Maximize the use of barcode verification prior to medication and vaccine administration.
Layer strategies throughout the medication-use process to improve safety with high-alert medications.
Because of the ability of artificial intelligence (AI) technology to process large volumes of data and provide patient-specific insights, it can help to reduce oxytocin errors and improve the clinical relevance of high-alert medications. Read on to learn how MedAware’s AI-enabled medication safety monitoring platform supports these two best practices.
Rule Recap: The core of this best practice is standardization of order sets, including doses, concentration, and rates. It also recommends aligning oxytocin infusions with the smart infusion pump dose error-reduction system.
Learnings: To achieve the level of order set standardization and connected device alignment proposed, significant EHR build and configuration will be required. Ensuring all patients are protected will still pose a risk—especially as their conditions change before, during, and after childbirth.
Workflow Implications: Without any new EHR build required, MedAware embeds clinically relevant, patient-specific insights within the existing ordering/prescribing, order review and verification, dispensing, and clinical monitoring workflow. This lends an added layer of safety and ensures oxytocin dosage and form are still appropriate as treatment evolves. In the case of an erroneous order, MedAware can trigger an alert to allow a dose or form correction before the patient is harmed.
A great demonstration of how MedAware is safeguarding infusion dosing is our work with Baxter International. Our collaboration highlights how leveraging smart infusion pumps that have MedAware’s technology embedded within can identify pump programming errors at the individual patient level. In doing so, MedAware’s technology is able to catch errors legacy systems often miss due to their lack of patient specificity.
Rule recap: This best practice highlights the need to layer numerous strategies throughout the medication-use process to improve the safety of high-alert medications. It recommends replacing low-leverage risk reduction strategies with higher-leverage tools that can address various stages of medication usage—including prescribing, dispensing, and monitoring.
Learnings: Legacy, rules-based clinical decision support applications are low-leverage tools. Their inability to incorporate more sophisticated logic that considers the patient context drives high alert fatigue and high provider burnout. As a result, these tools are unable to effectively prevent adverse drug events from prescribing errors with high-alert medications such as insulin, opiates, and narcotics.
Workflow Implications: MedAware’s technology reduces the volume of false medication-related alerts by applying a patient-specific logic to its rule architecture. In turn, MedAware is able to suppress the inaccurate, unnecessary alerts and only show ones that are clinically relevant. These alerts can be surfaced to care team members within workflow at the point of ordering, order review and verification, dispensing, and during clinical monitoring to ensure there’s a medication safety net in place at every step of care. If a dangerous medication-related event is indicated as imminent, MedAware pings the care team member within workflow to ensure intervention is possible.