Anesthesia Information Management System Research Articles

Factors affecting completeness of anaesthetic record: a cross-sectional study.

Anaesthesia Information Management Systems (AIMS) are of critical importance for ensuring the comprehensive recording of anaesthesia data. This study aimed to investigate the factors influencing the completeness of anaesthetic records created by various healthcare professionals including anesthesiologists, nurse anaesthetists, residents/fellows, and anaesthetic nurse trainees. Employing an online Google Forms questionnaire, this descriptive research focused on understanding the factors contributing to the completeness of anaesthetic records. The survey was distributed to all anaesthesia personnel in the department, comprising a total of 165 participants. The questionnaire encompassed three primary aspects:(1) personal information, (2) knowledge related to recording anaesthetic records, and (3) concerns related to achieving high-quality record-keeping. To assess completeness, anaesthetic record forms were evaluated based on the 2020 Medical Record Audit Guideline of Thailand's National Health Security Office, with a completeness rate exceeding 80% deemed acceptable. Spearman's rank correlation was employed to analyze the connections between the associated factors and completeness of anaesthetic records. From August to October 2021, 165 records were examined, indicating a completeness rate of 89.7%. Several factors exhibited significant correlations with the completeness of anaesthetic records. These included age (r = -0.223; P=0.004), job position (r = 0.44; P<0.001), years of anaesthesia experience (r = -0.208; P=0.007), experience in anaesthetic record training/teaching (r = 0.181; P=0.02), and attitude emphasizing high-quality record-keeping (r = 0.167; P=0.032). While personal attributes emerged as pivotal factors influencing record completeness, continuous training and a collaborative attitude were identified as critical for maintaining sustainability and achieving record-keeping goals.

A Comprehensive Analysis and Review of Artificial Intelligence in Anaesthesia.

In the field of anaesthesia, artificial intelligence (AI) has become a game-changing technology. Applications of AI include keeping records, monitoring patients, calculating and administering drugs, and carrying out mechanical procedures. This article explores the current uses, challenges, and prospective applications of AI in anaesthesia practices. This review discusses AI-supported systems like anaesthesia information management systems (AIMS), mechanical robots for carrying out procedures, and pharmacological models for drug delivery. AIMS has helped in automated record-keeping, predicting bad events, and monitoring the vital signs of the patient. Their application has a vital role in improving the efficacy of anaesthesia management and patient safety. The application of AI in anaesthesia comes with its own unique difficulties. Noteworthy obstacles include issues with data quantity and quality, technical limitations, and moral and legal dilemmas. The key to overcoming these barriers is to set guidelines for the ethical use of AI in healthcare, improve the reliability and comprehension of AI systems, and certify the health data precision and security. AI has very bright potential. Exciting future directions include developments in AI and machine learning thus development of new applications, and the possible enhancement in training and education. Potential research areas include the application of AI to chronic disease management, pain management, and the reinforcement of anaesthesiologists' education. AI could be used to design authentic lifelike training simulations and individualized student feedback systems, hence transforming anaesthesia education and training methodology. For this review, we conducted a PubMed, Google Scholar, and Cochrane Database search in 2022-2023 and retrieved articles on AI and its uses in anaesthesia. Recommendations for future research and development include strengthening the safety and reliability of health data, building a better understanding of AI systems, and looking into new areas of use. The power of AI can be used to innovate anaesthesia practices by concentrating on these areas.

Impact of SARS-CoV-2 infection on postoperative complications of patients undergoing surgery after general outbreak in China: a protocol for multicentre prospective cohort study

IntroductionThere is currently limited evidence addressing perioperative prognosis of surgical patients during COVID-19 pandemic; especially targeting on the Chinese population since the wave in 2022. Considering a distinct feature from...

Anesthesia quality indicators to measure and improve your practice: a modified delphi study

