Clinical Decision Support Alerts Research Articles

Optimising interruptive clinical decision support alerts for antithrombotic duplicate prescribing in hospital

IntroductionDuplicate prescribing clinical decision support alerts can prevent important prescribing errors but are frequently the cause of much alert fatigue. Stat dose prescriptions are a known reason for overriding these alerts. This study aimed to evaluate the effect of excluding stat dose prescriptions from duplicate prescribing alerts for antithrombotic medicines on alert burden, prescriber adherence, and prescribing. Materials and MethodsA before (January 1st, 2017 to August 31st, 2022) and after (October 5th, 2022 to September 30th, 2023) study was undertaken of antithrombotic duplicate prescribing alerts and prescribing following a change in alert settings. Alert and prescribing data for antithrombotic medicines were joined, processed, and analysed to compare alert rates, adherence, and prescribing. Alert burden was assessed as alerts per 100 prescriptions. Adherence was measured at the point of the alert as whether the prescriber accepted the alert and following the alert as whether a relevant prescription was ceased within an hour. Co-prescribing of antithrombotic stat dose prescriptions was assessed pre- and post-alert reconfiguration. ResultsReconfiguration of the alerts reduced the alert rate by 29 % (p < 0.001). The proportion of alerts associated with cessation of antithrombotic duplication significantly increased (32.8 % to 44.5 %, p < 0.001). Adherence at the point of the alert increased 1.2 % (4.8 % to 6.0 %, p = 0.012) and 11.5 % (29.4 % to 40.9 %, p < 0.001) within one hour of the alert. When ceased after the alert over 80 % of duplicate prescriptions were ceased within 2 min of overriding. Antithrombotic stat dose co-prescribing was unchanged for 4 out of 5 antithrombotic duplication alert rules. ConclusionBy reconfiguring our antithrombotic duplicate prescribing alerts, we reduced alert burden and increased alert adherence. Many prescribers ceased duplicate prescribing within 2 min of alert override highlighting the importance of incorporating post-alert measures in accurately determining prescriber alert adherence.

Clinical decision support to improve CBC and differential ordering.

Complete blood count and differential (CBC diff) is a common laboratory test that may be overused or misordered, particularly in an inpatient setting. We assessed the ability of a clinical decision support (CDS) alert to decrease unnecessary orders for CBC diff and analyzed its impact in the laboratory. We designed 3 CDS alerts to provide guidance to providers ordering CBC diff on inpatients at frequencies of daily, greater than once daily, or as needed. The 3 alerts were highly effective in reducing orders for CBC diff at the frequencies targeted by the alert. Overall, test volume for CBC diff decreased by 32% (mean of 5257 tests per month) after implementation of the alerts, with a corresponding decrease of 22% in manual differentials performed (mean of 898 per month). Turnaround time for manual differentials decreased by a mean of 41.5 minutes, with a mean decrease of up to 90 minutes during peak morning hours. The 3 CDS alerts successfully decreased inpatient orders for CBC diff and improved the quality of patient care by decreasing turnaround time for manual differentials.

Drug-Drug Interactions and Actual Harm to Hospitalized Patients: A Multicentre Study Examining the Prevalence Pre- and Post-Electronic Medication System Implementation.

Drug-drug interactions (DDIs) have potential to cause patient harm, including lowering therapeutic efficacy. This study aimed to (i) determine the prevalence of potential DDIs (pDDIs); clinically relevant DDIs (cDDIs), that is, DDIs that could lead to patient harm, taking into account a patient's individual clinical profile, drug effects and severity of potential harmful outcome; and subsequent actual harm among hospitalized patients and (ii) examine the impact of transitioning from paper-based medication charts to electronic medication management (eMM) on DDIs and patient harms. This was a secondary analysis of the control arm of a controlled pre-post study. Patients were randomly selected from three Australian hospitals. Retrospective chart review was conducted before and after the implementation of an eMM system, without accompanying clinical decision support alerts for DDIs. Harm was assessed by an expert panel. Of 1186 patient admissions, 70.1% (n = 831) experienced a pDDI, 42.6% (n = 505) a cDDI and 0.9% (n = 11) an actual harm in hospital. Of 15,860 pDDIs identified, 27.0% (n = 4285) were classified as cDDIs. The median number of pDDIs and cDDIs per 10 drugs were 6 [interquartile range (IQR) 2-13] and 0 (IQR 0-2), respectively. In cases where a cDDI was identified, both drugs were 44% less likely to be co-administered following eMM (adjusted odds ratio 0.56, 95% confidence interval 0.46-0.73). Although most patients experienced a pDDI during their hospital stay, less than one-third of pDDIs were clinically relevant. The low prevalence of harm identified raises questions about the value of incorporating DDI decision support into systems given the potential negative impacts of DDI alerts.

