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Anesthesia Information Management System-Based Near Real-Time Decision Support to Manage Intraoperative Hypotension and Hypertension

Nair, Bala G. PhD*; Horibe, Mayumi MD; Newman, Shu-Fang MS*; Wu, Wei-Ying PhD; Peterson, Gene N. MD, PhD*; Schwid, Howard A. MD*

doi: 10.1213/ANE.0000000000000027
Economics, Education, and Policy: Research Report
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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.

Published ahead of print November 15, 2013

From the *Department of Anesthesiology and Pain Medicine, University of Washington; Department of Anesthesiology, VA Puget Sound Health Care System, Seattle, Washington; and Department of Applied Mathematics, National Dong Hwa University, Hualien, Taiwan.

Accepted for publication September 27, 2013.

Published ahead of print November 15, 2013

Funding: This research was partly supported by Laura Cheney Patient Safety Grant provided by the University of Washington.

The authors declare no conflicts of interest.

Reprints will not be available from the authors.

Address correspondence to Bala G. Nair, PhD, Department of Anesthesiology and Pain Medicine, University of Washington, BB-1469 Health Sciences Bldg., Mail Box: 356540, 1959 NE Pacific St., Seattle, WA 98195. Address e-mail to nairbg@uw.edu.

Intraoperative hypotension, a common occurrence during general anesthesia, has been associated with adverse outcomes such as postoperative myocardial ischemia, infarction, and stroke.1–5 Similarly, intraoperative hypertension has been associated with negative outcomes such as in-hospital death or prolonged hospitalization with a morbid condition.6–8 For these reasons, management of arterial blood pressure within a safe range is a critical function performed by the anesthesia provider during surgery. However, continuous vigilance of arterial blood pressure and the various factors that affect it can be difficult in a busy operating room (OR).

Anesthesia information management systems (AIMS) have the advantage of acquiring and documenting real-time arterial blood pressure data automatically and at a higher frequency when compared with paper anesthesia records. Additionally, an AIMS also documents information related to factors that affect arterial blood pressure levels, such as medications and inhaled anesthetics. Hence, it is only natural to think that AIMS data could potentially be used for better management of intraoperative arterial blood pressure, particularly hypotension and hypertension.

AIMS-based clinical decision support systems have been developed and evaluated.9–26 They have primarily focused on detecting and notifying anesthesia providers concerning issues related to billing and compliance.12,18,19,21–26 Though lately, patient care and safety issues have been increasingly addressed.9,11,13–16 We describe the application of an AIMS-based decision support module, Smart Anesthesia Manager (SAM), to support the management of intraoperative hypotension and hypertension. Specifically, SAM was configured to address 2 scenarios: one that accentuates hypotension and the other hypertension. The considered scenario that accentuates hypotension was a high concentration of inhaled drug, while the scenario that accentuates hypertension was a continuing infusion of phenylephrine. SAM was configured to detect the following scenarios in near real-time: (1) hypotension with a concurrent high inhaled drug concentration as measured by minimum alveolar concentration (MAC), and (2) hypertension with concurrent infusion of phenylephrine. If the above scenarios were detected, the anesthesia providers were notified to decrease the inhaled drug concentration for scenario 1 or to stop or reduce phenylephrine infusion for scenario 2. To evaluate the effect of real-time notifications, we compared episodes of hypotension with high MAC, and hypertension with phenylephrine infusion, for 12 months before and after initiating SAM notifications.

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METHODS

Study Approval and Safety

This study was approved by the IRB of the University of Washington. The IRB waived the requirement for written informed consent. SAM decision rules do not recommend any clinical methods different from established guidelines and normal practice. Furthermore, SAM does not perform any direct intervention on patients. It simply detects potential issues, brings them to the attention of the anesthesia provider, and in some cases suggests remedial steps based on established clinical practice. Anesthesia providers can choose to ignore the suggestion and adopt an alternate remedial step that they consider appropriate. Last, SAM notifications are directed to qualified clinical personnel and not patients performing self-care. For these reasons, the authors and the University of Washington IRB considered the system minimal risk. The IRB did not request an Investigational Device Exemption.

