Individualized Drug Interaction Alerts

Electronic prescribing and pharmacy systems currently include alerts for drug-drug interactions (DDIs) as a form of clinical decision support (CDS) to warn prescribers and pharmacists of potentially harmful medication combinations. However, current CDS for DDIs is poorly designed, resulting in excessive non-clinically relevant alerts, alert fatigue, and the perception that these warnings are irrelevant and unhelpful. Current CDS relies on simple drug combination rules and ignores drug attributes and patient-specific information available in the electronic health record (EHR). Existing software fails to incorporate factors that influence the risk of an adverse drug reaction, such as the dose, route of administration, duration of therapy, and concomitant therapies. The simplistic logic of these systems also ignores patient-specific characteristics that influence an individual’s susceptibility to adverse drug reactions, such as genetics, age, and renal function. The lack of specificity has resulted in clinicians being inundated with interaction alerts that are irrelevant, leading to widespread desensitization to DDI warnings.

Drug knowledgebases (KBs) provide clinical drug codes and can be utilized to improve DDI checking. This project developed and pilot tested a DDI KB and algorithms to determine whether warnings about DDIs are relevant to a given patient. Researchers identified patient-specific modifiers and drug specific attributes that can be used to influence if a DDI alert is provided to a prescriber or pharmacist. They also examined DDI alerts that are commonly ignored or overridden by healthcare providers and whether clinical algorithms could reduce these alerts.

The specific aims of the project were as follows:

• Prototype a rules-based DDI KB with attributes necessary for patient-specific and contextual alerting. 
• Develop clinical algorithms that extract and use data from an existing commercially available EHR system and integrate with the DDI KB. 
• Conduct a validation of clinical algorithms using simulated and actual patient data. 

Investigators identified the most frequently overridden DDI warnings from a large tertiary care medical center and studied the drug combinations where contextual factors could eliminate the need to warn of potential harm. Data were extracted from EHRs and used to determine the reduction in alerts by retrospectively comparing the number of alerts generated with and without the clinical algorithms. Researchers found that for many of the drug combinations, implementing the algorithms would lead to a substantial reduction in the number of warnings without placing patients at harm with a positive predictive value ranging from 1.5 percent to 100 percent.

Applying specific alerting algorithms may lead to fewer overrides of clinically relevant, thereby reducing exposure to potentially harmful combinations. While the creation of a DDI-specific KB for medications is challenging, creating and implementing algorithms for specific drug combinations is feasible. Results from this study can inform future projects and research to broaden the development of drug- and patient-specific DDI alerting to increase the specificity of warnings.