Impact of a Novel Multiparameter Decision Support System on Intraoperative Processes of Care and Postoperative Outcomes

Effect of Telemedicine Support for Intraoperative Anaesthesia Care on Postoperative Outcomes: The TECTONICS Randomised Clinical Trial

Background

Novel applications of telemedicine can improve care quality and patient outcomes. Telemedicine for intraoperative decision support has not been rigorously studied.

Methods

This single centre randomised clinical trial ( clinicaltrials.gov NCT03923699 ) of unselected adult surgical patients was conducted between July 1, 2019 and January 31, 2023. Patients received usual care or decision support from a telemedicine service, the Anesthesiology Control Tower (ACT). The ACT provided real-time recommendations to intraoperative anaesthesia clinicians based on case reviews, machine-learning forecasting, and physiologic alerts. ORs were randomised 1:1. Co-primary outcomes of 30-day all-cause mortality, respiratory failure, acute kidney injury (AKI), and delirium were analysed as intention-to-treat.

Results

The trial completed planned enrolment with 71927 surgeries (35956 ACT; 35971 usual care). After multiple testing correction, there was no significant effect of the ACT vs. usual care on 30-day mortality [641/35956 (1.8%) vs 638/35971 (1.8%), risk difference 0.0% (95% CI -0.2% to 0.3%), p=0.96], respiratory failure [1089/34613 (3.1%) vs 1112/34619 (3.2%), risk difference -0.1% (95% CI -0.4% to 0.3%), p=0.96], AKI [2357/33897 (7%) vs 2391/33795 (7.1%), risk difference -0.1% (-0.6% to 0.4%), p=0.96], or delirium [1283/3928 (32.7%) vs 1279/3989 (32.1%), risk difference 0.6% (-2.0% to 3.2%), p=0.96]. There were no significant differences in secondary outcomes or in sensitivity analyses.

Conclusions

In this large RCT of a novel application of telemedicine-based remote monitoring and decision support using real-time alerts and case reviews, we found no significant differences in postoperative outcomes. Large-scale intraoperative telemedicine is feasible, and we suggest future avenues where it may be impactful.

Overview and Clinical Applications of Artificial Intelligence and Machine Learning in Cardiac Anesthesiology

Artificial intelligence- (AI) and machine learning (ML)-based applications are becoming increasingly pervasive in the healthcare setting. This has in turn challenged clinicians, hospital administrators, and health policymakers to understand such technologies and develop frameworks for safe and sustained clinical implementation. Within cardiac anesthesiology, challenges and opportunities for AI/ML to support patient care are presented by the vast amounts of electronic health data, which are collected rapidly, interpreted, and acted upon within the periprocedural area. To address such challenges and opportunities, in this article, the authors review 3 recent applications relevant to cardiac anesthesiology, including depth of anesthesia monitoring, operating room resource optimization, and transthoracic/transesophageal echocardiography, as conceptual examples to explore strengths and limitations of AI/ML within healthcare, and characterize this evolving landscape. Through reviewing such applications, the authors introduce basic AI/ML concepts and methodologies, as well as practical considerations and ethical concerns for initiating and maintaining safe clinical implementation of AI/ML-based algorithms for cardiac anesthesia patient care.

Broadening the scope and rising to the occasion, an opportunity for anaesthesiologists to take the lead in healthcare quality & patient safety (again)

PURPOSE OF REVIEW

The landscape of healthcare is characterized by high demands and scarce human and financial resources. This calls for action in improving healthcare quality. This review shows how anaesthesiologists are the designated medical specialist to share their affinity and knowledge in quality and safety, throughout the hospital and across the care continuum.

RECENT FINDINGS

Recent studies show excellent frameworks and examples of anaesthesiologist leading the way in patient safety and quality of care.

SUMMARY

Anaesthesiologist are early adapters of patient safety. In the last decades anaesthesia has become linked with patient safety and the quality of care. With the recent transition from peroperative to perioperative care; new opportunities are emerging, expanding our professional scope. Unfortunately, the anaesthesiologist is not often positioned in a leading role in quality of care and patient safety. After a brief rise during the COVID-19 pandemic, in which anaesthesiologists were visible in the frontline in many countries, we have unfortunately disappeared from the spotlight. This review shows numerous ideas, examples, and a framework how a leading position can be realized.

User-Centered Design of a Machine Learning Dashboard for Prediction of Postoperative Complications

BACKGROUND

Machine learning models can help anesthesiology clinicians assess patients and make clinical and operational decisions, but well-designed human-computer interfaces are necessary for machine learning model predictions to result in clinician actions that help patients. Therefore, the goal of this study was to apply a user-centered design framework to create a user interface for displaying machine learning model predictions of postoperative complications to anesthesiology clinicians.

METHODS

Twenty-five anesthesiology clinicians (attending anesthesiologists, resident physicians, and certified registered nurse anesthetists) participated in a 3-phase study that included (phase 1) semistructured focus group interviews and a card sorting activity to characterize user workflows and needs; (phase 2) simulated patient evaluation incorporating a low-fidelity static prototype display interface followed by a semistructured interview; and (phase 3) simulated patient evaluation with concurrent think-aloud incorporating a high-fidelity prototype display interface in the electronic health record. In each phase, data analysis included open coding of session transcripts and thematic analysis.

RESULTS

During the needs assessment phase (phase 1), participants voiced that (a) identifying preventable risk related to modifiable risk factors is more important than nonpreventable risk, (b) comprehensive patient evaluation follows a systematic approach that relies heavily on the electronic health record, and (c) an easy-to-use display interface should have a simple layout that uses color and graphs to minimize time and energy spent reading it. When performing simulations using the low-fidelity prototype (phase 2), participants reported that (a) the machine learning predictions helped them to evaluate patient risk, (b) additional information about how to act on the risk estimate would be useful, and (c) correctable problems related to textual content existed. When performing simulations using the high-fidelity prototype (phase 3), usability problems predominantly related to the presentation of information and functionality. Despite the usability problems, participants rated the system highly on the System Usability Scale (mean score, 82.5; standard deviation, 10.5).

CONCLUSIONS

Incorporating user needs and preferences into the design of a machine learning dashboard results in a display interface that clinicians rate as highly usable. Because the system demonstrates usability, evaluation of the effects of implementation on both process and clinical outcomes is warranted.

How anesthesiology can deal with innovation and new technologies?

Innovation and new technologies have always impacted significantly the anesthesiology practice all along the perioperative course, as it is recognized as one of the most transformative medical specialties specifically regarding patient's safety. Beside a number of major changes in procedures, equipment, training, and organization that aggregated to establish a strong safety culture with effective practices, anesthesiology is also a stakeholder in disruptive innovation. The present review is not exhaustive and aims to provide an overview on how innovation could change and improve anesthesiology practices through some examples as telemedicine (TM), machine learning and artificial intelligence (AI). For example, postoperative complications can be accurately predicted by AI from automated real-time electronic health record data, matching physicians' predictive accuracy. Clinical workflow could be facilitated and accelerated with mobile devices and applications, assuming that these tools should remain at the service of patients and care providers. Care providers and patients connections have improved, thanks to these digital and innovative transformations, without replacing existing relationships between them. It also should give time back to physicians and nurses to better spend it in the perioperative care, and to provide "personalized" medicine keeping a high level of standard of care.

