Electronic Audit and Feedback With Positive Rewards Improve Anesthesia Provider Compliance With a Barcode-Based Drug Safety System

Barcode medication administration system use and safety implications: a data-driven longitudinal study supported by clinical observation

OBJECTIVES

Barcode medication administration (BCMA) systems may improve patient safety with successful integration and use. This study aimed to explore the barriers and enablers for the successful use of a BCMA system by examining the patterns of medication and patient scanning over time and potential safety implications.

METHODS

Retrospective longitudinal study informed by prospective clinical observations using data extracted from five hospital wards over the first 16 months after implementation to determine trends in medication and patient scanning rates, reasons for non-compliance and scanning mismatch alerts. Regression models were applied to explore factors influencing medication scanning rates across wards of different specialties.

RESULTS

Electronic data on 613 868 medication administrations showed overall medication scanning rates per ward ranged from 5.6% to 67% and patient scanning rates from 4.6% to 89%. Reported reasons for not scanning medications were 'barcode not readable' and 'unavailability of scanners'. Scanning rates declined over time and the pattern of reason codes for not scanning also changed. Factors associated with higher scanning rates included a locally led quality improvement (QI) initiative, the medication administration time and the medication formulation, for example, tablets and liquids. Overall, 37% of scanning alerts resulted in a change in user action. Staff tried to comply with the BCMA system workflow, but workarounds were observed.

DISCUSSION

Compliance with BCMA systems varied across wards and changed over time. QI initiatives hold promise to ensure sustained use of BCMA systems.

CONCLUSIONS

BCMA systems may help to improve medication safety, but further research is needed to confirm sustained safety benefits.

Detecting clinical medication errors with AI enabled wearable cameras

Drug-related errors are a leading cause of preventable patient harm in the clinical setting. We present the first wearable camera system to automatically detect potential errors, prior to medication delivery. We demonstrate that using deep learning algorithms, our system can detect and classify drug labels on syringes and vials in drug preparation events recorded in real-world operating rooms. We created a first-of-its-kind large-scale video dataset from head-mounted cameras comprising 4K footage across 13 anesthesiology providers, 2 hospitals and 17 operating rooms over 55 days. The system was evaluated on 418 drug draw events in routine patient care and a controlled environment and achieved 99.6% sensitivity and 98.8% specificity at detecting vial swap errors. These results suggest that our wearable camera system has the potential to provide a secondary check when a medication is selected for a patient, and a chance to intervene before a potential medical error.

Uses of Electronic Health Record Data to Measure the Clinical Learning Environment of Graduate Medical Education Trainees: A Systematic Review

PURPOSE

This study systematically reviews the uses of electronic health record (EHR) data to measure graduate medical education (GME) trainee competencies.

METHOD

In January 2022, the authors conducted a systematic review of original research in MEDLINE from database start to December 31, 2021. The authors searched for articles that used the EHR as their data source and in which the individual GME trainee was the unit of observation and/or unit of analysis. The database query was intentionally broad because an initial survey of pertinent articles identified no unifying Medical Subject Heading terms. Articles were coded and clustered by theme and Accreditation Council for Graduate Medical Education (ACGME) core competency.

RESULTS

The database search yielded 3,540 articles, of which 86 met the study inclusion criteria. Articles clustered into 16 themes, the largest of which were trainee condition experience (17 articles), work patterns (16 articles), and continuity of care (12 articles). Five of the ACGME core competencies were represented (patient care and procedural skills, practice-based learning and improvement, systems-based practice, medical knowledge, and professionalism). In addition, 25 articles assessed the clinical learning environment.

CONCLUSIONS

This review identified 86 articles that used EHR data to measure individual GME trainee competencies, spanning 16 themes and 6 competencies and revealing marked between-trainee variation. The authors propose a digital learning cycle framework that arranges sequentially the uses of EHR data within the cycle of clinical experiential learning central to GME. Three technical components necessary to unlock the potential of EHR data to improve GME are described: measures, attribution, and visualization. Partnerships between GME programs and informatics departments will be pivotal in realizing this opportunity.

Neuroanesthesiology Quality Improvement Reporting Patterns: A Tertiary Medical Center Experience

BACKGROUND

Understanding quality improvement (QI) reporting patterns is important for practice-based improvement and for prioritizing QI initiatives. The aim of this project was to identify major domains of neuroanesthesiology QI reports at a single academic institution with 2 hospital-based practice sites.

METHODS

We retrospectively reviewed institutional QI databases to identify reports from neuroanesthesia cases between 2013 and 2021. Each report was categorized into one of the 16 primary predefined QI domains; the QI report domains were ranked by frequency. Descriptive statistics are used to present the analysis.

RESULTS

Seven hundred three QI reports (3.2% of all cases) were submitted for the 22,248 neurosurgical and neuroradiology procedures during the study period. Most of the QI reports across the institution were in the domain of communication/documentation (28.4%). Both hospitals shared the same 6 top QI report domains, although the relative frequency of each domain differed between the 2 hospitals. Drug error was the top QI report domain at one hospital, representing 19.3% of that site's neuroanesthesia QI reports. Communication/documentation was the top domain at the other hospital, representing 34.7% of that site's reports. The other 4 shared top domains were equipment/device failure, oropharyngeal injury, skin injury, and vascular catheter dislodgement.

CONCLUSIONS

The majority of neuroanesthesiology QI reports fell into 6 domains: drug error, communication/documentation, equipment/device failure, oropharyngeal injury, skin injury, and vascular catheter dislodgement. Similar analyses from other centers can guide generalizability and potential utility of using QI reporting domains to inform the development of neuroanesthesiology quality measures and reporting frameworks.

