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A coarse classification of medications into two risk categories, one for high-risk medications and one for all others, allows people to focus safety improvement work on medications that carry the highest risks of harm. However, such coarse categorization does not distinguish the relative risk of harm for the majority of medications. To begin to develop a more fine-grained measurement scale for the relative risk of harm spanning many medications, we performed an experiment with 18 practicing pharmacists. Each pharmacist-participant made 210 paired comparisons of 21 commonly prescribed medications to reveal a subjective scale of perceived medication worrisomeness (PMW). Statistical analyses of their collective judgments of medication pairs differentiated five levels of PMW. This study illuminates one path towards a fine-grained medication risk scale based on PMW. It also shows how the method of paired comparisons can be used to remotely crowdsource expert knowledge in support of learning health systems.

Many obstacles must be overcome to generate new biomedical knowledge from real-world data and then directly apply the newly generated knowledge for decision support. Attempts to bridge the processes of data analysis and technical implementation of analytic results reveal a number of gaps. As one example, the knowledge format used to communicate results from data analysis often differs from the knowledge format required by systems to compute advice. We asked whether a shared format could be used by both processes. To address this question, we developed a data-to-advice pipeline called ScriptNumerate. ScriptNumerate analyzes historical e-prescription data and communicates its results in a compound digital object format. ScriptNumerate then uses these same compound digital objects to compute its advice about whether new e-prescriptions have common, rare, or unprecedented instructions. ScriptNumerate demonstrates that data-to-advice pipelines are feasible. In the future, data-to-advice pipelines similar to ScriptNumerate may help support Learning Health Systems.