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

Effect of music on hemodynamic fluctuations in women during induction of general anesthesia: A prospective randomized controlled multicenter trial

BACKGROUND

The authors aim to investigate the effect of music on hemodynamic fluctuations during induction of general anesthesia and reducing preoperative anxiety for women who underwent elective non-cardiac surgery.

METHODS

It is a multicenter, double-blind, randomized, parallel-group clinical trial. Patients were randomized 1:1 to either a Music Intervention group (MI) or a Control group (Control). The MI participants listened to their preferred music for more than 30 minutes in the waiting area. The State-Trait Anxiety Inventory (STAI) was used to measure anxiety levels in the groups, and hemodynamic parameters (Heart Rate [HR], Mean Arterial Pressure [MAP]) were continuously recorded before induction (T0), at loss of consciousness (T1), immediately before intubation (T2), and after intubation (T3). Intubation-related adverse events were also recorded. The primary outcome was the incidence of MAP changes more than 20 % above baseline during T0-T2.

RESULTS

A total of 164 patients were included in the final analyses. The incidence of MAP instability during T0-T2 was lower in the MI, and the 95 % Confidence Interval for the rate difference demonstrated the superiority of MI. HR instability was less frequent in MI participants both in T0-T2 and T2-T3. The overall incidence of preoperative anxiety was 53.7 % (88/164). After the music intervention, the mean score of STAI was significantly lower in the MI than in the Control, with a between-group difference of 8.01.

CONCLUSIONS

Preoperative music intervention effectively prevented hemodynamic instability during anesthesia induction and significantly reduced preoperative anxiety in women undergoing elective non-cardiac surgery.

Effectiveness of different doses of dexmedetomidine on intraoperative haemodynamic profiles and postoperative pain in patients undergoing abdominal surgery at Dilla University Referral Hospital, Ethiopia, 2024: a double-blind randomized controlled trial

Background and objective

Abdominal surgery stands as one of the most frequently conducted procedures across surgical specialties, accounting for up to half of surgery-related expenses. Hemodynamic instability emerges as a significant concern during anaesthesia and surgery, provoked by the stress of intubation, surgical incision, and anaesthetic agents. Following abdominal surgery, pain is an inevitable consequence, typically managed with opioid-based analgesia. However, the adverse effects associated with opioids often overshadow their analgesic benefits, particularly in the context of abdominal surgery. Consequently, there exists a necessity to explore and assess alternative non-opioid pain management options post-abdominal surgery as part of a broader strategy to reduce opioid usage. The primary aim of this investigation is to assess the effectiveness of varying doses of dexmedetomidine in regulating intraoperative hemodynamics and alleviating postoperative pain in patients undergoing abdominal surgery.

Methods

Ethical clearance and institutional review board were obtained from the ethical clearance committee of Dilla University College of Medicine and Health Sciences with protocol unique number of duirb/008/22-01. Our trial has been prospectively registered on the Pan African Clinical Trial Registry with a unique identification number for the registry PACTR202208813896934. Statistical package and analysis were performed by using SPSS version 25. The distribution of data was checked by using Shapiro-Wilk test and the homogeneity of variance was checked by Levene's test. Analysis of variance (ANOVA) and Kruskal-Wallis H test were used for normally distributed continuous data and non-normally distributed or non-parametric data, respectively. P value less than 0.05 with a power of 90% was considered statistically significant.

Result

There was a statistically significant increase in mean SBP in the control group at the different critical time points (P<0.05), as compared to the baseline value, while there was no significant difference in mean systolic blood pressure (SBP) between the baseline and all other levels for group 2 and group 3. A statistically significant increase in mean arterial pressure (MAP) was detected in the control group at immediately after intubation (P=0.009) as compared to the baseline value, while a statistically significant reduction in mean heart rate (HR) was observed in group 3 at 15th min after infusion and at 30th 30 min after induction compared to baseline with a P value of 0.002 and 0.008, respectively.Conclusion:Perioperative low-dose infusion of dexmedetomidine at the rate of 0.4 mcg/kg/h is a useful anaesthesia adjuvant to control hemodynamic stress response to critical periods. It is wise to use this infusion dose as part of general anaesthesia to achieve better hemodynamic stability.

The bidirectional relationship between opioids and the gut microbiome: Implications for opioid tolerance and clinical interventions

Opioids are widely used in treating patients with acute and chronic pain; however, this class of drugs is also commonly abused. Opioid use disorder and associated overdoses are becoming more prevalent as the opioid crisis continues. Chronic opioid use is associated with tolerance, which decreases the efficacy of opioids over time, but also puts individuals at risk of fatal overdoses. Therefore, it is essential to identify strategies to reduce opioid tolerance in those that use these agents. The gut microbiome has been found to play a critical role in opioid tolerance, with opioids causing dysbiosis of the gut, and changes in the gut microbiome impacting opioid tolerance. These changes in turn have a detrimental effect on the gut microbiome, creating a positive feedback cycle. We review the bidirectional relationship between the gut microbiome and opioid tolerance, discuss the role of modulation of the gut microbiome as a potential therapeutic option in opioid-induced gut dysbiosis, and suggest opportunities for further research and clinical interventions.

Risk Factors for Cerebral Infarction After Microsurgical Clipping of Hunt-Hess Grade 0-2 Single Intracranial Aneurysm: A Retrospective Study

OBJECTIVE

The study aimed to explore risk factors for cerebral infarction after microsurgical clipping in patients with Hunt-Hess grade 0-2 single intracranial aneurysms.

METHODS

A total of 137 patients with Hunt-Hess grade 0-2 single intracranial aneurysms treated with microsurgical clipping between March 2017 and December 2020 were retrospectively enrolled. Patients were divided into 2 groups on the basis of the occurrence of cerebral infarction after surgery.

RESULTS

Of 137 enrolled patients, 14 (10.22%) showed cerebral infarction symptoms after surgery. Univariate analysis indicated that ruptured aneurysm status, aneurysm rupture during surgery, history of transient ischemic attack (TIA)/stroke, aneurysm size ≥7 mm, temporary clipping, intraoperative systolic hypotension (IOH), and occurrences of intraoperative motor-evoked potentials change were significantly related to postoperative cerebral infarction (PCI). However, using multivariate regression, only history of TIA/stroke (odds ratio = 0.124; 95% confidence interval [CI] = 0.021-0.748, P = 0.023) and IOH (odds ratio = 0.032; 95% CI = 0.005-0.210, P < 0.001) were independent predictors for PCI. Receiver operating characteristic curve analysis showed that the critical duration of temporary clipping and IOH that minimized the risk of PCI was 5.5 minutes and 7.5 minutes, respectively.

