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With increasing adoption of anesthesia information management systems (AIMS), there is growing interest in utilizing AIMS data for intraoperative clinical decision support (CDS). CDS for anesthesia has the potential for improving quality of care, patient safety, billing, and compliance. Intraoperative CDS can range from passive and post hoc systems to active real-time systems that can detect ongoing clinical issues and deviations from best practice care. Real-time CDS holds the most promise because real-time alerts and guidance can drive provider behavior toward evidence-based standardized care during the ongoing case. In this review, we describe the different types of intraoperative CDS systems with specific emphasis on real-time systems. The technical considerations in developing and implementing real-time CDS are systematically covered. This includes the functional modules of a CDS system, development and execution of decision rules, and modalities to alert anesthesia providers concerning clinical issues. We also describe the regulatory aspects that affect development, implementation, and use of intraoperative CDS. Methods and measures to assess the effectiveness of intraoperative CDS are discussed. Last, we outline areas of future development of intraoperative CDS, particularly the possibility of providing predictive and prescriptive decision support.

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OBJECTIVE: To understand the decisional practices of anesthesia providers in managing intraoperative glucose levels. DESIGN: This is a retrospective cohort study. SETTING: Operating rooms in an academic medical center. PATIENTS: Adult patients undergoing surgery. INTERVENTION: Intraoperative blood glucose management based on an institutional protocol. MEASUREMENTS: Glucose management data was extracted from electronic medical records to determine compliance to institutional glucose management protocol that prescribes hourly glucose measurements and insulin doses to maintain glucose levels between 100 to 140mg/dL. Effect of patient and surgery specific factors on compliance to glucose management protocol was explored. MAIN RESULTS: In 1903 adult patients compliances to hourly glucose measurements was 72.5% and correct insulin adjustments was 12.4%. Insulin was under-dosed compared to the prescribed value by a mean of 0.85U/h (95% CI 0.76-0.95). Multivariate analysis showed that compliance to hourly glucose measurements decreased with increasing length of the procedure (OR=0.92 per hour, 95% CI 0.89-0.95) but increased with ASA status codes (OR=1.25 per ASA unit, 95% CI=1.06-1.49). Greater compliance to correct insulin adjustment was found in diabetic patients compared with non-diabetic patients (OR=1.31, 95% CI 1.09-1.55). On average, providers administered progressively more insulin with an additional 0.11U/h (95% CI=0.00-0.21] for every additional 10kg/m(2) of BMI and 0.20U/h (95% CI=0.01-0.39) less in diabetic patients than in non-diabetic patients. With the above practice pattern, the mean±SD of glucose level was 158±36mg/dL. Hypoglycemic (<60mg/dL) incident rate was 0.1% (9/8301 measurements) while hyperglycemic (>180mg/dL) incident rate was 28%. Glucose levels were within the target range (100-140mg/dL) only 28% of the time. CONCLUSIONS: Low compliance and considerable variability in initiating and following institutional glucose management protocol were observed.

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Poor perioperative glycemic management can lead to negative surgical outcome. Improved compliance to glucose control protocol could lead to better glucose management. An Anesthesia Information Management System based decision support system-Smart Anesthesia Manager™ (SAM) was used to generate real-time reminders to the anesthesia providers to closely adhere to our institutional glucose management protocol. Compliance to hourly glucose measurements and correct insulin dose adjustments was compared for the baseline period (12 months) without SAM and the intervention period (12 months) with SAM decision support. Additionally, glucose management parameters were compared for the baseline and intervention periods. A total of 1587 cases during baseline and 1997 cases during intervention met the criteria for glucose management (diabetic patients or non-diabetic patients with glucose level >140 mg/dL). Among the intervention cases anesthesia providers chose to use SAM reminders 48.7 % of the time primarily for patients who had diabetes, higher HbA1C or body mass index, while disabling the system for the remaining cases. Compliance to hourly glucose measurement and correct insulin doses increased significantly during the intervention period when compared with the baseline (from 52.6 to 71.2 % and from 13.5 to 24.4 %, respectively). In spite of improved compliance to institutional protocol, the mean glucose levels and other glycemic management parameters did not show significant improvement with SAM reminders. Real-time electronic reminders improved intraoperative compliance to institutional glucose management protocol though glycemic parameters did not improve even when there was greater compliance to the protocol.

