Anesthesia Information Management Systems

Digitalization in the operating theatre- an interview study of operating room nurses' and nurse anesthetists' experiences in Norway

BACKGROUND

Digitalization in the health sector requires adaptive change in human attitudes and skills. The operating theatres have been introduced to digital innovations through centuries. The aim of this study was to explore operating room (OR) nurses' and Nurse Anesthetists' (NAs) experiences with digitalization in the operating theatre.

METHODS

The study had a qualitative design, using individual interviews with OR nurses and NAs at a Norwegian hospital. Data were analyzed using reflexive thematic analysis in-line with recommendations from Braun & Clarke.

RESULTS

Two themes were identified, namely (1) Impacting the work processes, and (2) Implications for patient safety. The OR nurses and NAs experienced that digitalization impacted on their work processes positively through making these smoother, but also negatively making the work processes vulnerable for disruptions, leading to a need for parallel actions. Digitalization was experienced to positively impact patient safety for example through making information more accessible. However, digital tools reduced focus on the patient, and then represented a risk to patient safety.

CONCLUSION

OR nurses and NAs perceive that digitalization on one side may facilitate work processes and information flow. However, on the other side digitalization may steel focus on the patient. These aspects should be taken into consideration in quality improvement initiatives and when introducing new digital tools.

Predicting Hypoxia Using Machine Learning: Systematic Review

Background

Hypoxia is an important risk factor and indicator for the declining health of inpatients. Predicting future hypoxic events using machine learning is a prospective area of study to facilitate time-critical interventions to counter patient health deterioration.

Objective

This systematic review aims to summarize and compare previous efforts to predict hypoxic events in the hospital setting using machine learning with respect to their methodology, predictive performance, and assessed population.

Methods

A systematic literature search was performed using Web of Science, Ovid with Embase and MEDLINE, and Google Scholar. Studies that investigated hypoxia or hypoxemia of hospitalized patients using machine learning models were considered. Risk of bias was assessed using the Prediction Model Risk of Bias Assessment Tool.

Results

After screening, a total of 12 papers were eligible for analysis, from which 32 models were extracted. The included studies showed a variety of population, methodology, and outcome definition. Comparability was further limited due to unclear or high risk of bias for most studies (10/12, 83%). The overall predictive performance ranged from moderate to high. Based on classification metrics, deep learning models performed similar to or outperformed conventional machine learning models within the same studies. Models using only prior peripheral oxygen saturation as a clinical variable showed better performance than models based on multiple variables, with most of these studies (2/3, 67%) using a long short-term memory algorithm.

Conclusions

Machine learning models provide the potential to accurately predict the occurrence of hypoxic events based on retrospective data. The heterogeneity of the studies and limited generalizability of their results highlight the need for further validation studies to assess their predictive performance.

Improving Anesthesia Start Time Documentation Through a Departmental Education Initiative at Yale New Haven Hospital, New Haven, United States

Background Reimbursement for anesthetic services in the United States utilizes a formula that incorporates procedural and patient factors with total anesthesia time. According to the Centers for Medicare & Medicaid Services and the American Society of Anesthesiologists, the period of billable time starts when the anesthesia practitioner assumes care of the patient and may include transport to the operating room from the preoperative holding area. In this report on a quality improvement effort, we implemented a departmental education initiative aimed at improving the accuracy of anesthesia start-time documentation. Methods Utilizing de-identified, internal data on surgical procedures at Yale New Haven Hospital (YNHH), New Haven, United States, the difference between documented anesthesia start and patient in-room time was determined for all cases. Those with a difference between 0-1 minute were assumed "likely underbilled," and the total revenue lost for these cases was estimated using a weighted average of institutional reimbursement per unit of time. A monthly, department-wide educational email was then introduced to inform practitioners about the guidelines around start-time documentation, and the percentage of "likely underbilled" cases and lost revenue estimates trended over a one-year period. Results Baseline data in December 2020 showed that of the 6,877 total surgical cases requiring anesthesia at YNHH, 55.1% (N=3,790) had an anesthesia start to in-room time of 0-1 minute, which were considered "likely underbilled." The average start-to-in-room time for properly recorded cases (44.9%, N=3,087) was 4.42 minutes. The baseline revenue lost in December 2020 for underbilled cases was estimated at $52,302. Over the one-year quality improvement initiative, the proportion of underbilled cases showed a downward trend, decreasing to 29.2% of total cases by November 2021. The estimate of revenue lost due to underbilling also showed a downward trend, decreasing to $29,300 in November 2021. Conclusion This quality improvement study demonstrated that a relatively simple, department-wide educational email sent monthly correlated with an improvement in anesthesia start-time documentation accuracy and a reduction in estimated revenue lost to underbilling over a one-year period.