CONCLUSIONS

Our study identified history of TIA/stroke and IOH as independent risk factors for cerebral infarction after microsurgical clipping.

Integrating machine learning predictions for perioperative risk management: Towards an empirical design of a flexible-standardized risk assessment tool

BACKGROUND

Surgical patients are complex, vulnerable, and prone to postoperative complications that can potentially be mitigated with quality perioperative risk assessment and management. Several institutions have incorporated machine learning (ML) into their patient care to improve awareness and support clinician decision-making along the perioperative spectrum. Recent research suggests that ML risk prediction can support perioperative patient risk monitoring and management across several situations, including the operating room (OR) to intensive care unit (ICU) handoffs.

OBJECTIVES

Our study objectives were threefold: (1) evaluate whether ML-generated postoperative predictions are concordant with clinician-generated risk rankings for acute kidney injury, delirium, pneumonia, deep vein thrombosis, and pulmonary embolism, and establish their associated risk factors; (2) ascertain clinician end-user suggestions to improve adoption of ML-generated risks and their integration into the perioperative workflow; and (3) develop a user-friendly visualization format for a tool to display ML-generated risks and risk factors to support postoperative care planning, for example, within the context of OR-ICU handoffs.

METHODS

Graphical user interfaces for postoperative risk prediction models were assessed for end-user usability through cognitive walkthroughs and interviews with anesthesiologists, surgeons, certified registered nurse anesthetists, registered nurses, and critical care physicians. Thematic analysis relying on an explanation design framework was used to identify feedback and suggestions for improvement.

RESULTS

17 clinicians participated in the evaluation. ML estimates of complication risks aligned with clinicians' independent rankings, and related displays were perceived as valuable for decision-making and care planning for postoperative care. During OR-ICU handoffs, the tool could speed up report preparation and remind clinicians to address patient-specific complications, thus providing more tailored care information. Suggestions for improvement centered on electronic tool delivery; methods to build trust in ML models; modifiable risks and risk mitigation strategies; and additional patient information based on individual preferences (e.g., surgical procedure).

CONCLUSIONS

ML estimates of postoperative complication risks can provide anticipatory guidance, potentially increasing the efficiency of care planning. We have offered an ML visualization framework for designing future ML-augmented tools and anticipate the development of tools that recommend specific actions to the user based on ML model output.

An ethnographic study on the impact of a novel telemedicine-based support system in the operating room

OBJECTIVE

The Anesthesiology Control Tower (ACT) for operating rooms (ORs) remotely assesses the progress of surgeries and provides real-time perioperative risk alerts, communicating risk mitigation recommendations to bedside clinicians. We aim to identify and map ACT-OR nonroutine events (NREs)-risk-inducing or risk-mitigating workflow deviations-and ascertain ACT's impact on clinical workflow and patient safety.

MATERIALS AND METHODS

We used ethnographic methods including shadowing ACT and OR clinicians during 83 surgeries, artifact collection, chart reviews for decision alerts sent to the OR, and 10 clinician interviews. We used hybrid thematic analysis informed by a human-factors systems-oriented approach to assess ACT's role and impact on safety, conducting content analysis to assess NREs.

RESULTS

Across 83 cases, 469 risk alerts were triggered, and the ACT sent 280 care recommendations to the OR. 135 NREs were observed. Critical factors facilitating ACT's role in supporting patient safety included providing backup support and offering a fresh-eye perspective on OR decisions. Factors impeding ACT included message timing and ACT and OR clinician cognitive lapses. Suggestions for improvement included tailoring ACT message content (structure, timing, presentation) and incorporating predictive analytics for advanced planning.

DISCUSSION

ACT served as a safety net with remote surveillance features and as a learning healthcare system with feedback/auditing features. Supporting strategies include adaptive coordination and harnessing clinician/patient support to improve ACT's sustainability. Study insights inform future intraoperative telemedicine design considerations to mitigate safety risks.

CONCLUSION

Incorporating similar remote technology enhancement into routine perioperative care could markedly improve safety and quality for millions of surgical patients.

VitalDB, a high-fidelity multi-parameter vital signs database in surgical patients

In modern anesthesia, multiple medical devices are used simultaneously to comprehensively monitor real-time vital signs to optimize patient care and improve surgical outcomes. However, interpreting the dynamic changes of time-series biosignals and their correlations is a difficult task even for experienced anesthesiologists. Recent advanced machine learning technologies have shown promising results in biosignal analysis, however, research and development in this area is relatively slow due to the lack of biosignal datasets for machine learning. The VitalDB (Vital Signs DataBase) is an open dataset created specifically to facilitate machine learning studies related to monitoring vital signs in surgical patients. This dataset contains high-resolution multi-parameter data from 6,388 cases, including 486,451 waveform and numeric data tracks of 196 intraoperative monitoring parameters, 73 perioperative clinical parameters, and 34 time-series laboratory result parameters. All data is stored in the public cloud after anonymization. The dataset can be freely accessed and analysed using application programming interfaces and Python library. The VitalDB public dataset is expected to be a valuable resource for biosignal research and development.

Measurement(s)	vital signs of patients during surgery • perioperative patient information
Technology Type(s)	Vital Signs Measurement • Electronic Medical Record
Factor Type(s)	vital signs data including various numeric and waveform data acquired from multiple patient monitors • perioperative patient information acquired from the electronic medical record system
Sample Characteristic - Organism	Homo sapiens
Sample Characteristic - Environment	hospital
Sample Characteristic - Location	South Korea

Opal: an implementation science tool for machine learning clinical decision support in anesthesia

Opal is the first published example of a full-stack platform infrastructure for an implementation science designed for ML in anesthesia that solves the problem of leveraging ML for clinical decision support. Users interact with a secure online Opal web application to select a desired operating room (OR) case cohort for data extraction, visualize datasets with built-in graphing techniques, and run in-client ML or extract data for external use. Opal was used to obtain data from 29,004 unique OR cases from a single academic institution for pre-operative prediction of post-operative acute kidney injury (AKI) based on creatinine KDIGO criteria using predictors which included pre-operative demographic, past medical history, medications, and flowsheet information. To demonstrate utility with unsupervised learning, Opal was also used to extract intra-operative flowsheet data from 2995 unique OR cases and patients were clustered using PCA analysis and k-means clustering. A gradient boosting machine model was developed using an 80/20 train to test ratio and yielded an area under the receiver operating curve (ROC-AUC) of 0.85 with 95% CI [0.80–0.90]. At the default probability decision threshold of 0.5, the model sensitivity was 0.9 and the specificity was 0.8. K-means clustering was performed to partition the cases into two clusters and for hypothesis generation of potential groups of outcomes related to intraoperative vitals. Opal’s design has created streamlined ML functionality for researchers and clinicians in the perioperative setting and opens the door for many future clinical applications, including data mining, clinical simulation, high-frequency prediction, and quality improvement.

