Format

Send to

Choose Destination
J Clin Anesth. 2016 Jun;31:238-46. doi: 10.1016/j.jclinane.2016.01.007. Epub 2016 Apr 16.

National incidences and predictors of inefficiencies in perioperative care.

Author information

1
Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women's Hospital/Harvard Medical School, 75 Francis Street, Boston, MA 02115, USA. Electronic address: rgabriel1@partners.org.
2
Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women's Hospital/Harvard Medical School, 75 Francis Street, Boston, MA 02115, USA. Electronic address: awu4@partners.org.
3
Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women's Hospital/Harvard Medical School, 75 Francis Street, Boston, MA 02115, USA. Electronic address: chuang17@partners.org.
4
Anesthesia Quality Institute, 1061 American Lane, Schaumburg, IL 60173, USA. Electronic address: r.dutton@asahq.org.
5
Department of Anesthesiology, Perioperative and Pain Medicine, Brigham and Women's Hospital/Harvard Medical School, 75 Francis Street, Boston, MA 02115, USA. Electronic address: rurman@partners.org.

Abstract

STUDY OBJECTIVE:

The operating room suite can be one of the most costly units within the hospital. Some of these costs stem from postoperative unplanned admissions, case cancellations, case delays, and extended recovery room times. The objective is to determine the clinical predictors of these operating room inefficiencies.

DESIGN:

Retrospective data analysis.

SETTING:

Operating room, postoperative recovery area.

PATIENTS:

Surgical patients whose perioperative data were reported to the Anesthesia Quality Institute's National Anesthesia Clinical Outcomes Registry from 2010 to 2015.

INTERVENTIONS:

We identified all cases that reported unplanned admissions, case cancellations, case delays, and extended recovery room times.

MEASUREMENTS:

Patient demographics, intraoperative characteristics, and provider information were collected for each case. Univariate and multivariate logistic regressions were fitted to determine if these various characteristics were associated with the outcomes of interest.

MAIN RESULTS:

The incidence of unplanned admissions (0.18%), case cancellations (0.05%), extended recovery room stays (1.12%), and case delays (14.43%) were reported. A positive predictor for unplanned admissions included elderly patients (odds ratio [OR], 1.34; 95% confidence interval [CI], 1.16-1.48), whereas cases not performed under general anesthesia had lower rates (P<.001). For case cancellations, higher American Society of Anesthesiologists classes had the highest risk (OR, 2.17; 95% CI, 1.81-2.60). Longer cases and elderly patients are the main predictors for extended postanesthetic care unit stays among all surgeries (OR, 1.54; 95% CI, 1.47-1.62; OR, 1.42; 95% CI, 1.34-1.50, respectively). Pediatric patients and monitored anesthetic care cases had highest odds for case delays (OR, 3.02; 95% CI, 2.93-3.11; OR, 4.98; 95% CI, 4.88-5.07, respectively).

CONCLUSIONS:

This study reports the national incidence and various clinical predictors for these 4 operating room metrics. This can serve as both a resource for operating room managers to compare their practice to national trends and a tool for strategically identifying at-risk surgical cases.

KEYWORDS:

Case cancellations; Case delay; Extended recovery; Operating room; Perioperative; Unplanned admissions

PMID:
27185719
DOI:
10.1016/j.jclinane.2016.01.007
[Indexed for MEDLINE]

Supplemental Content

Full text links

Icon for Elsevier Science
Loading ...
Support Center