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Patient

All elective, emergency, and intensive care unit patients having surgery at the University of Washington during the study period were included. Cardiac cases involving cardiopulmonary bypass were excluded.

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AIMS

Our institution (University of Washington Medical Center, Seattle, WA) has installed an AIMS (Merge AIMS, Merge Inc., Hartland, WI) in all its ORs. It automatically acquires data from the patient monitor and gas analysis module every minute. This includes noninvasive cuff blood pressures, arterial catheter invasive arterial blood pressures, and end-tidal expired concentrations of sevoflurane, desflurane, and isoflurane. Our AIMS does not interface directly with infusion pumps, so the anesthesia providers manually document the infusion medications in the AIMS record.

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SAM System

SAM is a real-time decision support module that was developed at the University of Washington for improving quality of care, billing, and compliance.11,12,18,19 Clinical data from the AIMS database are extracted and analyzed by SAM to detect selected issues related to quality of care, billing, and compliance based on a set of decision rules. If issues are detected, the anesthesia provider is notified via either a “pop-up” computer screen or a text page with a description of the issue and remedial steps. The module is currently configured to sample the AIMS database every 6 minutes to minimize the impact of SAM database queries on AIMS functionality and to ensure sufficient time for the AIMS database to refresh its data from the individual AIMS workstations in the OR (≤5 minutes for the AIMS system in our institution). The primary goal of SAM implementation was to meet institutional needs to improve quality of care, patient safety, billing, and compliance. To assess whether a set of decision rules made an improvement, we evaluated the target item before and after the rules were implemented. If the rules were found effective, we continued using them.

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Decision Rules

Decision support rules of SAM were enhanced to detect 2 scenarios: (1) hypotension with a concurrent high concentration of inhaled anesthetic, and (2) hypertension with concurrent phenylephrine infusion. The definition of hypotension is varied in the literature.3,5 For this study, hypotension was defined as systolic blood pressure below 80 mm Hg, a simple, yet commonly quoted definition.3 A high concentration of inhaled anesthetic that may contribute to hypotension was defined as >1.25 MAC. In the literature, we could not find a specific level of inhaled drug concentration related to hypotension. Hence, the limit of 1.25 MAC was arrived on as a consensus value based on the clinical practice of anesthesia providers that the research team consulted. MAC values were computed every minute based on the end-tidal expired drug concentrations. MAC computation did not include nitrous oxide concentration because it was rarely used in our institution and hence was not configured to be acquired by AIMS. For simplicity, and because our institution does not perform surgery on pediatric patients, MAC was not age adjusted for this study. For the second decision rule, hypertension was defined as systolic blood pressure higher than 160 mm Hg.6,7 Detection of hypotension and hypertension was performed based on the latest, artifact-reduced arterial blood pressure parameters acquired by AIMS from the patient monitor. Artifact reduction was primarily targeted toward arterial catheter invasive arterial blood pressure measurements. Specifically, filters were used to remove blood pressure values during zeroing of the invasive line (pressures = 0 mm Hg) and arterial line flushes (pressures >300 mm Hg). Additionally, to ensure a physiologically valid measurement, only measurement sets in which systolic >mean >diastolic pressures were considered for decision support. Both noninvasive blood pressure monitored episodically (at least every 5 minutes) via cuff oscillometry and continuous invasive blood pressure monitored via an arterial line were used. Arterial catheter invasive blood pressure variables were preferred over cuff blood pressures if both methods were used. At every SAM sampling instance, the most recent systolic blood pressure acquired by AIMS within the last 10 minutes was compared against the thresholds for hypotension (<80 mm Hg) and hypertension (>160 mm Hg) to determine hypo or hypertension. If the systolic blood pressure value was not measured in the last 10 minutes, the detection of hypo- or hypertension was not performed, and no decision logic was applied. Instead, SAM sent a pop-up message indicating that the blood pressure had not been measured recently.9 The condition of continuing phenylephrine infusion was determined based on the user-documented drug dosage information manually entered in the AIMS. Hypotension with concurrent inhaled anesthetic concentration >1.25 MAC or hypertension with continuing phenylephrine infusion triggered a pop-up notification message highlighting the issue along with the suggested action to resolve the issue. Figure 1 shows an example of the message when hypertension is detected with continuing phenylephrine infusion. The notification messages via pop-up were repeated every 6 minutes until the issue was resolved. The newly defined SAM decision rules termed Hypotension-High MAC and Hypertenstion-Phenylephrine rules outlined in Table 1 were tested and activated for use in all ORs in our institution.

