What is the current knowledge on the topic?
To identify patients at high risk of PEs and, thus, medication-related harm, a sensitive and specific prediction tool is needed to assign scarce time and resources. None of the currently available prediction tools are optimal for use in identifying adult hospitalized patients at risk for medication-related harm.
What question did this study address? Can doctors on clinical wards effectively identify patients at risk for medication-related harm as a result of PEs?
What does this study add to our knowledge? Doctors on clinical wards can effectively identify patients at risk of medication-related harm as a result of PEs. This may be a new and interesting selecting strategy for targeted PE-mitigating interventions.
How might this change clinical pharmacology or translational science? Prediction tools should be easy, user-friendly and accurate. The development of any prediction tools, including AI-driven ones, requires the collection, interpretion, and statistical analysis of clinical data, which is a complex, labor-intensive, and time-consuming endeavor. So perhaps there is power in simplicity. This simple prediction strategy uses ward doctors as a predictive indicator, and is based on a doctor’s clinical assessment, experience, and expectations. This may provide an easier, user-friendly, and accessible solution for predicting patients at risk for in-hospital PEs.