Through our eyes: reflections on artificial intelligence in hospital pharmacy
Ella Shearing
BPharm (Hons) | Clinical Pharmacist, Concord Repatriation General Hospital | Ellena.Shearing@health.nsw.gov.au
[Pharmacy GRIT article no: 20251417]
I first noticed artificial intelligence (AI) creeping into healthcare conversations during lunch breaks. It became a buzzword at conferences and came up in chats with friends in non-healthcare industries. It felt as exciting as it did daunting.
Despite AI’s growing presence in healthcare discourse, many frontline pharmacists still feel disconnected from its practical application. A clear divide is emerging between early adopters and those hesitant about change, making meaningful integration challenging. This is compounded by the absence of clear guidelines on what AI is suitable for, raising concerns about safety, security and accuracy. AI in healthcare could save lives and money — but change won’t happen overnight.
To explore this further, I undertook a project focusing on the use of AI tools in our hospital. In this piece, AI tools refers to generative models like ChatGPT (OpenAI, San Francisco, CA, USA) and CoPilot (Microsoft Corp., Redmond, WA, USA).1 I conducted an internal survey of 20 hospital pharmacists at a tertiary Sydney teaching hospital (March–April 2025) aiming to explore their thoughts regarding the use of AI in clinical practice. The survey included specific questions regarding previous experience with AI tools (both personal and occupational use), potential applications in the workplace and perceived limitations or concerns for the use of AI tools.
As an early career pharmacist, this was my first time leading a department-wide survey. Inspired by a Dutch study on ChatGPT use in community pharmacy,2 I designed the survey in Microsoft Forms (Microsoft Corp., Redmond, WA, USA), refined it with senior colleagues, and gained approval from the Director of Pharmacy. Responses from all participants were kept anonymous. A key challenge in undertaking this research was trying to encourage busy colleagues to participate. I found that existing rapport helped with engagement and direct one-on-one guidance through the survey questions guided survey completion. Liaising with senior pharmacists to promote the survey in department-wide meetings also assisted in the project's visibility and thus increased survey engagement.
This process deepened my appreciation for workplace research and the value of qualitative data to capture real perceptions. It highlighted the importance of bringing frontline voices into discussions about digital innovation.
Looking ahead, I hope to present these findings to pharmacy leadership and IT teams to support safe, ethical, and practical integration of AI into workflows. There is also a need for targeted education to build AI literacy and confidence.
At present, we only have access to freely available large language models, limiting what can be done safely. Data safety refers to ensuring the privacy of patient information, but also reliability of clinical information in which clinicians may use in decision making. Access to an enterprise-backed, purpose-built healthcare AI model in the future could transform practice more meaningfully.
This project reminded me that shaping the future of pharmacy isn’t just about adopting new tools: it’s about listening to those expected to use them. Policymakers and leaders need these perspectives to create frameworks that are practical, supportive and truly improve patient care.
References
- Yu P, Xu H, Hu X, Deng C. Leveraging generative AI and large language models: a comprehensive roadmap for healthcare integration. Healthcare (Basel) 2023; 11: 2776.
- de Ruiter EJ, Eimermann VM, Rijcken C, Taxis K, Borgsteede SD. The extent and type of use, opportunities and concerns of ChatGPT in community pharmacy: a survey of community pharmacy staff. Explor Res Clin Soc Pharm 2025; 17: 100575.