Why do users override alerts? Utilizing large language model to summarize comments and optimize clinical decision support.

To evaluate the capability of using generative artificial intelligence (AI) in summarizing alert comments and to determine if the AI-generated summary could be used to improve clinical decision support (CDS) alerts. We extracted user comments to alerts generated from September 1, 2022 to September 1, 2023 at Vanderbilt University Medical Center. For a subset of 8 alerts, comment summaries were generated independently by 2 physicians and then separately by GPT-4. We surveyed 5 CDS experts to rate the human-generated and AI-generated summaries on a scale from 1 (strongly disagree) to 5 (strongly agree) for the 4 metrics: clarity, completeness, accuracy, and usefulness. Five CDS experts participated in the survey. A total of 16 human-generated summaries and 8 AI-generated summaries were assessed. Among the top 8 rated summaries, five were generated by GPT-4. AI-generated summaries demonstrated high levels of clarity, accuracy, and usefulness, similar to the human-generated summaries. Moreover, AI-generated summaries exhibited significantly higher completeness and usefulness compared to the human-generated summaries (AI: 3.4 ± 1.2, human: 2.7 ± 1.2, P = .001). End-user comments provide clinicians' immediate feedback to CDS alerts and can serve as a direct and valuable data resource for improving CDS delivery. Traditionally, these comments may not be considered in the CDS review process due to their unstructured nature, large volume, and the presence of redundant or irrelevant content. Our study demonstrates that GPT-4 is capable of distilling these comments into summaries characterized by high clarity, accuracy, and completeness. AI-generated summaries are equivalent and potentially better than human-generated summaries. These AI-generated summaries could provide CDS experts with a novel means of reviewing user comments to rapidly optimize CDS alerts both online and offline.

Qualitative study of challenges with recruitment of hospitals into a cluster controlled trial of clinical decision support in Australia

ObjectiveTo identify barriers to hospital participation in controlled cluster trials of clinical decision support (CDS) and potential strategies for addressing barriers.DesignQualitative descriptive design comprising semistructured interviews.SettingFive hospitals in New South...

Resident-Driven Clinical Decision Support Governance to Improve the Utility of Clinical Decision Support.

This resident-driven quality improvement project aimed to better understand the known problem of a misaligned clinical decision support (CDS) strategy and improve CDS utilization. An internal survey was sent to all internal medicine (IM) residents to identify the most bothersome CDS alerts. Survey results were supported by electronic health record (EHR) data of CDS firing rates and response rates which were collected for each of the three most bothersome CDS tools. Changes to firing criteria were created to increase utilization and to better align with the five rights of CDS. Findings and proposed changes were presented to our institution's CDS Governance Committee. Changes were approved and implemented. Postintervention firing rates were then collected for 1 week. Twenty nine residents participated in the CDS survey and identified sepsis alerts, lipid profile reminders, and telemetry renewals to be the most bothersome alerts. EHR data showed action rates for these CDS as low as 1%. We implemented changes to focus emergency department (ED)-based sepsis alerts to the right provider, better address the right information for lipid profile reminders, and select the right time in workflow for telemetry renewals to be most effective. With these changes we successfully eliminated ED-based sepsis CDS reminders for IM providers, saw a 97% reduction in firing rates for the lipid profile CDS, and noted a 55% reduction in firing rates for telemetry CDS. This project highlighted that alert improvements spearheaded by resident teams can be completed successfully using robust CDS governance strategies and can effectively optimize interruptive alerts.

Leveraging explainable artificial intelligence to optimize clinical decision support.