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BACKGROUND: Intraoperative hypotension and hypertension are associated with adverse clinical outcomes and morbidity. Clinical decision support mediated through an anesthesia information management system (AIMS) has been shown to improve quality of care. We hypothesized that an AIMS-based clinical decision support system could be used to improve management of intraoperative hypotension and hypertension. METHODS: A near real-time AIMS-based decision support module, Smart Anesthesia Manager (SAM), was used to detect selected scenarios contributing to hypotension and hypertension. Specifically, hypotension (systolic blood pressure <80 mm Hg) with a concurrent high concentration (>1.25 minimum alveolar concentration [MAC]) of inhaled drug and hypertension (systolic blood pressure >160 mm Hg) with concurrent phenylephrine infusion were detected, and anesthesia providers were notified via "pop-up" computer screen messages. AIMS data were retrospectively analyzed to evaluate the effect of SAM notification messages on hypotensive and hypertensive episodes. RESULTS: For anesthetic cases 12 months before (N = 16913) and after (N = 17132) institution of SAM messages, the median duration of hypotensive episodes with concurrent high MAC decreased with notifications (Mann Whitney rank sum test, P = 0.031). However, the reduction in the median duration of hypertensive episodes with concurrent phenylephrine infusion was not significant (P = 0.47). The frequency of prolonged episodes that lasted >6 minutes (sampling period of SAM), represented in terms of the number of cases with episodes per 100 surgical cases (or percentage occurrence), declined with notifications for both hypotension with >1.25 MAC inhaled drug episodes (δ = -0.26% [confidence interval, -0.38% to -0.11%], P < 0.001) and hypertension with phenylephrine infusion episodes (δ = -0.92% [confidence interval, -1.79% to -0.04%], P = 0.035). For hypotensive events, the anesthesia providers reduced the inhaled drug concentrations to <1.25 MAC 81% of the time with notifications compared with 59% without notifications (P = 0.003). For hypertensive episodes, although the anesthesia providers' reduction or discontinuation of the phenylephrine infusion increased from 22% to 37% (P = 0.030) with notification messages, the overall response was less consistent than the response to hypotensive episodes. CONCLUSIONS: With automatic acquisition of arterial blood pressure and inhaled drug concentration variables in an AIMS, near real-time notification was effective in reducing the duration and frequency of hypotension with concurrent >1.25 MAC inhaled drug episodes. However, since phenylephrine infusion is manually documented in an AIMS, the impact of notification messages was less pronounced in reducing episodes of hypertension with concurrent phenylephrine infusion. Automated data capture and a higher frequency of data acquisition in an AIMS can improve the effectiveness of an intraoperative clinical decision support system.

