a more timely delivery of care. Thus, the business approaches, which were adapted for healthcare, were successfully applied. The method may serve as an example for other oncology settings with problems concerning waiting times, patient flow or lack of beds

Development of a benchmark tool for comprehensive cancer centers:results from a pilot exercise

Background: The BENCH-CAN project is an international benchmarking project which aims to develop a tool for the benchmarking of comprehensive cancer care and yield good practice examples across the EU in a way that contributes to improve the quality of interdisciplinary patient treatment. The Joint Commission defines benchmarking as “a systematic, data-driven process of continuous improvement that involves internally and/or externally comparing performance to identify, achieve, and sustain good practice”. This paper describes the development and results of a pilot of an institutional tool at 5 cancer centers. Methods: The 13 step benchmarking process by Van Lent et.al. was used. A framework based on the European Foundation of Quality Management model and the Institute of Medicine domains of quality was developed to structure the indicators. Indicators were primarily derived from literature and secondary from expert opinion. Centers provided data and a site visit was performed to grasp the context and to clarify additional questions. Based on the results of the pilot good practices were defined. Results: The final indicator list contains 81 qualitative and 141 quantitative indicators. Centers reported the data in the same way which enables comparison.Differences between pilot sites are found in for example length of stay (5 to 9 days), and staff turnover rates (for nurses it varied from 0,46% to 25,5%). Another difference can be found in the domain of Resources such as IT (for example the storage of patient records which was done digitally in some centers, but on paper in others), and patient involvement both in their own care process, as in the strategy development of an institute. Conclusions: A benchmarking tool for Comprehensive Cancer Care has been developed that will become available in an open format. Variation of the results provided improvement opportunities for all centers. Reliability and validity of the tool are ensured by the pilot of the indicators at 5 cancer centers. The pilot showed that the sites all reported data in the same way and all provided the type of data that was required. Apart from this institutional benchmark, a pathway benchmark was developed as well as a consumer experience questionnaire

Implementing Algorithms to Reduce Ward Occupancy Fluctuation Through Advanced Planning

As with many hospitals, NKI-AVL is eager to improve patient access through intelligent capacity investments. To this end, the hospital expanded its operating capacity from five to six operating rooms (ORs) and redesigned their master surgical schedule (MSS) in an effort to improve utilization and decrease hospital-wide congestion; an MSS is a cyclical schedule specifying when surgical specialties operate. Designing an efficient MSS is a complex task, requiring commitment and concessions on the part of competing stakeholders. There are many restrictions which need to be adhered to, including limited specialized equipment and physician availability. These restrictions are, for the most part, known in advance. The relationship between the MSS and the ward, however, is not known in advance and is plagued with uncertainties. For example, it may be known which patient type will be admitted to the ward after surgery; however, the number of patients changes from week to week, and it is not known with certainty how long each patient will stay in the hospital. Inpatient wards, furthermore, are one of the most expensive hospital resources and can be a major source of hospital congestion, as many departments rely on inpatient wards to receive and treat their patients prior to discharge from the hospital (e.g., the emergency department). This congestion leads to long waiting times for patients, patients receiving the wrong level of care, and extended lengths of stay for patients. Well-designed surgical schedules which take into account inpatient ward resources lead to reduced cancellations and higher and balanced utilization. We observed that peaks in the ward occupancy are particularly dependent on the MSS, and, as a result, ward occupancies can be leveled through careful MSS design. Avoiding peaks and leveling ward occupancy across weekdays makes staff scheduling easier and limits the risk of exceeding capacity, which causes congestion and perpetuates inefficiencies throughout the hospital. Working with NKI-AVL we developed an operations research model to support the redesign of theirMSS. The redesigned MSS improved the use of existing ward resources, thereby allowing an additional operating room to be built without additional investments in ward capacity. A post implementation review of bed use statistics validated our model’s projections. The success of the project served as proof-of-concept for our model, which has since been applied in several other hospitals

Een planningsmethode voor reductie van de fluctuaties in de belasting van verpleegafdelingen

Zorgvuldige afstemming tussen het schema van de operatiekamers en de verpleegafdeling balanceert de belasting van de verpleegafdeling en vermindert het aantal afgezegde operaties. In samenwerking tussen het Center for Healthcare Operations Improvement & Research (CHOIR, kenniscentrum voor optimalisatie van zorgprocessen en onderzoek in Nederland van de Universiteit Twente) en het Nederlands Kanker Instituut – Antoni van Leeuwenhoek Ziekenhuis (NKI-AVL) is door middel van een operations-researchmodel het Master Surgery Schedule van het NKI-AVL herzien. Hierdoor heeft het NKI-AVL een extra operatiekamer in gebruik kunnen nemen zonder de capaciteit van de verpleegafdeling te hebben moeten vergroten. Dit project heeft gediend als prototype voor succesvolle implementatie-projecten van het ontwikkelde operations-researchmodel in verscheidene ziekenhuizen. Als gevolg van deze resultaten is in samenwerking met Information Builders, ontwikkelaar van business intelligence- en integratiesoftware, een business intelligence-platform\ud ontwikkeld dat momenteel wereldwijd wordt aangeboden