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Background: The Accreditation Council of Graduate Medical Education requires monitoring of resident clinical and educational hours but does not require tracking daily work patterns or duty hour equity. Lack of such monitoring may allow for inequity that affects resident morale. No defined system for resident relief of weekday operating room (OR) clinical duties existed at our institution, leaving on-call residents to independently decide daily relief order. We developed an automated decision support tool (DST) to improve equitable decision making for clinical relief and assessed its impact on real and perceived relief equity., Methods: The DST sent a daily e-mail to the senior resident responsible for relief decisions. It contained a prioritized relief list of noncall residents who worked in the OR beyond 5 PM the prior clinical day. We assessed actual relief equity using the number of times a resident worked in the OR past 5:30 PM on 2 consecutive weekdays as our outcome, adjusting for the mean number of open ORs each day between 5:00 PM and 6:59 PM in our main OR areas. We analyzed 14 months of data before implementation and 16 months of data after implementation. We assessed perceived relief equity before and after implementation using a questionnaire., Results: After implementing the DST, the percentage of residents held 2 consecutive weekdays over the total of resident days worked decreased from 1.33% to 0.43%. The percentage of residents held beyond 5:30 PM on any given day decreased from 18.09% to 12.64%. Segmented regression analysis indicated that implementation of the DST was associated with a reduction in biweekly time series of residents kept late 2 days in a row, independent of the mean number of ORs in use. Surveyed residents reported the DST aided their ability to make equitable relief decisions (pre 60% versus post 94%; P = .0003). Eighty-five percent of residents strongly agreed that a prioritized relief list based on prior day work hours after 5 PM aided their decision making. After implementation, residents reported fewer instances of working past 5 PM within the past month (P < .005)., Conclusions: A DST systematizing the relief process for anesthesiology residents was associated with a lower frequency of residents working beyond 5:30 PM in the OR on 2 consecutive days. The DST improved the perceived ability to make equitable relief decisions by on-call senior residents and residents being relieved. Success with this tool allows for broader applications in resident education, enabling enhanced monitoring of resident experiences and support for OR assignment decisions.

The widely used American Society of Anesthesiologists Physical Status (ASA PS) classification is subjective, requires manual clinician review to score, and has limited granularity. Our objective was to develop a system that automatically generates an ASA PS with finer granularity by creating a continuous ASA PS score. Supervised machine learning methods were used to create a model that predicts a patient's ASA PS on a continuous scale using the patient's home medications and comorbidities. Three different types of predictive models were trained: regression models, ordinal models, and classification models. The performance and agreement of each model to anesthesiologists were compared by calculating the mean squared error (MSE), rounded MSE and Cohen's Kappa on a holdout set. To assess model performance on continuous ASA PS, model rankings were compared to two anesthesiologists on a subset of ASA PS 3 case pairs. The random forest regression model achieved the best MSE and rounded MSE. A model consisting of three random forest classifiers (split model) achieved the best Cohen's Kappa. The model's agreement with our anesthesiologists on the ASA PS 3 case pairs yielded fair to moderate Kappa values. The results suggest that the random forest split classification model can predict ASA PS with agreement similar to that of anesthesiologists reported in literature and produce a continuous score in which agreement in accurately judging granularity is fair to moderate.

Background: The American Society of Regional Anesthesia and Pain Medicine (ASRA) consensus statement on regional anesthesia in the patient receiving antithrombotic or thrombolytic therapy is the standard for evaluation and management of these patients. The authors hypothesized that an electronic decision support tool (eDST) would improve test performance compared with native physician behavior concerning the application of this guideline., Methods: Anesthesiology trainees and faculty at 8 institutions participated in a prospective, randomized trial in which they completed a 20-question test involving clinical scenarios related to the ASRA guidelines. The eDST group completed the test using an iOS app programmed to contain decision logic and content of the ASRA guidelines. The control group completed the test by using any resource in addition to the app. A generalized linear mixed-effects model was used to examine the effect of the intervention., Results: After obtaining institutional review board's approval and informed consent, 259 participants were enrolled and randomized (eDST = 122; control = 137). The mean score was 92.4 ± 6.6% in the eDST group and 68.0 ± 15.8% in the control group (P < 0.001). eDST use increased the odds of selecting correct answers (7.8; 95% CI, 5.7 to 10.7). Most control group participants (63%) used some cognitive aid during the test, and they scored higher than those who tested from memory alone (76 ± 15% vs. 57 ± 18%, P < 0.001). There was no difference in time to completion of the test (P = 0.15) and no effect of training level (P = 0.56)., Conclusions: eDST use improved application of the ASRA guidelines compared with the native clinician behavior in a testing environment.