BackgroundImplementation of the new competency-based post-graduate medical education curriculum has renewed the push by medical regulatory bodies in Canada to strongly advocate and/or mandate continuous quality improvement (cQI) for all physicians. Electronic anesthesia information management systems contain vast amounts of information yet it is unclear how this information could be used to promote cQI for practicing anesthesiologists. The aim of this study was to create a refined list of meaningful anesthesia quality indicators to assist anesthesiologists in the process of continuous self-assessment and feedback of their practice.MethodsAn initial list of quality indicators was created though a literature search. A modified-Delphi (mDelphi) method was used to rank these indicators and achieve consensus on those indicators considered to be most relevant. Fourteen anesthesiologists representing different regions across Canada participated in the panel.ResultsThe initial list contained 132 items and through 3 rounds of mDelphi the panelists selected 56 items from the list that they believed to be top priority. In the fourth round, a subset of 20 of these indicators were ranked as highest priority. The list included items related to process, structure and outcome.ConclusionThis ranked list of anesthesia quality indicators from this modified Delphi study could aid clinicians in their individual practice assessments for continuous quality improvement mandated by Canadian medical regulatory bodies. Feasibility and usability of these quality indicators, and the significance of process versus outcome measures in assessment, are areas of future research.

SURGICAL APGAR SCORE AS A GUIDE FOR PREDICTING POSTOPERATIVE ICU ADMISSIONS

Introduction: Immediate planned and appropriate postoperative intensive care unit (ICU) admission can increase the survival rate in patients undergoing high risk surgeries. Surgical Apgar Score is a simple, 10-point scoring system in which a low score reliably identifies those patients at risk for adverse perioperative outcomes. It can predict 30-day major complications or death after surgery. The aim of the present study was to evaluate the performance of the SAS for predicting complications 30 days after surgery in patients undergoing laparotomy surgeries Methods: Total 50 patients undergoing laparotomies and emergency surgeries were included in the study. Outcome data were collected after 30 days during an outpatient consultation. The following intraoperative data were extracted from the anesthesia information management system and used to calculate the SAS: maximum estimated blood loss (EBL), lowest mean arterial pressure (MAP), and lowest heart rate (HR) (all intraoperative parameters) Results: Our study was conducted on a total 50 patients, mean age of the patients in our study was 47.9%, among the study 58.5% were female, 91% underwent elective surgery. Majority of cases belong to ASA class II( 62%). In our study 8% of population were transferred directly to ICU following surgery. In our study males were more likely to be admitted to ICU postoperatively than females (11.3%). And patients undergoing emergency surgeries were more likely to be admitted in ICU than elective surgeries. Patients were ASA grade III and IV have more chances of ICU admission. AGE DISTRIBUTION IMMEDIATE ICU ADMISSION &lt;50 51.6% 4.1% 50-59 17.2% 8.1% 60-69 16.7% 13% 70-79 9.6% 12.4% &gt;80 5% 18.6% SEX DISTRIBUTION: SEX PERCENTAGE ICU ADMISSION MALE 41.5% 11.3% FEMALE 58.5% 5.2% Conclusion: SAS is an effective, simple score which is a practical and objective instrument that provides immediate feedback for predicting postoperative ICU admissions. Factors associated with a higher frequency of ICU admission are older age, male sex, emergency surgery, higher ASA class. Most patients had SAS of 7-8%(49.1%) and 9 -10 (19.3%). 86% of the cases had a ASA &gt; 3. Patient with SAS of 0-2(46.2%) and 3-4(17%)were admitted post operatively directly to ICU

Innovations in the Management of the Difficult Airway: A Narrative Review.

The difficult airway (DA) remains a perpetual challenge and its implications have led to multiple advances, technological and otherwise in this area. This article investigates the latest developments in the definition, prediction tools and diagnostics like airway and neck Ultrasonography (USG), Magnetic Resonance Imaging (MRI) and Computed tomography (CT) scans, preoperative Virtual endoscopy (VE) and 3D printing. Innovations in airway devices and adjuncts are analysed. Difficult airway society (DAS) guidelines, American Society of Anaesthesiologists (ASA) Practice Guidelines and Vortex approach for the management of DA are explored. Other breakthroughs include novel oxygen supplementation techniques throughout airway management and tools like Anaesthesia Information Management Systems (AIMS) and Clinical Decision Support (CDS) systems. The delivery of DA training and patient counselling has also undergone vast changes with emerging technology like Virtual Reality (VR), mobile applications and toolkits. The enormous, ever-evolving and endless possibilities in this area have only helped improve clinical standards and enhance patient safety.

Age-Stratified Propofol Dosage for Pediatric Procedural Sedation and Analgesia.