Development and evaluation of an anesthesia module for electronic medical records in the operating room: an applied developmental study

Developing an anesthesia module in the operating room is one of the significant steps toward the implementation of electronic medical records (EMR) in health care centers. This study aimed to develop and evaluate the web based-anesthesia module of an electronic medical record Sciences, in the operating room of the Namazi Medical Training Center of Shiraz University of Medical Iran. This developmental and applied study was conducted in steps including determining the functional and non-functional requirements, designing and implementing the anesthesia module, and usability evaluation. 3 anesthesiologists, 3 anesthesiologist assistants, and 12 anesthetist nurses were included in the study as a research community. React.js, Node.js programming language to program this module, Mongo dB database, and Windows server for data management and USE standard questionnaire were used. In the anesthesia module, software quality features were determined as functional requirements and non-functional requirements included 286 data elements in 25 categories (demographic information, surgery information, laboratory results, patient graphs, consults, consent letter, physical examinations, medication history, family disease records, social record, past medical history, type of anesthesia, anesthesia induction method, airway management, monitoring, anesthesia chart, blood and fluids, blood gases, tourniquets and warmers, accessories, positions, neuromuscular reversal, transfer the patient from the operating room, complications of anesthesia and, seal/ signature). Also, after implementing the anesthesia module, results of the usability evaluation showed that 69.1% of the users agreed with the use of this module in the operating room and considered it user-friendly.

A Spatiotemporal and Multisensory Approach to Designing Wearable Clinical ICU Alarms

In health care, auditory alarms are an important aspect of an informatics system that monitors patients and alerts clinicians attending to multiple concurrent tasks. However, the volume, design, and pervasiveness of existing Intensive Care Unit (ICU) alarms can make it difficult to quickly distinguish their meaning and importance. In this study, we evaluated the effectiveness of two design approaches not yet explored in a smartwatch-based alarm system designed for ICU use: (1) using audiovisual spatial colocalization and (2) adding haptic (i.e., touch) information. We compared the performance of 30 study participants using ICU smartwatch alarms containing auditory icons in two implementations of the audio modality: colocalized with the visual cue on the smartwatch's low-quality speaker versus delivered from a higher quality speaker located two feet away from participants (like a stationary alarm bay situated near patients in the ICU). Additionally, we compared participant performance using alarms with two sensory modalities (visual and audio) against alarms with three sensory modalities (adding haptic cues). Participants were 10.1% (0.24s) faster at responding to alarms when auditory information was delivered from the smartwatch instead of the higher quality external speaker. Meanwhile, adding haptic information to alarms improved response times to alarms by 12.2% (0.23s) and response times on their primary task by 10.3% (0.08s). Participants rated learnability and ease of use higher for alarms with haptic information. These small but statistically significant improvements demonstrate that audiovisual colocalization and multisensory alarm design can improve user response times.

SAP BusinessObjects in Medical Informatics

Electronic health record (EHR) generates a large amount of data filled with opportunities to enhance documentation compliance, quality improvement, and other metrics. Various software tools exist, but many clinicians are unaware of them. Our institution switched from a hybrid of paper and multiple small EHRs to one all-inclusive EHR system. We faced significant challenges beyond the typical new software deployment phase that affected our departmental regulatory compliance, quality measures, and research initiatives. We aimed to navigate these issues through the use of medical informatics. We used a multidimensional database software analysis tool called SAP BusinessObjects® (SAP SE. Released 2020. SAP BusinessObjects, Version 14.2.8.3671. Waldorf, Germany) to design automated queries for the patient database to generate various reports for our department. As a result, We improved our anesthesia documentation non-compliance from 13-17% of all cases to 4% within months. We have also used this tool to automatically generate various reports such as preoperative beta-blocker administrations, caseloads, case complications, procedure logs, and medication records. Even today many departments rely on manual checks for even the most basic documentation and quality metric compliance, which can be time consuming and costly. Using medical informatics tools is a highly efficient alternative. Fortunately, many software tools exist within most modern EHR packages, and most people can learn to use these tools productively.