The last decade has seen an explosion in the growth of digital data. Since 2005, the total amount of digital data created or replicated on all platforms and devices has been doubling every 2 years, from an estimated 132 exabytes (132 billion gigabytes) in 2005 to 4.4 zettabytes (4.4 trillion gigabytes) in 2013, and a projected 44 zettabytes (44 trillion gigabytes) in 2020. This growth has been driven in large part by the rise of social media along with more powerful and connected mobile devices, with an estimated 75% of information in the digital universe generated by individuals rather than entities. Transactions and communications including payments, instant messages, Web searches, social media updates, and online posts are all becoming part of a vast pool of data that live "in the cloud" on clusters of servers located in remote data centers. The amount of accumulating data has become so large that it has given rise to the term Big Data. In many ways, Big Data is just a buzzword, a phrase that is often misunderstood and misused to describe any sort of data, no matter the size or complexity. However, there is truth to the assertion that some data sets truly require new management and analysis techniques.

Background: Accreditation Council for Graduate Medical Education (ACGME) core competencies of systems-based practice and practice-based learning and improvement are difficult to assess, as they are often not directly measurable or observable. Reviewing day-of-surgery cancellations could provide resident learning opportunities in these areas., Objective: An automated system to facilitate anesthesiology resident review of cancelled cases was implemented on the Preoperative Evaluation Clinic (PEC) rotation at the authors' institution. This study aims to evaluate its impact on resident education., Methods: Residents on the PEC rotation during the 6 months preceding (n = 22) and following (n = 13) implementation in 2014 were surveyed about their experience performing cancelled case reviews in order to ascertain the effect of the intervention on their training., Results: Significant changes were reported in the number of cases reviewed by each resident (p < 0.0001), perceived importance of review (p = 0.03), and ease of review (p = 0.03) after system implementation. There was also an increase in the proportion of cancelled cases reviewed from 17.3% (34 of 196) to 95.6% (194 of 203) (p < 0.0001). Non-significant trends were seen in perceived rotation effect on ACGME competencies, including systems-based practice. Several specific improvements to our clinical practice, including the creation of standardized guidelines, arose from these case reviews., Conclusion: Implementation of automated systems can improve compliance with educational goals by clarifying priorities and simplifying workflow. This system increased the number of cases reviewed by residents and the perceived importance of this review as a part of their educational experience.

Background: Measurement of postoperative pain scores on arrival to the postanesthesia care unit (PACU) is a potential quality metric for supervising anesthesiologists. Our goal in this study was to determine whether rank-ordering by initial PACU numeric rating scale (NRS) pain score, as collected by nurses in a nonresearch clinical setting, could be used to compare anesthesiologists after adjusting for confounding factors., Methods: For a large population of adult patients, the admission PACU NRS pain scores (0-10) were evaluated using proportional odds mixed effects models. Fixed effects included age, gender, race, opioids in the preoperative medication list, American Society of Anesthesiologists (ASA) physical status classification, emergency surgery, laparoscopic approach, outpatient status, anesthesiologist, and PACU nurse; surgeon and surgical procedure were included as random effects., Results: A total of 26,680 initial PACU pain scores were analyzed. The PACU nurse had the largest observed association with initial PACU pain score. Compared with the nurse with the median covariate adjusted NRS score, the odds ratio (OR) for an increased reported pain score ranged from 0.16 (95% confidence interval [CI] 0.11 to 0.24) to 2.95 (95% CI 2.43 to 3.59). For anesthesiologists, the ORs for an increase in reported pain ranged from 0.60 (95% CI 0.37 to 0.99) to 1.44 (95% CI 0.98 to 2.11). Factors associated with increased pain scores were preoperative opioids, female gender, and ASA physical status 2 and 3. Lower pain scores were associated with outpatient surgery, laparoscopy, African American race, and older patients., Conclusions: There is little to no evidence to suggest that supervising anesthesiologists can be compared with one another effectively using admission PACU NRS pain scores. The confounding association of the PACU nurse eliciting the admission pain score greatly exceeded the contribution by the anesthesiologist.

