Archive \ Volume.17 2026 Issue 2

The Digital Pharmacist Proxy and the Clinical Acts That Must Remain Human

, , ,
  1. Department of Digital Proxies and Pharmacy Practice, Faculty of Pharmacy, Sofia University, Sofia, Bulgaria.
  2. Department of Human-Only Clinical Acts, Faculty of Pharmacy, University of Plovdiv, Plovdiv, Bulgaria.
  3. Department of AI Boundaries in Pharmacy, Faculty of Pharmacy, University of Ruse, Ruse, Bulgaria.

Abstract

Artificial intelligence is moving from supporting pharmacy work toward producing outputs that can appear to represent the pharmacist. This shift creates a boundary problem: technical capability to retrieve, classify, draft, or recommend does not by itself establish authority to perform a professional clinical act. This article proposes a Digital Pharmacist Proxy–Professional Boundary Model for distinguishing acts that may be delegated to a digital proxy, acts that require conditional human–AI co-production, and acts that must remain human-led. The model begins with the act rather than the technology and evaluates verifiability, reversibility, contextual sufficiency, relational dependence, ethical or normative load, authority and consequence, and the operational management of uncertainty and accountability. These dimensions are assembled within a proposed permission envelope specifying scope, users, population, inputs, outputs, autonomy, pharmacist oversight, fallback, monitoring, and expiry. Informational and administrative acts may be delegable when they are bounded, auditable, reversible, and low in normative consequence. Medication-related interpretation may require co-production when uncertainty, contextual incompleteness, or meaningful clinical consequence demands pharmacist review and authorization. Relational engagement, value elicitation, ethical exception handling, professional commitment, and final authority-bearing decisions remain human-led under the proposed model. Validation would require task-level technical assessment, human-factors testing, workflow evaluation, equity analysis, prospective safety study, and governance testing. The model is an original non-empirical synthesis. It does not establish professional equivalence, clinical benefit, regulatory acceptance, or deployment readiness, and its boundaries require empirical and normative validation across pharmacy settings.


Downloads: 27
Views: 67

How to cite:
Vancouver
Petrova E, Georgiev I, Stoyanov N, Kolev P. The Digital Pharmacist Proxy and the Clinical Acts That Must Remain Human. Arch Pharm Pract. 2026;17(2):67-74. https://doi.org/10.51847/pN0iDcYpWH
APA
Petrova, E., Georgiev, I., Stoyanov, N., & Kolev, P. (2026). The Digital Pharmacist Proxy and the Clinical Acts That Must Remain Human. Archives of Pharmacy Practice, 17(2), 67-74. https://doi.org/10.51847/pN0iDcYpWH