Process Optimization and Digital Quality Improvement to Enhance Timely Initiation of Epidural Infusions and Postoperative Pain Control

BACKGROUND

Although intraoperative epidural analgesia improves postoperative pain control, a recent quality improvement project demonstrated that only 59% of epidural infusions are started in the operating room before patient arrival in the postanesthesia care unit. We evaluated the combined effect of process and digital quality improvement efforts on provider compliance with starting continuous epidural infusions during surgery.

METHODS

In October 2014, we instituted 2 process improvement initiatives: (1) an electronic order queue to assist the operating room pharmacy with infusate preparation; and (2) a designated workspace for the storage of equipment related to epidural catheter placement and drug infusion delivery. In addition, we implemented a digital quality improvement initiative, an Anesthesia Information Management System-mediated clinical decision support, to prompt anesthesia providers to start and document epidural infusions in pertinent patients. We assessed anesthesia provider compliance with epidural infusion initiation in the operating room and postoperative pain-related outcomes before (PRE: October 1, 2012 to September 31, 2014) and after (POST: January 1, 2015 to December 31, 2016) implementation of the quality improvement initiatives.

RESULTS

Compliance with starting intraoperative epidural infusions was 59% in the PRE group and 85% in the POST group. After adjustment for confounders and preintervention time trends, segmented regression analysis demonstrated a statistically significant increase in compliance with the intervention in the POST phase (odds ratio, 2.78; 95% confidence interval, 1.73-4.49; P < .001). In the PRE and POST groups, cumulative postoperative intravenous opioid use (geometric mean) was 62 and 34 mg oral morphine equivalents, respectively. A segmented regression analysis did not demonstrate a statistically significant difference (P = .38) after adjustment for preintervention time trends.

CONCLUSIONS

Process workflow optimization along with Anesthesia Information Management System-mediated digital quality improvement efforts increased compliance to intraoperative epidural infusion initiation. Adjusted for preintervention time trends, these findings coincided with a statistically insignificant decrease in postoperative opioid use in the postanesthesia care unit during the POST phase.

Development and Implementation of the Portable Operating Room Tracker App With Vital Signs Streaming Infrastructure: Operational Feasibility Study

Background

In the perioperative environment, a multidisciplinary clinical team continually observes and evaluates patient information. However, data availability may be restricted to certain locations, cognitive workload may be high, and team communication may be constrained by availability and priorities. We developed the remote Portable Operating Room Tracker app (the telePORT app) to improve information exchange and communication between anesthesia team members. The telePORT app combines a real-time feed of waveforms and vital signs from the operating rooms with messaging, help request, and reminder features.

Objective

The aim of this paper is to describe the development of the app and the back-end infrastructure required to extract monitoring data, facilitate data exchange and ensure privacy and safety, which includes results from clinical feasibility testing.

Methods

telePORT’s client user interface was developed using user-centered design principles and workflow observations. The server architecture involves network-based data extraction and data processing. Baseline user workload was assessed using step counters and communication logs. Clinical feasibility testing analyzed device usage over 11 months.

Results

telePORT was more commonly used for help requests (approximately 4.5/day) than messaging between team members (approximately 1/day). Passive operating room monitoring was frequently utilized (34% of screen visits). Intermittent loss of wireless connectivity was a major barrier to adoption (decline of 0.3%/day).

Conclusions

The underlying server infrastructure was repurposed for real-time streaming of vital signs and their collection for research and quality improvement. Day-to-day activities of the anesthesia team can be supported by a mobile app that integrates real-time data from all operating rooms.

The development and validation of the Score for the Prediction of Postoperative Respiratory Complications (SPORC-2) to predict the requirement for early postoperative tracheal re-intubation: a hospital registry study

Postoperative pulmonary complications are associated with an increase in mortality, morbidity and healthcare utilisation. The Agency for Healthcare Research and Quality recommends risk assessment for postoperative respiratory complications in patients undergoing surgery. In this hospital registry study of adult patients undergoing non-cardiac surgery between 2005 and 2017 at two independent healthcare networks, a prediction instrument for early postoperative tracheal re-intubation was developed and externally validated. This was based on the development of the Score for Prediction Of Postoperative Respiratory Complications. For predictor selection, stepwise backward logistic regression and bootstrap resampling were applied. Development and validation cohorts were represented by 90,893 patients at Partners Healthcare and 67,046 patients at Beth Israel Deaconess Medical Center, of whom 699 (0.8%) and 587 (0.9%) patients, respectively, had their tracheas re-intubated. In addition to five pre-operative predictors identified in the Score for Prediction Of Postoperative Respiratory Complications, the final model included seven additional intra-operative predictors: early post-tracheal intubation desaturation; prolonged duration of surgery; high fraction of inspired oxygen; high vasopressor dose; blood transfusion; the absence of volatile anaesthetic use; and the absence of lung-protective ventilation. The area under the receiver operating characteristic curve for the new score was significantly greater than that of the original Score for Prediction Of Postoperative Respiratory Complications (0.84 [95%CI 0.82-0.85] vs. 0.76 [95%CI 0.75-0.78], respectively; p < 0.001). This may allow clinicians to develop and implement strategies to decrease the risk of early postoperative tracheal re-intubation.

Anesthesiology Control Tower-Feasibility Assessment to Support Translation (ACTFAST): Mixed-Methods Study of a Novel Telemedicine-Based Support System for the Operating Room

Background

Despite efforts to improve patient outcomes, major morbidity and mortality remain common after surgery. Health information technologies that provide decision support for clinicians might improve perioperative and postoperative patient care. Evaluating the usability of these technologies and barriers to their implementation can facilitate their acceptance within health systems.

Objective

This manuscript describes usability testing and refinement of an innovative telemedicine-based clinical support system, the Anesthesiology Control Tower (ACT). It also reports stakeholders’ perceptions of the barriers and facilitators to implementation of the intervention.

