The July Spike in Operating Room Management: Reality or Perception?

Sanford, Joseph A.; Tsai, Mitchell H.; Kadry, Bassam; Mayhew, Christopher R.; Adams, David C.

doi:10.4300/jgme-d-15-00170.1

Cited by 3 publications

(3 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, given a lack of seasonal effect at our institution, it was inferred that attending familiarity and guidance of techniques allowed for trainees to learn the robotic workflow without causing delay in patient care. This is similar to the Sanford et al [11] evaluation of the "July Effect" finding no seasonal difference in a single institution's total OR minutes, overutilization, and number of ORs running late by month. The staff neurosurgeon is normally present and involved in case setup perhaps mitigating any training effect on setup or case time.…”

Section: Discussionsupporting

confidence: 83%

Learning Curves during Implementation of Robotic Stereotactic Surgery

Hines,

Smit,

Vinjamuri

et al. 2024

Stereotact Funct Neurosurg

View full text Add to dashboard Cite

Introduction: Adoption of robotic techniques is increasing for neurosurgical applications. Common cranial applications include stereoelectroencephalography (sEEG) and deep brain stimulation (DBS). For surgeons to implement robotic techniques in these procedures, realistic learning curves must be anticipated for surgeons to overcome the challenges of integrating new techniques into surgical workflow. One such way of quantifying learning curves in surgery is cumulative sum (CUSUM) analysis. Methods: Here, the authors present retrospective review of stereotactic cases to perform a CUSUM analysis of operative time for robotic cases at a single institution performed by 2 surgeons. The authors demonstrate learning phase durations of 20 and 16 cases in DBS and sEEG, respectively. Results: After plateauing of operative time, mastery phases started at cases 132 and 72 in DBS and sEEG. A total of 273 cases (188 DBS and 85 sEEG) were included in the study. The authors observed a learning plateau concordant with change of location of surgery after exiting the learning phase. Conclusion: This study demonstrates the learning curve of 2 stereotactic workflows when integrating robotics as well as being the first study to examine the robotic learning curve in DBS via CUSUM analysis. This work provides data on what surgeons may expect when integrating this technology into their practice for cranial applications.

show abstract

Section: Discussionsupporting

confidence: 83%

Learning Curves during Implementation of Robotic Stereotactic Surgery

Hines,

Smit,

Vinjamuri

et al. 2024

Stereotact Funct Neurosurg

View full text Add to dashboard Cite

show abstract

“…Sanford and colleagues measured the monthly OR minutes (defined as the total OR time of all cases completed each month) over the course of almost 5 years to assess for seasonal inefficiency, particularly during the month of July. Compared with the baseline month of April, there was no significant difference in the average monthly OR minutes in July 54. Hoffman and colleagues analyzed turnover times for anesthesiology residents and certified registered nurse anesthetists (CRNAs).…”

Section: Impact Of Trainees and Clinical Expertisementioning

confidence: 99%

“…Compared with the baseline month of April, there was no significant difference in the average monthly OR minutes in July. 54 Hoffman and colleagues analyzed turnover times for anesthesiology residents and certified registered nurse anesthetists (CRNAs). Compared with a mean turnover time of 30 minutes for CRNAs, the mean turnover time for CA-1 residents was 32 minutes and that of CA-3 residents was 29 minutes.…”

Section: Impact Of Trainees and Clinical Expertisementioning

confidence: 99%

Efficiency in the operating room: optimizing patient throughput

Robertson

Kla

Yaghmour

2021

International Anesthesiology Clinics

View full text Add to dashboard Cite

The operating room (OR) is a complex environment that accounts for a significant proportion of total hospital revenue and expenses. Efficient management of patient throughput is necessary to optimize this valuable resource. Accurate scheduling of surgical cases can improve OR efficiency, and yet, it is fraught with inherent challenges. Various strategies aimed at first case ontime starts, OR turnover times, and delays in postanesthesia care unit (PACU) admissions are used to manage patient throughput and will be discussed in detail. The impact of perioperative teams, trainees, and clinical expertise are some of the additional considerations for OR efficiency. Machine learning methods, in combination with large electronic health databases, are a more recent approach to further optimize patient throughput from the preoperative arena to the PACU.

show abstract