Case Sequencing of Diagnostic Imaging Studies Performed Under General Anesthesia or Monitored Anesthesia Care During Nights and Weekends

Larger anesthesia practitioner per operating room ratios are needed to prevent unnecessary non-operative time than to mitigate patient risk: A narrative review

When choosing the anesthesia practitioner to operating room (OR) ratio for a hospital, objectives are applied to mitigate patient risk: 1) ensuring sufficient anesthesiologists to meet requirements for presence during critical intraoperative events (e.g., anesthesia induction) and 2) ensuring sufficient numbers to cover emergencies outside the ORs (e.g., emergent reintubation in the post-anesthesia care unit). At a 24-OR suite with each anesthesiologist supervising residents in 2 ORs, because critical events overlapped among ORs, ≥14 anesthesiologists were needed to be present for all critical events on >90% of days. The suitable anesthesia practitioner to OR ratio would be 1.58, where 1.58 = (24 + 14)/24. Our narrative review of 22 studies from 17 distinct hospitals shows that the practitioner to OR ratio needed to reduce non-operative time is reliably even larger. Activities to reduce non-operative times include performing preoperative evaluations, making prompt evidence-based decisions at the OR control desk, giving breaks during cases (e.g., lunch or lactation sessions), and using induction and block rooms in parallel to OR cases. The reviewed articles counted the frequency of these activities, finding them much more common than urgent patient-care events. Our review shows, also, that 1 anesthesiologist per OR, working without assistants, is often more expensive, from a societal perspective, than having a few more anesthesia practitioners (i.e., ratio > 1.00). These results are generalizable among hundreds of hospitals, based on managerial epidemiology studies. The implication of our narrative review is that existing studies have already shown, functionally, that artificial intelligence and monitoring technologies based on increasing the safety of intraoperative care have little to no potential to influence anesthesia or OR productivity. There are, in contrast, opportunities to use sensor data and decision-support to facilitate communication among anesthesiologists outside of ORs to choose optimal task sequences that reduce non-operative times, thereby increasing production and OR efficiency.

Night-time detection and response in relation to deteriorating inpatients: A scoping review

BACKGROUND

Although detection and response to clinical deterioration have been studied, the range and nature of studies focused on night-time clinical setting remain unclear.

AIM

This study aimed to identify and map existing research and findings concerning night-time detection and response to deteriorating inpatients in usual care or research settings.

STUDY DESIGN

A scoping review method was used. PubMed, CINAHL, Web of Science, and Ichushi-Web databases were systematically searched. We included studies focusing on night-time detection and response to clinical deterioration.

RESULTS

Twenty-eight studies were included. These studies were organized into five categories: night-time medical emergency team or rapid response team (MET/RRT) response, night-time observation using the early warning score (EWS), available resources for physicians' practice, continuous monitoring of specific parameters, and screening for night-time clinical deterioration. The first three categories were related to interventional measures in usual care settings, and relevant findings mainly demonstrated the actual situation and challenges of night-time practice. The final two categories were related to the interventions in the research settings and included innovative interventions to identify at-risk or deteriorating patients.

CONCLUSIONS

Systematic interventional measures, such as MET/RRT and EWS, could have been sub-optimally performed at night. Innovations in monitoring technologies or implementation of predictive models could be helpful in improving the detection of night-time deterioration.

RELEVANCE TO CLINICAL PRACTICE

This review provides a compilation of current evidence regarding night-time practice concerning patient deterioration. However, a lack of understanding exists on specific and effective practices regarding timely action for deteriorating patients at night.

US National Anesthesia Workload on Saturday and Sunday Mornings

BACKGROUND

In order to provide guidance to organizations considering elective weekend surgical case scheduling, we analyzed data from the American Society of Anesthesiologist's Anesthesia Quality Institute. We determined the US anesthesia workload on Saturdays and Sundays.

METHODS

The American Society of Anesthesiologist's Anesthesia Quality Institute data were from all US anesthesia groups that submitted cases to the National Anesthesia Clinical Outcomes Registry for 2013. For each of the N = 2,075,188 cases, we identified the local date and time of the start of anesthesia care and the duration of anesthesia care. Anesthesia workload was measured as the time from the start to the end of continuous anesthesia care. Because elective cases are rarely scheduled on Sundays, we considered the difference in workload between Saturday and Sunday to estimate elective case scheduling. This difference would be an overestimate if some patients' scheduled cases were postponed from Friday to Saturday. Data are reported as mean ± standard error; N = 13 four-week periods.

RESULTS

The difference in the anesthesia minutes between Saturdays versus Sundays 7:00 AM to 2:59 PM (ie, elective caseload) represented just 0.38% ± 0.02% of the total minutes nationwide; Saturday 1.57% ± 0.03% versus Sunday 1.19% ± 0.02%. The P < .00001 comparing the 0.38% with 1.0% and, also, with 0.5% (upper 99% confidence interval = 0.42%).

CONCLUSIONS

The imputed Saturday elective schedule represents a tiny percentage of overall anesthetic workload nationwide. Saturday elective surgery is currently an uncommon practice in the United States. Based on this prior knowledge, organizations considering changes to their current scheduling strategies should perform a thorough statistical analysis of their local workload prior to implementation and apply evidence-based criteria to guide their decision-making process.