Table 1

Table 1

Figure 1

Figure 1

Because the SAM system was already introduced to the anesthesia providers previously (in 2009) and had been in use ever since, no additional training or announcement was conducted before initiating the new decision rules. The mechanism of SAM message notification remained the same as for other previously reported studies.9–12,18,19

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Data Preparation

Anesthetic records in the AIMS database were analyzed for 12 months before starting SAM notifications and 12 months after initiating notifications. Arterial blood pressure, inhaled drug concentration, and phenylephrine infusion data were processed to determine the incidences of Hypotension-High MAC and Hypertension-Phenylephrine issues before and after starting SAM notifications. For evaluation of the Hypotension-High MAC issue, only cases that had both blood pressure monitored and inhaled drug used were considered. Additionally, cardiac patients who had cardiopulmonary bypass were excluded because of the artificially low blood pressures that were recorded in the AIMS during the bypass period. For evaluating the Hypertension-Phenylephrine issue, only patients who had both blood pressure monitored and phenylephrine infused were considered. Both the duration and frequency of Hypotension-High MAC and Hypertension-Phenylephrine incidents were analyzed before and after SAM notifications. To analyze incident duration, the duration of each episode of Hypotension-High MAC and Hypertension-Phenylephrine was computed. To analyze the frequency of occurrence of issues, the number of cases with issues (Nissue) was divided by the total number of cases that could potentially have the issue (Ntotal). This frequency ratio was normalized for 100 cases (100*Nissue/Ntotal) to represent it in terms of percentage occurrence. Only issue episodes that had artifact-reduced blood pressure measured with no gaps in measurement longer than 10 minutes were considered for analysis. The artifact reduction methods used for analysis were the same as those used during real-time notification with SAM. If a case had multiple episodes, only the episode with the maximum duration was selected for analysis.

SAM message logs were analyzed to determine whether real-time messages were generated for each episode of Hypotension-High MAC and Hypertension-Phenylephrine. Additionally, we closely examined cases that had episodes lasting 6 minutes (sampling period of SAM) or more by manually reviewing the anesthetic record and SAM message logs. Specifically, we determined whether the anesthesia providers responded in an expected fashion for each episode of Hypotension-High MAC or Hypertension-Phenylephrine with and without notification messages. For a Hypotension-High MAC episode, the expected provider response will be a reduction of inhaled drug concentration to below 1.25 MAC. For Hypertension-Phenylephrine episode, the expected provider response will be either stopping or reducing the phenylephrine infusion. The provider response was assessed after the notification message was issued for a time period of either 6 minutes (sampling period of SAM) or until the next blood pressure measurement that spontaneously resolved the hypotension or hypertension scenario, which ever occurred earlier.

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Statistical Analysis

Nonparametric analysis using Mann Whitney rank sum test was applied to check whether the medians of Hypotension-High MAC and Hypertension-Phenylephrine episode durations were different before and after initiating SAM notifications. Because the SAM sampling and notification rate were once every 6 minutes, we did not expect that the frequency of Hypotension-High MAC and Hypertension-Phenylephrine episodes 6 minutes or less would decrease due to SAM notifications. For this reason, the occurrence of episodes that lasted >6 minutes was separately analyzed. The frequency of episodes was analyzed using 2-tailed, 2 proportion z-test (similar to simple asymptotic method).27 Categorical parameters such as expected provider response when encountering either hypotension-high MAC or hypertension-phenylephrine episodes were analyzed using Fisher exact test. As a secondary analysis, the average phenylephrine infusion rate was compared before and after the intervention using 2-sample t test. Pertinent results are presented in terms of means, standard deviation (SD), and 95% CIs. Statistical analysis was performed using MatLab (Mathworks Inc. Portola Valley, CA) and GraphPad (GraphPad Inc., La Jolla, CA) software packages. Results were considered statistically significant if P < 0.05.