To develop and evaluate a data-driven process to generate suggestions for improving alert criteria using explainable artificial intelligence (XAI) approaches. We extracted data on alerts generated from January 1, 2019 to December 31, 2020, at Vanderbilt University Medical Center. We developed machine learning models to predict user responses to alerts. We applied XAI techniques to generate global explanations and local explanations. We evaluated the generated suggestions by comparing with alert's historical change logs and stakeholder interviews. Suggestions that either matched (or partially matched) changes already made to the alert or were considered clinically correct were classified as helpful. The final dataset included 2 991 823 firings with 2689 features. Among the 5 machine learning models, the LightGBM model achieved the highest Area under the ROC Curve: 0.919 [0.918, 0.920]. We identified 96 helpful suggestions. A total of 278 807 firings (9.3%) could have been eliminated. Some of the suggestions also revealed workflow and education issues. We developed a data-driven process to generate suggestions for improving alert criteria using XAI techniques. Our approach could identify improvements regarding clinical decision support (CDS) that might be overlooked or delayed in manual reviews. It also unveils a secondary purpose for the XAI: to improve quality by discovering scenarios where CDS alerts are not accepted due to workflow, education, or staffing issues.

Detecting atypical alert behavior through statistical process control: Clinical decision support alert frequency visualizations.

Clinical decision support (CDS) alerts are designed to work according to a set of clearly defined criteria and have the potential to improve clinical care. To efficiently and proactively review abnormally functioning CDS alerts, we postulate that the introduction of a dashboard with statistical process control (SPC) charting will lead to effective detection of erratic alert behavior. We identified custom CDS alerts from an academic medical center that were recorded and monitored in a longitudinal fashion and the data warehouses where this information was stored. We created a dashboard of alert frequency using SPC charts, applied SPC rules for classification of variation, and validated dashboard data. From June-August 2022, the dashboard effectively pulled in data to visually depict alert behavior. SPC-defined parameters for standard deviation from the mean were applied to visualizations and allowed for rapid review of alerts with greatest variation. These alerts were subsequently investigated, and it was determined that they were functioning correctly. The most profound abnormalities detected during implementation reflected changes in practice and not system errors, though further investigation into thresholds for statistical significance will benefit this field. We conclude that SPC visualizations are a time-efficient and effective method of identifying CDS malfunctions.

Impact of a Clinical Decision Support Alert on Informed Consent Documentation in the Neonatal Intensive Care Unit.

Informed consent is necessary to preserve patient autonomy and shared decision-making, yet compliant consent documentation is suboptimal in the intensive care unit (ICU). We aimed to increase compliance with bundled consent documentation, which provides consent for a predefined set of common procedures in the neonatal ICU from 0% to 50% over 1 year. We used the Plan-Do-Study-Act model for quality improvement. Interventions included education and performance awareness, delineation of the preferred consenting process, consent form revision, overlay tool creation, and clinical decision support (CDS) alert use within the electronic health record. Monthly audits categorized consent forms as missing, present but noncompliant, or compliant. We analyzed consent compliance on a run chart using standard run chart interpretation rules and obtained feedback on the CDS as a countermeasure. We conducted 564 audits over 37 months. Overall, median consent compliance increased from 0% to 86.6%. Upon initiating the CDS alert, we observed the highest monthly compliance of 93.3%, followed by a decrease to 33.3% with an inadvertent discontinuation of the CDS. Compliance subsequently increased to 73.3% after the restoration of the alert. We created a consultant opt-out selection to address negative feedback associated with CDS. There were no missing consent forms within the last 7 months of monitoring. A multi-faceted approach led to sustained improvement in bundled consent documentation compliance in our neonatal intensive care unit, with the direct contribution of the CDS observed. A CDS intervention directed at the informed consenting process may similarly benefit other ICUs.

Opioid Prescribing Rate for Nonoperative Distal Radius Fractures and Clinician Response to a Clinical Decision Support Alert

BackgroundOpioids are commonly prescribed for the management of acute orthopedic trauma pain, including nonoperative distal radius fractures. ObjectivesThis prospective study aimed to determine if a clinical decision support intervention influenced prescribing decisions for patients with known risk factors. We sought to quantify frequency of opioid prescriptions for acute nonoperative distal radius fractures treated. MethodsWe performed a prospective study at one large health care system. Utilizing umbrella code S52.5, we identified all distal radius fractures treated nonoperatively, and the encounters were merged with the Prescription Reporting with Immediate Medication Mapping (PRIMUM) database to identify encounters with opioid prescriptions and patients with risk factors for opioid use disorder. We used multivariable logistic regression to determine patient characteristics associated with the prescription of an opioid. Among encounters that triggered the PRIMUM alert, we calculated the percentage of encounters where the PRIMUM alert influenced the prescribing decision. ResultsOf 2984 encounters, 1244 (41.7%) included an opioid prescription. Age increment is a significant factor to more likely receive opioid prescriptions (p < 0.0001) after adjusting for other factors. Among encounters where the physician received an alert, those that triggered the alert for early refill were more likely to influence physicians’ opioid prescribing when compared with other risk factors (p = 0.0088). ConclusionOver 90% of patients (106/118) continued to receive an opioid medication despite having a known risk factor for abuse. Additionally, we found older patients were more likely to be prescribed opioids for nonoperatively managed distal radius fractures.

