A Narrative Review of Meaningful Use and Anesthesia Information Management Systems

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.

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.

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.

Logging in: a comparative analysis of electronic health records versus anesthesia resident-driven logbooks

PURPOSE

Resident logbooks (RLBs) documenting clinical case exposure are widespread in medical education despite evidence of poor accuracy. Electronic health records (e.g., anesthesia information management systems [AIMS]) may provide advantages for auditing longitudinal case exposure. We evaluated the agreement between AIMS and RLBs for tracking case exposure during anesthesiology residency.

METHODS

We performed a historical cohort study with anesthesiology residents (2011-2018, all of whom used a RLB contemporaneously with AIMS) working in a multisite academic health sciences network. The primary outcome was total case-load logging; secondary outcomes were volumes for seven surgical specialties (general, gynecology, neuro, orthopedic, thoracic, urology, and vascular surgery). Correlation of case numbers tracked by AIMS vs RLB was assessed using Pearson correlation; agreement was determined using Bland-Altman plots and intraclass correlation coefficients (ICC).

RESULTS

Data from 27 anesthesiology residents were collected. Overall, mean (standard deviation) case numbers were generally greater with AIMS vs RLB [649 (103) vs 583 (191); P = 0.049). Total case volumes between systems had moderate correlation (r = 0.50) and agreement (intraclass correlation coefficient [ICC], 0.42; 95% CI, 0.34 to 0.59). Bland-Altman plots showed variable agreement between AIMS and RLB data [mean (SD) bias = 66 (166) cases]. For general, gynecology, neuro, orthopedic, thoracic, urology, and vascular surgery, there was a range of poor to moderate agreement (ICC, 0.23-0.57) between AIMS and RLB.

CONCLUSION

For anesthesiology resident case-logging, the number of cases logged in an AIMS was higher with lower variance compared with RLBs. Anesthesia information management systems vs RLB data showed low-moderate correlation and agreement. Given the additional time and resources required for RLBs, AIMS may be a superior method for tracking cases where available.

Secondhand Smoke exposure and risk of Obstructive Sleep Apnea in Children

OBJECTIVES

Obstructive sleep apnea (OSA) has a prevalence of 4% in children. Few studies have explored the role of secondhand smoke (SHS) on OSA severity and have shown contradicting results. Most studies have focused on the effect of SHS on snoring. This study explored the association of SHS exposure and OSA severity in children aged 3-18 years.

METHODS

This is a retrospective single center IRB-approved study. Electronic Medical Records (EMR) were queried between 1/24/2015 and 1/24/2018 to obtain data on SHS exposure with standard questionnaires from perioperative database. SHS was analyzed as a binary variable and OSA was measured using obstructive apnea hypopnea index (OAHI) from polysomnography (PSG) as a continuous variable. Analyses were done on all children and in those with severe OSA (OAHI≥10/h) as a subgroup.

RESULTS

EMR query yielded 101,884 children of whom 3776 had PSG. Limiting baseline PSG in 3-18-year-old and reliable information on SHS yielded 167 analyzable children of whom 70 had severe OSA. Children exposed to SHS had significantly more public insurance than non-exposed (p < 0.0001). Among children with severe OSA, median OAHI was significantly higher in SHS exposed compared to non-exposed (29.0vs.19.5,p = 0.04), but not across all children. In multivariable analysis SHS exposure increased OAHI by 48% in severe OSA subgroup (95%CI: 8%-102%; p = 0.01) when adjusted for race, body mass index, and adjusted household income.

CONCLUSION

Children aged 3-18 years with severe OSA who were exposed to SHS were found to have 1.48 increase in odds of OAHI than those without SHS exposure. Results could be limited by retrospective nature of study and EMR tools.

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.

Anesthesia information management: clinical decision support

PURPOSE OF REVIEW

Perioperative informatics tools continue to be developed at a rapid pace and offer clinicians the potential to greatly enhance clinical decision making. The goal of this review is to bring the reader updates on perioperative information management and discuss future research directions in the field.

RECENT FINDINGS

Clinical decision support tools become more timely, accurate, and, in some instances, have been shown to improve patient outcomes. When correctly implemented, they are critical tools for optimization of perioperative care.

SUMMARY

Perioperative informaticians continue to test new and innovative ways to enhance the delivery of anesthesia care, improving the safety and efficacy of perioperative management. Future work will continue to refine tools to ensure that perioperative informatics provides clinicians timely and accurate feedback, with demonstrable evidence that a decision support system improves patient outcomes.

