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Recovering from COVID – Improving operating room capacity
INTRODUCTION: COVID-19 pandemic required that health systems made great efforts to mitigate the impact of high demands of patients requiring treatment. Triaging surgical cases reduced operating room capacity. Immunizations, massive testing, and personal protective equipment enabled re-activation of...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Journal of Pediatric Urology Company. Published by Elsevier Ltd.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9299983/ https://www.ncbi.nlm.nih.gov/pubmed/35999120 http://dx.doi.org/10.1016/j.jpurol.2022.07.006 |
Sumario: | INTRODUCTION: COVID-19 pandemic required that health systems made great efforts to mitigate the impact of high demands of patients requiring treatment. Triaging surgical cases reduced operating room capacity. Immunizations, massive testing, and personal protective equipment enabled re-activation of operating rooms. Delayed and newly added cases has placed stress on the system. We hypothesize that standardization in practice for tasks performed between anesthesia ready and surgery start time, also known as “prepping time”, can reduce operative time, improve efficiency and increase capacity. The aim of our project was to create and implement a best practice standardized prepping protocol, to explore its impact on operating room capacity. METHODS: Once local policies allowed re-opening of the operating rooms, our multidisciplinary group developed a working plan following Adaptive Clinical Management (ACM) principles to optimize surgical prepping time. Using electronic medical record (EMR) data, surgeons with the lowest surgical prepping times were identified (positive deviants). Their surgical prepping time workflows were reviewed. A clinical standard work (CSW) protocol was created by the team leader. New CSW protocol was defined and implemented by the leader and then by the rest of the surgeons. Baseline data was automatically extracted from EMR and analyzed by statistical process control (SPC) charts using AdaptX. Balancing measures included “last case end time” and rates of surgical site infections. RESULTS: A total of 2506 patients were included for analysis with 1333 prior to intervention and 1173 after. Team leader implementated the new CSW prepping protocol showing a special cause variation with an average time improvement from 14.6 min to 11.6 min and for all surgeons from 13.8 to 12.0 min. Total cases per month increased from 70 to 90 cases per month. Baseline ‘Last Case End Time’ was 15.7 min later than the scheduled. New CSW improve end time with an average of 20.8 min before the schedule. Baseline surgical site infection was 0.1% for the study population. No difference was seen after implementation. DISCUSSION: Variations in performance can be quantified using funnel plots showing individual practices allowing best practice to be identified, tested and scaled. Implementation of our surgical prepping time protocol showed a sustainable increase in efficiency without affecting quality, safety or workload. This additional increase is estimated to represent approximately $2–2.5M additional revenue per year. CONCLUSION: Adaptive clinical management is a practical solution to increase OR capacity by improving efficiency to reduce extra burden presented during COVID19 pandemic. [Figure: see text] |
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