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RESULTS

Patient demographic and case information during the preintervention (before initiating SAM messages) and postintervention (after initiating SAM messages) periods are presented in Table 2. The number of general anesthesia cases, American Society of Anesthesiologists classification, and the procedure duration were similar during the preintervention and postintervention periods. The mean patient age was slightly higher with a slightly larger proportion of elderly patients (> 60 years) during the postintervention period. The number of cases in which at least 1 episode of hypotension decreased during the postintervention period, while hypertension episodes that were encountered were similar between preintervention and postintervention periods. During the postintervention period, there was an increase in the number of cases in which phenylephrine infusion was initiated. The average phenylephrine infusion rate before and after intervention remained the same, and no time-trend in the average phenylephrine infusion rate was observed during the postintervention period.

Table 2

Table 2

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Effect of Real-Time Messages on Hypotension-High MAC episodes

Table 3 outlines the comparison of hypotension-high MAC episodes before and after real-time notifications with SAM. As shown by a lower mean rank, the median weight of episode duration was less after real-time notifications were instituted (Mann Whitney rank sum test, P = 0.031). This indicates a reduction in median episode duration >6 minutes with real-time alerts. Figure 2 shows the effect of real-time messages on the frequency of hypotension-high MAC episodes, with the frequency being represented in terms of the number of cases with hypotension-high MAC episodes per 100 cases (or percentage occurrence). For episodes of duration ≤6 minutes, an observed difference in frequency of −0.21% (CI, −0.56% to 0.14%) was not statistically significant (P = 0.218). However, for episodes that lasted >6 minutes (sampling period of SAM), the reduction in the frequency of episodes (δ = −0.26% [CI, −0.38% to −0.11%]) was statistically significant (P < 0.001).

Table 3

Table 3

Figure 2

Figure 2

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Effect of Real-Time Messages on Hypertension-Phenylephrine Episodes

Table 4 outlines the comparison of hypertension-phenylephrine episodes before and after real-time notifications with SAM. The median episode duration >6 minutes did not decline after real-time notifications were instituted (P= 0.47). Figure 3 shows the effect of real-time messages on the frequency of hypertension-phenylephrine episodes, with the frequency being represented in terms of the number of cases with hypotension-phenylephrine episodes per 100 cases (or percentage occurrence). For episodes ≤6 minutes, an observed difference in frequency of −0.39% (CI, −1.51% to 0.74%) was not statistically significant (P = 0.499). For episodes that lasted >6 minutes (sampling period of SAM), a statistically significant reduction in the frequency of episodes (δ = −0.92% [CI, −1.79% to −0.04%]) was observed (P = 0.035).

Table 4

Table 4

Figure 3

Figure 3

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Real-Time Messages and Anesthesia Provider Response

The instances when real-time notification messages were generated when episodes of hypotension-high MAC or hypertension-phenylephrine were encountered is shown in Table 5. It is evident that real-time messages were consistently generated only when hypotension and hypertension episodes were >6 minutes (or the sampling period of SAM). Messages were triggered less consistently for hypertension-phenylephrine episodes when compared with hypotension-high MAC episodes. Table 6 shows in percentage the time when the anesthesia providers responded in an expected fashion to hypotension-high MAC and hypertension-phenylephrine episodes with and without SAM messages. For hypotension-high MAC episodes, before real-time messages, the anesthesia provider responded by reducing inhaled anesthetic concentrations to below 1.25 MAC 59% of the time. With real-time messages, the compliance to the expected response by the provider response increased to 81% (P = 0.003). For hypertension-phenylephrine episodes, only 22% of the time, the anesthesia provider responded by either stopping or reducing phenylephrine infusion before real-time messages. With real-time messages, the compliance to expected provider response increased to 37% (P = 0.030). This improvement was statistically significant, but compliance still occurred in less than half of the instances.