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BACKGROUND: Since there are limitations on the amount of time residents can spend in full-scale simulation sessions, we introduced a virtual patient application into our anesthesiology program to su pplement mannequin-based simulation sessions. Previous investigations have demonstrated a positive educational effect for virtual patients, but suggest that further research is needed to clarify how to effectively implement virtual patients in medical education. We present a description of the implementation of a virtual patient application in our residency training program, the residents' evaluation of their experience with the application, and a cost analysis of incorporation of the application into the residency program, in order to determine the residents' perceptions of the value and estimate the cost of using virtual patients in anesthesia residency training. MATERIALS AND METHODS: For 20 years all anesthesia residents in our training program have been required to complete ten simulated cases using a virtual patient application prior to the end of their CA-1 year. Residents are given access to the anesthesia virtual patient application on departmental computers and also on their own personal computers. Residents complete 10 required cases on their own or in pairs and send printed case logs to an anesthesia attending for review and feedback. Participants anonymously completed surveys rating their perceptions of the virtual patient application's effectiveness. Cost to implement this program was estimated retrospectively. RESULTS: In total, 404 residents completed 3593 virtual patient cases in approximately 2800 hours. RESIDENT PERCEPTIONS: 252 residents completed the anonymous survey (62%). Almost all the respondents (97%) rated the virtual patient curriculum as worthwhile; 88% rated the application to be realistic; 97% felt better prepared to handle anesthesia-related critical incidents; 87% stated they had at least one event in a real operating room similar to an emergency presented in the virtual patient application, and 40% stated they experienced more than one such event. 93% were stimulated to read about management of anesthesia-related critical incidents after using the application. COST-ANALYSIS: The estimated cost to implement the screen-based curriculum for 20 years was $44,000 including the cost of software and faculty time. Therefore the cost for the 2800 hours of virtual patient simulation was about $16 per hour. CONCLUSIONS: The anesthesia virtual patient application was easily incorporated into our residency training program at the University of Washington. The application was well-received by anesthesia residents, helped them feel more prepared to manage critical incidents, and stimulated them to read more concerning the management of anesthesia-related critical incidents. The virtual patient application is far less expensive than mannequin-based simulation and much more available. Therefore, virtual patients should be considered an easily accessible supplement to mannequin-based simulation training.

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BACKGROUND: Reduced consumption of inhalation anesthetics can be safely achieved by reducing excess fresh gas flow (FGF). In this study the authors describe the use of a real-time decision support tool to reduce excess FGF to lower, less wasteful levels. METHOD: The authors applied a decision support tool called the Smart Anesthesia Manager™ (University of Washington, Seattle, WA) that analyzes real-time data from an Anesthesia Information Management System to notify the anesthesia team if FGF exceeds 1 l/min. If sevoflurane consumption reached 2 minimum alveolar concentration-hour under low flow anesthesia (FGF < 2 l/min), a second message was generated to increase FGF to 2 l/min, to comply with Food and Drug Administration guidelines. To evaluate the tool, mean FGF between surgical incision and the end of procedure was compared in four phases: (1) a baseline period before instituting decision rules, (2) Intervention-1 when decision support to reduce FGF was applied, (3) Intervention-2 when the decision rule to reduce flow was deliberately inactivated, and (4) Intervention-3 when decision rules were reactivated. RESULTS: The mean ± SD FGF reduced from 2.10 ± 1.12 l/min (n = 1,714) during baseline to 1.60 ± 1.01 l/min (n = 2,232) when decision rules were instituted (P < 0.001). When the decision rule to reduce flow was inactivated, mean FGF increased to 1.87 ± 1.15 l/min (n = 1,732) (P < 0.001), with an increasing trend in FGF of 0.1 l/min/month (P = 0.02). On reactivating the decision rules, the mean FGF came down to 1.59 ± 1.02 l/min (n = 1,845). Through the Smart Anesthesia Messenger™ system, the authors saved 9.5 l of sevoflurane, 6.0 l of desflurane, and 0.8 l isoflurane per month, translating to an annual savings of $104,916. CONCLUSIONS: Real-time notification is an effective way to reduce inhalation agent usage through decreased excess FGFs.

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Blood pressure monitoring during anesthesia is an American Society of Anesthesiology standard. However, the anesthesia provider sometimes fails to engage the patient monitor to make periodic (generally every 3-5 min) measurements of Non-Invasive Blood Pressure (NIBP), which can lead to extended periods (>5 min) when blood pressure is not monitored. We describe a system to automatically detect such gaps in NIBP measurement and notify clinicians in real-time to initiate measurement. We applied a decision support system called the Smart Anesthesia Messenger (SAM) to notify the anesthesia provider if NIBP measurements have not been made in the last 7 min. Notification messages were generated only if direct arterial blood pressure was not being monitored. NIBP gaps were analyzed for 9 months before and after SAM notification was initiated (12,000 cases for each period). SAM notification was able to reduce the occurrence of extended NIBP gaps >15 min from 15.7 ± 4.5 to 6.7 ± 2.0 instances per 1,000 cases (p < 0.001). In addition, for extended gaps (>15 min) the mean gap duration declined from 23.1 ± 2.0 to 18.6 ± 1.1 min after SAM notification was initiated (p < 0.001). However, for 7-15 min gaps, SAM notification was not effective in reducing the occurrence. The maximum gap encountered before SAM was 64 min, while it was 27 min with SAM notification. Real-time notification using SAM is an effective way to reduce both the number of instances and the duration of inadvertent, extended (>15 min) gaps in blood pressure measurements in the operating room. However, the frequency of gaps <15 min could not be reduced using the current configuration of SAM.