Systems Anesthesiology: Systems of Care Delivery and Optimization in the Operating Room

Anesthesiology presents a challenge to a traditional simplifying approach given the ever-increasing amount of medical data and a more demanding environment. Systems anesthesiology is a modern approach to perioperative care, integrating the complexity of multifactorial knowledge and data to achieve a more adequate representation of reality, while including both patient-related medical aspects as well as economic and organizational challenges. We discuss the value of some innovative technologies such as the emergence of anesthesia information systems, the use of tele-medicine, predictive monitoring, or closed-loop systems as it pertains to the changes in the current standards of care in anesthesiology. Furthermore, we highlight the importance of systems anesthesiology in operating room planning, anesthesia research, and education.

Patient Information Summarization in Clinical Settings: Scoping Review

BACKGROUND

Information overflow, a common problem in the present clinical environment, can be mitigated by summarizing clinical data. Although there are several solutions for clinical summarization, there is a lack of a complete overview of the research relevant to this field.

OBJECTIVE

This study aims to identify state-of-the-art solutions for clinical summarization, to analyze their capabilities, and to identify their properties.

METHODS

A scoping review of articles published between 2005 and 2022 was conducted. With a clinical focus, PubMed and Web of Science were queried to find an initial set of reports, later extended by articles found through a chain of citations. The included reports were analyzed to answer the questions of where, what, and how medical information is summarized; whether summarization conserves temporality, uncertainty, and medical pertinence; and how the propositions are evaluated and deployed. To answer how information is summarized, methods were compared through a new framework "collect-synthesize-communicate" referring to information gathering from data, its synthesis, and communication to the end user.

RESULTS

Overall, 128 articles were included, representing various medical fields. Exclusively structured data were used as input in 46.1% (59/128) of papers, text in 41.4% (53/128) of articles, and both in 10.2% (13/128) of papers. Using the proposed framework, 42.2% (54/128) of the records contributed to information collection, 27.3% (35/128) contributed to information synthesis, and 46.1% (59/128) presented solutions for summary communication. Numerous summarization approaches have been presented, including extractive (n=13) and abstractive summarization (n=19); topic modeling (n=5); summary specification (n=11); concept and relation extraction (n=30); visual design considerations (n=59); and complete pipelines (n=7) using information extraction, synthesis, and communication. Graphical displays (n=53), short texts (n=41), static reports (n=7), and problem-oriented views (n=7) were the most common types in terms of summary communication. Although temporality and uncertainty information were usually not conserved in most studies (74/128, 57.8% and 113/128, 88.3%, respectively), some studies presented solutions to treat this information. Overall, 115 (89.8%) articles showed results of an evaluation, and methods included evaluations with human participants (median 15, IQR 24 participants): measurements in experiments with human participants (n=31), real situations (n=8), and usability studies (n=28). Methods without human involvement included intrinsic evaluation (n=24), performance on a proxy (n=10), or domain-specific tasks (n=11). Overall, 11 (8.6%) reports described a system deployed in clinical settings.

CONCLUSIONS

The scientific literature contains many propositions for summarizing patient information but reports very few comparisons of these proposals. This work proposes to compare these algorithms through how they conserve essential aspects of clinical information and through the "collect-synthesize-communicate" framework. We found that current propositions usually address these 3 steps only partially. Moreover, they conserve and use temporality, uncertainty, and pertinent medical aspects to varying extents, and solutions are often preliminary.

Pediatric Anesthesia Providers' Perspective on the Real-Life Implementation of the Philips Visual Patient Avatar: A Qualitative Study

The Philips Visual Patient Avatar represents an alternative method of patient monitoring that, according to computer-based simulation studies, enhances diagnostic accuracy and confidence and reduces workload. After its clinical integration, we assessed pediatric anesthesia providers' perspectives on this technology. This is a single-center qualitative study, conducted at the University Hospital Zurich using in-depth individual interviews. We aimed to identify the advantages and limitations of the Visual Patient Avatar in pediatric anesthesia and to assess children's and parents' reactions from caregivers' perspectives. Thematic analysis was used to identify the dominant themes. Fourteen members of the institution's pediatric anesthesia team were interviewed. The most prevalent themes were children's positive reactions towards the Visual Patient Avatar (92.9%) and enhanced speed in problem identification (71.4%). Additionally, 50% of participants reported finding the Visual Patient Avatar useful for diverting children's attention during anesthesia induction, and 50% suggested that its vital sign thresholds should be adaptable for different age groups. The study revealed that the Visual Patient Avatar was recognized as a tool in pediatric anesthesia, enabling prompt identification of underlying issues and receiving positive feedback from both children and parents. The most commonly voiced wish for improvement in the study was the ability to customize the Visual Patient Avatar's thresholds for different age groups.

Efficacy of Electronic Reminders in Increasing the Enhanced Recovery After Surgery Protocol Use During Major Breast Surgery: Prospective Cohort Study

BACKGROUND

Enhanced recovery after surgery (ERAS) protocols are patient-centered, evidence-based guidelines for peri-, intra-, and postoperative management of surgical candidates that aim to decrease operative complications and facilitate recovery after surgery. Anesthesia providers can use these protocols to guide decision-making and standardize aspects of their anesthetic plan in the operating room.

OBJECTIVE

Research across multiple disciplines has demonstrated that clinical decision support systems have the potential to improve protocol adherence by reminding providers about departmental policies and protocols via notifications. There remains a gap in the literature about whether clinical decision support systems can improve patient outcomes by improving anesthesia providers' adherence to protocols. Our hypothesis is that the implementation of an electronic notification system to anesthesia providers the day prior to scheduled breast surgeries will increase the use of the already existing but underused ERAS protocols.

METHODS

This was a single-center prospective cohort study conducted between October 2017 and August 2018 at an urban academic medical center. After obtaining approval from the institutional review board, anesthesia providers assigned to major breast surgery cases were identified. Patient data were collected pre- and postimplementation of an electronic notification system that sent the anesthesia providers an email reminder of the ERAS breast protocol the night before scheduled surgeries. Each patient's record was then reviewed to assess the frequency of adherence to the various ERAS protocol elements.

RESULTS

Implementation of an electronic notification significantly improved overall protocol adherence and several preoperative markers of ERAS protocol adherence. Protocol adherence increased from 16% (n=14) to 44% (n=44; P<.001), preoperative administration of oral gabapentin (600 mg) increased from 13% (n=11) to 43% (n=43; P<.001), and oral celebrex (400 mg) use increased from 16% (n=14) to 35% (n=35; P=.006). There were no statistically significant differences in the use of scopolamine transdermal patch (P=.05), ketamine (P=.35), and oral acetaminophen (P=.31) between the groups. Secondary outcomes such as intraoperative and postoperative morphine equivalent administered, postanesthesia care unit length of stay, postoperative pain scores, and incidence of postoperative nausea and vomiting did not show statistical significance.