Methods

Three phases of testing were conducted in an iterative manner. Phase 1 testing employed a think-aloud protocol analysis to identify surface-level usability problems with individual software components of the ACT and its structure. Phase 2 testing involved an extended qualitative and quantitative real-world usability analysis. Phase 3 sought to identify major barriers and facilitators to implementation of the ACT through semistructured interviews with key stakeholders.

Results

Phase 1 and phase 2 usability testing sessions identified numerous usability problems with the software components of the ACT. The ACT platform was revised in seven iterations in response to these usability concerns. Initial satisfaction with the ACT, as measured by standardized instruments, was below commonly accepted cutoffs for these measures. Satisfaction improved to acceptable levels over the course of revision and testing. A number of barriers to implementation were also identified and addressed during the refinement of the ACT intervention.

Conclusions

The ACT model can improve the standard of perioperative anesthesia care. Through our thorough and iterative usability testing process and stakeholder assessment of barriers and facilitators, we enhanced the acceptability of this novel technology and improved our ability to implement this innovation into routine practice.

International Registered Report Identifier (IRRID)

RR2-10.1186/s40814-018-0233-4

Noninvasive continuous detection of arterial hypotension during induction of anaesthesia using a photoplethysmographic signal: proof of concept

BACKGROUND

During general anaesthesia, intraoperative hypotension (IOH), defined as a mean arterial pressure (MAP) reduction of > 20%, is frequent and may lead to complications. Pulse oximetry is mandatory in the operating room, making the photoplethysmographic signal and parameters, such as relative dicrotic notch height (Dic_pleth) or perfusion index (PI), readily available. The purpose of this study was to investigate whether relative variations of Dic_pleth and PI could detect IOH during anaesthesia induction, and to follow their variations during vasopressor boluses.

METHODS

MAP, Dic_pleth, and PI were monitored at 1-min intervals during target control induction of anaesthesia with propofol and remifentanil in 61 subjects. Vasopressor infusion (norepinephrine or phenylephrine) was performed when hypotension occurred according to the decision of the physician.

RESULTS

The delta in Dic_pleth and PI accurately detected IOH, with areas under the receiver operating characteristic curves (AUC) of 0.86 and 0.83, respectively. The optimal thresholds were -19% (sensitivity 79%; specificity 84%) and 51% (sensitivity 82%; specificity 74%) for ΔDic_pleth and ΔPI, respectively. There was no difference between the ROC of ΔDic_pleth and ΔPI (P=0.22). Combining both ΔDic_pleth and ΔPI further improved the hypotension detection power (AUC=0.91) with a sensitivity and specificity of 84%. MAP variations were correlated with ΔDic_pleth and ΔPI during vasopressor infusion (r=0.73 and -0.62, respectively; P<0.001).

CONCLUSIONS

The relative variation in Dic_pleth and PI derived from the photoplethysmographic signal can be used as a non invasive, continuous, and simple tool to detect intraoperative hypotension, and to track the vascular response to vasoconstrictor drugs during induction of general anaesthesia.

CLINICAL TRIAL REGISTRATION

NCT03756935.

Feasibility and indicator outcomes using computerized clinical decision support in pediatric traumatic brain injury anesthesia care

BACKGROUND

Traumatic brain injury anesthesia care is complex. The use of clinical decision support to improve pediatric trauma care has not been examined.

AIMS

The aim of this study was to examine feasibility, reliability, and key performance indicators for traumatic brain injury anesthesia care using clinical decision support.

METHODS

Clinical decision support was activated for patients under 19 years undergoing craniotomy for suspected traumatic brain injury. Anesthesia providers were prompted to adhere to process measures via on-screen alerts and notified in real time of abnormal monitor data or laboratory results (unwanted key performance indicator events). Process measures pertained to arterial line placement and blood gas draws, neuromuscular blockade, hypotension, anemia, coagulopathy, hyperglycemia, and intracranial hypertension. Unwanted key performance indicators were: hypotension, hypoxia, hypocarbia, hypercarbia, hypothermia, hyperthermia, anesthetic agent overdose; hypoxemia, coagulopathy, anemia, and hyperglycemia. Anesthesia records, vital signs, and alert logs were reviewed for 39 anesthetic cases (19 without clinical decision support and 20 with clinical decision support).

RESULTS

Data from 35 patients aged 11 months to 17 years and 77% males were examined. Clinical decision support reliably identified 39/46 eligible anesthetic cases, with 85% sensitivity and 100% specificity, and was highly sensitive, detecting 89% of monitor key performance indicator events and 100% of reported lab key performance indicator events. There were no false positive alerts. Median event duration was lower in the "with clinical decision support" group for 4/7 key performance indicators. Second insult duration was lower for duration of hypocarbia (by 44%), hypotension (29%), hypothermia (12%), and hyperthermia (15%).

CONCLUSION

Use of clinical decision support in pediatric traumatic brain injury anesthesia care is feasible, reliable, and may have the potential to improve key performance indicator outcomes. This observational study suggests the possibility of clinical decision support as a strategy to reduce second insults and improve traumatic brain injury guideline adherence during pediatric anesthesia care.

Utility of Geriatric Assessment in the Projection of Early Mortality Following Hip Fracture in the Elderly Patients

Hip fractures result in significant morbidity and mortality in elders. Indicators of frailty are associated with poor outcomes. Commonly used frailty tools rely on motor skills that cannot be performed by this population. We determined the association between the Charlson Comorbidity Score (CCS), intraoperative hypotension (IOH), and a geriatric medicine consult index (GCI) with short-term mortality in hip fracture patients. A retrospective cohort study was conducted at a single institution over a 2-year period. Patients aged 65 years and older who sustained a hip fracture following a low-energy mechanism were identified using billing records and our orthopedic fracture registry. Medical records were reviewed to collect demographic data, fracture classification and operative records, calculation of CCS, intraoperative details including hypotension, and assessments recorded in the geriatric consult notes. The GCI was calculated using 30 dichotomous variables contained within the geriatric consult note. The index, ranging from 0 to 1, included markers for physical and cognitive function, as well as medications. A higher GCI score indicated more markers for frailty. One hundred eight patients met inclusion criteria. Sixty-four (59%) were females and the average age was 77.3 years. Thirty-five (32%) patients sustained femoral neck fractures, and 73 (68%) patients sustained inter-/pertrochanteric hip fractures. The 30-day mortality was 6%; the 90-day mortality was 13%. The mean GCI was 0.30 in the 30-day survivor group as compared to 0.52 in those who died. The mean GCI was 0.28 in patients who were alive at 90 days as compared to 0.46 in those who died. In contrast, the CCS and IOH were not associated with 30- or 90-day mortality. In our older hip fracture patients, an index calculated from information routinely obtained in the geriatric consult evaluation was associated with 30- and 90-day mortality, whereas the CCS and measures of IOH were not.

