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The nature and sources of variability in pediatric surgical case duration
Author(s) -
Bravo Fernanda,
Levi Retsef,
Ferrari Lynne R.,
McManus Michael L.
Publication year - 2015
Publication title -
pediatric anesthesia
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.704
H-Index - 82
eISSN - 1460-9592
pISSN - 1155-5645
DOI - 10.1111/pan.12709
Subject(s) - medicine , descriptive statistics , pediatric surgeon , statistics , predictive power , regression analysis , variation (astronomy) , pediatric surgery , standard deviation , pediatrics , surgery , philosophy , physics , mathematics , epistemology , astrophysics
Summary Background Case time variability confounds surgical scheduling and decreases access to limited operating room resources. Variability arises from many sources and can differ among institutions serving different populations. A rich literature has developed around case time variability in adults, but little in pediatrics. Objective We studied the effect of commonly used patient and procedure factors in driving case time variability in a large, free‐standing, academic pediatric hospital. Methods We analyzed over 40 000 scheduled surgeries performed over 3 years. Using bootstrapping, we computed descriptive statistics for 249 procedures and reported variability statistics. We then used conditional inference regression trees to identify procedure and patient factors associated with pediatric case time and evaluated their predictive power by comparing prediction errors against current practice. Patient and procedure factors included patient's age and weight, medical status, surgeon identity, and ICU request indicator. Results Overall variability in pediatric case time, as reflected by standard deviation, was 30% (25.8, 34.7) of the median case time. Relative variability (coefficient of variation), was largest among short cases. For a few procedure types, the regression tree can improve prediction accuracy if extreme behavior cases are preemptively identified. However, for most procedure types, no useful predictive factors were identified and, most notably, surgeon identity was unimportant. Conclusions Pediatric case time variability, unlike adult cases, is poorly explained by surgeon effect or other characteristics that are commonly abstracted from electronic records. This largely relates to the ‘long‐tailed’ distribution of pediatric cases and unpredictably long cases. Surgeon‐specific scheduling is therefore unnecessary and similar cases may be pooled across surgeons. Future scheduling efforts in pediatrics should focus on prospective identification of patient and procedural specifics that are associated with and predictive of long cases. Until such predictors are identified, daily management of pediatric operating rooms will require compensatory overtime, capacity buffers, schedule flexibility, and cost.