CONCLUSIONS

This study examines whether sending automated notifications to anesthesia providers increases the use of ERAS protocols in a single academic medical center. Our analysis exhibited statistically significant increases in overall protocol adherence but failed to show significant differences in secondary outcome measures. Despite the lack of a statistically significant difference in secondary postoperative outcomes, our analysis contributes to the limited literature on the relationship between using push notifications and clinical decision support in guiding perioperative decision-making. A variety of techniques can be implemented, including technological solutions such as automated notifications to providers, to improve awareness and adherence to ERAS protocols.

An Intraoperative Telemedicine Program to Improve Perioperative Quality Measures: The ACTFAST-3 Randomized Clinical Trial

Importance

Telemedicine for clinical decision support has been adopted in many health care settings, but its utility in improving intraoperative care has not been assessed.

Objective

To pilot the implementation of a real-time intraoperative telemedicine decision support program and evaluate whether it reduces postoperative hypothermia and hyperglycemia as well as other quality of care measures.

Design, Setting, and Participants

This single-center pilot randomized clinical trial (Anesthesiology Control Tower-Feedback Alerts to Supplement Treatments [ACTFAST-3]) was conducted from April 3, 2017, to June 30, 2019, at a large academic medical center in the US. A total of 26 254 adult surgical patients were randomized to receive either usual intraoperative care (control group; n = 12 980) or usual care augmented by telemedicine decision support (intervention group; n = 13 274). Data were initially analyzed from April 22 to May 19, 2021, with updates in November 2022 and February 2023.

Intervention

Patients received either usual care (medical direction from the anesthesia care team) or intraoperative anesthesia care monitored and augmented by decision support from the Anesthesiology Control Tower (ACT), a real-time, live telemedicine intervention. The ACT incorporated remote monitoring of operating rooms by a team of anesthesia clinicians with customized analysis software. The ACT reviewed alerts and electronic health record data to inform recommendations to operating room clinicians.

Main Outcomes and Measures

The primary outcomes were avoidance of postoperative hypothermia (defined as the proportion of patients with a final recorded intraoperative core temperature >36 °C) and hyperglycemia (defined as the proportion of patients with diabetes who had a blood glucose level ≤180 mg/dL on arrival to the postanesthesia recovery area). Secondary outcomes included intraoperative hypotension, temperature monitoring, timely antibiotic redosing, intraoperative glucose evaluation and management, neuromuscular blockade documentation, ventilator management, and volatile anesthetic overuse.

Results

Among 26 254 participants, 13 393 (51.0%) were female and 20 169 (76.8%) were White, with a median (IQR) age of 60 (47-69) years. There was no treatment effect on avoidance of hyperglycemia (7445 of 8676 patients [85.8%] in the intervention group vs 7559 of 8815 [85.8%] in the control group; rate ratio [RR], 1.00; 95% CI, 0.99-1.01) or hypothermia (7602 of 11 447 patients [66.4%] in the intervention group vs 7783 of 11 672 [66.7.%] in the control group; RR, 1.00; 95% CI, 0.97-1.02). Intraoperative glucose measurement was more common among patients with diabetes in the intervention group (RR, 1.07; 95% CI, 1.01-1.15), but other secondary outcomes were not significantly different.

Conclusions and Relevance

In this randomized clinical trial, anesthesia care quality measures did not differ between groups, with high confidence in the findings. These results suggest that the intervention did not affect the targeted care practices. Further streamlining of clinical decision support and workflows may help the intraoperative telemedicine program achieve improvement in targeted clinical measures.

Trial Registration

ClinicalTrials.gov Identifier: NCT02830126.

Best Practice Alerts Informed by Inpatient Opioid Intake to Reduce Opioid Prescribing after Surgery (PRIOR): A Cluster Randomized Multiple Crossover Trial

BACKGROUND

Overprescription of opioids after surgery remains common. Residual and unnecessarily prescribed opioids can provide a reservoir for nonmedical use. This study therefore tested the hypothesis that a decision-support tool embedded in electronic health records guides clinicians to prescribe fewer opioids at discharge after inpatient surgery.

METHODS

This study included 21,689 surgical inpatient discharges in a cluster randomized multiple crossover trial from July 2020 to June 2021 in four Colorado hospitals. Hospital-level clusters were randomized to alternating 8-week periods during which an electronic decision-support tool recommended tailored discharge opioid prescriptions based on previous inpatient opioid intake. During active alert periods, the alert was displayed to clinicians when the proposed opioid prescription exceeded recommended amounts. No alerts were displayed during inactive periods. Carryover effects were mitigated by including 4-week washout periods. The primary outcome was oral morphine milligram equivalents prescribed at discharge. Secondary outcomes included combination opioid and nonopioid prescriptions and additional opioid prescriptions until day 28 after discharge. A vigorous state-wide opioid education and awareness campaign was in place during the trial.

RESULTS

The total postdischarge opioid prescription was a median [quartile 1, quartile 3] of 75 [0, 225] oral morphine milligram equivalents among 11,003 patients discharged when the alerts were active and 100 [0, 225] morphine milligram equivalents in 10,686 patients when the alerts were inactive, with an estimated ratio of geometric means of 0.95 (95% CI, 0.80 to 1.13; P = 0.586). The alert was displayed in 28% (3,074 of 11,003) of the discharges during the active alert period. There was no relationship between the alert and prescribed opioid and nonopioid combination medications or additional opioid prescriptions written after discharge.

CONCLUSIONS

A decision-support tool incorporated into electronic medical records did not reduce discharge opioid prescribing for postoperative patients in the context of vigorous opioid education and awareness efforts. Opioid prescribing alerts might yet be valuable in other contexts.(Anesthesiology 2023; 139:186-96).

EDITOR’S PERSPECTIVE

Human-centered visualization technologies for patient monitoring are the future: a narrative review

Medical technology innovation has improved patient monitoring in perioperative and intensive care medicine and continuous improvement in the technology is now a central focus in this field. Because data density increases with the number of parameters captured by patient-monitoring devices, its interpretation has become more challenging. Therefore, it is necessary to support clinicians in managing information overload while improving their awareness and understanding about the patient's health status. Patient monitoring has almost exclusively operated on the single-sensor-single-indicator principle-a technology-centered way of presenting data in which specific parameters are measured and displayed individually as separate numbers and waves. An alternative is user-centered medical visualization technology, which integrates multiple pieces of information (e.g., vital signs), derived from multiple sensors into a single indicator-an avatar-based visualization-that is a meaningful representation of the real-world situation. Data are presented as changing shapes, colors, and animation frequencies, which can be perceived, integrated, and interpreted much more efficiently than other formats (e.g., numbers). The beneficial effects of these technologies have been confirmed in computer-based simulation studies; visualization technologies improved clinicians' situation awareness by helping them effectively perceive and verbalize the underlying medical issue, while improving diagnostic confidence and reducing workload. This review presents an overview of the scientific results and the evidence for the validity of these technologies.