Procedural sedation and analgesia (PSA) for diagnostic and minimally invasive therapeutic procedures has become common practice in children of all ages. Based on our clinical experience, we suspected an inverse relation between age and dosage. However, a schedule for age-stratified propofol induction and maintenance dosage for PSA was not available and could be helpful to many anesthesiologists as a reference. We performed a retrospective cohort study of children who received procedural sedation at the Wilhelmina Children's Hospital (WKZ), a tertiary pediatric hospital part of the University Medical Center Utrecht (UMCU), between June 2007 and December 2020. We studied whether the induction (mg·kg -1 ) and maintenance (mg·kg -1 ·h -1 ) dosage is age-dependent using linear regression models. A total of 6438 pediatric procedures were retrieved from Anesthesia Information Management Systems (AIMS). A total of 5567 records were available for induction dose analysis and 5420 records for analysis of the maintenance dose. After adjustment for sex, American Society of Anesthesiologists (ASA) physical status classification, opioid administration, and diagnostic or interventional, we obtained a coefficient of -0.11 (95% confidence interval [CI], -0.12 to -0.11) for age (years) from a multivariable linear regression model for propofol induction dosage (mg·kg -1 ) and a coefficient of -0.36 (95% CI, -0.39 to -0.34) for age (years) for propofol maintenance dosage. We found a noteworthy inverse age-effect on propofol dosage for both induction and maintenance of pediatric procedural sedation. Furthermore, our study revealed that remarkably higher propofol sedation doses were needed for infants and toddlers than previously expected and reported.

Big data in anaesthesia: a narrative, nonsystematic review

Data generation is growing with the use of ‘anaesthesia information management systems’ (AIMS), but the appropriate use of data for scientific purposes is often wasted by a lack of integration. This narrative review aims to describe the use of routinely collected data and its potential usefulness to improve the quality of care, first by defining the six levels of integration of electronic health records as proposed by the National Health Service (NHS) illustrated by examples in anaesthesia practice. Secondly, by explaining what measures can be taken to profit from those data on the micro-system level (for the patient), the meso-system (for the department and the hospital institution) and the macro-system (for healthcare and public health). We will next describe a homemade AIMS solution and the opportunities which result from his integration on the different levels and the research prospects implied. Opportunities outside of high-income countries will also be presented. All lead to the conclusion that a core dataset for peri-operative global research may facilitate a framework for the integration of large volumes of data from electronic health records. It will allow a constant re-evaluation of our practice as anaesthesiologists to offer the best care for patients. In this regard, the training of some anaesthesiologists in data science and artificial intelligence is of paramount importance. We must also take into account the ecological footprint of data centres as these are energy-consuming. It is essential to prepare for these changes and turn the speciality of anaesthesia, collaborating with data scientists, into a more prominent role of peri-operative medicine.

An Accessible Clinical Decision Support System to Curtail Anesthetic Greenhouse Gases in a Large Health Network: Implementation Study.

Inhaled anesthetics in the operating room are potent greenhouse gases and are a key contributor to carbon emissions from health care facilities. Real-time clinical decision support (CDS) systems lower anesthetic gas waste by prompting anesthesia professionals to reduce fresh gas flow (FGF) when a set threshold is exceeded. However, previous CDS systems have relied on proprietary or highly customized anesthesia information management systems, significantly reducing other institutions' accessibility to the technology and thus limiting overall environmental benefit. In 2018, a CDS system that lowers anesthetic gas waste using methods that can be easily adopted by other institutions was developed at the University of California San Francisco (UCSF). This study aims to facilitate wider uptake of our CDS system and further reduce gas waste by describing the implementation of the FGF CDS toolkit at UCSF and the subsequent implementation at other medical campuses within the University of California Health network. We developed a noninterruptive active CDS system to alert anesthesia professionals when FGF rates exceeded 0.7 L per minute for common volatile anesthetics. The implementation process at UCSF was documented and assembled into an informational toolkit to aid in the integration of the CDS system at other health care institutions. Before implementation, presentation-based education initiatives were used to disseminate information regarding the safety of low FGF use and its relationship to environmental sustainability. Our FGF CDS toolkit consisted of 4 main components for implementation: sustainability-focused education of anesthesia professionals, hardware integration of the CDS technology, software build of the CDS system, and data reporting of measured outcomes. The FGF CDS system was successfully deployed at 5 University of California Health network campuses. Four of the institutions are independent from the institution that created the CDS system. The CDS system was deployed at each facility using the FGF CDS toolkit, which describes the main components of the technology and implementation. Each campus made modifications to the CDS tool to best suit their institution, emphasizing the versatility and adoptability of the technology and implementation framework. It has previously been shown that the FGF CDS system reduces anesthetic gas waste, leading to environmental and fiscal benefits. Here, we demonstrate that the CDS system can be transferred to other medical facilities using our toolkit for implementation, making the technology and associated benefits globally accessible to advance mitigation of health care-related emissions.