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Anesthesia information management systems (AIMS) are being increasingly used in the operating room to document anesthesia care. We developed a system, Smart Anesthesia Manager™ (SAM) that works in conjunction with an AIMS to provide clinical and billing decision support. SAM interrogates AIMS database in near real time, detects issues related to clinical care, billing and compliance, and material waste. Issues and the steps for their resolution are brought to the attention of the anesthesia provider in real time through "pop-up" messages overlaid on top of AIMS screens or text pages. SAM improved compliance to antibiotic initial dose and redose to 99.3 ± 0.7% and 83.9 ± 3.4% from 88.5 ± 1.4% and 62.5 ± 1.6%, respectively. Beta-blocker protocol compliance increased to 94.6 ± 3.5% from 60.5 ± 8.6%. Inadvertent gaps (>15 min) in blood pressure monitoring were reduced to 34 ± 30 min/1000 cases from 192 ± 58 min/1000 cases. Additional billing charge capture of invasive lines procedures worth $144,732 per year and 1,200 compliant records were achieved with SAM. SAM was also able to reduce wastage of inhalation anesthetic agents worth $120,168 per year.

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BACKGROUND: Continuation of perioperative beta-blockers for surgical patients who are receiving beta-blockers prior to arrival for surgery is an important quality measure (SCIP-Card-2). For this measure to be considered successful, name, date, and time of the perioperative beta-blocker must be documented. Alternately, if the beta-blocker is not given, the medical reason for not administering must be documented. METHODS: Before the study was conducted, the institution lacked a highly reliable process to document the date and time of self-administration of beta-blockers prior to hospital admission. Because of this, compliance with the beta-blocker quality measure was poor (-65%). To improve this measure, the anesthesia care team was made responsible for documenting perioperative beta-blockade. Clear documentation guidelines were outlined, and an electronic Anesthesia Information Management System (AIMS) was configured to facilitate complete documentation of the beta-blocker quality measure. In addition, real-time electronic alerts were generated using Smart Anesthesia Messenger (SAM), an internally developed decision-support system, to notify users concerning incomplete beta-blocker documentation. RESULTS: Weekly compliance for perioperative beta-blocker documentation before the study was 65.8 +/- 16.6%, which served as the baseline value. When the anesthesia care team started documenting perioperative beta-blocker in AIMS, compliance was 60.5 +/- 8.6% (p = .677 as compared with baseline). Electronic alerts with SAM improved documentation compliance to 94.6 +/- 3.5% (p < .001 as compared with baseline). CONCLUSIONS: To achieve high compliance for the beta-blocker measure, it is essential to (1) clearly assign a medical team to perform beta-blocker documentation and (2) enhance features in the electronic medical systems to alert the user concerning incomplete documentation.

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BACKGROUND: Timely re-dosing of antibiotic for prolonged surgical cases is an important measure in reducing the risk of surgical site infections. For the anesthesia team, which generally administers the antibiotic re-doses, it is difficult to keep track of and remember the exact timing requirements. We explored the efficacy of two types of electronic reminders to aid the anesthesia team in performing timely antibiotic re-doses. METHODS: The first electronic reminder was a timer-triggered "blinking button" feature in the Anesthesia Information Management System (AIMS). The second was generated with a real-time decision support system, the Smart Anesthesia Messenger (SAM). The AIMS reminder was applied for the first five months of the study, whereas the SAM reminder was applied for the second five months. A retrospective analysis was performed to evaluate the efficacy of the reminder messages in improving the antibiotic re-dose success rate. RESULTS: In a total of 940 cases, the anesthesia team was reminded of the need for antibiotic re-dosing with AIMS, whereas in 922 cases, the SAM system gave the reminder. The AIMS reminders achieved a timely re-dose success rate of 62.5% ± 1.6%, whereas the SAM reminders achieved a significantly higher success rate: 83.9% ± 3.4% (p < 0.001). CONCLUSIONS: Compared with the simple reminders generated with AIMS, the relevant, informative messages generated with SAM were more effective in improving compliance with timely antibiotic re-doses.

