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.

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

BACKGROUND: Implementation 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. METHODS: An 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. RESULTS: The 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. CONCLUSION: This 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.

Quantifying and Interpreting Inequality in Surgical Site Infections per Quarter Among Anesthetizing Locations and Specialties.

Background Earlier studies have shown that prevention of surgical site infection can achieve net cost savings when targeted to operating rooms with the most surgical site infections. Methodology This retrospective cohort study included all 231,057 anesthetics between May 2017 and June 2022 at a large teaching hospital. The anesthetics were administered in operating rooms, procedure rooms, radiology, and other sites. The 8,941 postoperative infections were identified from International Classification of Diseases diagnosis codes relevant to surgical site infections documented during all follow-up encounters over 90 days postoperatively. To quantify the inequality in the counts of infections among anesthetizing locations, the Gini index was used, with the Gini index being proportional to the sum of the absolute pairwise differences among anesthetizing locations in the counts of infections. Results The Gini index for infections among the 112 anesthetizing locations at the hospital was 0.64 (99% confidence interval = 0.56 to 0.71). The value of 0.64 is so large that, for comparison, it exceeds nearly all countries' Gini index for income inequality. The 50% of locations with the fewest infections accounted for 5% of infections. The 10% of locations with the most infections accounted for 40% of infections and 15% of anesthetics. Among the 57 operating room locations, there was no association between counts of cases and infections (Spearman correlation coefficient r = 0.01). Among the non-operating room locations (e.g., interventional radiology), there was a significant association (Spearman r = 0.79). Conclusions Targeting specific anesthetizing locations is important for the multiple interventions to reduce surgical site infections that represent fixed costs irrespective of the number of patients (e.g., specialized ventilatory systems and nightly ultraviolet-C disinfection).

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.

Anesthesiologists in the Ether: Technology and Telemedicine in Anesthesiology.

A new frontier in veterinary anesthesia telehealth has begun. With the adoption of electronic anesthetic records and video, phone, and chat consultations, an anesthesiologist can be integrated into the care team of any patient, anywhere in the world. This article reviews the benefits of adopting an electronic anesthetic record system, and the ways that practitioners can incorporate a virtual anesthesiologist into their care team.

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.

Comparison of the Effects of Automated and Manual Record Keeping on Anesthetists' Monitoring Performance: Randomized Controlled Simulation Study.

BACKGROUND: Anesthesia information management systems (AIMSs) automatically import real-time vital signs from physiological monitors to anesthetic records, replacing part of anesthetists' traditional manual record keeping. However, only a handful of studies have examined the effects of AIMSs on anesthetists' monitoring performance. OBJECTIVE: This study aimed to compare the effects of AIMS use and manual record keeping on anesthetists' monitoring performance, using a full-scale high-fidelity simulation. METHODS: This simulation study was a randomized controlled trial with a parallel group design that compared the effects of two record-keeping methods (AIMS vs manual) on anesthetists' monitoring performance. Twenty anesthetists at a tertiary hospital in Hong Kong were randomly assigned to either the AIMS or manual condition, and they participated in a 45-minute scenario in a high-fidelity simulation environment. Participants took over a case involving general anesthesia for below-knee amputation surgery and performed record keeping. The three primary outcomes were participants' (1) vigilance detection accuracy (%), (2) situation awareness accuracy (%), and (3) subjective mental workload (0-100). RESULTS: With regard to the primary outcomes, there was no significant difference in participants' vigilance detection accuracy (AIMS, 56.7% vs manual, 56.7%; P=.50), and subjective mental workload was significantly lower in the AIMS condition than in the manual condition (AIMS, 34.2 vs manual, 46.7; P=.02). However, the result for situation awareness accuracy was inconclusive as the study did not have enough power to detect a difference between the two conditions. CONCLUSIONS: Our findings suggest that it is promising for AIMS use to become a mainstay of anesthesia record keeping. AIMSs are effective in reducing anesthetists' workload and improving the quality of their anesthetic record keeping, without compromising vigilance.

Effect of forced-air warming by an underbody blanket on end-of-surgery hypothermia: a propensity score-matched analysis of 5063 patients.