Digitization of Symbol-Denoted Blood Pressure Data From Intraoperative Paper Health Records in a Low-Middle-Income Country Using Deep Image Segmentation and Associated Postoperative Outcomes: A Feasibility Study

BACKGROUND

In low-middle-income countries (LMICs), perioperative clinical information is almost universally collected on paper health records (PHRs). The lack of accessible digital databases limits LMICs in leveraging data to predict and improve patient outcomes after surgery. In this feasibility study, our aims were to: (1) determine the detection performance and prediction error of the U-Net deep image segmentation approach for digitization of hand-drawn blood pressure symbols from an image of the intraoperative PHRs and (2) evaluate the association between deep image segmentation-derived blood pressure parameters and postoperative mortality and length of stay.

METHODS

A smartphone mHealth platform developed by our team was used to capture images of completed intraoperative PHRs. A 2-stage deep image segmentation modeling approach was used to create 2 separate segmentation masks for systolic blood pressure (SBP) and diastolic blood pressure (DBP). Iterative postprocessing was utilized to convert the segmentation mask results into numerical SBP and DBP values. Detection performance and prediction errors were evaluated for the U-Net models by comparison with ground-truth values. Using multivariate regression analysis, we investigated the association of deep image segmentation-derived blood pressure values, total time spent in predefined blood pressure ranges, and postoperative outcomes including in-hospital mortality and length of stay.

RESULTS

A total of 350 intraoperative PHRs were imaged following surgery. Overall accuracy was 0.839 and 0.911 for SBP and DBP symbol detections, respectively. The mean error rate and standard deviation for the difference between the actual and predicted blood pressure values were 2.1 ± 4.9 and -0.8 ± 3.9 mm Hg for SBP and DBP, respectively. Using the U-Net model-derived blood pressures, minutes of time where DBP <50 mm Hg (odds ratio [OR], 1.03; CI, 1.01-1.05; P = .003) was associated with an increased in-hospital mortality. In addition, increased cumulative minutes of time with SBP between 80 and 90 mm Hg was significantly associated with a longer length of stay (incidence rate ratio, 1.02 [1.0-1.03]; P < .05), while increased cumulative minutes of time where SBP between 140 and 160 mm Hg was associated with a shorter length of stay (incidence rate ratio, 0.9 [0.96-0.99]; P < .05).

CONCLUSIONS

In this study, we report our experience with a deep image segmentation model for digitization of symbol-denoted blood pressure from intraoperative anesthesia PHRs. Our data support further development of this novel approach to digitize PHRs from LMICs, to provide accessible, curated, and reproducible data for both quality improvement- and outcome-based research.

Maternal heart rate variability patterns associated with maternal hypotension and non-reassuring fetal heart rate patterns following initiation of combined spinal-epidural labor analgesia: a prospective observational trial

BACKGROUND

We evaluated whether baseline maternal heart rate variability (HRV), including the Analgesia Nociception Index (ANI), is associated with maternal hypotension and fetal heart rate (FHR) abnormalities following combined spinal-epidural (CSE) labor analgesia.

METHODS

Laboring women were enrolled in this prospective observational study. The primary endpoint was maternal hypotension. The secondary endpoint was FHR abnormalities within 30 min following CSE analgesia initiated with intrathecal plain bupivacaine 1.0 mg and fentanyl 20 µg. The maternal ANI, electrocardiogram, blood pressure, heart rate, oxygen saturation, and FHR tracings were recorded 15 min before and 30 min after CSE. Parturients were grouped based on presence of hypotension and FHR abnormalities. Patient demographics and HRV metrics were compared. Receiver operating characteristics (ROC) curves were constructed for the prediction of hypotension and FHR abnormalities.

RESULTS

No significant intergroup differences were detected in patient characteristics. Several baseline HRV metrics and ANI differed significantly between the normotensive (n = 50) and hypotensive (n = 31) groups and between parturients showing FHR abnormalities (n = 19) and those showing reassuring FHR traces (n = 62). The area under the ROC curve (AUC) for predicting hypotension of the baseline low-frequency (LF)/high-frequency (HF) ratio was 0.677 (95% CI 0.55 to 0.80), and that of the ANI was 0.858 (95% CI 0.78 to 0.94). For predicting non-reassuring FHR patterns, the AUC of the LF/HF ratio was 0.77 (95% CI 0.65 to 0.89), and that of the ANI was 0.833 (95% CI 0.72 to 0.94).

CONCLUSIONS

The ANI can predict the propensity for maternal hypotension and non-reassuring FHR patterns following CSE.

Prediction of Postoperative Deterioration in Cardiac Surgery Patients Using Electronic Health Record and Physiologic Waveform Data

BACKGROUND

Postoperative hemodynamic deterioration among cardiac surgical patients can indicate or lead to adverse outcomes. Whereas prediction models for such events using electronic health records or physiologic waveform data are previously described, their combined value remains incompletely defined. The authors hypothesized that models incorporating electronic health record and processed waveform signal data (electrocardiogram lead II, pulse plethysmography, arterial catheter tracing) would yield improved performance versus either modality alone.

METHODS

Intensive care unit data were reviewed after elective adult cardiac surgical procedures at an academic center between 2013 and 2020. Model features included electronic health record features and physiologic waveforms. Tensor decomposition was used for waveform feature reduction. Machine learning-based prediction models included a 2013 to 2017 training set and a 2017 to 2020 temporal holdout test set. The primary outcome was a postoperative deterioration event, defined as a composite of low cardiac index of less than 2.0 ml min-1 m-2, mean arterial pressure of less than 55 mmHg sustained for 120 min or longer, new or escalated inotrope/vasopressor infusion, epinephrine bolus of 1 mg or more, or intensive care unit mortality. Prediction models analyzed data 8 h before events.

RESULTS

Among 1,555 cases, 185 (12%) experienced 276 deterioration events, most commonly including low cardiac index (7.0% of patients), new inotrope (1.9%), and sustained hypotension (1.4%). The best performing model on the 2013 to 2017 training set yielded a C-statistic of 0.803 (95% CI, 0.799 to 0.807), although performance was substantially lower in the 2017 to 2020 test set (0.709, 0.705 to 0.712). Test set performance of the combined model was greater than corresponding models limited to solely electronic health record features (0.641; 95% CI, 0.637 to 0.646) or waveform features (0.697; 95% CI, 0.693 to 0.701).

CONCLUSIONS

Clinical deterioration prediction models combining electronic health record data and waveform data were superior to either modality alone, and performance of combined models was primarily driven by waveform data. Decreased performance of prediction models during temporal validation may be explained by data set shift, a core challenge of healthcare prediction modeling.

EDITOR’S PERSPECTIVE

Reflections on the Discipline Construction Environment of World Literature and Comparative Literature in the Era of Big Data Analysis

Comparative literature and world literature were originally two independent disciplines, but now they are merged into one. In the era of big data, building an efficient information management method is one of the important contents of university reform. Based on the construction of comparative literature and world literature, this paper applies the DSS (Decision Support System) structure based on data warehouse to the construction of comparative literature and world literature, and constructs the DSS structure of comparative literature and world literature. The related knowledge of DM (data mining) is used for research, and the application design and implementation process of DM are introduced into the subject quality evaluation system. The research shows that the experimental results of NB (Naive Bayes) algorithm will not take too long, about 56.08 s, and the compression ratio is 0.897, when the parameters are basically the same. This model can effectively help schools to analyze data and make decisions, and improve the level of information construction in schools.

