RESUMO
The introduction of Operational Excellence in the Maastricht University Medical Center (MUMC+) has been the first of its kind and scale for a university hospital. The policy makers of the MUMC+ have combined different elements from various other business, management, and healthcare philosophies and frameworks into a unique mix. This paper summarizes the journey of developing this system and its most important aspects. Special attention is paid to the role of the operating rooms and the improvements that have taken place there, because of their central role in the working of the hospital. The MUMC+ is the leading tertiary healthcare center for the South-East region of The Netherlands and beyond. Regional, national, and international developments encouraged the MUMC+ to start significantly reorganizing its care processes from 2009 onward. First experiments with Lean Six Sigma and Business Modeling were combined with lessons learned from other centers around the world to form the MUMC+'s own type of Operational Excellence. At the time of writing, many improvement projects of different types have been successfully completed. Every single department in the hospital now uses Operational Excellence and design thinking in general as a method to develop new models of care. An evaluation in 2014 revealed several opportunities for improvement. A large number of projects were in progress, but 75% of all projects had not been completed, despite the first projects being initiated back in 2012. This led to a number of policy changes, mainly focusing on more intensive monitoring of projects and trying to do more improvement projects directly under the responsibility of the line manager. Focusing on patient value, continuous improvement, and the reduction of waste have proven to be very fitting principles for healthcare in general and specifically for application in a university hospital. Approaching improvement at a systems level while directly involving the people on the work floor in observing opportunities for improvement and realizing these has shown itself to be essential.
RESUMO
For efficient utilization of operating rooms (ORs), accurate schedules of assigned block time and sequences of patient cases need to be made. The quality of these planning tools is dependent on the accurate prediction of total procedure time (TPT) per case. In this paper, we attempt to improve the accuracy of TPT predictions by using linear regression models based on estimated surgeon-controlled time (eSCT) and other variables relevant to TPT. We extracted data from a Dutch benchmarking database of all surgeries performed in six academic hospitals in The Netherlands from 2012 till 2016. The final dataset consisted of 79,983 records, describing 199,772 h of total OR time. Potential predictors of TPT that were included in the subsequent analysis were eSCT, patient age, type of operation, American Society of Anesthesiologists (ASA) physical status classification, and type of anesthesia used. First, we computed the predicted TPT based on a previously described fixed ratio model for each record, multiplying eSCT by 1.33. This number is based on the research performed by van Veen-Berkx et al., which showed that 33% of SCT is generally a good approximation of anesthesia-controlled time (ACT). We then systematically tested all possible linear regression models to predict TPT using eSCT in combination with the other available independent variables. In addition, all regression models were again tested without eSCT as a predictor to predict ACT separately (which leads to TPT by adding SCT). TPT was most accurately predicted using a linear regression model based on the independent variables eSCT, type of operation, ASA classification, and type of anesthesia. This model performed significantly better than the fixed ratio model and the method of predicting ACT separately. Making use of these more accurate predictions in planning and sequencing algorithms may enable an increase in utilization of ORs, leading to significant financial and productivity related benefits.