Prediction of hemodynamic fluctuations after induction of general anesthesia using propofol in non-cardiac surgery: a retrospective cohort study

Background

Although propofol is a common anesthetic agent for the induction of general anesthesia, hemodynamic fluctuations are occasionally prominent during induction/intubation. The aims of this study were to determine the influential factors on enhanced hemodynamic fluctuation and to establish a prediction formula to quickly determine the dose of propofol to protect against hemodynamic fluctuations.

Methods

This retrospective cohort study patients (n = 2097) were 18 years or older. They underwent general anesthesia induction using propofol and orotracheal intubation for non-cardiac surgery at Kyushu University Hospital during April 2015 to March 2016. Preoperative patient clinical information was collected from anesthesia preoperative evaluation records. Intraoperative data were obtained from computerized anesthesia records. If patients’ post-induction mean arterial blood pressure (MAP) decreased or increased 30% or more from their pre-induction MAP, they were determined to have enhanced hemodynamic fluctuations. Unconditional logistic regression was used to assess the adjusted odds ratios (ORs) and 95% confidence intervals (CIs). Structural equation modeling (SEM) was conducted to simultaneously examine the direct and indirect effect (path coefficient = r) of potential variables.

Results

In the SEM analysis, age was significantly associated with enhanced hemodynamic fluctuations (adjusted odds ratio = 1.008, 95% CI = 1.001–1.015, P = 0.03). Age (path coefficient (r) = − 0.0113, 95% CI = − 0.0126–0.010, P < 0.001), American Society of Anesthesiologists physical status (ASA-PS) (r = − 0.0788, 95% CI = − 0.1431–0.0145, P = 0.02), sex (r = 0.057, 95% CI = 0.0149–0.9906, P = 0.01), and fentanyl dose (r = 0.1087, 95% CI = 0.0707–0.1467, P < 0.001) influenced the dose of propofol in induction. The prediction formula of “Propofol dose (mg) = [2.374 – 0.0113 × age (year) – 0.0788 (if ASA-PS 3 or 4) + 0.057 (if female) + 0.1087 × fentanyl dose (μg/kg)] × body weight (kg)” was derived.

Conclusions

Age was associated with hemodynamic fluctuations in induction. Although the prediction formula is considered to be acceptable, future studies validating whether it can decrease patients’ risk of enhanced hemodynamic fluctuations in clinical situations are necessary.

Supervised Machine-learning Predictive Analytics for Prediction of Postinduction Hypotension

Editor’s Perspective

What We Already Know about This Topic

What This Article Tells Us That Is New

Background

Hypotension is a risk factor for adverse perioperative outcomes. Machine-learning methods allow large amounts of data for development of robust predictive analytics. The authors hypothesized that machine-learning methods can provide prediction for the risk of postinduction hypotension.

Methods

Data was extracted from the electronic health record of a single quaternary care center from November 2015 to May 2016 for patients over age 12 that underwent general anesthesia, without procedure exclusions. Multiple supervised machine-learning classification techniques were attempted, with postinduction hypotension (mean arterial pressure less than 55 mmHg within 10 min of induction by any measurement) as primary outcome, and preoperative medications, medical comorbidities, induction medications, and intraoperative vital signs as features. Discrimination was assessed using cross-validated area under the receiver operating characteristic curve. The best performing model was tuned and final performance assessed using split-set validation.

Results

Out of 13,323 cases, 1,185 (8.9%) experienced postinduction hypotension. Area under the receiver operating characteristic curve using logistic regression was 0.71 (95% CI, 0.70 to 0.72), support vector machines was 0.63 (95% CI, 0.58 to 0.60), naive Bayes was 0.69 (95% CI, 0.67 to 0.69), k-nearest neighbor was 0.64 (95% CI, 0.63 to 0.65), linear discriminant analysis was 0.72 (95% CI, 0.71 to 0.73), random forest was 0.74 (95% CI, 0.73 to 0.75), neural nets 0.71 (95% CI, 0.69 to 0.71), and gradient boosting machine 0.76 (95% CI, 0.75 to 0.77). Test set area for the gradient boosting machine was 0.74 (95% CI, 0.72 to 0.77).

Conclusions

The success of this technique in predicting postinduction hypotension demonstrates feasibility of machine-learning models for predictive analytics in the field of anesthesiology, with performance dependent on model selection and appropriate tuning.

Incidence of Artifacts and Deviating Values in Research Data Obtained from an Anesthesia Information Management System in Children

BACKGROUND

Vital parameter data collected in anesthesia information management systems are often used for clinical research. The validity of this type of research is dependent on the number of artifacts.

METHODS

In this prospective observational cohort study, the incidence of artifacts in anesthesia information management system data was investigated in children undergoing anesthesia for noncardiac procedures. Secondary outcomes included the incidence of artifacts among deviating and nondeviating values, among the anesthesia phases, and among different anesthetic techniques.

RESULTS

We included 136 anesthetics representing 10,236 min of anesthesia time. The incidence of artifacts was 0.5% for heart rate (95% CI: 0.4 to 0.7%), 1.3% for oxygen saturation (1.1 to 1.5%), 7.5% for end-tidal carbon dioxide (6.9 to 8.0%), 5.0% for noninvasive blood pressure (4.0 to 6.0%), and 7.3% for invasive blood pressure (5.9 to 8.8%). The incidence of artifacts among deviating values was 3.1% for heart rate (2.1 to 4.4%), 10.8% for oxygen saturation (7.6 to 14.8%), 14.1% for end-tidal carbon dioxide (13.0 to 15.2%), 14.4% for noninvasive blood pressure (10.3 to 19.4%), and 38.4% for invasive blood pressure (30.3 to 47.1%).

CONCLUSIONS

Not all values in anesthesia information management systems are valid. The incidence of artifacts stored in the present pediatric anesthesia practice was low for heart rate and oxygen saturation, whereas noninvasive and invasive blood pressure and end-tidal carbon dioxide had higher artifact incidences. Deviating values are more often artifacts than values in a normal range, and artifacts are associated with the phase of anesthesia and anesthetic technique. Development of (automatic) data validation systems or solutions to deal with artifacts in data is warranted.