Medication safety in the perioperative setting: A comparison of methods for detecting medication errors and adverse medication events.

The purpose of this study was to evaluate perioperative medication-related incidents (medication errors (MEs) and/or adverse medication events (AMEs)) identified by 2 different reporting methods (self-report and direct observation), and to compare the types and severity of incidents identified by each method. We compared perioperative medication-related incidents identified by direct observation in Nanji et al's 2016 study[1] to those identified by self-report via a facilitated incident reporting system at the same 1046-bed tertiary care academic medical center during the same 8-month period. Incidents, including MEs and AMEs were classified by type and severity. In 277 operations involving 3671 medication administrations, 193 MEs and/or AMEs were observed (5.3% incident rate). While none of the observed incidents were self-reported, 10 separate medication-related incidents were self-reported from different (unobserved) operations that occurred during the same time period, which involved a total of 21,576 operations and approximately 280,488 medication administrations (0.004% self-reported incident rate). The distribution of incidents (ME, AME, or both) did not differ by direct observation versus self-report methodology. The types of MEs identified by direct observation differed from those identified by self-report (P = .005). Specifically, the most frequent types of MEs identified by direct observation were labeling errors (N = 37; 24.2%), wrong dose errors (N = 35; 22.9%) and errors of omission (N = 27; 17.6%). The most frequent types of MEs identified by self-report were wrong dose (N = 5; 50%) and wrong medication (N = 4; 40%). The severity of incidents identified by direct observation and self-report differed, with self-reported incidents having a higher average severity (P < .001). The procedure types associated with medication-related incidents did not differ by direct observation versus self-report methodology. Direct observation captured many more perioperative medication-related incidents than self-report. The ME types identified and their severity differed between the 2 methods, with a higher average incident severity in the self-reported data.

Reducing Volatile Anesthetic Waste Using a Commercial Electronic Health Record Clinical Decision Support Tool to Lower Fresh Gas Flows.

Volatile anesthetic consumption can be reduced by minimizing excessive fresh gas flows (FGFs). Currently, it is unknown whether decision support tools embedded within commercial electronic health record systems can be successfully adopted to achieve long-term reductions in FGF rates. The authors describe the implementation of an electronic health record-based clinical decision support tool aimed at reducing FGF and evaluate the effectiveness of this intervention in achieving sustained reductions in FGF rates and volatile anesthetic consumption. On August 29, 2018, we implemented a decision support tool within the Epic Anesthesia Information Management System (AIMS) to alert providers of high FGF (>0.7 L/min for desflurane and >1 L/min for sevoflurane) during maintenance of anesthesia. July 22, 2015, to July 10, 2018, served as our baseline period before the intervention. The intervention period spanned from August 29, 2018, to December 31, 2019. Our primary outcomes were mean FGF (L/min) and volatile agent consumption (mL/MAC-h). Because a simple comparison of 2 time periods may result in false conclusions due to underlying trends independent of the intervention, we performed segmented regression of the interrupted time series to assess the change in level at the start of the intervention and the differences in slopes before and after the intervention. The analysis was also adjusted for potential confounding variables. Data included 44,899 cases using sevoflurane preintervention with 26,911 cases postintervention, and 17,472 cases using desflurane with 1185 cases postintervention. Segmented regression of the interrupted times series demonstrated a decrease in mean FGF by 0.6 L/min (95% CI, 0.6-0.6 L/min; P < .0001) for sevoflurane and 0.2 L/min (95% CI, 0.2-0.3 L/min; P < .0001) for desflurane immediately after implementation of the intervention. For sevoflurane, mL/MAC-h decreased by 3.8 mL/MAC-h (95% CI, 3.6-4.1 mL/MAC-h; P < .0001) after implementation of the intervention and decreased by 4.1 mL/MAC-h (95% CI, 2.6-5.6 mL/MAC-h; P < .0001) for desflurane. Slopes for both FGF and mL/MAC-h in the postintervention period were statistically less negative than the preintervention slopes (P < .0001 for sevoflurane and P < .01 for desflurane). A commercial AIMS-based decision support tool can be adopted to change provider FGF management patterns and reduce volatile anesthetic consumption in a sustainable fashion.

