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.