Anesthesiology Control Tower: Feasibility Assessment to Support Translation (ACT-FAST)-a feasibility study protocol

BACKGROUND

Major postoperative morbidity and mortality remain common despite efforts to improve patient outcomes. Health information technologies have the potential to actualize advances in perioperative patient care, but failure to evaluate the usability of these technologies may hinder their implementation and acceptance. This protocol describes the usability testing of an innovative telemedicine-based intra-operative clinical support system, the Anesthesiology Control Tower, in which a team led by an attending anesthesiologist will use a combination of established and novel information technologies to provide evidence-based support to their colleagues in the operating room.

METHODS

Two phases of mixed-methods usability testing will be conducted in an iterative manner and will evaluate both the individual components of the Anesthesiology Control Tower and their integration as a whole. Phase I testing will employ two separate "think-aloud" protocol analyses with the two groups of end users. Segments will be coded and analyzed for usability issues. Phase II will involve a qualitative and quantitative in situ usability and feasibility analysis. Results from each phase will inform the revision and improvement of the Control Tower prototype throughout our testing and analysis process. The final prototype will be evaluated in the form of a pragmatic randomized controlled clinical trial.

DISCUSSION

The Anesthesiology Control Tower has the potential to revolutionize the standard of care for perioperative medicine. Through the thorough and iterative usability testing process described in this protocol, we will maximize the usefulness of this novel technology for our clinicians, thus improving our ability to implement this innovation into the model of care for perioperative medicine.

TRIAL REGISTRATION

The study that this protocol describes has been registered in clinicaltrials.gov as NCT02830126.

Vital Recorder-a free research tool for automatic recording of high-resolution time-synchronised physiological data from multiple anaesthesia devices

The current anaesthesia information management system (AIMS) has limited capability for the acquisition of high-quality vital signs data. We have developed a Vital Recorder program to overcome the disadvantages of AIMS and to support research. Physiological data of surgical patients were collected from 10 operating rooms using the Vital Recorder. The basic equipment used were a patient monitor, the anaesthesia machine, and the bispectral index (BIS) monitor. Infusion pumps, cardiac output monitors, regional oximeter, and rapid infusion device were added as required. The automatic recording option was used exclusively and the status of recording was frequently checked through web monitoring. Automatic recording was successful in 98.5% (4,272/4,335) cases during eight months of operation. The total recorded time was 13,489 h (3.2 ± 1.9 h/case). The Vital Recorder's automatic recording and remote monitoring capabilities enabled us to record physiological big data with minimal effort. The Vital Recorder also provided time-synchronised data captured from a variety of devices to facilitate an integrated analysis of vital signs data. The free distribution of the Vital Recorder is expected to improve data access for researchers attempting physiological data studies and to eliminate inequalities in research opportunities due to differences in data collection capabilities.

Obstetric anaesthesia practice: Dashboard as a dynamic audit tool

Rapid advances and improved networking abilities have led to the widespread adoption of technology in healthcare, especially focused on diagnostics, documentation and evaluation, or mining of data to improve outcomes. Current technology allows for rapid and accurate decision-making in clinical care decisions for individual patients, collation and analysis at different levels for administrative and financial purposes, and the ability to visualise, analyse, and share data in real time for departmental needs. The adoption of technology may help to improve efficiency and efficacy of healthcare services. Obstetric anaesthesia is a specialised area that has to address the well-being of the pregnant woman and the unborn baby simultaneously. A shift toward caesarean sections as the major mode of childbirth has led to an increased involvement of anaesthesiologists with childbirth. Decisions are often made in high pressure, time intense situations to protect maternal and foetal health. Furthermore, labour analgesia using various neuraxial and non-neuraxial techniques is being demanded by parturients frequently, and for the materno-foetal safety, risk management is the core issue. Hence, it is essential that obstetric anaesthesia teams regularly audit their outcomes to improve services and to identify potential trouble spots earlier. It may be helpful to have audit parameters displayed as visual data, rather than complex tabular and numerical data, for ease of sharing, analysis, and redressal of problem areas. We describe the design and use of an obstetric anaesthesia dashboard that we have used in our department for the past 5 years.

Development and Feasibility of a Real-Time Clinical Decision Support System for Traumatic Brain Injury Anesthesia Care

BACKGROUND

Real-time clinical decision support (CDS) integrated with anesthesia information management systems (AIMS) can generate point of care reminders to improve quality of care.

OBJECTIVE

To develop, implement and evaluate a real-time clinical decision support system for anesthetic management of pediatric traumatic brain injury (TBI) patients undergoing urgent neurosurgery.

METHODS

We iteratively developed a CDS system for pediatric TBI patients undergoing urgent neurosurgery. The system automatically detects eligible cases and evidence-based key performance indicators (KPIs). Unwanted clinical events trigger and display real-time messages on the AIMS computer screen. Main outcomes were feasibility of detecting eligible cases and KPIs, and user acceptance.

RESULTS

The CDS system was triggered in 22 out of 28 (79%) patients. The sensitivity of detecting continuously sampled KPIs reached 93.8%. For intermittently sampled KPIs, sensitivity and specificity reached 90.9% and 100%, respectively. 88% of providers reported that CDS helped with TBI anesthesia care.

CONCLUSIONS

CDS implementation is feasible and acceptable with a high rate of case capture and appropriate generation of alert and guidance messages for TBI anesthesia care.

The timing and prevalence of intraoperative hypotension in infants undergoing laparoscopic pyloromyotomy at a tertiary pediatric hospital

BACKGROUND

Intraoperative hypotension may be associated with adverse outcomes in children undergoing surgery. Infants and neonates under 6 months of age have less autoregulatory cerebral reserve than older infants, yet little information exists regarding when and how often intraoperative hypotension occurs in infants.

AIMS

To better understand the epidemiology of intraoperative hypotension in infants, we aimed to determine the prevalence of intraoperative hypotension in a generally uniform population of infants undergoing laparoscopic pyloromyotomy.

METHODS

Vital sign data from electronic records of infants who underwent laparoscopic pyloromyotomy with general anesthesia at a children's hospital between January 1, 1998 and October 4, 2013 were analyzed. Baseline blood pressure (BP) values and intraoperative BPs were identified during eight perioperative stages based on anesthesia event timestamps. We determined the occurrence of relative (systolic BP <20% below baseline) and absolute (mean arterial BP <35 mmHg) intraoperative hypotension within each stage.