Objectives: Although recent literature has demonstrated the utility of the ASA score in predicting postoperative length of stay, complication risk and potential utilization of other hospital resources, the ASA score has been inconsistently assigned by anaesthesia providers. This study tested the reliability of assignment of the ASA score classification by both attending anaesthesiologists and anaesthesia residents specifically among the orthopaedic trauma patient population., Methods: Nine case-based scenarios were created involving preoperative patients with isolated operative orthopaedic trauma injuries. The cases were created and assigned a reference score by both an attending anaesthesiologist and orthopaedic trauma surgeon. Attending and resident anaesthesiologists were asked to assign an ASA score for each case. Rater versus reference and inter-rater agreement amongst respondents was then analyzed utilizing Fleiss's Kappa and weighted and unweighted Cohen's Kappa., Results: Thirty three individuals provided ASA scores for each of the scenarios. The average rater versus reference reliability was substantial (Kw=0.78, SD=0.131, 95% CI=0.73-0.83). The average rater versus reference Kuw was also substantial (Kuw=0.64, SD=0.21, 95% CI=0.56-0.71). The inter-rater reliability as evaluated by Fleiss's Kappa was moderate (K=0.51, p<.001). An inter-rater comparison within the group of attendings (K=0.50, p<.001) and within the group of residents were both moderate (K=0.55, p<.001). There was a significant increase in the level of inter-rater reliability from the self-reported 'very uncomfortable' participants to the 'very comfortable' participants (uncomfortable K=0.43, comfortable K=0.59, p<.001)., Conclusions: This study shows substantial agreement strength for reliability of the ASA score among anaesthesiologists when evaluating orthopaedic trauma patients. The significant increase in inter-rater reliability based on anaesthesiologists' comfort with the ASA scoring method implies a need for further evaluation of ASA assessment training and routine use on the ground. These findings support the use of the ASA score as a statistically reliable tool in orthopaedic trauma., (Copyright © 2014 Elsevier Ltd. All rights reserved.)

Background: Hypoxemia, as measured by pulse oximetry (SpO2), is common in postanesthesia care unit (PACU) patients. The temporal distribution of desaturation has managerial implications because treatment may necessitate the presence of an anesthesiologist., Methods: We retrieved SpO2 values recorded electronically every 30 to 60 seconds from 137,757 PACU patients over n = 80 four-week periods at an academic medical center. Batch mean methods of analysis were used. Onset times of hypoxemic episodes (defined, on the basis of previous studies, as SpO2 <90% lasting at least 2 minutes) were determined and resolution at 3, 5, and 10 minutes was assessed. Episodes beginning <30 minutes and ≥30 minutes after PACU admission were compared. Patients undergoing intubation in the PACU were identified by doing a free text search of electronically recorded nursing notes for phrases suggesting intubation, followed by a confirmatory manual chart review. Intervals from PACU admission to intubation were determined., Results: Fewer than half (31.2% ± 0.05%) of episodes of PACU hypoxemia lasting ≥2 minutes occurred <30 minutes after PACU admission. Most (i.e., >50%) occurred ≥30 minutes after admission (P < 0.0001). Few (<1%) anesthesia providers transporting patients to the PACU were still present in the PACU 30 minutes after arrival in the PACU. Fewer than half (37%; 95% confidence interval, 27.4% to 48.8%) of PACU intubations occurred <30 minutes after PACU admission. Most (i.e., >50%) occurred ≥30 minutes after admission (P = 0.029). Hypoxemic episodes in the PACU resolved more slowly than episodes in operating rooms (P < 0.0001). After 3 minutes, 40.9% ± 0.6% were unresolved in the PACU versus 23% (99% upper confidence limit) in operating rooms, and 32.6% ± 0.5% vs 9% (99% upper confidence limit) after 5 minutes., Conclusions: Because most (68.8%) hypoxemic episodes in the PACU occur ≥30 minutes after admission, a time by which the anesthesia provider who transported the patient usually would no longer be present (>99% of cases), the PACU needs to be considered when anesthesiologist operating room staffing and assignment decisions are made.