Anaesthesia personnels' perspectives on digital anaesthesia information management systems - a qualitative study

Background

In Norway, the anaesthesia team normally consists of a nurse anaesthetist and an anaesthetist. Digital anesthesia information management systems (AIMS) that collect patient information directly from the anaesthesia workstation, and transmit the data into documentation systems have recently been implemented in Norway. Earlier studies have indicated that implementation of digital AIMS impacts the clinical workflow patterns and distracts the anaesthesia providers. These studies have mainly had a quantitative design and focused on functionality, installation designs, benefits and challenges associated with implementing and using AIMS. Hence, the aim of this study was to qualitatively explore anaesthesia personnel’s perspectives on implementing and using digital AIMS.

Methods

The study had an exploratory and descriptive design. The study was conducted within three non-university hospitals in Southern Norway. Qualitative, individual interviews with nurse anaesthetists (n = 9) and anaesthetists (n = 9) were conducted in the period September to December 2020. Data were analysed using qualitative content analysis according to the recommendations of Graneheim and Lundman.

Results

Four categories were identified: 1) Balance between clinical assessment and monitoring, 2) Vigilance in relation to the patient, 3) The nurse-physician collaboration, and 4) Software issues. Participants described that anaesthesia included a continuous balance between clinical assessment and monitoring. They experienced that the digital AIMS had an impact on their vigilance in relation to the patient during anaesthesia. The digital AIMS affected the nurse-physician collaboration. Moreover, participants emphasised a lack of user participation and aspects of user-friendliness regarding the implementation of digital AIMS.

Conclusion

Digital AIMS impacts vigilance in relation to the patient. Hence, collaboration and acceptance of the mutual responsibility between nurse anaesthetists and anaesthetists for both clinical observation and digital AIMS administration is essential. Anaesthesia personnel should be included in development and implementation processes to facilitate implementation.

A Century of Technology in Anesthesia & Analgesia

Technological innovation has been closely intertwined with the growth of modern anesthesiology as a medical and scientific discipline. Anesthesia & Analgesia, the longest-running physician anesthesiology journal in the world, has documented key technological developments in the specialty over the past 100 years. What began as a focus on the fundamental tools needed for effective anesthetic delivery has evolved over the century into an increasing emphasis on automation, portability, and machine intelligence to improve the quality, safety, and efficiency of patient care.

The Case for the Anesthesiologist-Informaticist

Health care has been transformed by computerization, and the use of electronic health record systems has become widespread. Anesthesia information management systems are commonly used in the operating room to maintain records of anesthetic care delivery. The perioperative environment and the practice of anesthesia generate a large volume of data that may be reused to support clinical decision-making, research, and process improvement. Anesthesiologists trained in clinical informatics, referred to as informaticists or informaticians, may help implement and optimize anesthesia information management systems. They may also participate in clinical research, management of information systems, and quality improvement in the operating room or throughout a health care system. Here, we describe the specialty of clinical informatics, how anesthesiologists may obtain training in clinical informatics, and the considerations particular to the subspecialty of anesthesia informatics. Management of perioperative information systems, implementation of computerized clinical decision support systems in the perioperative environment, the role of virtual visits and remote monitoring, perioperative informatics research, perioperative process improvement, leadership, and change management are described from the perspective of the anesthesiologist-informaticist.

Machine learning methods for perioperative anesthetic management in cardiac surgery patients: a scoping review

BACKGROUND

Machine learning (ML) is developing fast with promising prospects within medicine and already has several applications in perioperative care. We conducted a scoping review to examine the extent and potential limitations of ML implementation in perioperative anesthetic care, specifically in cardiac surgery patients.

METHODS

We mapped the current literature by searching three databases: MEDLINE (Ovid), EMBASE (Ovid), and Cochrane Library. Articles were eligible if they reported on perioperative ML use in the field of cardiac surgery with relevance to anesthetic practices. Data on the applicability of ML and comparability to conventional statistical methods were extracted.

RESULTS

Forty-six articles on ML relevant to the work of the anesthesiologist in cardiac surgery were identified. Three main categories emerged: (I) event and risk prediction, (II) hemodynamic monitoring, and (III) automation of echocardiography. Prediction models based on ML tend to behave similarly to conventional statistical methods. Using dynamic hemodynamic or ultrasound data in ML models, however, shifts the potential to promising results.