RESULTS

A total of 735 full-term infants and 82 preterm infants met the study criteria. Relative intraoperative hypotension occurred in 77%, 72%, and 58% of infants in the 1-30, 31-60, and 61-90 days age groups, respectively. Absolute intraoperative hypotension was seen in 21%, 12%, and 4% of infants in the 1-30, 31-60, and 61-90 days age groups, respectively. Intraoperative hypotension occurred primarily during surgical prep and throughout the surgical procedure. Preterm infants had higher rates of absolute intraoperative hypotension than full-term infants.

CONCLUSIONS

Relative intraoperative hypotension was routine and absolute intraoperative hypotension was common in neonates and infants under 91 days of age. Preterm infants and infants under 61 days of age experienced the highest rates of absolute and relative intraoperative hypotension, particularly during surgical prep and throughout surgery.

Effectiveness of an Electronic Alert for Hypotension and Low Bispectral Index on 90-day Postoperative Mortality

Background

We tested the hypothesis that an electronic alert for a “double low” of mean arterial pressure less than 75 mmHg and a bispectral index less than 45 reduces the primary outcome of 90-day mortality.

Methods

Adults having noncardiac surgery were randomized to receive either intraoperative alerts for double-low events or no alerts. Anesthesiologists were not blinded and not required to alter care based upon the alerts. The primary outcome was all-cause 90-day mortality.

Results

Patients (20,239) were randomized over 33 months, and 19,092 were analyzed. After adjusting for age, comorbidities, and perioperative factors, patients with more than 60 min of cumulative double-low time were twice as likely to die (hazard ratio, 1.99; 95% CI, 1.2 to 3.2; P = 0.005). The median number of double-low minutes (quartiles) was only slightly lower in the alert arm: 10 (2 to 30) versus 12 (2 to 34) min. Ninety-day mortality was 135 (1.4%) in the alert arm and 123 (1.3%) in the control arm. The difference in percent mortality was 0.18% (99% CI, −0.25 to 0.61).

Conclusions

Ninety-day mortality was not significantly lower in patients cared for by anesthesiologists who received automated alerts to double-low states. Prolonged cumulative double-low conditions were strongly associated with mortality.

A systematic review of near real-time and point-of-care clinical decision support in anesthesia information management systems

Anesthesia information management systems (AIMS) are sophisticated hardware and software technology solutions that can provide electronic feedback to anesthesia providers. This feedback can be tailored to provide clinical decision support (CDS) to aid clinicians with patient care processes, documentation compliance, and resource utilization. We conducted a systematic review of peer-reviewed articles on near real-time and point-of-care CDS within AIMS using the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols. Studies were identified by searches of the electronic databases Medline and EMBASE. Two reviewers screened studies based on title, abstract, and full text. Studies that were similar in intervention and desired outcome were grouped into CDS categories. Three reviewers graded the evidence within each category. The final analysis included 25 articles on CDS as implemented within AIMS. CDS categories included perioperative antibiotic prophylaxis, post-operative nausea and vomiting prophylaxis, vital sign monitors and alarms, glucose management, blood pressure management, ventilator management, clinical documentation, and resource utilization. Of these categories, the reviewers graded perioperative antibiotic prophylaxis and clinical documentation as having strong evidence per the peer reviewed literature. There is strong evidence for the inclusion of near real-time and point-of-care CDS in AIMS to enhance compliance with perioperative antibiotic prophylaxis and clinical documentation. Additional research is needed in many other areas of AIMS-based CDS.

Methodology to automatically detect abnormal values of vital parameters in anesthesia time-series: Proposal for an adaptable algorithm

Abnormal values of vital parameters such as hypotension or tachycardia may occur during anesthesia and may be detected by analyzing time-series data collected during the procedure by the Anesthesia Information Management System. When crossed with other data from the Hospital Information System, abnormal values of vital parameters have been linked with postoperative morbidity and mortality. However, methods for the automatic detection of these events are poorly documented in the literature and differ between studies, making it difficult to reproduce results. In this paper, we propose a methodology for the automatic detection of abnormal values of vital parameters. This methodology uses an algorithm allowing the configuration of threshold values for any vital parameters as well as the management of missing data. Four examples illustrate the application of the algorithm, after which it is applied to three vital signs (heart rate, SpO2, and mean arterial pressure) to all 2014 anesthetic records at our institution.

Paperless anesthesia: uses and abuses of these data

Demonstrably accurate records facilitate clinical decision making, improve patient safety, provide better defense against frivolous lawsuits, and enable better medical policy decisions. Anesthesia Information Management Systems (AIMS) have the potential to improve on the accuracy and reliability of handwritten records. Interfaces with electronic recording systems within the hospital or wider community allow correlation of anesthesia relevant data with biochemistry laboratory results, billing sections, radiological units, pharmacy, earlier patient records, and other systems. Electronic storage of large and accurate datasets has lent itself to quality assurance, enhancement of patient safety, research, cost containment, scheduling, anesthesia training initiatives, and has even stimulated organizational change. The time for record making may be increased by AIMS, but in some cases has been reduced. The question of impact on vigilance is not entirely settled, but substantial negative effects seem to be unlikely. The usefulness of these large databases depends on the accuracy of data and they may be incorrect or incomplete. Consequent biases are threats to the validity of research results. Data mining of biomedical databases makes it easier for individuals with political, social, or economic agendas to generate misleading research findings for the purpose of manipulating public opinion and swaying policymakers. There remains a fear that accessibility of data may have undesirable regulatory or legal consequences. Increasing regulation of treatment options during the perioperative period through regulated policies could reduce autonomy for clinicians. These fears are as yet unsubstantiated.

The perioperative surgical home: An innovative, patient-centred and cost-effective perioperative care model

Contrary to the intraoperative period, the current perioperative environment is known to be fragmented and expensive. One of the potential solutions to this problem is the newly proposed perioperative surgical home (PSH) model of care. The PSH is a patient-centred micro healthcare system, which begins at the time the decision for surgery is made, is continuous through the perioperative period and concludes 30 days after discharge from the hospital. The model is based on multidisciplinary involvement: coordination of care, consistent application of best evidence/best practice protocols, full transparency with continuous monitoring and reporting of safety, quality, and cost data to optimize and decrease variation in care practices. To reduce said variation in care, the entire continuum of the perioperative process must evolve into a unique care environment handled by one perioperative team and coordinated by a leader. Anaesthesiologists are ideally positioned to lead this new model and thus significantly contribute to the highest standards in transitional medicine. The unique characteristics that place Anaesthesiologists in this framework include their systematic role in hospitals (as coordinators between patients/medical staff and institutions), the culture of safety and health care metrics innate to the specialty, and a significant role in the preoperative evaluation and counselling process, making them ideal leaders in perioperative medicine.