The balance between reducing costs and inefficiencies with that of patient safety is a challenging problem faced in the operating room suite. An ongoing challenge is the creation of effective strategies that reduce these inefficiencies and provide real-time personalized metrics and electronic feedback to anesthesia practitioners. We created a sample report card structure, utilizing existing informatics systems. This system allows to gather and analyze operating room metrics for each anesthesia provider and offer personalized feedback. To accomplish this task, we identified key metrics that represented time and quality parameters. We collected these data for individual anesthesiologists and compared performance to the overall group average. Data were presented as an electronic score card and made available to individual clinicians on a real-time basis in an effort to provide effective feedback. These metrics included number of cancelled cases, average turnover time, average time to operating room ready and patient in room, number of delayed first case starts, average induction time, average extubation time, average time to recovery room arrival to discharge, performance feedback from other providers, compliance to various protocols, and total anesthetic costs. The concept we propose can easily be generalized to a variety of operating room settings, types of facilities and OR health care professionals. Such a scorecard can be created using content that is important for operating room efficiency, research, and practice improvement for anesthesia providers.

Purpose: Measuring and providing performance feedback to physicians has gained momentum not only as a way to comply with regulatory requirements, but also as a way to improve patient care. Measurement of structural, process, and outcome metrics in a reliable, evidence-based, specialty-specific manner maximizes the probability of improving physician performance. The manner in which feedback is provided influences whether the measurement tool will be successful in changing behavior. We created an innovative reporting tool template for anesthesiology practitioners designed to provide detailed, continuous feedback covering many aspects of clinical practice., Methods: The literature regarding quality metric measurement and feedback strategies was examined to design a reporting tool that could provide high quality information and result in improved performance of clinical and academic tasks. A committee of department leaders and information technology professionals was tasked with determining the measurement criteria and infrastructure needed to generate these reports. Data was collected in a systematic, unbiased manner, and reports were populated with information from multiple databases and software systems. Feedback would be based on frequently updated information and allow for analysis of historical performance as well as comparison amongst peers., Results: A template for an anesthesia report card was created. Categories included compliance, credentialing and qualifications, education, clinical and operating room responsibilities, and academic achievements. Physicians were able to choose to be evaluated in some of the categories and had to meet a minimum number of criteria within each category. This allowed for customization to each practitioner's practice. Criteria were derived from the measures of academic and clinical proficiency, as well as quality metrics. Criteria were objective measures and data gathering was often automated. Reports could be generated that were updated daily and provided historical information, and information about peers in the department and within each subspecialty group., Conclusions: We demonstrate the creation of an online anesthesia report card that incorporates metrics most likely to engender positive changes in practice and academic responsibilities. This tool provides timely and customized information for each anesthesia practitioner, designed to be easily modifiable to improve the quantity, quality, and substance of metrics being measured. Finally, our tool could serve as a template for a performance measuring tool that can be customizable to a wide variety of practice settings, and upon which both monetary and non-monetary incentives might be based in the future.

Background: The 2007 American College of Cardiologists/American Heart Association Guidelines on Perioperative Cardiac Evaluation and Care for Noncardiac Surgery is the standard for perioperative cardiac evaluation. Recent work has shown that residents and anesthesiologists do not apply these guidelines when tested. This research hypothesized that a decision support tool would improve adherence to this consensus guideline., Methods: Anesthesiology residents at four training programs participated in an unblinded, prospective, randomized, cross-over trial in which they completed two tests covering clinical scenarios. One quiz was completed from memory and one with the aid of an electronic decision support tool. Performance was evaluated by overall score (% correct), number of incorrect answers with possibly increased cost or risk of care, and the amount of time required to complete the quizzes both with and without the cognitive aid. The primary outcome was the proportion of correct responses attributable to the use of the decision support tool., Results: All anesthesiology residents at four institutions were recruited and 111 residents participated. Use of the decision support tool resulted in a 25% improvement in adherence to guidelines compared with memory alone (P < 0.0001), and participants made 77% fewer incorrect responses that would have resulted in increased costs. Use of the tool was associated with a 3.4-min increase in time to complete the test (P < 0.001)., Conclusions: Use of an electronic decision support tool significantly improved adherence to the guidelines as compared with memory alone. The decision support tool also prevented inappropriate management steps possibly associated with increased healthcare costs.