Table 5

Table 5

Table 6

Table 6

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Phenylephrine Infusion and Real-Time Alerts

We explored whether the providers learned from SAM notifications to run phenylephrine infusions in a manner designed to avoid alerts. Specifically, mean phenylephrine infusion rate before and after the intervention was compared (Table 2) and found similar (P = 0.491 [CI, 0.019 to −0.009], 2-sample t test). Additionally, no time-trend in mean phenylephrine infusion rate during the postintervention phase was found. To examine whether the providers backdated the phenylephrine infusion rate in the AIMS to avoid alerts, we analyzed the documentation patterns of phenylephrine infusion before and after intervention. No difference was found in the proportion of times providers backdated phenylephrine infusion before (89/543 = 16%) and after (116/670 = 17%) intervention (P = 0.700, Fisher exact test).

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DISCUSSION

Prolonged intraoperative hypotension and hypertension are associated with negative clinical outcome. In this study, we detected 2 scenarios of hypotension and hypertension in near real-time and brought them to the attention of the anesthesia provider via pop-up computer screens. The specific scenarios include hypotension with concurrently high inhaled drug MAC and hypotension with continuing phenylephrine infusion. Notification messages did decrease the instances of prolonged episodes (≥6 minutes) of both Hypotension-high MAC and Hypertension-phenylephrine infusion incidents. Also, notification messages for hypotensive episodes with concurrent high MAC decreased the mean duration of such episodes. However, notification of hypertensive episodes with concurrent phenylephrine infusion did not decrease the mean duration of such episodes. Therefore, the overall efficacy of near real-time notifications was less pronounced for Hypertension-phenylephrine infusion events than with Hypotension-high MAC events. The provider response to both Hypotension-high MAC and Hypertension-phenylephrine infusion events improved with notification messages. For Hypotension-high MAC events, the providers reduced the anesthetic concentration to below 1.25 MAC close to 80% of the time after notification messages were initiated, an increase of 20% from before notifications. Though a similar percentage improvement for correct provider response was also noticed for hypertension-phenylephrine infusion events, the overall compliance to correct provider response was less consistent (37%) even after notification messages.

The goal of this study was to reduce hypotensive and hypertensive episodes through near real-time notification of contributing factors, with the hypothesis that near real-time notifications can elicit near real-time response from anesthesia providers to take actions to mitigate hypotension and hypertension. We used the common definitions of hypotension as systolic blood pressure <80 mm Hg and hypertension as systolic blood pressure higher than 160 mm Hg. As a future enhancement to our rules, we may consider thresholds customized to a patient or case. The median duration of > 6-minute hypotension-high MAC episodes reduced with SAM notifications, while that of hypertension-phenylephrine episodes did not show a statistically significant reduction. Perhaps what may be more clinically significant is the reduction in the frequency of > 6-minute episodes of hypotension-high MAC and hypertension-phenylephrine episodes. Also, it was observed that the maximum duration of hypotensive episodes reduced from 44 to 15 minutes while that of hypertensive episodes reduced from 30 to 21 minutes. Reduction of such prolonged episodes could potentially minimize the risks of intraoperative hypotension and hypertension: myocardial ischemia, infarction, stroke and prolonged hospital stay. Though we did not investigate whether a reduction in unsafe blood pressure episodes actually translated into better patent outcome, near real-time notifications seem to have had a positive impact toward improved intraoperative patient management.

There may not be a single reason why providers did not respond as expected: 19% of the time for hypotension-high MAC episodes and the majority of the time (63%) for hypertension-phenylephrine episodes, even after notification messages were generated. Because messages are displayed on the AIMS screen for 2 minutes (unless the user acknowledges the message), in some instances the providers may not have seen the message if they were busy with patient care. The SAM system did not keep an audit log whether the provider acknowledged a message or not. Thus, it was not possible to determine whether the provider did not take an action after acknowledging a message or whether the provider did not see the message. In many instances, when cuff blood pressure was measured episodically, the notification was based on a single blood pressure measurement being either hypotensive or hypertensive. Some providers may have felt that a single measurement was not clinically relevant to initiate a corrective action and may have waited for a subsequent blood pressure measurement by which time the episode may have ended. Additionally, there may have also been instances when hypertension and hypotension may have had to be deliberatively maintained, in which case the providers may have simply ignored the notification. The reason why the hypertension-phenylephrine alerts were ignored a large number of times could also be related to the relevance of a SAM message that was displayed. A SAM alert recommended stopping the phenylephrine infusion during hypertension-phenylephrine episodes, when in fact most providers felt reducing the phenylephrine infusion rate was a more appropriate intervention (Table 6). We have subsequently changed the SAM message to remove this recommendation and simply highlight the presence of a hypertension-phenylephrine episode.

