Powered by AI: advancing towards artificial intelligence algorithms in Australian hospital pharmacy

Imagine hospitals where clinicians can quickly and accurately identify patients at risk of medication harm and why. This is what artificial intelligence (AI) promises, and it’s closer than we think.

While the past decade brought electronic health records (EHRs) and decision support systems, AI-enabled machine learning (ML) prediction models and large language models have emerged, with the potential to greatly assist clinical decision-making and improve patient outcomes. For example, AI can predict optimal doses of pharmacokinetically complex medications¹ and identify adverse drug reactions among coded discharge data. These new tools can support busy pharmacists by automating tedious tasks and discerning clinical scenarios warranting pharmacist intervention.

This editorial highlights considerations relating to AI/ML technologies applied to medicine management in Australian hospitals, drawing insights from local experience in designing and evaluating a ML dosing algorithm for unfractionated heparin (UFH).

Risk prediction algorithms are common, such as the CHADS-VASc and HAS-BLED scores, developed using conventional statistical (regression) methods. But with the availability of ‘big data’ from EHRs within multiple hospitals, clinician researchers, data scientists, and informaticians can now collaborate to develop more accurate real-time predictive algorithms using AI/ML. Some examples include predicting an individual’s likelihood of a medication-related hospital readmission, suffering a bleed with anticoagulant therapy, or rapid deterioration due to undertreated illness. Detecting and treating these conditions can optimise patient outcomes.

The ultimate question is whether AI tools enable clinicians to work smarter and more efficiently, save healthcare costs, and render patient care more effective and safe. Machines don’t tire and are not influenced by emotions, and they can learn and process vast amounts of information faster and more accurately than humans. But human oversight and judgement remains crucial in ensuring the appropriate design and use of algorithms and monitoring their performance. Machines exist to augment, not usurp, clinician decision-making, empowering pharmacists to focus more on empathic patient interactions, education, and counselling and fostering interprofessional healthcare delivery; integral care components for which no machine can substitute.

The future of hospital pharmacy is undeniably intertwined with the evolution of AI, and we should embrace and lead the agenda in using them as supportive tools to enhance our clinical practice.

The authors declare that they have no conflicts of interest.

Conceptualisation: NF, IS, MB. Investigation: NF. Writing — original draft: NF, IS, MB. Writing — review and editing: NF, IS, MB.

Ethical approval was not required for this editorial as it did not contain any human data or participants.

Not commissioned, not externally peer reviewed.

This editorial received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.