Original ContributionNational incidences and predictors of inefficiencies in perioperative care☆,☆☆,★
Introduction
The operating room suite is one of the largest contributors to a hospital's economic success. However, it is also one of the most costly units within this environment [1]. Major sources of costs associated with running an operating room suite stem from unforeseen events and inefficiencies. Unplanned admissions and extended stay in the postanesthetic care unit (PACU) after surgery are examples of such a costly event [2], [3], [4], [5], [6]. Likewise, case cancellations and delays play a major role in the economic burden coming from operating room inefficiencies [7], [8], [9], [10]. Prioritizing patient safety is key; however, balancing this with improving cost efficiency is exceptionally challenging.
Creating highly efficient operating rooms and reducing unforeseen events such as unplanned admissions or extended PACU stays are difficult because of the highly variable presentations of patient problems, operating types, and unexpected clinical events. Additional barriers include existing infrastructure, human resource management, scheduling variation, process flow, technology issues, and information systems [11]. One appropriate step to achieve this goal is for operating room managers to benchmark their metrics against national trends. Of course, this introduces biases because the institutions are very heterogeneous, resulting in comparisons that may not be completely accurate. In any case, it is a strategic starting point, as having these data can provide managers with a tangible goal to reach for the success of their own facilities.
Data from the National Anesthesia Clinical Outcomes Registry (NACOR) was used from the Anesthesia Quality Institute (AQI), the largest anesthesia database in the United States [12]. NACOR has collected data on more than 20% of all anesthetics administered in the United States since 2010, gathering information on all aspects of the patients who undergo anesthesia for various procedures in all settings. The data are collected from a combination of clinical programs that electively approach AQI and include billing records, clinical data, and quality outcomes, which AQI receives electronically and houses securely in their headquarters.
Here, we analyze national trends for unplanned admissions, case cancellations, extended PACU stays, and case delays, as these are all outcomes related to the subject of interest. Furthermore, characteristics related to both patient demographics and intraoperative care were analyzed and tested for association with the aforementioned outcomes.
Section snippets
Data source
Data were collected by NACOR from January 2010 to June 2015 and consisted of 26,568,734 records accumulated through the NACOR from more than 100 heterogeneous sources [12], [13]. NACOR is a voluntary submission registry with institutions that participate in the sharing of anesthesia-related data and outcomes to evaluate the quality of care both nationally and locally [14]. Deidentified data are abstracted from NACOR on a quarterly basis into the Participant User File (PUF), as a tool for
Results
From 2010 to 2015, NACOR contained data for 26,568,734 anesthesia cases, ranging from nonoperating room procedures to surgeries in the operating room, all surgical subspecialties (ie, thoracic surgery, general surgery, neurosurgery, etc), across all age groups throughout the United States. Associated with 1,773,051 of these cases, there are data related to various clinical outcomes related to anesthesia that occurred in medium-sized community hospitals. With a particular interest in studying
Discussion
Four key parameters related to increased hospital costs during the perioperative period were analyzed here: unplanned admissions after surgery, case cancellations, prolonged PACU stay, and case delays. These are all recurring and relatively unexpected events that occur not uncommonly. Although NACOR provides some definitions for these parameters, due to the heterogeneous spread of surgical institutions and their practice styles, variations in these metrics throughout the United States can be
Acknowledgments
The authors would like to thank Benjamin Westlake, Anesthesia Quality Institute, Schaumburg, IL.
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Preventing Surgical Delay and Cancellation with Patient-Centered Interventions
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2020, British Journal of AnaesthesiaCitation Excerpt :This research builds on previous studies in two ways. Although others have predicted PACU LOS,17,19–27,30 none have examined the impact of using these predictions to generate doctor-specific benchmarks. Moreover, although others have recognised the potential value of visual analytics, or business intelligence systems, in anaesthesia,12 none have attempted to incorporate machine learning algorithms into metrics relating to individual anaesthetists.
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This work is attributed to the Department of Anesthesiology, Brigham and Women's Hospital, Boston, MA, and Anesthesia Quality Institute, Schaumberg, IL.
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Meetings at which work has been previously presented: None.
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Disclosures: None.