The 2 blood pressure management issues chosen for the study were deliberate. For the hypotension-high MAC issue, both the systolic blood pressure and the inhaled drug concentration data were automatically acquired by the AIMS and thus by SAM. However, for the hypertension-phenylephrine issue, only systolic blood pressure was automatically acquired, while the information related to phenylephrine infusion (the time of dose adjustment and the actual dose) was entered manually by the anesthesia provider. In our AIMS, as is the case with most other AIMS, manual entry of information often occurs retrospectively. This explains why the SAM messages were not generated in some cases (Table 5) when retrospective analysis revealed that there were episodes of hypertension with continuing phenylephrine infusion. On closely analyzing the audit log of the AIMS, we were able to determine that in such instances the users actually entered the phenylephrine infusion information after the hypertension episode has ended. Conversely, we also found instances where a SAM notification message was generated, but we could not find a corresponding hypertension-phenylephrine episode during retrospective analysis. In such cases, the anesthesia provider had edited the phenylephrine infusion information retrospectively such that it was documented as being stopped before the hypertension episode.

SAM alerts did not seem to have influenced the manner in which providers administered and documented phenylephrine infusion. This may be due to the fact that SAM alerts related to hypotension and hypertension are infrequent (<1% of cases), and the anesthesia personnel changes at our institution are fairly frequent. However, the number of cases that had phenylephrine infusion was higher during the postintervention period when compared with preintervention (Table 2). The reasons for this observation are not quite clear. It was also noticed that hypotensive events decreased during the postintervention period. It could be that providers used more phenylephrine to avoid hypotension. Additionally, we noticed that there were slightly more elderly patients (>60 years) during the postintervention period that may also have influenced the use of more phenylephrine. Yet another influencing factor could be the changes in anesthesia personnel during the course of the study and the associated differences in their anesthetic management. It is possible that SAM notifications could also have indirectly contributed to the increased use of phenylephrine. However, as SAM hypotension messages are infrequent and are directed toward reduction of inhaled drug MAC, this possibility seems unlikely.

The SAM decision support program is an add-on module separate from AIMS. This constrains SAM to perform conservatively without negatively impacting the performance of AIMS. Additionally, its sampling of AIMS data is limited by how fast the AIMS database refreshes its data from the AIMS workstations. For the AIMS installed in our hospital, the data refresh rate is variable and can be as slow as every 5 minutes. To accommodate these considerations, SAM was configured to sample the AIMS database every 6 minutes. A 6-minute sampling period meant that hypotension-high MAC and hypertension-phenylephrine episodes lasting less that 6 minutes could not be reliably detected (Table 5) as they could occur within the sampling period without being detected. Additionally, a variable refresh rate of the AIMS meant that data in the AIMS database could be as old as 5 minutes. For this reason, even hypotension/hypertension episodes of up to 11 minutes (6 minutes sampling period + 5-minute data refresh period) are sometimes not detected by SAM (Table 5).

Real-time detection of continuing patient safety issues is where AIMS-based decision support will have the most significant impact on patient care. In this study, we attempted to detect and notify 2 unsafe clinical scenarios: hypotension with a high concentration of inhaled drugs and hypertension with continuing phenylephrine infusion. Though near real-time detection and notification of these scenarios decreased the episodes of these scenarios lasting >6 minutes, near-instantaneous detection and notification are possible only by increasing the data sampling rate and decreasing data latencies in the system.20 It is suggested that the decision support module be integrated as part of an AIMS rather than function as an add-on module. This can promote faster sampling of an AIMS database without risking system failure. Data latency can be reduced by more frequent refreshing of an AIMS database with data from the AIMS workstation. Alternately, for AIMS configuration that follows a thick client model (majority of the computing occurs on the client workstation), the time-critical decision support logic can run on the data collected by the client module (or OR workstation) instead of waiting for these data to reach the server database. Our study also revealed that the efficacy of real-time detection and notification of unsafe events is dependent on how data are acquired in an AIMS. Automated acquisition of relevant data meant that detection of hypotension-high inhaled drug concentration events were more effective when compared with hypertension-phenylephrine events for which phenylephrine infusion data were manually entered. For effective clinical decision support, all data relevant for decision support should be automatically acquired to eliminate the possibility of retrospective data entry or delayed data corrections. Integration with infusion pumps and other monitoring equipment, bar-coded data entry of medication, and blood products are mechanisms to increase automated data entry into an AIMS. Increased automation and frequency of data acquisition in an AIMS ensure real-time availability of a larger information source, which in turn can lead to effective and near-instantaneous clinical decision support.

