Operating Rooms efficiency: leveraging data to improve management of elective surgery

Abstract Background Careful scheduling of elective surgery Operating Rooms (ORs) is crucial for their efficient use, to avoid low/over utilization and staff overtime. Accurate estimation of procedures duration is essential to improve ORs scheduling. Therefore analysis of historical data about surgic...

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Bibliographic Details
Published in:European journal of public health 2020-09, Vol.30 (Supplement_5)
Main Authors: Quercioli, C, Carta, G A, Cevenini, G, Messina, G, Nante, N, Becattini, G, Sancasciani, S
Format: Article
Language:English
Subjects:
Data
Data analysis
Efficiency
Elective surgery
Gynecology
Intervention
Management
Multivariate analysis
Optimization
Orthopedics
Pilot projects
Public health
Regression analysis
Resource management
Risk
Scheduling
Statistical analysis
Surgery
Time
Urology
Utilization
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Summary:Abstract Background Careful scheduling of elective surgery Operating Rooms (ORs) is crucial for their efficient use, to avoid low/over utilization and staff overtime. Accurate estimation of procedures duration is essential to improve ORs scheduling. Therefore analysis of historical data about surgical times is fundamental to ORs management. We analyzed the effect, in a real setting, of an ORs scheduling model based on estimated optimum surgical time in improving ORs efficiency and decreasing the risk of overtime. Methods We studied all the 2014-2019 elective surgery sessions (3,758 sessions, 12,449 interventions) of a district general hospital in Siena's Province, Italy. The hospital had3 ORs open 5 days/week 08:00-14:00. Surgery specialties were general surgery, orthopedics, gynecology and urology. Based on a pilot study conducted in 2016, which estimated a 5 times greater risk of having an OR overtime for sessions with a surgical time (incision-suture)>200 minutes, from 2017 all the ORs were scheduled using a maximum surgical time of 200 minutes calculated summing the mean surgical times for intervention and surgeon (obtained from 2014-2016 data). We carried out multivariate logistic regression to calculate the probability of ORs overtime (of 15 and 30 minutes) for the periods 2014-2016 and 2017-2019adjusting for raw ORs utilization. Results The 2017-2019 risk of an OR overtime of 15 minutes decreased by 25% compared to the 2014-2016 period (OR = 0.75, 95%CI=0.618-0.902, p = 0.003); the risk of a OR overtime of 30 minutes decreased by 33% (OR = 0.67, 95%CI= 0.543-0.831, p 
ISSN:1101-1262
1464-360X
DOI:10.1093/eurpub/ckaa166.528