Archive \ Volume.16 2025 Issue 1

Where Should Automation Stop in Community Pharmacy? A Boundary Model for Delegating Medication Tasks

, ,
  1. Department of Pharmacy Automation and Task Delegation, Faculty of Pharmaceutical Sciences, University of Bonn, Bonn, Germany.
  2. Department of Clinical Pharmacy and Digital Boundaries, Faculty of Pharmacy, LMU Munich, Munich, Germany.

Abstract

Automation in community pharmacy is often discussed as though medication tasks can be transferred to digital systems whenever adequate technical performance is demonstrated. This framing overlooks differences in clinical consequence, uncertainty, contextual dependence, patient vulnerability, error detectability, and the possibility of correcting an inappropriate action. This article develops an original, non-empirical delegation-boundary model for determining when automation may proceed, when professional review is required, and when delegation should stop. Delegability is defined as a conditional property of a specific medication-task instance rather than an inherent characteristic of an entire service, technology, or occupational role. The proposed model assesses six interacting dimensions: clinical consequence, ambiguity, reversibility, contextual dependence, patient vulnerability, and detectability of error. These dimensions inform three conditional states. Proceed permits bounded automated execution under defined safeguards; Escalate requires review by an authorized professional before action; and Stop retains human control because necessary context, recovery capacity, accountability, or safety conditions are insufficient. The architecture also includes task specification, uncertainty communication, professional authority, monitoring, incident review, and boundary reassessment. Application is intended to distinguish automatable components from judgment-dependent components within dispensing, verification, counselling, and follow-up rather than classify whole services as automatable or non-automatable. Empirical evaluation would require task-specific technical validation, workflow simulation, human-factors assessment, medication-safety analysis, patient and equity evaluation, external validation, and lifecycle monitoring. The model is a proposed conceptual synthesis, not a clinical recommendation, legal standard, validated scoring system, or deployment-ready framework. Its principal contribution is to reposition pharmacy automation as a conditional delegation decision requiring evidence about both system performance and the consequences of transferring professional work.


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How to cite:
Vancouver
Müller H, Schmidt A, Weber T. Where Should Automation Stop in Community Pharmacy? A Boundary Model for Delegating Medication Tasks. Arch Pharm Pract. 2025;16(1):50-9. https://doi.org/10.51847/lgc8J2bCwU
APA
Müller, H., Schmidt, A., & Weber, T. (2025). Where Should Automation Stop in Community Pharmacy? A Boundary Model for Delegating Medication Tasks. Archives of Pharmacy Practice, 16(1), 50-59. https://doi.org/10.51847/lgc8J2bCwU

