Importantly, the traditional medication safety approach also falls short on personalization. It does not consider how an individual patient might react to a medication based on their pharmacogenomic data. Take various opioids like codeine, hydrocodone, oxycodone and tramadol, as examples. These drugs must be transformed by a specific enzyme in the liver to effectively relieve pain. If a patient lacks functional activity (a poor metabolizer) for that enzyme or if other medications they are taking inhibit the opioid’s ability to be transformed, the patient may encounter diminished pain relief. Without this pharmacogenomic insight, the patient may be prescribed codeine for pain relief and then a higher dose when their pain persists. This could lead to unintentional misuse, instances of overdosing and drug abuse.
In lacking precision and personalization, the traditional approach to medication safety has fostered reactive practices. Instead, the new medication safety paradigm should center on proactive analysis. This means understanding how a new prescription, over-the-counter medication, supplement or herbal could impact a patient’s risk for adverse drug events before it gets introduced into their medication regimen. The results would help healthcare providers determine if there might be a better fit for the patient, ultimately avoiding adverse drug events and saving lives.
Today, advanced clinical decision support systems that use algorithms and artificial intelligence are available to support pharmacists in their query of medication safety. Shifting the medication safety paradigm to emphasize precision, personalization and proactive analysis can prevent adverse drug events and help save lives.