Influencing Anesthesia Provider Behavior Using Anesthesia Information Management System Data for Near Real-Time Alerts and Post Hoc Reports

In this review article, we address issues related to using data from anesthesia information management systems (AIMS) to deliver near real-time alerts via AIMS workstation popups and/or alphanumeric pagers and post hoc reports via e-mail. We focus on reports and alerts for influencing the behavior of anesthesia providers (i.e., anesthesiologists, anesthesia residents, and nurse anesthetists). Multiple studies have shown that anesthesia clinical decision support (CDS) improves adherence to protocols and increases financial performance through facilitation of billing, regulatory, and compliance documentation; however, improved clinical outcomes have not been demonstrated. We inform developers and users of feedback systems about the multitude of concerns to consider during development and implementation of CDS to increase its effectiveness and to mitigate its potentially disruptive aspects. We discuss the timing and modalities used to deliver messages, implications of outlier-only versus individualized feedback, the need to consider possible unintended consequences of such feedback, regulations, sustainability, and portability among systems. We discuss statistical issues related to the appropriate evaluation of CDS efficacy. We provide a systematic review of the published literature (indexed in PubMed) of anesthesia CDS and offer 2 case studies of CDS interventions using AIMS data from our own institution illustrating the salient points. Because of the considerable expense and complexity of maintaining near real-time CDS systems, as compared with providing individual reports via e-mail after the fact, we suggest that if the same goal can be accomplished via delayed reporting versus immediate feedback, the former approach is preferable. Nevertheless, some processes require near real-time alerts to produce the desired improvement. Post hoc e-mail reporting from enterprise-wide electronic health record systems is straightforward and can be accomplished using system-independent pathways (e.g., via built-in e-mail support provided by the relational database management system). However, for some of these enterprise-wide systems, near real-time data access, necessary for CDS that generates concurrent alerts, has been challenging to implement.

A Narrative Review of Meaningful Use and Anesthesia Information Management Systems

The US federal government has enacted legislation for a federal incentive program for health care providers and hospitals to implement electronic health records. The primary goal of the Meaningful Use (MU) program is to drive adoption of electronic health records nationwide and set the stage to monitor and guide efforts to improve population health and outcomes. The MU program provides incentives for the adoption and use of electronic health record technology and, in some cases, penalties for hospitals or providers not using the technology. The MU program is administrated by the Department of Health and Human Services and is divided into 3 stages that include specific reporting and compliance metrics. The rationale is that increased use of electronic health records will improve the process of delivering care at the individual level by improving the communication and allow for tracking population health and quality improvement metrics at a national level in the long run. The goal of this narrative review is to describe the MU program as it applies to anesthesiologists in the United States. This narrative review will discuss how anesthesiologists can meet the eligible provider reporting criteria of MU by applying anesthesia information management systems (AIMS) in various contexts in the United States. Subsequently, AIMS will be described in the context of MU criteria. This narrative literature review also will evaluate the evidence supporting the electronic health record technology in the operating room, including AIMS, independent of certification requirements for the electronic health record technology under MU in the United States.

Association between Intraoperative Hypotension and Hypertension and 30-day Postoperative Mortality in Noncardiac Surgery

BACKGROUND

Although deviations in intraoperative blood pressure are assumed to be associated with postoperative mortality, critical blood pressure thresholds remain undefined. Therefore, the authors estimated the intraoperative thresholds of systolic blood pressure (SBP), mean blood pressure (MAP), and diastolic blood pressure (DBP) associated with increased risk-adjusted 30-day mortality.

METHODS

This retrospective cohort study combined intraoperative blood pressure data from six Veterans Affairs medical centers with 30-day outcomes to determine the risk-adjusted associations between intraoperative blood pressure and 30-day mortality. Deviations in blood pressure were assessed using three methods: (1) population thresholds (individual patient sum of area under threshold [AUT] or area over threshold 2 SDs from the mean of the population intraoperative blood pressure values), (2). absolute thresholds, and (3) percent change from baseline blood pressure.

RESULTS

Thirty-day mortality was associated with (1) population threshold: systolic AUT (odds ratio, 3.3; 95% CI, 2.2 to 4.8), mean AUT (2.8; 1.9 to 4.3), and diastolic AUT (2.4; 1.6 to 3.8). Approximate conversions of AUT into its separate components of pressure and time were SBP < 67 mmHg for more than 8.2 min, MAP < 49 mmHg for more than 3.9 min, DBP < 33 mmHg for more than 4.4 min. (2) Absolute threshold: SBP < 70 mmHg for more than or equal to 5 min (odds ratio, 2.9; 95% CI, 1.7 to 4.9), MAP < 49 mmHg for more than or equal to 5 min (2.4; 1.3 to 4.6), and DBP < 30 mmHg for more than or equal to 5 min (3.2; 1.8 to 5.5). (3) Percent change: MAP decreases to more than 50% from baseline for more than or equal to 5 min (2.7; 1.5 to 5.0). Intraoperative hypertension was not associated with 30-day mortality with any of these techniques.

CONCLUSION

Intraoperative hypotension, but not hypertension, is associated with increased 30-day operative mortality.

Value of expert systems, quick reference guides and other cognitive aids

PURPOSE OF REVIEW

Cognitive aids and other methods of decision support are receiving increased interest by the anesthesia community. These tools have significant safety implications because of the possibility to decrease variability in human performance.

RECENT FINDINGS

Studies of the use of cognitive aids during realistic simulations supports use of cognitive aids and other decision support tools.

SUMMARY

The early work in this field of decision support is encouraging but there are many questions regarding the optimal design, presentation and use.

Big data and visual analytics in anaesthesia and health care

Advances in computer technology, patient monitoring systems, and electronic health record systems have enabled rapid accumulation of patient data in electronic form (i.e. big data). Organizations such as the Anesthesia Quality Institute and Multicenter Perioperative Outcomes Group have spearheaded large-scale efforts to collect anaesthesia big data for outcomes research and quality improvement. Analytics--the systematic use of data combined with quantitative and qualitative analysis to make decisions--can be applied to big data for quality and performance improvements, such as predictive risk assessment, clinical decision support, and resource management. Visual analytics is the science of analytical reasoning facilitated by interactive visual interfaces, and it can facilitate performance of cognitive activities involving big data. Ongoing integration of big data and analytics within anaesthesia and health care will increase demand for anaesthesia professionals who are well versed in both the medical and the information sciences.