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DISCLOSURES

Name: Bala G. Nair, PhD.

Contribution: The author is the primary investigator. The author is the primary individual who performed design and conduct of the study, data analysis, and manuscript preparation.

Attestation: Bala G. Nair approved the final manuscript, also attests to the integrity of the original data and the analysis reported in this manuscript, and is the archival author.

Name: Mayumi Horibe, MD.

Contribution: The author is the coinvestigator. The author is the secondary individual who performed data preparation, analysis, and manuscript preparation.

Attestation: Mayumi Horibe approved the final manuscript and also attests to the integrity of the original data and the analysis reported in this manuscript.

Name: Shu-Fang Newman, MS.

Contribution: The author is the coinvestigator and Smart Anesthesia Manager programmer.

Attestation: Shu-Fang Newman approved the final manuscript.

Name: Wei-Ying Wu, PhD.

Contribution: The author helped design and analyze the statistical study and assisted prepare the manuscript.

Attestation: Wei-Ying Wu approved the final manuscript and also attests to the integrity of the statistical analysis reported in this manuscript.

Name: Gene N. Peterson, MD, PhD.

Contribution: The author is the clinical advisor for research study and manuscript preparation.

Attestation: Gene N. Peterson approved the final manuscript.

Name: Howard A. Schwid, MD.

Contribution: The author is the clinical advisor for decision support rules, research study, and manuscript preparation.

Attestation: Howard A. Schwid approved the final manuscript.

This manuscript was handled by: Franklin Dexter, MD, PhD.

