Archive \ Volume.17 2026 Issue 3

Keeping Algorithmic Medication Advice Open to Challenge in Routine Pharmacy Care

, , ,
  1. Department of Open-to-Challenge Medication Advice, Faculty of Pharmacy, Semmelweis University, Budapest, Hungary.
  2. Department of Algorithmic Accountability in Pharmacy, Faculty of Pharmacy, University of Debrecen, Debrecen, Hungary.
  3. Department of Clinical Challenge and AI, Faculty of Pharmacy, University of Pécs, Pécs, Hungary.

Abstract

Algorithmic systems increasingly generate medication alerts, risk estimates, prioritizations, and treatment suggestions that can influence pharmacists, prescribers, patients, and organizations. Their safety cannot be secured solely through predictive performance, the presence of a professional reviewer, or the provision of a technical explanation. When advice is difficult to question, interrupt, reassess, or correct, it may acquire practical authority beyond its evidential strength. This article develops an original contestability framework for algorithmic medication advice in routine pharmacy care. Contestability is defined as the practical capacity of an affected or responsible person to obtain decision-relevant information, raise a challenge, activate a proportionate protective response, secure accountable review, and achieve correction or justified confirmation. The proposed framework connects six functions: registration of the advice object; access to reasons, evidence, alternatives, uncertainty, and limitations; human deliberation; challenge and escalation; correction or supersession; and organizational learning. Accountability, accessibility, equity, traceability, response time, and auditability operate as cross-cutting conditions. The framework distinguishes model output from medication decisions, explanation from justification, nominal oversight from meaningful review, and patient-level correction from system-level learning. Evaluation should assess whether contestability can be located, understood, initiated, answered, and closed under realistic workload and consequence conditions. Validation requires prospective pharmacy-workflow studies, patient and professional usability research, safety evaluation, equity analysis, external assessment, and longitudinal monitoring. Contestability is presented as a necessary governance capability rather than proof of algorithmic validity, clinical benefit, regulatory acceptability, or deployment readiness.


Downloads: 27
Views: 72

How to cite:
Vancouver
Kovács Z, Szabo K, Nagy G, Toth E. Keeping Algorithmic Medication Advice Open to Challenge in Routine Pharmacy Care. Arch Pharm Pract. 2026;17(3):75-83. https://doi.org/10.51847/5ojs70Ftj2
APA
Kovács, Z., Szabo, K., Nagy, G., & Toth, E. (2026). Keeping Algorithmic Medication Advice Open to Challenge in Routine Pharmacy Care. Archives of Pharmacy Practice, 17(3), 75-83. https://doi.org/10.51847/5ojs70Ftj2

Download Citation
References
  1. 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
  2. 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
  3. Chen CY, Chen YL, Scholl J, Yang HC, Li YCJ. Ability of machine-learning based clinical decision support system to reduce alert fatigue, wrong-drug errors, and alert users about look alike, sound alike medication. Comput Methods Programs Biomed. 2024;243:107869. doi:10.1016/j.cmpb.2023.107869
  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. Vayena E, Blasimme A, Cohen IG. Machine learning in medicine: Addressing ethical challenges. PLoS Med. 2018;15(11). doi:10.1371/journal.pmed.1002689
  6. Grote T, Berens P. On the ethics of algorithmic decision-making in healthcare. J Med Ethics. 2020;46(3):205-11. doi:10.1136/medethics-2019-105586
  7. 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
  8. Ploug T, Holm S. The four dimensions of contestable AI diagnostics—A patient-centric approach to explainable AI. Artif Intell Med. 2020;107:101901. doi:10.1016/j.artmed.2020.101901
  9. 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
  10. Gaube S, Suresh H, Raue M, Merritt A, Berkowitz SJ, Lermer E, et al. Do as AI say: Susceptibility in deployment of clinical decision-aids. NPJ Digit Med. 2021;4(1):31. doi:10.1038/s41746-021-00385-9
  11. 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
  12. Carayon P, Wooldridge A, Hoonakker P, Hundt AS, Kelly MM. SEIPS 3.0: Human-centered design of the patient journey for patient safety. Appl Ergon. 2020;84:103033. doi:10.1016/j.apergo.2019.103033
  13. 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
  14. 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
  15. 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
  16. Van Calster B, McLernon DJ, van Smeden M, Wynants L, Steyerberg EW. Calibration: The Achilles heel of predictive analytics. BMC Med. 2019;17(1):230. doi:10.1186/s12916-019-1466-7
  17. 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
  18. 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
  19. Sendak MP, Ratliff W, Sarro D, Alderton E, Futoma J, Gao M, et al. Real-world integration of a sepsis deep learning technology into routine clinical care: Implementation study. JMIR Med Inform. 2020;8(7). doi:10.2196/15182
  20. 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
  21. Nagendran M, Chen Y, Lovejoy CA, Gordon AC, Komorowski M, Harvey H, et al. Artificial intelligence versus clinicians: Systematic review of design, reporting standards, and claims of deep learning studies. BMJ. 2020;368. doi:10.1136/bmj.m689
  22. 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
  23. Longoni C, Bonezzi A, Morewedge CK. Resistance to medical artificial intelligence. J Consum Res. 2019;46(4):629-50. doi:10.1093/jcr/ucz013
  24. Richardson JP, Smith C, Curtis S, Watson S, Zhu X, Barry B, et al. Patient apprehensions about the use of artificial intelligence in healthcare. NPJ Digit Med. 2021;4(1):140. doi:10.1038/s41746-021-00509-1
  25. Char DS, Shah NH, Magnus D. Implementing machine learning in health care—addressing ethical challenges. N Engl J Med. 2018;378(11):981-3. doi:10.1056/NEJMp1714229
  26. 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
  27. Subbaswamy A, Saria S. From development to deployment: Dataset shift, causality, and shift-stable models in health AI. Biostatistics. 2020;21(2):345-52. doi:10.1093/biostatistics/kxz041
  28. Finlayson SG, Bowers JD, Ito J, Zittrain JL, Beam AL, Kohane IS. Adversarial attacks on medical machine learning. Science. 2019;363(6433):1287-9. doi:10.1126/science.aaw4399

 

 

 


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