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References
  1. 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
  2. Baines D, Nørgaard LS, Babar ZUD, Rossing C. The Fourth Industrial Revolution: Will it change pharmacy practice? Res Social Adm Pharm. 2020;16(9):1279-81. doi:10.1016/j.sapharm.2019.04.003
  3. Sutton RT, Pincock D, Baumgart DC, Sadowski DC, Fedorak RN, Kroeker KI. An overview of clinical decision support systems: Benefits, risks, and strategies for success. NPJ Digit Med. 2020;3:17. doi:10.1038/s41746-020-0221-y
  4. 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
  5. 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
  6. Nelson SD, Poikonen J, Reese T, El Halta D, Weir C. The pharmacist and the EHR. J Am Med Inform Assoc. 2017;24(1):193-7. doi:10.1093/jamia/ocw044
  7. Lyell D, Magrabi F, Raban MZ, Pont LG, Baysari MT, Day RO, et al. Automation bias in electronic prescribing. BMC Med Inform Decis Mak. 2017;17(1):28. doi:10.1186/s12911-017-0425-5
  8. Mouazer A, Léguillon R, Boudegzdame N, Levrard T, Le Bars Y, Simon C, et al. ABiMed: An intelligent and visual clinical decision support system for medication reviews and polypharmacy management. BMC Med Inform Decis Mak. 2025;25(1):173. doi:10.1186/s12911-025-03002-x
  9. Rudin C. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat Mach Intell. 2019;1(5):206-15. doi:10.1038/s42256-019-0048-x
  10. Kompa B, Snoek J, Beam AL. Second opinion needed: Communicating uncertainty in medical machine learning. NPJ Digit Med. 2021;4(1):4. doi:10.1038/s41746-020-00367-3
  11. Jabbour S, Fouhey D, Shepard S, Valley TS, Kazerooni EA, Banovic N, et al. Measuring the impact of AI in the diagnosis of hospitalized patients: A randomized clinical vignette survey study. JAMA. 2023;330(23):2275-84. doi:10.1001/jama.2023.22295
  12. 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
  13. Tschandl P, Rinner C, Apalla Z, Argenziano G, Codella N, Halpern A, et al. Human-computer collaboration for skin cancer recognition. Nat Med. 2020;26(8):1229-34. doi:10.1038/s41591-020-0942-0
  14. Asan O, Bayrak AE, Choudhury A. Artificial intelligence and human trust in healthcare: Focus on clinicians. J Med Internet Res. 2020;22(6). doi:10.2196/15154
  15. Amann J, Blasimme A, Vayena E, Frey D, Madai VI, Precise4Q Consortium. Explainability for artificial intelligence in healthcare: A multidisciplinary perspective. BMC Med Inform Decis Mak. 2020;20(1):310. doi:10.1186/s12911-020-01332-6
  16. Wiens J, Saria S, Sendak M, Ghassemi M, Liu VX, Doshi-Velez F, et al. Do no harm: A roadmap for responsible machine learning for health care. Nat Med. 2019;25(9):1337-40. doi:10.1038/s41591-019-0548-6
  17. Reddy A, Abebe E, Rivera AJ, Stone JA, Chui MA. Interruptions in community pharmacies: Frequency, sources, and mitigation strategies. Res Social Adm Pharm. 2019;15(10):1243-50. doi:10.1016/j.sapharm.2018.10.030
  18. Graafsma J, Murphy RM, van de Garde EMW, Karapinar-Çarkit F, Derijks HJ, Hoge RHL, et al. The use of artificial intelligence to optimize medication alerts generated by clinical decision support systems: A scoping review. J Am Med Inform Assoc. 2024;31(6):1411-22. doi:10.1093/jamia/ocae076
  19. Qudah B, Chewning B. Exploring the impact of a digital health tool on patients’ interaction with community pharmacists: A pilot randomized controlled study. Res Social Adm Pharm. 2024;20(10):986-94. doi:10.1016/j.sapharm.2024.06.011
  20. Milosavljevic A, Aspden T, Harrison J. Community pharmacist-led interventions and their impact on patients’ medication adherence and other health outcomes: A systematic review. Int J Pharm Pract. 2018;26(5):387-97. doi:10.1111/ijpp.12462
  21. 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
  22. 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
  23. Davis SE, Lasko TA, Chen G, Siew ED, Matheny ME. Calibration drift in regression and machine learning models for acute kidney injury. J Am Med Inform Assoc. 2017;24(6):1052-61. doi:10.1093/jamia/ocx030
  24. Gregório J, Cavaco AM, Lapão LV. How to best manage time interaction with patients? Community pharmacist workload and service provision analysis. Res Social Adm Pharm. 2017;13(1):133-47. doi:10.1016/j.sapharm.2016.02.008
  25. Rahimi AK, Pienaar O, Ghadimi M, Canfell OJ, Pole JD, Shrapnel S, et al. Implementing AI in hospitals to achieve a learning health system: Systematic review of current enablers and barriers. J Med Internet Res. 2024;26. doi:10.2196/49655
  26. Jarab AS, Al-Qerem W, Alzoubi KH, Obeidat H, Abu Heshmeh S, Mukattash TL, et al. Artificial intelligence in pharmacy practice: Attitude and willingness of the community pharmacists and the barriers for its implementation. Saudi Pharm J. 2023;31(8):101700. doi:10.1016/j.jsps.2023.101700
  27. Kelly CJ, Karthikesalingam A, Suleyman M, Corrado G, King D. Key challenges for delivering clinical impact with artificial intelligence. BMC Med. 2019;17(1):195. doi:10.1186/s12916-019-1426-2
  28. Gianfrancesco MA, Tamang S, Yazdany J, Schmajuk G. Potential biases in machine learning algorithms using electronic health record data. JAMA Intern Med. 2018;178(11):1544-7. doi:10.1001/jamainternmed.2018.3763
  29. Price WN 2nd, Gerke S, Cohen IG. Potential liability for physicians using artificial intelligence. JAMA. 2019;322(18):1765-6. doi:10.1001/jama.2019.15064

 

 


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