BACKGROUND: Underbody blankets have recently been launched and are used by anesthesiologists for surgical patients. However, the forced-air warming effect of underbody blankets is still controversial. The aim of this study was to determine the effect of forced-air warming by an underbody blanket on body temperature in anesthetized patients. METHODS: We retrospectively analyzed 5063 surgical patients. We used propensity score matching to reduce the bias caused by a lack of randomization. After propensity score matching, the change in body temperature from before to after surgery was compared between patients who used underbody blankets (Under group) and those who used other types of warming blankets (Control group). The incidence of hypothermia (i.e., body temperature < 36.0 °C at the end of surgery) was compared between the two groups. A p value < 0.05 was considered to indicate statistical significance. RESULTS: We obtained 489 propensity score-matched pairs of patients from the two groups, of whom 33 and 63 had hypothermia in the Under and Control groups, respectively (odds ratio: 0.49, 95% confidence interval: 0.31-0.76, p = 0.0013). CONCLUSIONS: The present study suggests that the underbody blanket may help reduce the incidence of intraoperative hypothermia and may be more efficient in warming anesthetized patients compared with other types of warming blankets. TRIAL REGISTRATION: UMIN Clinical Trials Registry (Identifier: UMIN000022909 ; retrospectively registered on June 27, 2016).

The effect of data-entry template design and anesthesia provider workload on documentation accuracy, documentation efficiency, and user-satisfaction.

INTRODUCTION: Currently, there are few evidence-based guidelines to inform optimal clinical data-entry template design that maximizes usability while reducing unintended consequences. This study explored the impact of data-entry template design and anesthesia provider workload on documentation accuracy, documentation efficiency, and user-satisfaction to identify the most beneficial data-entry methods for use in future documentation interface design. METHODOLOGY: A study using observational data collection and psychometric instruments (for perceived workload and user-satisfaction) was conducted at three hospitals using different methods of data-entry for perioperative documentation (auto-filling with unstructured data, computer-assisted data selection with semi-structured documentation, and paper-based documentation). Nurse anesthetists at each hospital (N = 30) were observed completing documentation on routine abdominal surgical cases. RESULTS: Auto-filling (61.2%) had the lowest documentation accuracy scores compared to computer-assisted (81.3%) and paper-based documentation (76.2%). Computer-assisted data-entry had the best documentation efficiency scores and required the least percentage of the nurse anesthetists' time (9.65%) compared to auto-filling (11.43%) and paper-based documentation (15.23%). Paper-based documentation had the highest perceived workload scores (M = 288, SD = 88) compared to auto-filling (M = 160, SD = 93, U = 16.5, p < 0.01) and computer assisted data-entry (M = 93, SD = 50, U = 4.0, P < 0.001). CONCLUSIONS: Auto-filling with unstructured data needs to be used sparingly because of its low documentation accuracy. Computer-assisted data entry with semi-structured data needs to be further study because of its better documentation accuracy, documentation efficiency, and perceived workload.

Implementation and Use of Anesthesia Information Management Systems for Non-operating Room Locations.

Non-operating room anesthesia (NORA) encounters comprise a significant fraction of contemporary anesthesia practice. With the implemention of an aneshtesia information management system (AIMS), anesthesia practitioners can better streamline preoperative assessment, intraoperative automated documentation, real-time decision support, and remote surveillance. Despite the large personal and financial commitments involved in adoption and implementation of AIMS and other electronic health records in these settings, the benefits to safety, efficacy, and efficiency are far too great to be ignored. Continued future innovation of AIMS technology only promises to further improve on our NORA experience and improve care quality and safety.

Documentation and Treatment of Intraoperative Hypotension: Electronic Anesthesia Records versus Paper Anesthesia Records.