Abstract 13433: Outcomes of Chest Pain Discharge Decisions Following Launch of a Heart Score Clinical Decision Support Alert

Background: The use of a bioclinical risk score such as the HEART score is recommended for the evaluation of pts presenting to the ED with chest pain (CP). Prior studies have demonstrated that CP pts with HEART score &lt; 3 and “ruled out” with cardiac troponin series are low risk for 30d death/MI (MACE). There is limited data regarding whether the addition of the HEART score to a validated high sensitivity troponin accelerated diagnostic protocol (ADP) has beneficial effects on CP patient discharge decisions in the ED. Research Question: Did the addition of an EHR-embedded automated HEART score clinical decision support alert (CDSA) have a beneficial effect on discharge decision making of ED CP pts in Geisinger Health System? Methods: Retrospective analysis of 30day MACE following ED visit for CP for the 6mo period prior to and 10mo following the launch of a HEART score CDSA (11/2021-3/2023). Discharges were stratified using a previously validated accelerated hsTnT ADP into those that ruled in (ADP+) or ruled out (ADP-). Comparison of MACE in pre- vs. post-CDSA periods was performed using chi square analysis. Results: MACE stratified by study period and ADP results are summarized in the Table. There were no significant differences in 30-day MACE pre- vs. post-CDSA, both overall and in ADP+ and ADP- subgroups. In contrast, the presence or absence of a high risk (&gt;3) HEART score in isolation did significantly predict the risk of MACE [11/2360 (0.47%) for HEART score &lt; 3 vs 36/966 (4.04%) for HEART score &gt;3, p&lt;0.001]. Conclusions: Addition of an EHR-embedded HEART Score CDSA did not have a significant effect on 30d outcomes following ED provider discharge decisions. While the HEART score by itself did stratify risk of 30d MACE, our study suggests the additive benefit of the HS CDSA to clinical decision making based on an established hsTnT ADP unclear.

Clinician Response to Pharmacogenetic Clinical Decision Support Alerts.

The objective of this study was to characterize clinician response following standardization of pharmacogenetic (PGx) clinical decision support alerts at University of Florida (UF) Health. A retrospective analysis of all PGx alerts that fired at a tertiary academic medical center from August 2020 through May 2022 was performed. Alert acceptance rate was calculated and compared across six gene-drug pairs, patient care setting, and clinician specialty. The disposition of the triggering medication was compared with the alert response and evaluated for congruence. There were a total of 818 alerts included for analysis of alert response, 557 alerts included in acceptance rate calculations, and 392 alerts with sufficient information to assess clinical response. The overall acceptance rate was 63%. The alert response and clinical response were congruent for 47% of alerts. Alert response was influenced by the triggering gene-drug pair, clinician specialty, patient care setting, and specialty of the provider who initially ordered the PGx test. Clinical response was mostly incongruent with alert response. Alert acceptance is influenced by the triggering gene-drug pair, clinician specialty, and care setting. Alert response is not a reliable surrogate marker for clinical action. Any future research into the impact of clinical decision support (CDS) alerts should focus on clinical response rather than alert response. Given the reliance on CDS alerts to enhance uptake of PGx, it is worthwhile to further investigate their impact on prescribing and patient outcomes.