Design of a novel multifunction decision support/alerting system for in-patient acute care, ICU and floor (AlertWatch AC)

Background

Multifunction surveillance alerting systems have been found to be beneficial for the operating room and labor and delivery. This paper describes a similar system developed for in-hospital acute care environments, AlertWatch Acute Care (AWAC).

Results

A decision support surveillance system has been developed which extracts comprehensive electronic health record (EHR) data including live data from physiologic monitors and ventilators and incorporates them into an integrated organ icon-based patient display. Live data retrieved from the hospitals network are processed by presenting scrolling median values to reduce artifacts. A total of 48 possible alerts are generated covering a broad range of critical patient care concerns. Notification is achieved by paging or texting the appropriated member of the critical care team. Alerts range from simple out of range values to more complex programing of impending Ventilator Associated Events, SOFA, qSOFA, SIRS scores and process of care reminders for the management of glucose and sepsis. As with similar systems developed for the operating room and labor and delivery, there are green, yellow, and red configurable ranges for all parameters. A census view allows surveillance of an entire unit with flashing or text to voice alerting and enables detailed information by windowing into an individual patient view including live physiologic waveforms. The system runs via web interface on desktop as well as mobile devices, with iOS native app available, for ease of communication from any location. The goal is to improve safety and adherence to standard management protocols.

Conclusions

AWAC is designed to provide a high level surveillance view for multi-bed hospital units with varying acuity from standard floor patients to complex ICU care. Alerts are generated by algorithms running in the background and automatically notify the selected member of the patients care team. Its value has been demonstrated for low acuity patients, further study is required to determine its effectiveness in high acuity patients.

The Impact of Clinical Decision Support Alerts on Clostridioides difficile Testing: A Systematic Review

BACKGROUND

Several studies have investigated the utility of electronic decision support alerts in diagnostic stewardship for Clostridioides difficile infection (CDI). However, it is unclear if alerts are effective in reducing inappropriate CDI testing and/or CDI rates. The aim of this systematic review was to determine if alerts related to CDI diagnostic stewardship are effective at reducing inappropriate CDI testing volume and CDI rates among hospitalized adult patients.

METHODS

We searched Ovid Medline and 5 other databases for original studies evaluating the association between alerts for CDI diagnosis and CDI testing volume and/or CDI rate. Two investigators independently extracted data on study characteristics, study design, alert triggers, cointerventions, and study outcomes.

RESULTS

Eleven studies met criteria for inclusion. Studies varied significantly in alert triggers and in study outcomes. Six of 11 studies demonstrated a statistically significant decrease in CDI testing volume, 6 of 6 studies evaluating appropriateness of CDI testing found a significant reduction in the proportion of inappropriate testing, and 4 of 7 studies measuring CDI rate demonstrated a significant decrease in the CDI rate in the postintervention vs preintervention period. The magnitude of the increase in appropriate CDI testing varied, with some studies reporting an increase with minimal clinical significance.

CONCLUSIONS

The use of electronic alerts for diagnostic stewardship for C. difficile was associated with reductions in CDI testing, the proportion of inappropriate CDI testing, and rates of CDI in most studies. However, broader concerns related to alerts remain understudied, including unintended adverse consequences and alert fatigue.

A Remote Surveillance Platform to Monitor General Care Ward Surgical Patients for Acute Physiologic Deterioration

BACKGROUND

The traditional paradigm of hospital surgical ward care consists of episodic bedside visits by providers with periodic perusals of the patient's electronic health record (EHR). Vital signs and laboratory results are directly pushed to the EHR but not to providers themselves. Results that require intervention may not be recognized for hours. Remote surveillance programs continuously monitor electronic data and provide automatic alerts that can be routed to multidisciplinary providers. Such programs have not been explored in surgical general care wards.

METHODS

We performed a quality improvement observational study of otolaryngology and ophthalmology patients on a general care ward from October 2017 to March 2019 during nighttime hours (17:00-07:00). The study was initiated due to the loss of on-site anesthesiology resources that historically helped respond to acute physiologic deterioration events. We implemented a remote surveillance software program to continuously monitor patients for severe vital signs and laboratory abnormalities and automatically alert the ward team and a remote critical care anesthesiology team. The primary end point was the true positive rate, defined as the proportion of alerts that were associated with a downstream action that changed the care of the patient. This was determined using systematic chart review. The secondary end point, as a measure of alarm fatigue, was the average number of alerts per clinician shift.

RESULTS

The software monitored 3926 hospital visits and analyzed 1,560,999 vitals signs and 16,635 laboratories. It generated 151 alerts, averaging 2.6 alerts per week. Of these, 143 (94.7%) were numerically accurate and 8 (5.3%) were inaccurate. Hypoxemia with oxygen saturation <88% was the most common etiology (92, 63%) followed by tachycardia >130 beats per minute (19, 13.3%). Among the accurate alerts, 133 (88.1%) were true positives with an associated clinical action. Actions included a change in management 113 (67.7%), new diagnostic test 26 (15.6%), change in discharge planning 20 (12.0%), and change in level of care to the intensive care unit (ICU) 8 (4.8%). As a measure of alarm fatigue, there were 0.4 alerts per clinician shift.

CONCLUSIONS

In a surgical general care ward, a remote surveillance software program that continually and automatically monitors physiologic data streams from the EHR and alerts multidisciplinary providers for severe derangements provided highly actionable alarms at a rate that is unlikely to cause alarm fatigue. Such programs are feasible and could be used to change the paradigm of monitoring.

Impact of integrated graphical display on expert and novice diagnostic performance in critical care

OBJECTIVE

To determine the impact of a graphical information display on diagnosing circulatory shock.

MATERIALS AND METHODS

This was an experimental study comparing integrated and conventional information displays. Participants were intensivists or critical care fellows (experts) and first-year medical residents (novices).

RESULTS

The integrated display was associated with higher performance (87% vs 82%; P < .001), less time (2.9 vs 3.5 min; P = .008), and more accurate etiology (67% vs 54%; P = .048) compared to the conventional display. When stratified by experience, novice physicians using the integrated display had higher performance (86% vs 69%; P < .001), less time (2.9 vs 3.7 min; P = .03), and more accurate etiology (65% vs 42%; P = .02); expert physicians using the integrated display had nonsignificantly improved performance (87% vs 82%; P = .09), time (2.9 vs 3.3; P = .28), and etiology (69% vs 67%; P = .81).

DISCUSSION

The integrated display appeared to support efficient information processing, which resulted in more rapid and accurate circulatory shock diagnosis. Evidence more strongly supported a difference for novices, suggesting that graphical displays may help reduce expert-novice performance gaps.

Opportunities to Improve the Capacity to Rescue: Intraoperative and Perioperative Tools

Postoperative complications, which occur in approximately 23% of surgeries, are a major source of patient mortality. Some of these deaths may be preventable. This article explores factors and contexts during the intraoperative period, in the postanesthesia care unit, perioperatively, and after discharge that may represent opportunities to intervene and prevent mortality after a potentially treatable complication. Tools to improve the identification and response to life-threatening complications in these unique care settings are discussed.

