Digital Quality Improvement Approach Reduces the Need for Rescue Antiemetics in High-Risk Patients

Comparison of prophylaxis strategy for postoperative nausea and vomiting and its incidence before and after the implementation of 5-hydroxytryptamine 3 in surgical setting: a single-center, retrospective study

PURPOSE

To investigate the association between adherence to guideline-recommended risk-based postoperative nausea and vomiting (PONV) prophylaxis, the antiemetics used for PONV prophylaxis, and the incidence of PONV in patients who were underwent general anesthesia before and after 5-HT3 receptor antagonists became available.

METHODS

Patients (≥ 20 years old) who were extubated after scheduled surgery and returned to general wards between January 2021 and February 2022 and between June 2022 and July 2023 were included. Risk factors included age < 50, female, motion sickness, nonsmoker, surgical factors, and postoperative opioid use. Two and three or more prophylaxis were recommended for patients with one or two and three or more risk factors, respectively. The primary outcome was the number of patients who received adequate prophylaxis, and the secondary outcomes were antiemetic agents used during anesthesia and the incidence of PONV on postoperative days 0 and 1. PONV was defined as documented PONV or rescue antiemetic administration.

RESULTS

From January 2021 to February 2022 and from June 2022 to July 2023, 2342 and 2682 patients were included, respectively. Before ondansetron became available, more D2 receptor antagonists were used (p < 0.001), and after ondansetron became available, both ondansetron (p < 0.001) and propofol (p < 0.001) were given more frequently. Before and after ondansetron became available, the number of patients with adequate prophylaxis was 3.7% and 9.2%, respectively (p < 0.001), and the incidence of PONV on postoperative days 0 and 1 was 44.6% and 44.0%, respectively (p = 0.67).

CONCLUSION

The availability of ondansetron increased the number of patients with adequate PONV prophylaxis, but did not decrease the incidence of PONV.

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

BACKGROUND

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.

METHODS

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.

RESULTS

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).

CONCLUSIONS

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.

Quality improvement in New Zealand pediatric anesthesia: National quality direction, patient experience, equity, and collaboration

The current priorities of the quality and safety of patient care in New Zealand at a central government level are described, with a focus on equity and patient experience. Priorities between stakeholders differ. We report the current quality activities of New Zealand pediatric anesthetists in relation to these governance aims, seeking gaps and suggesting future directions to align governance priorities and local activities. International relevance is also outlined. New Zealand Māori experience health inequity. Complex systemic factors including those of systemic racism and prejudice contribute to the inequity. The specific contributions to health inequity from pediatric anesthetists are unknown but could include aspects of cultural safety, delays in treatment and treatment deficits. Patient experience is correlated positively with other quality domains. Peri-operative patient experience tools require outcomes of interest that matter to patients, including relevant cultural safety domains. Risk identification and critical event review contribute to local learnings in departments and institutions, and more widely to national and binational (with Australia) learnings. Several collaborative projects in Australia and New Zealand, whilst not primarily quality improvement projects, may improve pediatric anesthesia. These collaborations include a pediatric anesthesia professional network, a curriculum for a pediatric anesthetic fellowship, contributions to a document on standards for pediatric anesthesia, and a national quality group researching key performance indicators across New Zealand.

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.

Optimizing Hospital Electronic Prescribing Systems: A Systematic Scoping Review

OBJECTIVE

Considerable international investment in hospital electronic prescribing (ePrescribing) systems has been made, but despite this, it is proving difficult for most organizations to realize safety, quality, and efficiency gains in prescribing. The objective of this work was to develop policy-relevant insights into the optimization of hospital ePrescribing systems to maximize the benefits and minimize the risks of these expensive digital health infrastructures.

METHODS

We undertook a systematic scoping review of the literature by searching MEDLINE, Embase, and CINAHL databases. We searched for primary studies reporting on ePrescribing optimization strategies and independently screened and abstracted data until saturation was achieved. Findings were theoretically and thematically synthesized taking a medicine life-cycle perspective, incorporating consultative phases with domain experts.

RESULTS

We identified 23,609 potentially eligible studies from which 1367 satisfied our inclusion criteria. Thematic synthesis was conducted on a data set of 76 studies, of which 48 were based in the United States. Key approaches to optimization included the following: stakeholder engagement, system or process redesign, technological innovations, and education and training packages. Single-component interventions (n = 26) described technological optimization strategies focusing on a single, specific step in the prescribing process. Multicomponent interventions (n = 50) used a combination of optimization strategies, typically targeting multiple steps in the medicines management process.

DISCUSSION

We identified numerous optimization strategies for enhancing the performance of ePrescribing systems. Key considerations for ePrescribing optimization include meaningful stakeholder engagement to reconceptualize the service delivery model and implementing technological innovations with supporting training packages to simultaneously impact on different facets of the medicines management process.

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.

A retrospective single-site data-linkage study comparing manual to electronic data abstraction for routine post-operative nausea and vomiting audit

BACKGROUND

Post-operative nausea and vomiting (PONV) is a common cause of patient dissatisfaction following anaesthesia. Audit of adherence to PONV prevention guidelines is resource intensive when performed by manual chart extraction. Electronic audit can require costly anaesthetic and medical records.

OBJECTIVE

In our single-site study we sought to compare manual and electronic PONV audits by utilizing existing non-anaesthetic electronic medical records to avoid expensive additional software.