Impact of Predictive Text Clinical Decision Support on Imaging Order Entry in the Emergency Department

PurposeImaging clinical decision support (CDS) is designed to assist providers in selecting appropriate imaging studies and is now federally required. The aim of this study was to understand the effect of CDS on decisions and workflows in the emergency department (ED). MethodsThe authors’ institution’s order entry platform serves up structured indications for imaging orders. Imaging orders are scored by CDS on the basis of appropriate use criteria (AUC). CDS triggers alerts for imaging orders with low AUC scores. Because free text alone cannot be scored by CDS, an artificial intelligence predictive text (AIPT) module was implemented to guide the selection of structured indications when free-text indications are entered. A total of 17,355 imaging orders in the ED over 6 months were retrospectively analyzed. ResultsCDS alerts for low AUC scores were triggered for 3% of all imaging study orders (522 of 17,355). Providers spent an average of 24 seconds interacting with alerts. In 18 of 522 imaging orders with alerts, alternative studies were ordered. After AIPT implementation, the percentage of unscored studies significantly decreased from 81% to 45% (P < .001). ConclusionsIn a quaternary academic ED, CDS alerts triggered by low AUC scores caused minimal increase in time spent on imaging order entry but had a relatively marginal impact on imaging study selection. AIPT implementation increased the number of scored studies and could potentially enhance CDS effects. CDS implementation enables the collection of novel data regarding which imaging studies receive low AUC scores. Future work could include exploring alternative models of CDS implementation to maximize its impact.

Implementation of Electronic Health Record Integration and Clinical Decision Support to Improve Emergency Department Prescription Drug Monitoring Program Use

To evaluate the implementation of 3 electronic health record (EHR)-based interventions to increase prescription drug monitoring program (PDMP) use in the emergency department (ED): EHR-PDMP integration, addition of a PDMP risk score, and addition of EHR-based clinical decision support alert to review the PDMP when prescribing an opioid. Three intervention stages were implemented using a prospective stepped-wedge design at 5 university-affiliated EDs split into 3 practice groups. The PDMP use and prescribing rates during the 3 stages were compared with baseline before EHR integration and a sustainability stage where the clinical decision support alert was removed, but EHR integration and risk score remained. Generalized linear mixed model with logit link function and a random intercept for clinicians was analyzed. The ED provider PDMP review before opioid prescribing was low in all stages. The highest review rate occurred during interruptive clinical decision support alerts, 23.8% (interquartile range 10.6 to 37.5). Overall, opioid prescribing declined, and PDMP review was not associated with a decrease in opioid prescribing. PDMP review was associated with a reduction in the probability of prescribing an opioid as the number of prior opioid prescriptions increased (odds ratio: 0.92 [95% confidence interval: 0.91 to 0.94] for every additional prescription). The EHR-PDMP integration did not increase PDMP use in the ED, but a PDMP risk score and a clinical decision support alert were associated with modest increases in the probability of PDMP review. When the PDMP is reviewed, ED clinicians are less likely to prescribe opioids to patients with a high number of prior opioid prescriptions.

Optimizing Best Practice Advisory alerts in electronic medical records with a multi-pronged strategy at a tertiary care hospital in Singapore.

Clinical decision support (CDS) alerts can aid in improving patient care. One CDS functionality is the Best Practice Advisory (BPA) alert notification system, wherein BPA alerts are automated alerts embedded in the hospital's electronic medical records (EMR). However, excessive alerts can change clinician behavior; redundant and repetitive alerts can contribute to alert fatigue. Alerts can be optimized through a multipronged strategy. Our study aims to describe these strategies adopted and evaluate the resultant BPA alert optimization outcomes. This retrospective single-center study was done at Jurong Health Campus. Aggregated, anonymized data on patient demographics and alert statistics were collected from January 1, 2018 to December 31, 2021. "Preintervention" period was January 1-December 31, 2018, and "postintervention" period was January 1-December 31, 2021. The intervention period was the intervening period. Categorical variables were reported as frequencies and proportions and compared using the chi-square test. Continuous data were reported as median (interquartile range, IQR) and compared using the Wilcoxon rank-sum test. Statistical significance was defined at P < .05. There was a significant reduction of 59.6% in the total number of interruptive BPA alerts, despite an increase in the number of unique BPAs from 54 to 360 from pre- to postintervention. There was a 74% reduction in the number of alerts from the 7 BPAs that were optimized from the pre- to postintervention period. There was a significant increase in percentage of overall interruptive BPA alerts with action taken (8 [IQR 7.7-8.4] to 54.7 [IQR 52.5-58.9], P-value < .05) and optimized BPAs with action taken (32.6 [IQR 32.3-32.9] to 72.6 [IQR 64.3-73.4], P-value < .05). We estimate that the reduction in alerts saved 3600 h of providers' time per year. A significant reduction in interruptive alert volume, and a significant increase in action taken rates despite manifold increase in the number of unique BPAs could be achieved through concentrated efforts focusing on governance, data review, and visualization using a system-embedded tool, combined with the CDS Five Rights framework, to optimize alerts. Improved alert compliance was likely multifactorial-due to decreased repeated alert firing for the same patient; better awareness due to stakeholders' involvement; and less fatigue since unnecessary alerts were removed. Future studies should prospectively focus on patients' clinical chart reviews to assess downstream effects of various actions taken, identify any possibility of harm, and collect end-user feedback regarding the utility of alerts.