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REFERENCES

1. Bijker JB, Persoon S, Peelen LM, Moons KGM, Kalkman CJ, Kappelle JL, van Klei WA. Intraoperative hypotension and perioperative ischemic stroke after general surgery. Anesthesiology. 2012;116:658–64
2. Sharma D, Brown MJ, Curry P, Noda S, Chesnut RM, Vavilala MS. Prevalence and risk factors for intraoperative hypotension during craniotomy for traumatic brain injury. J Neurosurg Anesthesiol. 2012;24:178–84
3. Frank M, Radtke FK, Prahs C, Seeling M, Papkalla N, Wernecke KD, Spies CD. Documented intraoperative hypotesion according to the three most common definitions does not match the application of antihypotensive Medication. J International Med Res. 2011;39:846–56
4. Bijker JB, van Klei WA, Vergouwe Y, Eleveld DJ, van Wolfswinkel L, Moons KG, Kalkman CJ. Intraoperative hypotension and 1-year mortality after noncardiac surgery. Anesthesiology. 2009;111:1217–26
5. Bijker JB, van Klei WA, Kappen TH, van Wolfswinkel L, Moons KGM, Kalkman CJ. Incidence of intraoperative hypotension as a function of the chosen definition. Anesthesiology. 2007;107:213–20
6. Lien SF, Bisognano JD. Perioperative hypertension: defining at-risk patients and their management. Curr Hypertens Rep. 2012;14:432–41
7. Reich DL, Bennet-Guerrero E, Bodian CA, Hossain S, Winfree W, Krol M. Intraoperative tachycardia and hypertension are independently associated with adverse outcome in noncardiac surgey of long duration. Anesth Analg. 2002;95:273–7
8. Reich DL, Bodian CA, Krol M, Kuroda M, Osinski T, Thys DM. Intraoperative hemodynamic predictors of mortality, stroke, and myocardial infarction after coronary artery bypass surgery. Anesth Analg. 1999;89:814–22
9. Nair BG, Horibe M, Newman SF, Wu WY, Schwid HA. Near real-time notification of gaps in cuff blood pressure recordings for improved patient monitoring. J Clin Monit Comput. 2013;27:265–71
10. Nair BG, Peterson GN, Neradilek MB, Newman SF, Huang EY, Schwid HA. Reducing wastage of inhalation anesthetics using real-time decision support to notify of excessive fresh gas flow. Anesthesiology. 2013;118:874–84
11. Nair BG, Newman SF, Peterson GN, Schwid HA. Smart Anesthesia Manager™ (SAM)–a real-time decision support system for anesthesia care during surgery. IEEE Trans Biomed Eng. 2013;60:207–10
12. Nair BG, Peterson GN, Newman S, Wu W, Kolios-Morris V, Schwid HA. Improved documentation of β-blocker quality measure through anesthesia information management system and real-time notification of documentation errors. Jt Comm J Qual Saf. 2012;38:283–8
13. Rothman B, Leonard JC, Vigoda MM. Future of electronic health records: implications for decision support. Mt Sinai J Med. 2012;79:757–68
14. Epstein RH, Dexter F. Implications of resolved hypoxemia on the utility of desaturation alerts sent from an anesthesia decision support system to supervising anesthesiologists. Anesth Analg. 2012;115:929–33
15. Ehrenfeld JM, Epstein RH, Bader S, Kheterpal S, Sandberg WS. Automatic notifications mediated by anesthesia information management systems reduce the frequency of prolonged gaps in blood pressure documentation. Anesth Analg. 2011;113:356–63
16. Chau A, Ehrenfeld JM. Using real-time clinical decision support to improve performance on perioperative quality and process measures. Anesthesiol Clin. 2011;29:57–69
17. Wanderer JP, Sandberg WS, Ehrenfeld JM. Real-time alerts and reminders using information systems. Anesthesiol Clin. 2011;29:389–96
18. Nair BG, Newman SF, Peterson GN, Schwid HA. Automated electronic reminders to improve redosing of antibiotics during surgical cases: comparison of two approaches. Surg Infect (Larchmt). 2011;12:57–63
19. Nair BG, Newman SF, Peterson GN, Wu WY, Schwid HA. Feedback mechanisms including real-time electronic alerts to achieve near 100% timely prophylactic antibiotic administration in surgical cases. Anesth Analg. 2010;111:1293–300
20. Epstein RH, Dexter F, Ehrenfeld JM, Sandberg WS. Implications of event entry latency on anesthesia information management decision support systems. Anesth Analg. 2009;108:941–7
21. Dexter F, Epstein RH, Lee JD, Ledolter J. Automatic updating of times remaining in surgical cases using bayesian analysis of historical case duration data and “instant messaging” updates from anesthesia providers. Anesth Analg. 2009;108:929–40
22. Sandberg WS, Sandberg EH, Seim AR, Anupama S, Ehrenfeld JM, Spring SF, Walsh JL. Real-time checking of electronic anesthesia records for documentation errors and automatically text messaging clinicians improves quality of documentation. Anesth Analg. 2008;106:192–201
23. Kheterpal S, Gupta R, Blum JM, Tremper KK, O’Reilly M, Kazanjian PE. Electronic reminders improve procedure documentation compliance and professional fee reimbursement. Anesth Analg. 2007;104:592–7
24. Epstein RH, Dexter F, Piotrowski E. Automated correction of room location errors in anesthesia information management systems. Anesth Analg. 2008;107:965–71
25. Spring SF, Sandberg WS, Anupama S, Walsh JL, Driscoll WD, Raines DE. Automated documentation error detection and notification improves anesthesia billing performance. Anesthesiology. 2007;106:157–63
26. Wax DB, Beilin Y, Levin M, Chadha N, Krol M, Reich DL. The effect of an interactive visual reminder in an anesthesia information management system on timeliness of prophylactic antibiotic administration. Anesth Analg. 2007;104:1462–6
27. Newcombe RG. Interval estimation for the difference between independent proportions: comparison of eleven methods. Stat Med. 1998;17:873–90
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