In this study, we examined anesthetic records before and after the implementation of an electronic anesthetic record documentation (AIMS) in a single surgical population. The purpose of this study was to identify any inconsistencies in anesthetic care based on handwritten documentation (paper) or AIMS. We hypothesized that the type of anesthetic record (paper or AIMS) would lead to differences in the documentation and management of hypotension. Consecutive patients who underwent esophageal surgery between 2009 and 2014 by a single surgeon were eligible for the study. Patients were grouped in to 'paper' or 'AIMS' based on the type of anesthetic record identified in the chart. Pertinent patient identifiers were removed and data collated after collection. Predetermined preoperative and intraoperative data variables were reviewed. Consecutive esophageal surgery patients (N = 189) between 2009 and 2014 were evaluated. 92 patients had an anesthetic record documented on paper and 97 using AIMS. The median number of unique blood pressure recordings was lower in the AIMS group (median (Q1,Q3) AIMS 30.0 (24.0, 39.0) vs. Paper 35.0 (28.5, 43.5), p < 0.01). However, the median number of hypotensive events (HTEs) was higher in the AIMS group (median (Q1,Q3) 8.0 (4.0, 18.0) vs. 4.0 (1.0, 10.5), p < 0.001), and the percentage of HTEs per blood pressure recording was higher in the AIMS group (30.4 ((Q1, Q3) (9.5, 60.9)% vs. 12.5 (2.4, 27.5)%), p < 0.01). Multivariable regression analysis identified independent predictors of HTEs. The incidence of HTEs was found to increase with AIMS (IRR = 1.88, p < 0.01). Preoperative systolic blood pressure, increased blood loss, and phenylephrine. A phenylephrine infusion was negatively associated with hypotensive events (IRR = 0.99, p = 0.03). We noted an increased incidence of HTEs associated with the institution of an AIMS. Despite this increase, no change in medical therapy for hypotension was seen. AIMS did not appear to have an effect on the management of intraoperative hypotension in this patient population.

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.

Study on design and application management of anesthesia information management system / 医疗卫生装备

Objective To design an anesthesia information management system and apply it preliminarily so as to facilitate informatized anesthesia.Methods The system was developed with C/S architecture,thin clients distributed in all operating rooms as well as the terminals in postanesthesia care units and management office.Trial of the system was carried out in Chinese PLA General Hospital,and solutions were put forward for the problems during the trial.Results The system enhanced anesthesiology in clinical use,scientific research,teaching and management,though some defects still existed due to its development level.Conclusion The system promotes hospital informatization greatly,and has practical values.

Study on accuracy of information collection of anesthesia information management system / 医疗卫生装备

Objective To explore the data accuracy acquired by the anesthesia information management system.Methods Totally 100 patients from two hospitals were selected randomly,whose anesthesia time was estimated more than 1 h.The vital signs data acquired by the system were compared with those by the monitor once every 5 min±30 s,and totally there were 12 times of comparison executed.SAS 9.2 software was used for statistical analysis.Results In FAS set the system had the total data accuracy being 100％,95％ credibility interval from 86.87％ to 97.30％ and the BMDL higher than 85％;in PPS set he total data accuracy was 100％,95％ credibility interval was from 92.89％ to 100％ and the BMDL was also higher than 85％.The system gained "Excellent" or "Good" grade in system response,stability,functional interface operability and the accuracy of special functions.Conclusion The system acquires and stores the vital signs data automatically and accurately,enhances anesthesia information in objectivity,authenticity and tractability,and has high values for enhancing anesthesia safety,medical safety and scientific research.

Study on design and application management of anesthesia information management system / 医疗卫生装备

Objective To design an anesthesia information management system and apply it preliminarily so as to facilitate informatized anesthesia.Methods The system was developed with C/S architecture,thin clients distributed in all operating rooms as well as the terminals in postanesthesia care units and management office.Trial of the system was carried out in Chinese PLA General Hospital,and solutions were put forward for the problems during the trial.Results The system enhanced anesthesiology in clinical use,scientific research,teaching and management,though some defects still existed due to its development level.Conclusion The system promotes hospital informatization greatly,and has practical values.

Study on accuracy of information collection of anesthesia information management system / 医疗卫生装备

Objective To explore the data accuracy acquired by the anesthesia information management system.Methods Totally 100 patients from two hospitals were selected randomly,whose anesthesia time was estimated more than 1 h.The vital signs data acquired by the system were compared with those by the monitor once every 5 min±30 s,and totally there were 12 times of comparison executed.SAS 9.2 software was used for statistical analysis.Results In FAS set the system had the total data accuracy being 100％,95％ credibility interval from 86.87％ to 97.30％ and the BMDL higher than 85％;in PPS set he total data accuracy was 100％,95％ credibility interval was from 92.89％ to 100％ and the BMDL was also higher than 85％.The system gained "Excellent" or "Good" grade in system response,stability,functional interface operability and the accuracy of special functions.Conclusion The system acquires and stores the vital signs data automatically and accurately,enhances anesthesia information in objectivity,authenticity and tractability,and has high values for enhancing anesthesia safety,medical safety and scientific research.