Refining Clinician Workflow as a Means to Improving Catheter Quality Measures

Abstract Objective This study aimed to improve the quality measure performance for indwelling urinary catheter (IUC) duration, central venous catheter (CVC) duration, and telemetry duration by redesigning clinical decision support (CDS) tools within the documentation process and order workflow. Methods The effectiveness of the redesign was evaluated using system standard quality reporting methodology to observe device duration, central-line-associated bloodstream infection (CLABSI) rate, and catheter-associated urinary tract infection (CAUTI) rate preintervention (FY2017) and postintervention (FY2018). Electronic health record (EHR) reporting tools were used to evaluate CDS alert data both preintervention and postintervention. Results Total device duration and line days per patient days were reduced for CVC (12.8% [0.305–0.266]) and IUC (4.68% [0.171–0.163]). Mean telemetry duration was reduced by 16.94% (3.72–3.09 days), and CDS alert volume decreased 18.6% from a preintervention mean of 1.18 alerts per patient per day (81,190 total alerts) to a postintervention mean of 0.96 alerts per patient per day (61,899 total alerts). Both CLABSI (2.8% [1.07–1.04]) and CAUTI (8.1% [1.61–1.48]) rates were reduced, resulting in approximately $926,000 in savings. Conclusion In this novel model, the redesigned CDS tools improved clinician response to CDS alerts, prompting providers to take action on relevant orders that automatically updated the clinical documentation to reflect their actions. The study demonstrated that effective redesign of CDS tools within the documentation process and order workflow can reduce device duration, improve patient outcomes, and decrease CDS alert volume.

Congruence rates for pharmacogenomic noninterruptive alerts.

Meaningful clinical decision support (CDS) recommendations are vital for implementation of pharmacogenomics (PGx) into routine clinical care. PGx CDS alerts include interruptive and noninterruptive alerts. The objective of this study was to evaluate provider ordering behavior after noninterruptive alerts are displayed. A retrospective manual chart review was conducted from the time of noninterruptive alert implementation to the time of data analysis to determine congruence with CDS recommendations. The congruence rate for noninterruptive alerts was 89.8% across all drug-gene interactions. The drug-gene interaction with the most alerts for analysis included metoclopramide (n = 138). The high rate of medication order congruence after noninterruptive alerts were deployed suggests this modality may be appropriate for PGx CDS as a method for best practice adherence.

Implementation of Clinical Cytochrome P450 3A Genotyping for Tacrolimus Dosing in a Large Kidney Transplant Program.

Tacrolimus is a calcineurin inhibitor with a narrow therapeutic range and is metabolized by cytochrome P450 (CYP) isoenzymes CYP3A4 and CYP3A5. The Clinical Pharmacogenetic Implementation Consortium published evidence-based guidelines for CYP3A5 normal/intermediate metabolizers prescribed tacrolimus, yet few transplant centers have implemented routine testing. The objective of this study was to implement preemptive CYP3A genotyping into clinical practice in a large kidney transplant program and to evaluate workflow feasibility, potential clinical benefit, and reimbursement to identify barriers and determine sustainability. Preemptive pharmacogenetic testing for CYP3A5 and CYP3A4 was implemented in all patients listed for a kidney transplant as part of standard clinical care. Genotyping was performed at the listing appointment, results were reported as discrete data in the electronic medical record, and education and clinical decision support alerts were developed to provide pharmacogenetic-recommended tacrolimus dosing. During this initial phase, all patients were administered standard tacrolimus dosing, and clinical and reimbursement outcomes were collected. Greater than 99.5% of genotyping claims were reimbursed by third-party payers. CYP3A5 normal/intermediate metabolizers had significantly fewer tacrolimus trough concentrations within the target range and a significantly longer time to their first therapeutic trough compared to poor metabolizers. The challenge of tacrolimus dosing is magnified in the African American population. The US Food and Drug Administration drug label recommends increased starting doses in African ancestry, yet only ≈66% of African Americans in our cohort were normal/intermediate metabolizers who required higher doses. Routine CYP3A5 genotyping may overcome this issue by using genotype over race as a more accurate predictor of drug response.

