Physicians' behaviors are nearly all AI needs to head off faulty drug prescriptions
Many errors in drug ordering are caused or worsened by the intricacies of the electronic health records (EHR). These problems often stem from mistakes made at the point of the clinician–computer interface. To avert the resulting risk, researchers have designed an AI-based system that can flag errors going just by the ordering clinician’s behavior and the context in which it occurs. The innovation may not only reduce prescription-drug errors but also relieve pharmacists’ workloads while better ensuring patient privacy and security, the team suggests.
To build and test their system, a team led by Martina Balestra, a member of NYU Tandon’s Center for Urban Science and Progress, and including Oded Nov, Chair of the Department of Technology Management and Innovation at NYU Tandon, collected data on drug prescribers’ actions over a two-week period at an academic medical center. They built a machine learning classification model to predict drug orders questionable enough that they needed a pharmacist to stop and examine.
The team notes that conventional EHR drug alerting has entailed reviewing drug orders alongside the patient’s medical records. The shortcoming in this approach, they maintain, is its lack of attention to the EHR’s inherent vulnerabilities to error and how these potential fail points may show up in the orderer’s actions.