The incidence of awake tracheal intubation in anesthetic practice is decreasing: a historical cohort study of the years 2014-2020 at a single tertiary care institution.

PurposeAwake tracheal intubation (ATI) is recommended in airway management guidelines when significant difficulty is predicted with airway management. Use of the technique may be declining, which may have implications for patient safety or for skills acquisition and maintenance. This historical cohort database study sought to determine if the use of ATI was decreasing in our adult tertiary care center.MethodsWith institutional research ethics board approval, we queried our anesthesia information management system for cases with ATI descriptors for each year from 2014 to 2020. Records of the retrieved cases were independently reviewed by all three authors to verify they met inclusion criteria for the ATI cohort prior to analysis for the primary outcome. Secondary outcome measures included airway device and route used for ATI, first attempt and ultimate success rates, and reported adverse issues recorded in cases of failed ATI or those requiring more than one attempt.ResultsA total of 692 cases of ATI were identified between 2014 and 2020. There was a statistically significant decrease in yearly ATIs over the seven-year study period (Chi square goodness of fit, P < 0.001), with ATI use decreasing by about 50%. First attempt success was significantly greater with use of flexible bronchoscopy vs video laryngoscopy to facilitate ATI (84% vs 60%; P < 0.001), while there was no difference in first attempt success with the oral vs nasal route (82% vs 82%; P = 1.0).ConclusionIn this single-center historical cohort study, the use of ATI decreased significantly from 2014 to 2020. Whether this decrease will result in morbidity or mortality related to airway management is currently unclear. Regardless, it has implications for training opportunities and maintenance of competence in performing the procedure.Supplementary InformationThe online version contains supplementary material available at 10.1007/s12630-022-02344-8.

Definitions of low cardiac output syndrome after cardiac surgery and their effect on the incidence of intraoperative LCOS: A literature review and cohort study

ObjectivesLow cardiac output syndrome (LCOS) is a serious complication after cardiac surgery. Despite scientific interest in LCOS, there is no uniform definition used in current research and clinicians cannot properly compare different study findings. We aimed to collect the LCOS definitions used in literature and subsequently applied the definitions obtained to existing data to estimate their effect on the intraoperative LCOS incidences in adults, children and infants.DesignThis is a literature review, followed by a retrospective cohort study.SettingThis is a single-institutional study from a university hospital in the Netherlands.ParticipantsPatients from all ages undergoing cardiac surgery with cardiopulmonary bypass between June 2011 and August 2018.InterventionsWe obtained different definitions of LCOS used in the literature and applied these to data obtained from an anesthesia information management system to estimate intraoperative incidences of LCOS. We compared intraoperative incidences of LCOS in different populations based on age (infants, children and adults).Measurements and main resultsThe literature search identified 262 LCOS definitions, that were applied to intraoperative data from 7,366 patients. Using the 10 most frequently published LCOS definitions, the obtained incidence estimates ranged from 0.4 to 82% in infants, from 0.6 to 56% in children and from 1.5 to 91% in adults.ConclusionThere is an important variety in definitions used to describe LCOS. When applied to data obtained from clinical care, these different definitions resulted in large distribution of intraoperative LCOS incidence rates. We therefore advocate for standardization of the LCOS definition to improve clinical understanding and enable adequate comparison of outcomes and treatment effects both in daily care and in research.

Development and usage of an anesthesia data warehouse: lessons learnt from a 10-year project

This paper describes the development and implementation of an anesthesia data warehouse in the Lille University Hospital. We share the lessons learned from a ten-year project and provide guidance for the implementation of such a project. Our clinical data warehouse is mainly fed with data collected by the anesthesia information management system and hospital discharge reports. The data warehouse stores historical and accurate data with an accuracy level of the day for administrative data, and of the second for monitoring data. Datamarts complete the architecture and provide secondary computed data and indicators, in order to execute queries faster and easily. Between 2010 and 2021, 636 784 anesthesia records were integrated for 353 152 patients. We reported the main concerns and barriers during the development of this project and we provided 8 tips to handle them. We have implemented our data warehouse into the OMOP common data model as a complementary downstream data model. The next step of the project will be to disseminate the use of the OMOP data model for anesthesia and critical care, and drive the trend towards federated learning to enhance collaborations and multicenter studies.