Genetic mutations associated with susceptibility to perioperative complications in a longitudinal biorepository with integrated genomic and electronic health records

BACKGROUND

Existing genetic information can be leveraged to identify patients with susceptibilities to conditions that might impact their perioperative care, but clinicians generally have limited exposure and are not trained to contextualise this information. We identified patients with genetic susceptibilities to anaesthetic complications using a perioperative biorepository and characterised the concordance with existing diagnoses.

METHODS

Adult patients undergoing surgery within Michigan Medicine from 2012 to 2017 were consented for genotyping. Genotypes were integrated with the electronic health record (EHR). We retrospectively characterised frequencies of variants associated with butyrylcholinesterase deficiency, factor V Leiden, and malignant hyperthermia, three pharmacogenetic factors with perioperative implications. We calculated the percentage homozygous and heterozygous for each that had been diagnosed previously and searched for EHR findings consistent with a predisposition.

RESULTS

Analysis of genetic data revealed that 25 out of 40 769 (0.1%) patients were homozygous and 1918 (4.7%) were heterozygous for mutations associated with butyrylcholinesterase deficiency. Of the homozygous individuals, 14 (56%) carried a pre-existing diagnosis. For factor V Leiden, 29 (0.1%) were homozygous and 2153 (5.3%) heterozygous. Of the homozygous individuals, three (10%) were diagnosed by EHR-derived phenotype and six (21%) by clinician review. Malignant hyperthermia was assessed in a subset of patients. We detected two patients with associated mutations. Neither carried clinical diagnoses.

CONCLUSIONS

We identified patients with genetic susceptibility to perioperative complications using an open source script designed for clinician use. We validated this application in a retrospective analysis for three conditions with well-characterised inheritance, and showed that not all genetic susceptibilities were documented in the EHR.

Considerations for Integration of Perioperative Electronic Health Records Across Institutions for Research and Quality Improvement: The Approach Taken by the Multicenter Perioperative Outcomes Group

Use of the electronic health record (EHR) has become a routine part of perioperative care in the United States. Secondary use of EHR data includes research, quality, and educational initiatives. Fundamental to secondary use is a framework to ensure fidelity, transparency, and completeness of the source data. In developing this framework, competing priorities must be considered as to which data sources are used and how data are organized and incorporated into a useable format. In assembling perioperative data from diverse institutions across the United States and Europe, the Multicenter Perioperative Outcomes Group (MPOG) has developed methods to support such a framework. This special article outlines how MPOG has approached considerations of data structure, validation, and accessibility to support multicenter integration of perioperative EHRs. In this multicenter practice registry, MPOG has developed processes to extract data from the perioperative EHR; transform data into a standardized format; and validate, deidentify, and transfer data to a secure central Coordinating Center database. Participating institutions may obtain access to this central database, governed by quality and research committees, to inform clinical practice and contribute to the scientific and clinical communities. Through a rigorous and standardized approach to ensure data integrity, MPOG enables data to be usable for quality improvement and advancing scientific knowledge. As of March 2019, our collaboration of 46 hospitals has accrued 10.7 million anesthesia records with associated perioperative EHR data across heterogeneous vendors. Facilitated by MPOG, each site retains access to a local repository containing all site-specific perioperative data, distinct from source EHRs and readily available for local research, quality, and educational initiatives. Through committee approval processes, investigators at participating sites may additionally access multicenter data for similar initiatives. Emerging from this work are 4 considerations that our group has prioritized to improve data quality: (1) data should be available at the local level before Coordinating Center transfer; (2) data should be rigorously validated against standardized metrics before use; (3) data should be curated into computable phenotypes that are easily accessible; and (4) data should be collected for both research and quality improvement purposes because these complementary goals bolster the strength of each endeavor.

Use of a Polygenic Risk Score Improves Prediction of Myocardial Injury After Non-Cardiac Surgery

BACKGROUND

While postoperative myocardial injury remains a major driver of morbidity and mortality, the ability to accurately identify patients at risk remains limited despite decades of clinical research. The role of genetic information in predicting myocardial injury after noncardiac surgery (MINS) remains unknown and requires large scale electronic health record and genomic data sets.

METHODS

In this retrospective observational study of adult patients undergoing noncardiac surgery, we defined MINS as new troponin elevation within 30 days following surgery. To determine the incremental value of polygenic risk score (PRS) for coronary artery disease, we added the score to 3 models of MINS risk: revised cardiac risk index, a model comprised entirely of preoperative variables, and a model with combined preoperative plus intraoperative variables. We assessed performance without and with PRSs via area under the receiver operating characteristic curve and net reclassification index.

RESULTS

Among 90 053 procedures across 40 498 genotyped individuals, we observed 429 cases with MINS (0.5%). PRS for coronary artery disease was independently associated with MINS for each multivariable model created (odds ratio=1.12 [95% CI, 1.02-1.24], P=0.023 in the revised cardiac risk index-based model; odds ratio, 1.19 [95% CI, 1.07-1.31], P=0.001 in the preoperative model; and odds ratio, 1.17 [95% CI, 1.06-1.30], P=0.003 in the preoperative plus intraoperative model). The addition of clinical risk factors improved model discrimination. When PRS was included with preoperative and preoperative plus intraoperative models, up to 3.6% of procedures were shifted into a new outcome classification.

CONCLUSIONS

The addition of a PRS does not significantly improve discrimination but remains independently associated with MINS and improves goodness of fit. As genetic analysis becomes more common, clinicians will have an opportunity to use polygenic risk to predict perioperative complications. Further studies are necessary to determine if PRSs can inform MINS surveillance.

Intraoperative Electronic Alerts Improve Compliance With National Quality Program Measure for Perioperative Temperature Management

BACKGROUND

Reimbursement for anesthesia services has been shifting from a fee-for-service model to a value-based model that ties payment to quality metrics. The Centers for Medicare & Medicaid Service's (CMS) value-based payment program includes a quality measure for perioperative temperature management (Measure #424, Perioperative Temperature Management). Compliance may impose new challenges in clinical practice, data collection, and reporting. We investigated the impact of an electronic decision-support tool on adherence to this emerging standard.

METHODS

In this retrospective observational study, perioperative temperature data were collected from cases eligible for reporting this measure to CMS from a single academic medical center before and after the implementation of an electronic decision-support tool that prompted temperature measurement and maintenance of normothermia. Proportions of measure compliance were assessed using segmented regression analysis. Proportions of intraoperative temperature measurement were also assessed, and multivariable logistic regression was performed to assess the association between patient and surgical factors and measure compliance.

RESULTS

A total of 24,755 cases eligible for reporting in 2017 were assessed, and 25,274 cases from 2016 were included as an extended baseline. Segmented time-series regression did not show a significant baseline trend in measure compliance. Introduction of the alerts was associated with an increase in overall compliance from 84.4% (95% confidence interval [CI], 83.6%-85.2%) to 92.4% (91.4%-93.4%), and an increase in intraoperative compliance from 26.8% (25.8%-27.8%) to 71.0% (69.6%-72.4%). The association between the alerts and overall compliance was also present on multivariable analysis.