Accuracy of Vasopressor Documentation in Anesthesia Records.

OBJECTIVES: To determine the accuracy of documentation of vasoactive medication administration in anesthetic records. DESIGN: Cross-sectional observational study. SETTING: Single academic center. PARTICIPANTS: Attending and resident anesthesiologists. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: An auditor inspected the anesthesia worktop between cases looking for partially used syringes of vasopressors, and the anesthesia record for the preceding case was reviewed for entries related to administration of these agents. In 100 anesthesia records for cases in which a phenylephrine and/or ephedrine bolus was apparently administered, 26% (95% CI: 18-35%) had full documentation and 36% (95% CI: 27-46%) had no documentation. In the 38% of cases that had partial documentation, a median of 50% (interquartile range 33%, 67%) of the total amounts given were documented. CONCLUSIONS: The authors found complete or partial omission of documentation of bolus doses of vasopressors in anesthesia records in the majority of cases in which such drugs were given. This finding has the potential to jeopardize the data integrity of local and pooled case registries and conclusions of retrospective studies that utilize these data.

Implementation of an Anesthesia Information Management System in an Ambulatory Surgery Center.

Anesthesia information management systems (AIMS) are increasingly being implemented throughout the United States. However, little information exists on the implementation process for AIMS within ambulatory surgery centers (ASC). The objectives of this descriptive study are to document: 1) the phases of implementation of an AIMS at an ASC; and 2) lessons learnt from a socio-technical perspective. The ASC, within the Veterans Health Administration (VHA), has hosted an AIMS since 2008. As a quality improvement effort, we implemented a new version of the AIMS. This new version involved fundamental software changes to enhance clinical care such as real-time importing of laboratory data and total hardware exchange. The pre-implementation phase involved coordinated preparation over six months between multiple informatics teams along with local leadership. During this time, we conducted component, integration, and validation testing to ensure correct data flow from medical devices to AIMS and centralized databases. The implementation phase occurred in September 2014 over three days and was successful. Over the next several months, during post-implementation phase, we addressed residual items like latency of the application. Important lessons learnt from the implementation included the utility of partnering early with executive leadership; ensuring end user acceptance of new clinical workflow; continuous testing of data flow; use of a staged rollout; and providing additional personnel throughout implementation. Implementation of an AIMS at an ASC can utilize methods developed for large hospitals. However, issues unique to an ASC such as limited number of support personnel and distinctive workflows must be considered.

Breaking Through the Economic Barriers of Anesthesia Information Management Systems.

Anesthesia information management systems (AIMS) are being increasingly used to assist the delivery and documentation of anesthesia services in the United States. The major benefits of AIMS cited in the literature are the ability to (1) reduce costs, (2) facilitate quality assurance and quality improvement processes, (3) increase the accuracy and completeness of the anesthesia record, and (4) improve adherence to recommended guidelines. The major drawback, especially for rural hospitals, is the cost. This article identifies 2 of the main elements of AIMS that exist in information technology and data systems already present in a hospital. In this project, the medication dispensing system was used to achieve a 70% reduction in drug costs, and a simple electronic database was created, which detected adverse events that were missed with the legacy quality assurance system. There was no additional cost to the hospital to realize these benefits because this project utilized technologies and data systems already in place and staff time was part of the normal workload. This project showed that it is possible to achieve 2 of the major benefits of an AIMS without the major cost of purchasing one.

Anesthesia information management systems in the ambulatory setting: benefits and challenges.

Adopting an anesthesia information management system (AIMS) is a challenge for anesthesia departments. The transition requires a physician champion and the support of members in every section. This change can be facilitated by visiting similar institutions that are already using AIMS, shadow charting for a sufficient period of time, and understanding that optimization continues after the go-live date. Once implemented, the benefits outweigh the challenges, but understanding where the potential obstacles lie is critical to removing them efficiently and effectively. As different AIMS continue to spread throughout the medical world, so will their benefits.