Perioperative Information Systems: Opportunities to Improve Delivery of Care and Clinical Outcomes in Cardiac and Vascular Surgery

A variety of existing perioperative informatics tools offer clinicians and researchers the opportunity to improve the delivery of care and clinical outcomes for patients undergoing cardiac and vascular surgery. Many of these tools can be used to improve the reliability of the care delivery process through the application of clinical decision support tools and/or quality improvement methodologies at a number of junctures. In this review, the authors will offer a concise overview of the existing perioperative informatics literature, with a focus on tools considered to be of utility in confronting the unique challenges inherent to cardiac and vascular surgery. The authors also highlight areas that they believe are of interest for future targeted inquiry.

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

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

Neural Network Classifier for Automatic Detection of Invasive Versus Noninvasive Airway Management Technique Based on Respiratory Monitoring Parameters in a Pediatric Anesthesia

Children undergoing general anesthesia require airway monitoring by an anesthesia provider. The airway may be supported with noninvasive devices such as face mask or invasive devices such as a laryngeal mask airway or an endotracheal tube. The physiologic data stored provides an opportunity to apply machine learning algorithms distinguish between these modes based on pattern recognition. We retrieved three data sets from patients receiving general anesthesia in 2015 with either mask, laryngeal mask airway or endotracheal tube. Patients underwent myringotomy, tonsillectomy, adenoidectomy or inguinal hernia repair procedures. We retrieved measurements for end-tidal carbon dioxide, tidal volume, and peak inspiratory pressure and calculated statistical features for each data element per patient. We applied machine learning algorithms (decision tree, support vector machine, and neural network) to classify patients into noninvasive or invasive airway device support. We identified 300 patients per group (mask, laryngeal mask airway, and endotracheal tube) for a total of 900 patients. The neural network classifier performed better than the boosted trees and support vector machine classifiers based on the test data sets. The sensitivity, specificity, and accuracy for neural network classification are 97.5%, 96.3%, and 95.8%. In contrast, the sensitivity, specificity, and accuracy of support vector machine are 89.1%, 92.3%, and 88.3% and with the boosted tree classifier they are 93.8%, 92.1%, and 91.4%. We describe a method to automatically distinguish between noninvasive and invasive airway device support in a pediatric surgical setting based on respiratory monitoring parameters. The results show that the neural network classifier algorithm can accurately classify noninvasive and invasive airway device support.

Development of a Minimum Data Set (MDS) for C-Section Anesthesia Information Management System (AIMS)

Background

Caesarean section, also known as C-section, is a very common procedure in the world. Minimum data set (MDS) is defined as a set of data elements holding information regarding a series of target entities to provide a basis for planning, management, and performance evaluation. MDS has found a great use in health care information systems. Also, it can be considered as a basis for medical information management and has shown a great potential for contributing to the provision of high quality care and disease control measures.

Objectives

The principal aim of this research was to determine MDS and required capabilities for Anesthesia information management system (AIMS) in C-section in Iran.

Methods

Data items collected from several selected AIMS were studied to establish an initial set of data. The population of this study composed of 115 anesthesiologists was asked to review the proposed data elements and score them in order of importance by using a five-point Likert scale. The items scored as important or highly important by at least 75% of the experts were included in the final list of minimum data set.

Results

Overall 8 classes of data (consisted of 81 key data elements) were determined as final set. Also, the most important required capabilities were related to airway management and hypertension and hypotension management.

Conclusions

In the development of information system (IS) based on MDS and identification, because of the broad involvement of users, IS capabilities must focus on the users’ needs to form a successful system. Therefore, it is essential to assess MDS watchfully by considering the planned uses of data. Also, IS should have essential capabilities to meet the needs of its users.

Comparison of minute distribution frequency for anesthesia start and end times from an anesthesia information management system and paper records

Use of an anesthesia information management system (AIMS) has been reported to improve accuracy of recorded information. We tested the hypothesis that analyzing the distribution of times charted on paper and computerized records could reveal possible rounding errors, and that this effect could be modulated by differences in the user interface for documenting certain event times with an AIMS. We compared the frequency distribution of start and end times for anesthesia cases completed with paper records and an AIMS. Paper anesthesia records had significantly more times ending with "0" and "5" compared to those from the AIMS (p < 0.001). For case start times, AIMS still exhibited end-digit preference, with times whose last digits had significantly higher frequencies of "0" and "5" than other integers. This effect, however, was attenuated compared to that for paper anesthesia records. For case end times, the distribution of minutes recorded with AIMS was almost evenly distributed, unlike those from paper records that still showed significant end-digit preference. The accuracy of anesthesia case start times and case end times, as inferred by statistical analysis of the distribution of the times, is enhanced with the use of an AIMS. Furthermore, the differences in AIMS user interface for documenting case start and case end times likely affects the degree of end-digit preference, and likely accuracy, of those times.