CONCLUSIONS

ML in cardiac surgery is increasingly used in perioperative anesthetic management. The majority is used for prediction purposes similar to conventional clinical scores. Remarkable ML model performances are achieved when using real-time dynamic parameters. However, beneficial clinical outcomes of ML integration have yet to be determined. Nonetheless, the first steps introducing ML in perioperative anesthetic care for cardiac surgery have been taken.

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.

An Environmental Scan of Anesthesia Information Management Systems in the American and Canadian Marketplace

Anesthesia Information Management Systems are specialized forms of electronic medical records used by anesthesiologists to automatically and reliably collect, store, and present perioperative patient data. There are no recent academic publications that outline the names and features of AIMS in the current American and Canadian marketplace. An environmental scan was performed to first identify existing AIMS in this marketplace, and then describe and compare these AIMS. We found 13 commercially available AIMS but were able to describe in detail the features and functionalities of only 10 of these systems, as three vendors did not participate in the study. While all AIMS have certain key features, other features and functionalities are only offered by some of the AIMS. Features less commonly offered included patient portals for pre-operative questionnaires, clinical decision support systems, and voice-to-text capability for documentation. The findings of this study can inform AIMS procurement efforts by enabling anesthesia departments to compare features across AIMS and find an AIMS whose features best fit their needs and priorities. Future studies are needed to describe the features and functionalities of these AIMS at a more granular level, and also assess the usability and costs of these systems.

Clinician Monitoring

Since the first public demonstration of general anesthesia in 1846, anesthesiology has seen major advancements as a specialty. These include both important technological improvements and the development and implementation of internationally accepted patient safety standards. Together, these ultimately resulted in the recognition of anesthesiology as the leading medical specialty advocating for patient safety. Modern-day anesthesiology faces a new challenge of automated anesthesia delivery. Despite evidence for a more refined and precise delivery of anesthesia through this platform, there is currently no substitute for the presence of an appropriately trained anesthesia clinician to manage the complex interplay of human factors and patient safety in the perioperative setting.

Recommendations for standards of monitoring during anaesthesia and recovery 2021: Guideline from the Association of Anaesthetists

This guideline updates and replaces the 5th edition of the Standards of Monitoring published in 2015. The aim of this document is to provide guidance on the minimum standards for monitoring of any patient undergoing anaesthesia or sedation under the care of an anaesthetist. The recommendations are primarily aimed at anaesthetists practising in the UK and Ireland, but it is recognised that these guidelines may also be of use in other areas of the world. Minimum standards for monitoring patients during anaesthesia and in the recovery phase are included. There is also guidance on monitoring patients undergoing sedation and during transfer. There are new sections specifically discussing capnography, sedation and regional anaesthesia. In addition, the indications for processed electroencephalogram and neuromuscular monitoring have been updated.

The state of adoption of anesthesia information management systems in Canadian academic anesthesia departments: a survey

PURPOSE

Anesthesia information management systems (AIMS) are gradually replacing paper documentation of anesthesia care. This study sought to determine the current status of AIMS adoption and the level of health informatics expertise in Canadian academic anesthesia departments.

METHODS

Department heads or their designates of Canadian academic anesthesia departments were invited by e-mail to complete an online survey between September 2019 and February 2020. The survey elicited information on current AIMS or future plans for an AIMS installation, the number of department members dedicated to clinical informatics issues, the gross level of health informatics expertise at each department, perceived advantages of AIMS, and perceived disadvantages of and barriers to implementation of AIMS.

RESULTS

Of the 64 departments invited to participate, 63 (98.4%) completed the survey. Only 21 (33.3%) of the departments had AIMS. Of the 42 departments still charting on paper, 23 (54.8%) reported planning to install an AIMS within the next five years. Forty-six departments (73%) had at least one anesthesiologist tasked with dealing with AIMS or electronic health record issues. Most reported having no department members with extensive knowledge or formal training in health informatics. The top three perceived barriers and disadvantages to an AIMS installation were its initial cost, lack of funding, and a lack of technical support dedicated specifically to AIMS. The top three advantages departments wished to prioritize with AIMS were accurate clinical documentation, better data for quality improvement initiatives, and better data for research.

CONCLUSIONS

A majority of Canadian academic anesthesia departments are still using paper records, but this trend is expected to reverse in the next five years as more departments install an AIMS. Health informatics expertise is lacking in most of the departments, with a minority planning to support the training of future anesthesia informaticians.

