Clinical Informatics Pharmacist: PSSNY Summer Convention 2026
Target Audience
All pharmacists
Learning Objectives
At the completion of the activity viewers will be able to;
Describe the current and emerging landscape of AI in pharmacy practice and identify areas where AI is likely to influence future pharmacy workflows.
State high-level factors that influence the race-to-value when deciding whether to buy, build, or pilot AI solutions in pharmacy, including basic considerations for prioritization and governance.
Define artificial intelligence and large language models in practical terms relevant to pharmacists, technicians, and pharmacy staff.
Explain how large language models generate outputs and demonstrate why understanding their strengths and limitations improves safe and effective prompting.
State the basic prompting techniques with large language models to improve efficiency, clarity, and usefulness for pharmacy-related tasks.
Duncan X. Dobbins, PharmD, BSPF, MHI, is a fourth-generation pharmacist on his father’s side and a third-generation pharmacist on his mother’s side. He graduated from Duquesne University with his Doctor of Pharmacy and Bachelor of Science in Pharmacy Foundations, where he also completed biopharmaceutical research. He then completed a two-year postdoctoral fellowship in Health System Science at Geisinger’s Center for Pharmacy Innovations and Outcomes while earning his Master of Health Informatics from Wake Forest University. Duncan currently serves as a clinical informatics pharmacist overseeing pharmacy AI initiatives across Geisinger, including AI vendor evaluation, building AI solutions, governance and staff education. He is also involved in several national healthcare AI committees, including NCPDP, ACCP & HCSRN.
ACPE UAN # 0042-9999-26-040-L99-P
Available Credit
- 1.00 ACPEThe Arnold & Marie Schwartz College of Pharmacy and Health Sciences is accredited by the Accreditation Council for Pharmacy Education as a provider of continuing pharmacy education.

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