CONCLUSIONS

Implementation of an intraoperative decision-support tool was associated with statistically significant improvement in the maintenance of normothermia in cases eligible for reporting to CMS. This led to improved compliance with Measure #424 and suggests that electronic alerts can help practices improve their performance and payment bonus eligibility.

Situation Awareness-Oriented Patient Monitoring with Visual Patient Technology: A Qualitative Review of the Primary Research

Visual Patient technology is a situation awareness-oriented visualization technology that translates numerical and waveform patient monitoring data into a new user-centered visual language. Vital sign values are converted into colors, shapes, and rhythmic movements-a language humans can easily perceive and interpret-on a patient avatar model in real time. In this review, we summarize the current state of the research on the Visual Patient, including the technology, its history, and its scientific context. We also provide a summary of our primary research and a brief overview of research work on similar user-centered visualizations in medicine. In several computer-based studies under various experimental conditions, Visual Patient transferred more information per unit time, increased perceived diagnostic certainty, and lowered perceived workload. Eye tracking showed the technology worked because of the way it synthesizes and transforms vital sign information into new and logical forms corresponding to the real phenomena. The technology could be particularly useful for improving situation awareness in settings with high cognitive demand or when users must make quick decisions. This comprehensive review of Visual Patient research is the foundation for an evaluation of the technology in clinical applications, starting with a high-fidelity simulation study in early 2020.

Linking Big Data and Prediction Strategies: Tools, Pitfalls, and Lessons Learned

OBJECTIVES

Modern critical care amasses unprecedented amounts of clinical data-so called "big data"-on a minute-by-minute basis. Innovative processing of these data has the potential to revolutionize clinical prognostics and decision support in the care of the critically ill but also forces clinicians to depend on new and complex tools of which they may have limited understanding and over which they have little control. This concise review aims to provide bedside clinicians with ways to think about common methods being used to extract information from clinical big datasets and to judge the quality and utility of that information.

DATA SOURCES

We searched the free-access search engines PubMed and Google Scholar using the MeSH terms "big data", "prediction", and "intensive care" with iterations of a range of additional potentially associated factors, along with published bibliographies, to find papers suggesting illustration of key points in the structuring and analysis of clinical "big data," with special focus on outcomes prediction and major clinical concerns in critical care.

STUDY SELECTION

Three reviewers independently screened preliminary citation lists.

DATA EXTRACTION

Summary data were tabulated for review.

DATA SYNTHESIS

To date, most relevant big data research has focused on development of and attempts to validate patient outcome scoring systems and has yet to fully make use of the potential for automation and novel uses of continuous data streams such as those available from clinical care monitoring devices.

CONCLUSIONS

Realizing the potential for big data to improve critical care patient outcomes will require unprecedented team building across disparate competencies. It will also require clinicians to develop statistical awareness and thinking as yet another critical judgment skill they bring to their patients' bedsides and to the array of evidence presented to them about their patients over the course of care.

Novel displays of patient information in critical care settings: a systematic review

OBJECTIVE

Clinician information overload is prevalent in critical care settings. Improved visualization of patient information may help clinicians cope with information overload, increase efficiency, and improve quality. We compared the effect of information display interventions with usual care on patient care outcomes.

MATERIALS AND METHODS

We conducted a systematic review including experimental and quasi-experimental studies of information display interventions conducted in critical care and anesthesiology settings. Citations from January 1990 to June 2018 were searched in PubMed and IEEE Xplore. Reviewers worked independently to screen articles, evaluate quality, and abstract primary outcomes and display features.

RESULTS

Of 6742 studies identified, 22 studies evaluating 17 information displays met the study inclusion criteria. Information display categories included comprehensive integrated displays (3 displays), multipatient dashboards (7 displays), physiologic and laboratory monitoring (5 displays), and expert systems (2 displays). Significant improvement on primary outcomes over usual care was reported in 12 studies for 9 unique displays. Improvement was found mostly with comprehensive integrated displays (4 of 6 studies) and multipatient dashboards (5 of 7 studies). Only 1 of 5 randomized controlled trials had a positive effect in the primary outcome.

CONCLUSION

We found weak evidence suggesting comprehensive integrated displays improve provider efficiency and process outcomes, and multipatient dashboards improve compliance with care protocols and patient outcomes. Randomized controlled trials of physiologic and laboratory monitoring displays did not show improvement in primary outcomes, despite positive results in simulated settings. Important research translation gaps from laboratory to actual critical care settings exist.

Remote Surveillance Technologies: Realizing the Aim of Right Patient, Right Data, Right Time

The convergence of multiple recent developments in health care information technology and monitoring devices has made possible the creation of remote patient surveillance systems that increase the timeliness and quality of patient care. More convenient, less invasive monitoring devices, including patches, wearables, and biosensors, now allow for continuous physiological data to be gleaned from patients in a variety of care settings across the perioperative experience. These data can be bound into a single data repository, creating so-called data lakes. The high volume and diversity of data in these repositories must be processed into standard formats that can be queried in real time. These data can then be used by sophisticated prediction algorithms currently under development, enabling the early recognition of patterns of clinical deterioration otherwise undetectable to humans. Improved predictions can reduce alarm fatigue. In addition, data are now automatically queriable on a real-time basis such that they can be fed back to clinicians in a time frame that allows for meaningful intervention. These advancements are key components of successful remote surveillance systems. Anesthesiologists have the opportunity to be at the forefront of remote surveillance in the care they provide in the operating room, postanesthesia care unit, and intensive care unit, while also expanding their scope to include high-risk preoperative and postoperative patients on the general care wards. These systems hold the promise of enabling anesthesiologists to detect and intervene upon changes in the clinical status of the patient before adverse events have occurred. Importantly, however, significant barriers still exist to the effective deployment of these technologies and their study in impacting patient outcomes. Studies demonstrating the impact of remote surveillance on patient outcomes are limited. Critical to the impact of the technology are strategies of implementation, including who should receive and respond to alerts and how they should respond. Moreover, the lack of cost-effectiveness data and the uncertainty of whether clinical activities surrounding these technologies will be financially reimbursed remain significant challenges to future scale and sustainability. This narrative review will discuss the evolving technical components of remote surveillance systems, the clinical use cases relevant to the anesthesiologist's practice, the existing evidence for their impact on patients, the barriers that exist to their effective implementation and study, and important considerations regarding sustainability and cost-effectiveness.

Contributions on Clinical Decision Support from the 2018 Literature

Objectives : To summarize recent research and select the best papers published in 2018 in the field of computerized clinical decision support for the Decision Support section of the International Medical Informatics Association (IMIA) yearbook.

Methods : A literature review was performed by searching two bibliographic databases for papers referring to clinical decision support systems (CDSSs). The aim was to identify a list of candidate best papers from the retrieved bibliographic records, which were then peer-reviewed by external reviewers. A consensus meeting of the IMIA editorial team finally selected the best papers on the basis of all reviews and the section editors' evaluation.