METHODS

The audits were performed from 13 January 2020 to 1 February 2020 for surgical inpatients. Two PONV periods were captured-the post-anaesthetic recovery unit and on the ward (to 24 h). Electronic PONV was defined as the administration of an anti-emetic medication. A 6-month electronic PONV rate was also calculated.

RESULTS

Manual audit captured 142 patients and electronic audit captured 294 patients, over the same time period. The manual PONV rate was 10% (95% confidence interval (CI) 5-16%) in the post-anaesthetic recovery unit and 20% (95% CI 14-28%) the next day. The electronic rate was 5% (95% CI 3-8%) in the post-anaesthetic recovery unit and 15% (11-19%) in a 24-h period. The 6-month electronic audit found 3510 patients, with a post-anaesthetic recovery unit and 24-h PONV rates of 5% (4-6%) and 14% (13-16%), respectively. Electronic audit did not identify 5.8% of PONV patients in the manual audit.

CONCLUSION

Electronic audit enrolled more patients and identified a lower PONV rate than manual audit, likely from less enrolment bias. Electronic audit was easily repeated over a 6-month period. While electronic PONV audit is possible without additional software, an electronic anaesthetic chart would greatly improve audit quality.

Drugs in anesthesia: preventing postoperative nausea and vomiting

PURPOSE OF REVIEW

Postoperative nausea and vomiting (PONV) continues to be a burden for patients, medical staff and healthcare facilities because of inadequate adherence to available recommendations. This review gives an overview on recent recommendations, new evidence and remaining issues in the field of PONV management.

RECENT FINDINGS

A wide range of drugs is available for the management of PONV including corticosteroids, 5-HT3-antagonists, dopamine-antagonists, neurokinin-receptor-1 (NK1)-antagonists, antihistamines and anticholinergics. The updated PONV guidelines from 2020 recommend a universal multimodal strategy for PONV prophylaxis, which is an important paradigm shift to improve implementation of the existing evidence. A recent Cochrane network meta-analysis ranked single drugs and drug combinations for PONV prophylaxis in terms of efficacy and safety. Notably, NK1-antagonists and new 5-HT3-antagonists ranged among the most effective drugs. However, safety data on antiemetics are generally scarce.

SUMMARY

Numerous drug (combinations) and strategies are available for PONV management. New and very effective (single) drugs could result in a simplification compared with a combination of several drugs, and thus lead to better implementation.

Anesthesiologists' Role in Value-based Perioperative Care and Healthcare Transformation

Health care is undergoing major transformation with a shift from fee-for-service care to fee-for-value. The advent of new care delivery and payment models is serving as a driver for value-based care. Hospitals, payors, and patients increasingly expect physicians and healthcare systems to improve outcomes and manage costs. The impact of the coronavirus disease 2019 (COVID-19) pandemic on surgical and procedural practices further highlights the urgency and need for anesthesiologists to expand their roles in perioperative care, and to impact system improvement. While there have been substantial advances in anesthesia care, perioperative complications and mortality after surgery remain a key concern. Anesthesiologists are in a unique position to impact perioperative health care through their multitude of interactions and influences on various aspects of the perioperative domain, by using the surgical experience as the first touchpoint to reengage the patient in their own health care. Among the key interventions that are being effectively instituted by anesthesiologists include proactive engagement in preoperative optimization of patients' health; personalization and standardization of care delivery by segmenting patients based upon their complexity and risk; and implementation of best practices that are data-driven and evidence-based and provide structure that allow the patient to return to their optimal state of functional, cognitive, and psychologic health. Through collaborative relationships with other perioperative stakeholders, anesthesiologists can consolidate their role as clinical leaders driving value-based care and healthcare transformation in the best interests of patients.

Machine Learning Prediction of Postoperative Emergency Department Hospital Readmission

Background

Although prediction of hospital readmissions has been studied in medical patients, it has received relatively little attention in surgical patient populations. Published predictors require information only available at the moment of discharge. The authors hypothesized that machine learning approaches can be leveraged to accurately predict readmissions in postoperative patients from the emergency department. Further, the authors hypothesize that these approaches can accurately predict the risk of readmission much sooner than hospital discharge.

Methods

Using a cohort of surgical patients at a tertiary care academic medical center, surgical, demographic, lab, medication, care team, and current procedural terminology data were extracted from the electronic health record. The primary outcome was whether there existed a future hospital readmission originating from the emergency department within 30 days of surgery. Secondarily, the time interval from surgery to the prediction was analyzed at 0, 12, 24, 36, 48, and 60 h. Different machine learning models for predicting the primary outcome were evaluated with respect to the area under the receiver-operator characteristic curve metric using different permutations of the available features.

Results

Surgical hospital admissions (N = 34,532) from April 2013 to December 2016 were included in the analysis. Surgical and demographic features led to moderate discrimination for prediction after discharge (area under the curve: 0.74 to 0.76), whereas medication, consulting team, and current procedural terminology features did not improve the discrimination. Lab features improved discrimination, with gradient-boosted trees attaining the best performance (area under the curve: 0.866, SD 0.006). This performance was sustained during temporal validation with 2017 to 2018 data (area under the curve: 0.85 to 0.88). Lastly, the discrimination of the predictions calculated 36 h after surgery (area under the curve: 0.88 to 0.89) nearly matched those from time of discharge.

Conclusions

A machine learning approach to predicting postoperative readmission can produce hospital-specific models for accurately predicting 30-day readmissions via the emergency department. Moreover, these predictions can be confidently calculated at 36 h after surgery without consideration of discharge-level data.

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