Background: Anesthesiology residencies are developing trainee assessment tools to evaluate 25 milestones that map to the six core competencies. The effort will be facilitated by development of automated methods to capture, assess, and report trainee performance to program directors, the Accreditation Council for Graduate Medical Education and the trainees themselves., Methods: The authors leveraged a perioperative information management system to develop an automated, near-real-time performance capture and feedback tool that provides objective data on clinical performance and requires minimal administrative effort. Before development, the authors surveyed trainees about satisfaction with clinical performance feedback and about preferences for future feedback., Results: Resident performance on 24,154 completed cases has been incorporated into the authors' automated dashboard, and trainees now have access to their own performance data. Eighty percent (48 of 60) of the residents responded to the feedback survey. Overall, residents "agreed/strongly agreed" that they desire frequent updates on their clinical performance on defined quality metrics and that they desired to see how they compared with the residency as a whole. Before deployment of the new tool, they "disagreed" that they were receiving feedback in a timely manner. Survey results were used to guide the format of the feedback tool that has been implemented., Conclusion: The authors demonstrate the implementation of a system that provides near-real-time feedback concerning resident performance on an extensible series of quality metrics, and which is responsive to requests arising from resident feedback about desired reporting mechanisms.

Background: The Massachusetts General Hospital (Boston), a large academic center providing anesthesia services for more than 49,000 procedures each year, created an Ongoing Professional Practice Evaluation (OPPE) process that could use readily available, automatically captured electronic information from its vendor-provided anesthesia information management system., Methods: The OPPE credentialing committee selected the following initial metrics: Blood pressure (BP) monitoring, end tidal CO2 monitoring, and timely documentation of compliance statements. Baseline data on the metrics were collected in an eight-month period (January 1, 2008-August 31, 2008). In February 2009 information on the metrics was provided to the department's staff members, and the ongoing evaluation process began. On the basis of three months of data, final reports for physicians being credentialed were distributed. Each report included a listing for each metric of the total number of compliant cases and noncompliant cases and a comparison by percentage to the baseline departmental evaluation. A summary statement indicated whether a physician's performance was within the group representing 95% of all department physicians. Noncompliant cases were listed by medical record number and case date so providers and reviewers could examine individual cases., Conclusion: A novel, automated, and continuous reporting system for physician credentialing that uses the existing clinical information system infrastructure can serve as a key element of a comprehensive clinical performance evaluation that measures both technical and generalizable clinical skill sets. It is not intended to provide a complete system for measuring competence but rather to serve as a first-round warning mechanism and metric scoring tool to identify problems and potential performance noncompliance issues.

Adoption of information systems throughout the hospital environment has enabled the development of real-time physiologic alerts and clinician reminder systems. These clinical tools can be made available through the deployment of anesthesia information management systems (AIMS). Creating usable alert systems requires understanding of technical considerations. Various successful implementations are reviewed, encompassing cost reduction, improved revenue capture, timely antibiotic administration, and postoperative nausea and vomiting prophylaxis. Challenges to the widespread use of real-time alerts and reminders include AIMS adoption rates and the difficulty in choosing appropriate areas and approaches for information systems support., (Copyright © 2011 Elsevier Inc. All rights reserved.)

The functionality and rate of implementation of Anesthesia Information Management Systems (AIMS) has markedly risen over the past decade. These systems have now become much more than the generic automated record keepers, originally proposed and developed in the 1980s. AIMS have now become complex integrated systems, which have been shown to improve patient care and, in some cases, the financial performance of a department. Although the underlying technology has improved greatly over the past 5 years, the process of selecting and completing an AIMS installation still presents a number of challenges, and must be approached carefully in order to maximize the benefits provided by these systems.