Results : Among 1,148 retrieved articles, 15 best paper candidates were selected, the review of which resulted in the selection of four best papers. The first paper introduces a deep learning model for estimating short-term life expectancy (>3 months) of metastatic cancer patients by analyzing free-text clinical notes in electronic medical records, while maintaining the temporal visit sequence. The second paper takes note that CDSSs become routinely integrated in health information systems and compares statistical anomaly detection models to identify CDSS malfunctions which, if remain unnoticed, may have a negative impact on care delivery. The third paper fairly reports on lessons learnt from the development of an oncology CDSS using artificial intelligence techniques and from its assessment in a large US cancer center. The fourth paper implements a preference learning methodology for detecting inconsistencies in clinical practice guidelines and illustrates the applicability of the proposed methodology to antibiotherapy.

Conclusions : Three of the four best papers rely on data-driven methods, and one builds on a knowledge-based approach. While there is currently a trend for data-driven decision support, the promising results of such approaches still need to be confirmed by the adoption of these systems and their routine use.

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.

Research of data mining and web technology in university discipline construction decision support system based on MVC model

Purpose

Taking the discipline construction in colleges and universities as the application background, based on the research on data mining technology and decision support system technology, the data generated by university management information system are effectively utilized. The paper aims to discuss these issues.

Design/methodology/approach

Based on the Beijing Key Discipline Information Platform as the data source, the decision tree algorithm of data mining is studied. On the basis of decision tree C4.5, the Bayesian theory is applied to the post-pruning operation of the decision tree.

Findings

A decision tree post-pruning algorithm based on the Bayesian theory is studied and put forward in order to simplify the decision tree, which improves the generalization ability of the whole algorithm. Finally, the algorithm is used to build the prediction model of key disciplines. Combined with the decision support system architecture, data warehouse and the data mining algorithm constructed by university discipline, based on J2EE standard enterprise system specification, MVC model is applied. Moreover, a prototype system of decision support system for discipline construction in colleges and universities with browser/server (B/S) structure is completed and implemented.

Originality/value

A decision tree post-pruning algorithm based on the Bayesian theory is studied and put forward in order to simplify the decision tree, which improves the generalization ability of the whole algorithm. Finally, the algorithm is used to build the prediction model of key disciplines. Combined with the decision support system architecture, data warehouse and the data mining algorithm constructed by university discipline, based on J2EE standard enterprise system specification, MVC model is applied. Moreover, a prototype system of decision support system for discipline construction in colleges and universities with B/S structure is completed and implemented.

Modules for the Technical Skills Section of the OSCE Component of the American Board of Anesthesiology APPLIED Examination

INTRODUCTION

To assess communication and professionalism, as well as technical skills related to patient care, the American Board of Anesthesiology (ABA) has begun administering an Objective Structured Clinical Examination (OSCE) portion of the APPLIED Examination in addition to the Standard Oral Examination component.

METHODS

We created video modules and a curriculum for anesthesiology resident OSCE preparation for the Interpretation of Monitors and Interpretation of Echocardiography components. The modules can be used individually by trainees or included as part of an OSCE workshop led by faculty educators with seven individual stations matching the content of the actual ABA examination. These modules are recommended for all levels of anesthesiology trainees so that they can gain exposure to the format and the fast pace of the examination.

RESULTS

Sixty-six junior and senior anesthesiology residents, fellows, and junior faculty successfully participated in these modules. Seventy-three percent of the participants agreed that after completing these modules, they now had a good understanding of the Interpretation of Monitors and Interpretation of Echocardiography technical skills stations. More than 90% of participants reported that the modules were useful, and more than 70% reported that they now felt prepared for these stations of the OSCE.

DISCUSSION

Developing technical skills stations for deliberate practice and preparation for the ABA OSCE is resource intensive. Finding time and faculty to facilitate OSCE preparation is also challenging. With the video modules and scripts included in this publication, residents can practice independently or as part of larger preparation course.

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

What we can learn from Big Data about factors influencing perioperative outcome

PURPOSE OF REVIEW

This narrative review will discuss what value Big Data has to offer anesthesiology and aims to highlight recently published articles of large databases exploring factors influencing perioperative outcome. Additionally, the future perspectives of Big Data and its major pitfalls will be discussed.

RECENT FINDINGS

The potential of Big Data has given an incentive to create nationwide and anesthesia-initiated registries like the MPOG and NACOR. These large databases have contributed in elucidating some of the rare perioperative complications, such as declined cognition after exposure to general anesthesia and epidural hematomas in parturients. Additionally, they are useful in finding patterns such as similar outcome in subtypes of beta-blockers and lower incidence of pneumonia in preoperative influenza vaccinations in the elderly.

SUMMARY

Big Data is becoming increasingly popular with the collaborative collection of registries offering anesthesia a way to explore rare perioperative complications and outcome to encourage further hypotheses testing. Although Big Data has its flaws in security, lack of expertise and methodological concerns, the future potential of analytics combined with genomics, machine learning and real-time decision support looks promising.

Triple-low Alerts Do Not Reduce Mortality

EDITOR’S PERSPECTIVE

What We Already Know about This Topic

Intraoperative triple-low events (mean arterial pressure less than 75 mmHg, Bispectral Index less than 45, and minimum alveolar fraction of anesthetic less than 0.8) have been found to be associated with increased risk of mortality

What This Article Tells Us That Is New

A randomized electronic alert of triple-low events to treating clinicians did not reduce 90-day mortality

The alerts minimally influenced clinician responses, assessed as vasopressor administration or reduction in end-tidal volatile anesthetic partial pressure, and there was no association between response to alerts and mortality

Triple-low events predict mortality but do not appear to be causally related

Background

Triple-low events (mean arterial pressure less than 75 mmHg, Bispectral Index less than 45, and minimum alveolar fraction less than 0.8) are associated with mortality but may not be causal. This study tested the hypothesis that providing triple-low alerts to clinicians reduces 90-day mortality.

Methods

Adults having noncardiac surgery with volatile anesthesia and Bispectral Index monitoring were electronically screened for triple-low events. Patients having triple-low events were randomized in real time, with clinicians either receiving an alert, “consider hemodynamic support,” or not. Patients were blinded to treatment. Helpful responses to triple-low events were defined by administration of a vasopressor within 5 min or a 20% reduction in end-tidal volatile anesthetic concentration within 15 min.

Results

Of the qualifying patients, 7,569 of 36,670 (20%) had triple-low events and were randomized. All 7,569 were included in the primary analysis. Ninety-day mortality was 8.3% in the alert group and 7.3% in the nonalert group. The hazard ratio (95% CI) for alert versus nonalert was 1.14 (0.96, 1.35); P = 0.12, crossing a prespecified futility boundary. Clinical responses were helpful in about half the patients in each group, with 51% of alert patients and 47% of nonalert patients receiving vasopressors or having anesthetics lowered after start of triple low (P &lt; 0.001). There was no relationship between the response to triple-low events and adjusted 90-day mortality.

Conclusions

Real-time alerts to triple-low events did not lead to a reduction in 90-day mortality, and there were fewer responses to alerts than expected. However, similar mortality with and without responses suggests that there is no strong relationship between responses to triple-low events and mortality.