Dashboard of Short-Term Postoperative Patient Outcomes for Anesthesiologists: Development and Preliminary Evaluation

BACKGROUND

Anesthesiologists require an understanding of their patients' outcomes to evaluate their performance and improve their practice. Traditionally, anesthesiologists had limited information about their surgical outpatients' outcomes due to minimal contact post discharge. Leveraging digital health innovations for analyzing personal and population outcomes may improve perioperative care. BC Children's Hospital's postoperative follow-up registry for outpatient surgeries collects short-term outcomes such as pain, nausea, and vomiting. Yet, these data were previously not available to anesthesiologists.

OBJECTIVE

This quality improvement study aimed to visualize postoperative outcome data to allow anesthesiologists to reflect on their care and compare their performance with their peers.

METHODS

The postoperative follow-up registry contains nurse-reported postoperative outcomes, including opioid and antiemetic administration in the postanesthetic care unit (PACU), and family-reported outcomes, including pain, nausea, and vomiting, within 24 hours post discharge. Dashboards were iteratively co-designed with 5 anesthesiologists, and a department-wide usability survey gathered anesthesiologists' feedback on the dashboards, allowing further design improvements. A final dashboard version has been deployed, with data updated weekly.

RESULTS

The dashboard contains three sections: (1) 24-hour outcomes, (2) PACU outcomes, and (3) a practice profile containing individual anesthesiologist's case mix, grouped by age groups, sex, and surgical service. At the time of evaluation, the dashboard included 24-hour data from 7877 cases collected from September 2020 to February 2023 and PACU data from 8716 cases collected from April 2021 to February 2023. The co-design process and usability evaluation indicated that anesthesiologists preferred simpler designs for data summaries but also required the ability to explore details of specific outcomes and cases if needed. Anesthesiologists considered security and confidentiality to be key features of the design and most deemed the dashboard information useful and potentially beneficial for their practice.

CONCLUSIONS

We designed and deployed a dynamic, personalized dashboard for anesthesiologists to review their outpatients' short-term postoperative outcomes. This dashboard facilitates personal reflection on individual practice in the context of peer and departmental performance and, hence, the opportunity to evaluate iterative practice changes. Further work is required to establish their effect on improving individual and department performance and patient outcomes.

Evaluation of the Efficiency and Safety of a Safe Label System: A Prospective Simulation Study

OBJECTIVES

Our study aims to investigate the safety and efficiency of the Codonics Safe Label System (SLS) in a prospective simulation study.

METHODS

Three sets of simulated experiments involving 82 anesthetists were carried out on patient simulator mannequins. The primary outcome assessed through the simulated experiments was the effectiveness of the SLS in avoiding vial swap errors. Secondary outcomes analyzed included the efficacy of the SLS in preventing syringe swap and the difference in time taken to prepare standardized drugs as compared with conventional methods.

RESULTS

The SLS was associated with a significant reduction in all 4 stages of vial swap error. The incidence of wrong ampoule breakage was significantly lower in the SLS group compared with the conventional group (12.1% versus 38.5%, P = 0.007). The number of staff who drew the wrong ampoule was similarly lower in the SLS group compared with the conventional group (4.9% versus 33.3%, P = 0.001). The proportions of staff who eventually wrongly labeled the loaded syringe were 0% in the SLS group and 17.9% in the conventional group (P = 0.005).Drug preparation time was longer for the SLS group than for the conventional group (239.6 ± 45.9 versus 160.3 ± 46.5 seconds, P < 0.001).There was no significant difference in the incidence of syringe swap with the use of the SLS.

CONCLUSIONS

The use of the SLS is effective in reducing vial swap error, but not syringe swap errors, and is associated with increased time taken for anesthetic drug preparation.

Use of provider education, intra-operative decision support, and an email-feedback system in improving compliance with sugammadex dosage guideline and reducing drug expenditures

STUDY OBJECTIVE

Due to excessive sugammadex expenditures at our institution, we designed dosing guidelines that utilize adjusted body weight and informatics-based tools aimed at reducing variability in dosing practices.

DESIGN

We retrospectively reviewed rates of high-dose sugammadex administration in three phases: Pre-intervention - May 2018 to November 2018; First intervention - November 2018 to April 2019; and Second intervention - April 2019 to July 2019.

SETTING

Academic medical center in the United States - Vanderbilt University Medical Center (VUMC) PATIENTS: N/A INTERVENTIONS: First, anesthesia providers were educated on adjusted body weight-based dosing guidelines. Providers also received intraoperative decision support displaying a patient's actual and adjusted body weight along with rates of high-dose (>200 mg) sugammadex administration for each respective provider. Second, we implemented an email-feedback system to remind providers of the new guidelines.

MEASUREMENTS

Weekly rate of high-dose sugammadex cases.

MAIN RESULTS

During the pre-intervention stage, 1556 (12.3%) cases involved high-dose sugammadex. Comparatively, 550 (4.3%) and 187 (3.1%) high-dose sugammadex cases occurred during the first and second intervention stages, respectively. Segmented regression analysis demonstrated a significant rate change of -3.51% (95% CI: -5.64%, -1.38%) in sugammadex dosing practices after provider education and the implementation of digital improvement initiatives but failed to reveal a significant change after implementation of the email-feedback system. Overall, our interventions were associated with $2563.05 in estimated weekly savings of sugammadex expenditures.

CONCLUSIONS

Provider education and digital quality improvement was associated with reduced rates of high-dose sugammadex administration, generating cost savings at a large academic medical institution.