Evidence map›Paper›PMID 24469685›Full record

ArticleJournal of medical systems2014

Variability of subspecialty-specific anesthesia-controlled times at two academic institutions.

Bhavani Shankar Kodali, K Dennie Kim, Hugh Flanagan, Jesse M Ehrenfeld, Richard D Urman

Abstract readMulticenter Study
PubMed Publisher
In one paragraph

Article in Journal of medical systems, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

13 citing papers in PubMed.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

5 authors.

Bhavani Shankar KodaliHarvard Medical School/Brigham and Women's Hospital, Boston, MA, USA.
K Dennie Kim
Hugh Flanagan
Jesse M Ehrenfeld
Richard D Urman

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Realistic scheduling of operating room cases decreases costs, optimizes utilization and improves staff and patient satisfaction. Currently limited data exists to establish anesthesia-controlled time benchmarks based on specific subspecialty service. In this multicenter retrospective analysis of cases performed during a 53 month period at two large multispecialty academic institutions, data were retrieved from the perioperative information systems at each center. Both induction and emergence times were calculated. We then determined mean and median anesthesia controlled times based on each subspecialty service and compared them to previously published anesthesia-controlled time data. We obtained data on 104,184 cases at hospital A, and 122,560 cases at Hospital B. For all specialties at hospital A and hospital B, median induction time was 16.0 min and 17.0 min, emergence time was 14.0 and 8.0 min, and total anesthesia controlled time was 31.0 min and 27.0 min respectively. There was considerable variability among different surgical specialties deviating from the previously established 30 min benchmark. Subspecialties with lower total anesthesia controlled times in both centers were pain, general surgery, gynecology, plastic surgery and urology. Subspecialties with higher total anesthesia controlled times in both centers included cardiac surgery, neurosurgery, transplant and vascular. Cardiac surgery had the highest total time of 60 min and 50 min at Hospital A and B respectively. Individual specialty-specific anesthesia controlled times should be used for case scheduling and to benchmark anesthesia performance.

Indexed as

Appointments and SchedulesEfficiency, OrganizationalAcademic Medical CentersAnesthesiaHumansMedicineOperating RoomsRetrospective StudiesTime Factors

Identifiers

What OpenQuestion holds

Textmetadata
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.