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BACKGROUND: Administration of prophylactic antibiotics during surgery is generally performed by the anesthesia providers. Timely antibiotic administration within the optimal time window before incision is critical for prevention of surgical site infections. However, this often becomes a difficult task for the anesthesia team during the busy part of a case when the patient is being anesthetized. METHODS: Starting with the implementation of an anesthesia information management system (AIMS), we designed and implemented several feedback mechanisms to improve compliance of proper antibiotic delivery and documentation. This included generating e-mail feedback of missed documentation, distributing monthly summary reports, and generating real-time electronic alerts with a decision support system. RESULTS: In 20,974 surgical cases for the period, June 2008 to January 2010, the interventions of AIMS install, e-mail feedback, summary reports, and real-time alerts changed antibiotic compliance by -1.5%, 2.3%, 4.9%, and 9.3%, respectively, when compared with the baseline value of 90.0% ± 2.9% when paper anesthesia records were used. Highest antibiotic compliance was achieved when using real-time alerts. With real-time alerts, monthly compliance was >99% for every month between June 2009 and January 2010. CONCLUSIONS: Installation of AIMS itself did not improve antibiotic compliance over that achieved with paper anesthesia records. However, real-time guidance and reminders through electronic messages generated by a computerized decision support system (Smart Anesthesia Messenger, or SAM) significantly improved compliance. With such a system a consistent compliance of >99% was achieved.

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OBJECTIVE: To develop a computer screen-based simulator that may be used as a pediatric advanced life support (PALS) cognitive skill assessment tool and to pilot test a consensus-based scoring system for the simulator. DESIGN: Development of an evaluation tool, followed by prospective, observational study of tool performance. SETTING: Tertiary care pediatric hospital. SUBJECTS: A total of 100 PALS providers from multiple disciplines. INTERVENTIONS: Using a consensus process with a group of six experts in pediatric emergency and critical care medicine, we developed scoring algorithms to measure performance on four interactive PALS scenarios (supraventricular tachycardia, pulseless electrical activity, ventricular fibrillation, and bradycardia). PALS providers (n = 100) completed the scenarios on the simulator and the computer assessed their performance using the scoring algorithm. MEASUREMENTS AND MAIN RESULTS: Case management scoring audits agreed 100% with computer scoring during pilot testing, indicating excellent reliability. The mean time to complete all four cases was 13.8 mins. Performance scores were highest for supraventricular tachycardia management and lowest for pulseless electrical activity management. Survival was significantly more common than death in the supraventricular tachycardia and ventricular fibrillation scenarios, whereas death was more common in the pulseless electrical activity scenario (p < .004). Physician status predicted a higher aggregate score as well as higher scores in the supraventricular tachycardia (p < .001), pulseless electrical activity (p = .041), and bradycardia (p = .006) scenarios. Participants who completed the PALS course on the same day as their assessment scored higher on the supraventricular tachycardia scenario (p = .041). CONCLUSIONS: Personal computer-based simulation can be used to evaluate performance against consensus criteria in a large number of PALS providers. This technology could supplement traditional curricula by facilitating frequent knowledge assessments as part of a PALS competency maintenance regimen.

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Sharing simulator case scenarios among educators will greatly reduce the cost and effort involved in bringing simulation to our students. A library of 72 cases was posted to the internet to freely share. The cases in the library consist of text files utilizing a simple open-source format. In the first four months after posting, cases were downloaded over 18,000 times. In addition, the library encouraged the development of 23 new cases by 16 new contributors. This experience indicates a strong interest in obtaining free cases, but only modest willingness to share new scenarios among authors and institutions.

