Relating the master surgery schedule to the workload at the nursing wards

Introduction: This master thesis has been conducted from February through September 2011 at the Isala Klinieken Zwolle. The goal of the research was to reduce the variability of the workload on the nursing wards. The study shows that stream of elective surgery patients is responsible for most of the erratic behaviour at the nursing wards. High variability of workload in the nursing wards results in many unfavourable consequences: peak workloads, cancellation of patients, empty beds, the allocation of patients to wards that are non-optimal for their healing process, difficulties handling emergency patients, higher risk of mortality, higher failure to rescue rates, lower job satisfaction and higher likelihood of nurse burn-out. The study proposes a decision tool that quantifies the required bed demand for the current master surgery schedule and offers alternative MSSs that are expected to perform substantially better. Research approach: To conduct this research, we use Vanberkel’s model to relate recovering surgical patient workload to the Master Surgery Schedule. The model is used to evaluate variants of the current MSS. In this research the term OR-block is defined as a shift (morning or afternoon) in any of the operating rooms on any of the 10 days within an MSS-cycle (De Weezenlanden there are 10 (ORs) times 2 (shifts per day) times 10 (Days) = 200 OR-blocks). The heuristic developed in this research swaps OR-blocks and compares the calculated expected patient workload. The performance of an MSS is defined along three dimensions: the variability of the expected bed demand per day, of the expected number of admissions per day, and of the expected number of discharges per day. The model records the best possible swaps. After implementing the change the model can be rerun. We refer to a rerun as an iteration. Results: Two interventions are tested: restricting the amount of patients that are allowed to be operated during one OR-block, and altering the MSS. The first intervention yields no consistent results. The later, however, shows considerable improvements. The interventions are tested for both of the hospital’s major locations, De Weezenlanden (WL) and Sophia (SZ). For De Weezenlanden, the workload performance measure has been reduced by about 50 percent after five iterations. The total bed demand for the location is reduced from 137 to 131 beds after five iterations. For the Sophia location, the workload performance measure is reduced by about 60 percent after four iterations, and the bed demand is reduced by 3 beds. Ward specific distributions: The situation of individual wards as a result of the changes is explored as well. It turns out that for the Weezenlanden location the performance for the specific wards improves as well. For the Sophia location the net workload for the individual wards is about the same. Conclusions: The best swaps for the Weezenlanden location are: swapping urology from the first Wednesday to the last Friday afternoon, then swapping urology from the second Wednesday to the last Friday of the cycle, then swapping ear, nose and throat surgery from the first Thursday for orthopaedics from the second Monday, then ear, nose and throat surgery from the first Friday for orthopaedics from the first Monday. Table 5.5 and 5.6 give an overview of the best swaps. The best swaps for the Sophia location are: swapping neurosurgery from the first Monday with plastic surgery from the last Friday, then swapping neurosurgery from the second Monday with plastic surgery from the first Friday, then neurosurgery from the second Monday with general surgery from the last Friday, then neurosurgery from the first Monday with general surgery from the second Monday. A complete overview of the best swaps can be found in the Appendix. The heuristic is flexible. The performance measure can be adapted to minimize variance over all days (both week and weekend days), or to minimize total bed demand, or give different weights to either the variance of bed demand during the week and the variance of bed demand during the weekend. It is also possible to swap OR-days instead of OR-blocks. The workload is reduced considerably for the hospital in general, but further investigation shows that we need to be aware of changes at specific wards. As a consequence of the reduction in workload some wards perform better as well. There are some wards that perform worse after the intervention however, and it is important to be aware of this.