Anesthesia in the modern world of apps and technology: Implications and impact on wellness

Recent decades have seen an unprecedented leap in digital innovation, with far-reaching implications in healthcare. Anesthesiologists have historically championed the adoption of new technologies. However, the rapid evolution of these technologies has outpaced attempts at studying their potential impact on healthcare providers' well-being. This document introduces several categories of workplace technologies commonly encountered by the anesthesiologist. We examine examples of novel technology and the impact of these digital interventions on the anesthesiologist's well-being. We also review popular personalized technology aimed at improving wellness and the impact on well-being examined. Finally, technology acceptance models are introduced to improve technology adoption, which, when appropriately applied, may minimize the negative impacts of technology on anesthesiologists' well-being. Incorporating quantitative, serial assessments of well-being as part of technology implementation are proposed as a future direction for examining the wellness impact of technology on anesthesiologists.

It's the Culture!-How systemic and societal constructs impact well-being

Pediatric anesthesiologists practice within a culture, a system, and a society. In this article, we provide an overview of the influence these have on the well-being or the unwellness of pediatric anesthesiologists. The scope of these issues is broad and far-reaching; thus, our goal has been to highlight those areas which would be likely to have the largest impact on well-being if addressed fully by society, institutions, and leaders in our field. We discuss the burnout-promoting aspects of medical education and training. We survey occupational factors, such as the high-stake pediatric anesthesia environment, occupational health hazards, time pressure, and the reduction in physician autonomy. We then describe societal barriers, such as the marginalization of certain populations, the US system of malpractice litigation, the stigma surrounding psychiatric care, and some of the issues related to physician reimbursement in the United States. We conclude that in order to move forward, improving physician wellness must be a focus of society, of the medical system as a whole, and of individual departments and leaders in pediatric anesthesia.

Combined Monitoring-Brain Function Monitoring and Cerebral Oximetry

Peri-operative brain function monitoring is still seen by most clinicians as complex, difficult to interpret and is therefore adopted very slowly. Current available technology mainly focusses on either a processed parameter based on the electroencephalogram to titrate anesthetics and central acting agents or on cerebral oximetry, a wider term to obtain information on the cerebral oxygen balance. There is still a lack of technological offerings that allow to monitor both entities in one device. However, there is scientific evidence that it is possible to combine measurements in an algorithmic approach that allows to better manage brain function in the surgical setting. Such integrated solutions should be made available to clinicians as they are likely to optimize patient care dependent on a sound health technology assessment.

Anesthesia Information Management Systems: Evolution of the Paper Anesthetic Record to a Multisystem Electronic Medical Record Network That Streamlines Perioperative Care

Initially devised in the 1890s, the traditional anesthetic record comprises physiological changes, crucial anesthetic or surgical events, and medications administered during the perioperative period. The timely collection of quality data facilitates situational awareness and point-of-care clinical decision making. The burgeoning volume and complexity of data in conjunction with financial incentives and the push for improved clinical documentation by regulatory bodies have prompted the transition away from paper records. Anesthesia Information Management Systems (AIMS) are specialized electronic health record networks that allow the anesthesia record to interface with hospital clinical data repositories, resulting in improvements in quality of care, patient safety, operations management, reimbursement, and translational research. Like most new technological advances, adoption was slow at first due to the challenges of integrating complex systems into daily clinical practice, questions about return on investment, and medicolegal liability. Recent technological advances, coupled with government incentives, have allowed AIMS adoption to reach an acceleration phase among US academic medical centers; widespread utilization of AIMS by 84% of US academic medical centers is expected by 2018-2020. Adoption among nonacademic US and European medical centers still remains low; information concerning Asian countries is limited to literature describing only single-hospital center experiences.

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.

A Narrative Review of Analytics in Pediatric Cardiac Anesthesia and Critical Care Medicine

Congenital heart disease (CHD) is one of the most common birth anomalies, and the care of children with CHD has improved over the past 4 decades. However, children with CHD who undergo general anesthesia remain at increased risk for morbidity and mortality. The proliferation of electronic health record systems and sophisticated patient monitors affords the opportunity to capture and analyze large amounts of CHD patient data, and the application of novel, effective analytics methods to these data can enable clinicians to enhance their care of pediatric CHD patients. This narrative review covers recent efforts to leverage analytics in pediatric cardiac anesthesia and critical care to improve the care of children with CHD.