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OBJECTIVES: This study intended to create symptom-based triage algorithms for the initial encounter with terror-attack victims. The goals of the triage algorithms include: (1) early recognition; (2) avoiding contamination; (3) early use of antidotes; (4) appropriate handling of unstable, contaminated victims; and (5) provisions of force protection. The algorithms also address industrial accidents and emerging infections, which have similar clinical presentations and risks for contamination as weapons of mass destruction (WMD). METHODS: The algorithms were developed using references from military and civilian sources. They were tested and adjusted using a series of theoretical patients from a CD-ROM chemical, biological, radiological/nuclear, and explosive victim simulator. Then, the algorithms were placed into a card format and sent to experts in relevant fields for academic review. RESULTS: Six inter-connected algorithms were created, described, and presented in figure form. The "attack" algorithm, for example, begins by differentiating between overt and covert attack victims (A covert attack is defined by epidemiological criteria adapted from the Centers for Disease Control and Prevention (CDC) recommendations). The attack algorithm then categorizes patients either as stable or unstable. Unstable patients flow to the "Dirty Resuscitation" algorithm, whereas, stable patients flow to the "Chemical Agent" and "Biological Agent" algorithms. The two remaining algorithms include the "Suicide Bomb/Blast/Explosion" and the "Radiation Dispersal Device" algorithms, which are inter-connected through the overt pathway in the "Attack" algorithm. CONCLUSION: A civilian, symptom-based, algorithmic approach to the initial encounter with victims of terrorist attacks, industrial accidents, or emerging infections was created. Future studies will address the usability of the algorithms with theoretical cases and utility in prospective, announced and unannounced, field drills. Additionally, future studies will assess the effectiveness of teaching modalities used to reinforce the algorithmic approach.

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BACKGROUND: Anesthesia simulators can generate reproducible, standardized clinical scenarios for instruction and evaluation purposes. Valid and reliable simulated scenarios and grading systems must be developed to use simulation for evaluation of anesthesia residents. METHODS: After obtaining Human Subjects approval at each of the 10 participating institutions, 99 anesthesia residents consented to be videotaped during their management of four simulated scenarios on MedSim or METI mannequin-based anesthesia simulators. Using two different grading forms, two evaluators at each department independently reviewed the videotapes of the subjects from their institution to score the residents' performance. A third evaluator, at an outside institution, reviewed the videotape again. Statistical analysis was performed for construct- and criterion-related validity, internal consistency, interrater reliability, and intersimulator reliability. A single evaluator reviewed all videotapes a fourth time to determine the frequency of certain management errors. RESULTS: Even advanced anesthesia residents nearing completion of their training made numerous management errors; however, construct-related validity of mannequin-based simulator assessment was supported by an overall improvement in simulator scores from CB and CA-1 to CA-2 and CA-3 levels of training. Subjects rated the simulator scenarios as realistic (3.47 out of possible 4), further supporting construct-related validity. Criterion-related validity was supported by moderate correlation of simulator scores with departmental faculty evaluations (0.37-0.41, P < 0.01), ABA written in-training scores (0.44-0.49, < 0.01), and departmental mock oral board scores (0.44-0.47, P < 0.01). Reliability of the simulator assessment was demonstrated by very good internal consistency (alpha = 0.71-0.76) and excellent interrater reliability (correlation = 0.94-0.96; P < 0.01; kappa = 0.81-0.90). There was no significant difference in METI versus MedSim scores for residents in the same year of training. CONCLUSIONS: Numerous management errors were identified in this study of anesthesia residents from 10 institutions. Further attention to these problems may benefit residency training since advanced residents continued to make these errors. Evaluation of anesthesia residents using mannequin-based simulators shows promise, adding a new dimension to current assessment methods. Further improvements are necessary in the simulation scenarios and grading criteria before mannequin-based simulation is used for accreditation purposes.

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