Impact of transitioning to an active, noninterruptive CYP2C19/proton pump inhibitor alert on prescribing patterns.

To compare rates of prescriber acceptance of interruptive and noninterruptive clinical decision support (CDS) alerts regarding potential diminished therapeutic effectiveness and safety risks associated with proton pump inhibitor (PPI) use in carriers of gene variants affecting cytochrome P450 (CYP) isozyme 2C19 metabolism. A retrospective study was conducted at a large rural health system to examine different approaches to improving CDS alert acceptance while minimizing alert fatigue. Manual reviews were conducted to identify alerts regarding CYP2C19 metabolizer status displayed at the time of PPI ordering over 30-day periods before and after the transition from interruptive to noninterruptive CDS alert functionality. A chi-square test was conducted to analyze prescriber acceptance of CDS recommendations by alert modality and type of treatment modification. Overall, interruptive alerts had an acceptance rate of 18.6% (64/344), compared to 8.4% acceptance (30/357 alerts) for noninterruptive alerts (P ≤ 0.0001). Analysis of acceptance criteria -revealed the noninterruptive alert cohort had higher acceptance, as determined by documented medication dose adjustments, than the interruptive alert cohort (53.3% [16/30] and 4.7% [3/64], respectively). The difference in acceptance rates by CDS modality and treatment modification was statistically significant (P ≤ 0.00001). The predominant indication for PPI use was gastroesophageal reflux disease (GERD) in both cohorts. Interruptive alerts that actively influenced workflow had higher acceptance rates than noninterruptive alerts that served an informational purpose without a direct disruption of workflow. The study results suggest the utilization of noninterruptive alerts may be a beneficial tool for prompting clinicians to alter dosing regimens rather than transition to an alternative agent.

Project SPRUCE: Saving Our Planet by Reducing Carbon Emissions, a Pediatric Anesthesia Sustainability Quality Improvement Initiative.

Children are particularly vulnerable to adverse health outcomes related to climate change. Inhalational anesthetics are potent greenhouse gasses (GHGs) and contribute significantly to health care-generated emissions. Desflurane and nitrous oxide have very high global warming potentials. Eliminating their use, as well as lowering fresh gas flows (FGFs), will lead to reduced emissions. Using published calculations for converting volatile anesthetic concentrations to carbon dioxide equivalents (CO 2 e), we derived the average kilograms (kg) CO 2 e/min for every anesthetic administered in the operating rooms at our pediatric hospital and ambulatory surgical center between October 2017 and October 2022. We leveraged real-world data captured from our electronic medical record systems and used AdaptX to extract and present those data as statistical process control (SPC) charts. We implemented recommended strategies aimed at reducing emissions from inhalational anesthetics, including removing desflurane vaporizers, unplugging nitrous oxide hoses, decreasing the default anesthesia machine FGF, clinical decision support tools, and educational initiatives. Our primary outcome measure was average kg CO 2 e/min. A combination of educational initiatives, practice constraints, protocol changes, and access to real-world data were associated with an 87% reduction in measured GHG emissions from inhaled anesthesia agents used in the operating rooms over a 5-year period. Shorter cases (<30 minutes duration) had 3 times higher average CO 2 e, likely due to higher FGF and nitrous oxide use associated with inhalational inductions, and higher proportion of mask-only anesthetics. Removing desflurane vaporizers corresponded with a >50% reduction of CO 2 e. A subsequent decrease in anesthesia machine default FGF was associated with a similarly robust emissions reduction. Another significant decrease in emissions was noted with educational efforts, clinical decision support alerts, and feedback from real-time data. Providing environmentally responsible anesthesia in a pediatric setting is a challenging but achievable goal, and it is imperative to help mitigate the impact of climate change. Large systems changes, such as eliminating desflurane, limiting access to nitrous oxide, and changing default anesthesia machine FGF rates, were associated with rapid and lasting emissions reduction. Measuring and reporting GHG emissions from volatile anesthetics allows practitioners to explore and implement methods of decreasing the environmental impact of their individual anesthesia delivery practices.