Double lumen tubes (DLT) are commonly used to achieve lung isolation (LI). Not all anesthesiologists are frequent DLT users. Our thoracic surgical service is covered by sub-specialty anesthesiologists who are frequent DLT users. Thus, we are in a position to evaluate the performance of infrequent DLT users relative to frequent DLT users. Using statistical methods, we examined the incidence, duration and severity of hypoxia, hypercapnea and high airway pressures for patients receiving LI via DLTs placed by infrequent versus frequent users. The incidence of low SpO2, high EtCO2, or high PIP was not different between frequent and infrequent DLT users. However, when these events do occur, they are more severe (elevated EtCO2 duration, lower SpO2, higher EtCO2, higher airway pressure) among infrequent than frequent DLT users. The practical significance of these differences, which are small, is unproven. However, when episodes of hypercapnea do occur, they last much longer among infrequent than frequent DLT users. [ABSTRACT FROM AUTHOR]

Background: Rapid response teams (RRT) are used to prevent adverse events in patients with acute clinical deterioration, and to save costs of unnecessary transfer in patients with lower-acuity problems. However, determining the optimal use of RRT services is challenging. One method of benchmarking performance is to determine whether a department's event rate is commensurate with its volume and acuity. Study Design: Using admissions between 2009 and 2011 to 18 distinct surgical services at a tertiary care center, we developed logistic regression models to predict RRT activation, accounting for days at-risk for RRT and patient acuity, using claims modifiers for risk of mortality (ROM) and severity of illness (SOI). The model was used to compute observed-to-expected (O/E) RRT use by service. Results: Of 45,651 admissions, 728 (1.6%, or 3.2 per 1,000 inpatient days) resulted in 1 or more RRT activations. Use varied widely across services (0.4% to 6.2% of admissions; 1.39 to 8.73 per 1,000 inpatient days, unadjusted). In the multivariable model, the greatest contributors to the likelihood of RRT were days at risk, SOI, and ROM. The O/E RRT use ranged from 0.32 to 2.82 across services, with 8 services having an observed value that was significantly higher or lower than predicted by the model. Conclusions: We developed a tool for identifying outlying use of an important institutional medical resource. The O/E computation provides a starting point for further investigation into the reasons for variability among services, and a benchmark for quality and process improvement efforts in patient safety. [ABSTRACT FROM AUTHOR]

Study Objective: Protocol changes at Vanderbilt have been adopted with the intention of reducing unnecessary preoperative testing. We sought to evaluate their success and association with clinical decisions.Design: Retrospective Observational Study SETTING: Vanderbilt's Preoperative Evaluation Clinic MEASUREMENTS: We reviewed and identified a key interval of change on clinical workup protocols which led to a reduction in preoperative testing. We queried Data Warehouse for preoperative chemistry tests, complete blood counts, coagulation blood draws, electrocardiograms, and chest x-rays done before and after these intervals. Chi-square, univariate and mixed effect multivariable regressions were performed to determine the significance of testing reduction and tendency of readmission rates and length-of-stay; Welch's t-test with Freeman-Tukey transformation was conducted to identify the differences in case cancellation rates.Main Results: We analyzed 56,425 anesthetic cases and there was a statistically significant downward trend in all preoperative testing performed: electrocardiograms (61.90% to 31.66% [OR 0.151; 95% CI 0.144 to 0.159]), coagulation blood draws (37.57% to 29.74% [OR 0.392; 95% CI 0.370 to 0.416]), basic metabolic panels (70.64% to 51.29% [OR 0.294; 95% CI 0.280 to 0.309]), blood cell counts (71.38% to 51.42% [OR 0.264; 95% CI 0.251 to 0.277]) and chest x-rays (11.80% to 6.04% [OR 0.417; 95% CI 0.384 to 0.452], to 3.13% [OR 0.473; 95% CI 0.431 to 0.519]) after protocol changed. The changes didn't induce a significant increase in case cancellations, length-of-stay, readmission or most DOS testing; except for BMPs (0.28% to 0.66% [OR 1.307; 95% CI 1.104 to 1.549]).Conclusions: A net reduction in preoperative testing was seen at our institution from 2012 to 2015 due to anesthesia protocol changes intended to limit routine ordering of labs and imaging. While there was a significant increase in DOS testing for BMPs, these increases were not enough to offset the decrease in testing observed preoperatively. [ABSTRACT FROM AUTHOR]