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Resource and cost constraints in hospitals demand thorough planning of operating room schedules. Ideally, exact start times and durations are known in advance for each case. However, aside from the first case’s start, most factors are hard to predict. While the role of the start of the first case for optimal room utilization has been shown before, data for to-follow cases are lacking. The present study therefore aimed to analyze all elective surgery cases of a university hospital within 1 year in search of visible patterns. A total of 14,014 cases scheduled on 254 regular working days at a university hospital between September 2015 and August 2016 underwent screening. After eliminating 112 emergencies during regular working hours, 13,547 elective daytime cases were analyzed, out of which 4,346 ranked first, 3,723 second, and 5,478 third or higher in the daily schedule. Also, 36% of cases changed start times from the day before to 7:00 a.m., with half of these (52%) resulting in a delay of more than 15 min. After 7:00 a.m., 87% of cases started more than 10 min off schedule, with 26% being early and 74% late. Timeliness was 15 ± 72 min (mean ± SD) for first, 21 ± 84 min for second, and 25 ± 93 min for all to-follow cases, compared to preoperative day planning, and 21 ± 45, 23 ± 61, and 19 ± 74 min compared to 7:00 a.m. status. Start time deviations were also related to procedure duration, with cases of 61–90 min duration being most reliable (deviation 9.8 ± 67 min compared to 7:00 a.m.), regardless of order. In consequence, cases following after 61–90 min long cases had the shortest deviations of incision time from schedule (16 ± 66 min). Taken together, start times for elective surgery cases deviate substantially from schedule, with first and second cases falling into the highest mean deviation category. Second cases had the largest deviations from scheduled times compared to first and all to-follow cases. While planned vs. actual start times differ among specialties, cases of 61–90 min duration had the most reliable start times, with neither shorter nor longer cases seeming to improve timeliness of start times.
Purpose
Due to the demographic change morbidity raises the demand for medical hospital services as well as a need for medical specialization, while economic and human resources are diminishing. Unlike other industries hospitals do not have sufficient data and adequate models to relate growing demands and increasing performance to growth in staff capacity and to increase in staff competences.
Method
Based on huge medical data sample covering the years from 2010 to 2014 with more than 150,000 operations of the Department for Anesthesiology at the University Hospital Muenster, Germany, comparisons are drawn between the development of medical services and the development of personnel capacity and expertise.
Results
The numbers of surgical operations increased by 21% and “skin incision to closure” time by 17%. Simultaneously, personnel capacity grew by 16% largely resting upon recruiting first-time employees. Expertise measured as “years of professional experience” dwindled from 10 years to 5.4 years on average and staff turnover accelerated.
Conclusion
Static benchmark data collected at fixed reference dates do not sufficiently reflect the nexus between capacity and competence and do not reflect the dynamic changes in a hospital’s requirements for expertise and specialization, at all. Staff turnover leads to a loss of experience, which jeopardizes patient safety and hampers medical specialization. In consequence of the dramatic shortage of medical specialists, drop-off rates must be reduced and retention rates must be increased. To that end, working conditions need to be fundamentally converted for a multigeneration, multicultural, and increasingly female workforce.