The Unseen Hand: AI-Based Prescribing Decision Support Tools and the Evaluation of Drug Safety and Effectiveness

Harriet Dickinson*, Dana Y. Teltsch, Jan Feifel, Philip Hunt, Enriqueta Vallejo-Yagüe, Arti V. Virkud, Katoo M. Muylle, Taichi Ochi, Macarius Donneyong, Joseph Zabinski, Victoria Y. Strauss, Juan M. Hincapie-Castillo

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

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The use of artificial intelligence (AI)-based tools to guide prescribing decisions is full of promise and may enhance patient outcomes. These tools can perform actions such as choosing the ‘safest’ medication, choosing between competing medications, promoting de-prescribing or even predicting non-adherence. These tools can exist in a variety of formats; for example, they may be directly integrated into electronic medical records or they may exist in a stand-alone website accessible by a web browser. One potential impact of these tools is that they could manipulate our understanding of the benefit-risk of medicines in the real world. Currently, the benefit risk of approved medications is assessed according to carefully planned agreements covering spontaneous reporting systems and planned surveillance studies. But AI-based tools may limit or even block prescription to high-risk patients or prevent off-label use. The uptake and temporal availability of these tools may be uneven across healthcare systems and geographies, creating artefacts in data that are difficult to account for. It is also hard to estimate the ‘true impact’ that a tool had on a prescribing decision. International borders may also be highly porous to these tools, especially in cases where tools are available over the web. These tools already exist, and their use is likely to increase in the coming years. How they can be accounted for in benefit-risk decisions is yet to be seen.

Original languageEnglish
Pages (from-to)117–123
Number of pages7
JournalDrug Safety
Publication statusPublished - 29-Nov-2023


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