Cardiac surgery productivity and throughput improvements.
OBJECTIVE
The high variability in cardiac surgery length--is one of the main challenges for staff managing productivity. This study aims to evaluate the impact of six interventions on open-heart surgery operating theatre productivity.


METHODS
A discrete operating theatre event simulation model with empirical operation time input data from 2603 patients is used to evaluate the effect that these process interventions have on the surgery output and overtime work. A linear regression model was used to get operation time forecasts for surgery scheduling while it also could be used to explain operation time.


RESULTS
A forecasting model based on the linear regression of variables available before the surgery explains 46 per cent operating time variance. The main factors influencing operation length were type of operation, redoing the operation and the head surgeon. Reduction of changeover time between surgeries by inducing anaesthesia outside an operating theatre and by reducing slack time at the end of day after a second surgery have the strongest effects on surgery output and productivity. A more accurate operation time forecast did not have any effect on output, although improved operation time forecast did decrease overtime work.


CONCLUSIONS
A reduction in the operation time itself is not studied in this article. However, the forecasting model can also be applied to discover which factors are most significant in explaining variation in the length of open-heart surgery.


CONCLUSIONS
The challenge in scheduling two open-heart surgeries in one day can be partly resolved by increasing the length of the day, decreasing the time between two surgeries or by improving patient scheduling procedures so that two short surgeries can be paired.


CONCLUSIONS
A linear regression model is created in the paper to increase the accuracy of operation time forecasting and to identify factors that have the most influence on operation time. A simulation model is used to analyse the impact of improved surgical length forecasting and five selected process interventions on productivity in cardiac surgery.
Lehtonen
Juha-Matti
JM
Laboratory of Industrial Engineering and Management, Helsinki University of Technology, Helsinki, Finland.
Kujala
Jaakko
J
Kouri
Juhani
J
Hippeläinen
Mikko
M
D000465:Algorithms; D001071:Appointments and Schedules; D006348:Cardiac Surgical Procedures; D017598:Efficiency, Organizational; D006801:Humans; D016014:Linear Models; D017146:Models, Organizational