Anaesthesia personnels’ perspectives on digital anaesthesia information management systems – a qualitative study

BackgroundIn 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.MethodsThe 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.ResultsFour 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.ConclusionDigital 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.

Usability of a perioperative medication-related clinical decision support software application: a randomized controlled trial.

We developed a comprehensive, medication-related clinical decision support (CDS) software prototype for use in the operating room. The purpose of this study was to compare the usability of the CDS software to the current standard electronic health record (EHR) medication administration and documentation workflow. The primary outcome was the time taken to complete all simulation tasks. Secondary outcomes were the total number of mouse clicks and the total distance traveled on the screen in pixels. Forty participants were randomized and assigned to complete 7 simulation tasks in 1 of 2 groups: (1) the CDS group (n = 20), who completed tasks using the CDS and (2) the Control group (n = 20), who completed tasks using the standard medication workflow with retrospective manual documentation in our anesthesia information management system. Blinding was not possible. We video- and audio-recorded the participants to capture quantitative data (time on task, mouse clicks, and pixels traveled on the screen) and qualitative data (think-aloud verbalization). The CDS group mean total task time (402.2 ± 85.9 s) was less than the Control group (509.8 ± 103.6 s), with a mean difference of 107.6 s (95% confidence interval [CI], 60.5-179.5 s, P < .001). The CDS group used fewer mouse clicks (26.4 ± 4.5 clicks) than the Control group (56.0 ± 15.0 clicks) with a mean difference of 29.6 clicks (95% CI, 23.2-37.6, P < .001). The CDS group had fewer pixels traveled on the computer monitor (59.5 ± 20.0 thousand pixels) than the Control group (109.3 ± 40.8 thousand pixels) with a mean difference of 49.8 thousand pixels (95% CI, 33.0-73.7, P < .001). The perioperative medication-related CDS software prototype substantially outperformed standard EHR workflow by decreasing task time and improving efficiency and quality of care in a simulation setting.

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.

Data science and its application in anesthesiology

Electronic health records have brought about vast improvement in all aspects of healthcare. ‘Anesthesia Information Management System’ is a specialized form of electronic health record system that is used to record all of the events taking place during the perioperative period, such as clinical procedures performed, physiologic changes that may happen and the medications administered. Based on anesthetic data many databases have been developed internationally for quality improvement in anesthesiology and to know the research outcomes. At an individual clinical level, big anesthesia data is not yet present, unless waveforms and continuous numerical data of intraoperative physiologic variables is recorded. Though the initiative for big data has taken up by some healthcare institutions in the health data management but our country is still far behind in this field. This editorial is aimed to highlight the necessity of this system and to draw the attention of the concerned authorities to plan provision of electronic record keeping in operating rooms.&#x0D; Key words: Electronic Health Records; Data; Data Science; Anesthesiology&#x0D; Citation: Abbasi S, Ahmed U, Khan FA. Data science and its application in anesthesiology. Anaesth. pain intensive care 2021;26(1):1–3;&#x0D; DOI: 10.35975/apic.v26i1.1757&#x0D; Received: January 11, 2022, Accepted: January 14, 2022

Considerations for the implementation of machine learning into acute care settings.

Management of patients in the acute care setting requires accurate diagnosis and rapid initiation of validated treatments; therefore, this setting is likely to be an environment in which cognitive augmentation of the clinician's provision of care with technology rooted in artificial intelligence, such as machine learning (ML), is likely to eventuate. PubMed and Google Scholar with search terms that included ML, intensive/critical care unit, electronic health records (EHR), anesthesia information management systems and clinical decision support were the primary sources for this report. Different categories of learning of large clinical datasets, often contained in EHRs, are used for training in ML. Supervised learning uses algorithm-based models, including support vector machines, to pair patients' attributes with an expected outcome. Unsupervised learning uses clustering algorithms to define to which disease grouping a patient's attributes most closely approximates. Reinforcement learning algorithms use ongoing environmental feedback to deterministically pursue likely patient outcome. Application of ML can result in undesirable outcomes over concerns related to fairness, transparency, privacy and accountability. Whether these ML technologies irrevocably change the healthcare workforce remains unresolved. Well-resourced Learning Health Systems are likely to exploit ML technology to gain the fullest benefits for their patients. How these clinical advantages can be extended to patients in health systems that are neither well-endowed, nor have the necessary data gathering technologies, needs to be urgently addressed to avoid further disparities in healthcare.

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.