Download Citation
References
  1. Topol EJ. High-performance medicine: The convergence of human and artificial intelligence. Nat Med. 2019;25(1):44-56. doi:10.1038/s41591-018-0300-7
  2. Ranchon F, Chanoine S, Lambert-Lacroix S, Bosson JL, Moreau-Gaudry A, Bedouch P. Development of artificial intelligence powered apps and tools for clinical pharmacy services: A systematic review. Int J Med Inform. 2023;172:104983. doi:10.1016/j.ijmedinf.2022.104983
  3. Hatzimanolis J, Riley B, El-Den S, Aslani P, Zhou J, Chaar BB. Applications of artificial intelligence in current pharmacy practice: A scoping review. Res Social Adm Pharm. 2025;21(3):134-41. doi:10.1016/j.sapharm.2024.12.007
  4. Li X, Guo H, Li D, Zheng Y. Engine of innovation in hospital pharmacy: Applications and reflections of ChatGPT. J Med Internet Res. 2024;26. doi:10.2196/51635
  5. Guignard B, Crevier F, Charlin B, Audétat MC. A graphical model to make explicit pharmacist clinical reasoning during medication review. Res Social Adm Pharm. 2024;20(12 Pt A):1142-50. doi:10.1016/j.sapharm.2024.09.005
  6. Gregory PAM, Austin Z. How do patients develop trust in community pharmacists? Res Social Adm Pharm. 2021;17(5):911-20. doi:10.1016/j.sapharm.2020.07.023
  7. Qudah B, Thakur T, Chewning B. Factors influencing patient participation in medication counseling at the community pharmacy: A systematic review. Res Social Adm Pharm. 2021;17(11):1863-76. doi:10.1016/j.sapharm.2021.03.005
  8. Nabhani-Gebara S, Fletcher S, Shamim A, May L, Butt N, Chagger S, et al. General practice pharmacists in England: Integration, mediation and professional dynamics. Res Social Adm Pharm. 2020;16(1):17-24. doi:10.1016/j.sapharm.2019.01.014
  9. Vaccaro M, Almaatouq A, Malone T. When combinations of humans and AI are useful: A systematic review and meta-analysis. Nat Hum Behav. 2024;8(12):2293-303. doi:10.1038/s41562-024-02024-1
  10. Reverberi C, Rigon T, Solari A, Hassan C, Cherubini P, Antonelli G, et al. Experimental evidence of effective human-AI collaboration in medical decision-making. Sci Rep. 2022;12(1):14952. doi:10.1038/s41598-022-18751-2
  11. Cabitza F, Rasoini R, Gensini GF. Unintended consequences of machine learning in medicine. JAMA. 2017;318(6):517-8. doi:10.1001/jama.2017.7797
  12. Meskó B, Görög M. A short guide for medical professionals in the era of artificial intelligence. NPJ Digit Med. 2020;3:126. doi:10.1038/s41746-020-00333-z
  13. Huang X, Estau D, Liu X, Yu Y, Qin J, Li Z. Evaluating the performance of ChatGPT in clinical pharmacy: A comparative study of ChatGPT and clinical pharmacists. Br J Clin Pharmacol. 2024;90(1):232-8. doi:10.1111/bcp.15896
  14. Tsai CC, Kim JY, Chen Q, Rowell B, Yang XJ, Kontar R, et al. Effect of artificial intelligence helpfulness and uncertainty on cognitive interactions with pharmacists: Randomized controlled trial. J Med Internet Res. 2025;27. doi:10.2196/59946
  15. Lester C, Rowell B, Zheng Y, Co Z, Marshall V, Kim JY, et al. Effect of uncertainty-aware AI models on pharmacists’ reaction time and decision-making in a web-based mock medication verification task: Randomized controlled trial. JMIR Med Inform. 2025;13. doi:10.2196/64902
  16. Hassan N, Slight R, Bimpong K, Bates DW, Weiand D, Vellinga A, et al. Systematic review to understand users perspectives on AI-enabled decision aids to inform shared decision making. NPJ Digit Med. 2024;7:332. doi:10.1038/s41746-024-01326-y
  17. Morley J, Machado CCV, Burr C, Cowls J, Joshi I, Taddeo M, et al. The ethics of AI in health care: A mapping review. Soc Sci Med. 2020;260:113172. doi:10.1016/j.socscimed.2020.113172
  18. Heyen NB, Salloch S. The ethics of machine learning-based clinical decision support: An analysis through the lens of professionalisation theory. BMC Med Ethics. 2021;22:112. doi:10.1186/s12910-021-00679-3
  19. Vasey B, Nagendran M, Campbell B, Clifton DA, Collins GS, Denaxas S, et al. Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI. Nat Med. 2022;28(5):924-33. doi:10.1038/s41591-022-01772-9
  20. Lekadir K, Frangi AF, Porras AR, Glocker B, Cintas C, Langlotz CP, et al. FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare. BMJ. 2025;388. doi:10.1136/bmj-2024-081554
  21. Lyell D, Coiera E. Automation bias and verification complexity: A systematic review. J Am Med Inform Assoc. 2017;24(2):423-31. doi:10.1093/jamia/ocw105
  22. Traulsen JM, Druedahl LC. Shifting perspectives—Planning for the future of the pharmacy profession taking current labor market trends into consideration. Res Social Adm Pharm. 2018;14(12):1189-94. doi:10.1016/j.sapharm.2018.02.006
  23. Farič N, Hinder S, Williams R, Ramaesh R, Bernabeu MO, van Beek E, et al. Early experiences of integrating an artificial intelligence-based diagnostic decision support system into radiology settings: A qualitative study. J Am Med Inform Assoc. 2024;31(1):24-34. doi:10.1093/jamia/ocad191
  24. Gregório J, Cavaco A. The pharmacist’s guide to the future: Are we there yet? Res Social Adm Pharm. 2021;17(4):795-8. doi:10.1016/j.sapharm.2020.05.029
  25. Obermeyer Z, Powers B, Vogeli C, Mullainathan S. Dissecting racial bias in an algorithm used to manage the health of populations. Science. 2019;366(6464):447-53. doi:10.1126/science.aax2342
  26. Ghassemi M, Oakden-Rayner L, Beam AL. The false hope of current approaches to explainable artificial intelligence in health care. Lancet Digit Health. 2021;3(11). doi:10.1016/S2589-7500(21)00208-9
  27. Kelly CJ, Karthikesalingam A, Suleyman M, Corrado G, King D. Key challenges for delivering clinical impact with artificial intelligence. BMC Med. 2019;17:195. doi:10.1186/s12916-019-1426-2

 

 

 


Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.