Archive \ Volume.17 2026 Issue 2

Safety Boundaries for Autonomous Sterile Compounding Cells

, , ,
  1. Department of Sterile Compounding Safety and AI, Faculty of Pharmacy, University of Leeds, Leeds, United Kingdom.
  2. Department of Autonomous Compounding Boundaries, Faculty of Pharmacy, University of Sheffield, Sheffield, United Kingdom.
  3. Department of Safety-Critical Pharmacy Automation, Faculty of Pharmacy, University of York, York, United Kingdom.

Abstract

Autonomous sterile compounding combines digital decision systems with a high-consequence physical process in which errors may affect product identity, dose, sterility, occupational exposure, workflow integrity, and medication release. Existing technologies can automate selected preparation, measurement, documentation, and verification tasks, but no single sensor, accuracy measure, artificial-intelligence model, or professional check is sufficient to establish system safety. This article proposes a cyber-physical safety architecture for autonomous sterile compounding cells. The architecture treats autonomy as conditional authority bounded by an authorized operating envelope, material and order controls, environmental and process sensing, independent verification, interlocks, hazard-specific safe states, accountable human supervision, evidence preservation, and controlled incident recovery. It distinguishes technical performance from medication-use decisions, automated execution from professional release authority, and successful implementation from validated benefit. The proposed relationships are organized as testable safety claims rather than assumed properties of automation. Evaluation would require component verification, aseptic and environmental qualification, fault injection, human-factors testing, workflow assessment, traceable safety-case evidence, and prospective monitoring under defined operating conditions. Changes in products, software, equipment, environment, staffing, or workflow would require reassessment of the applicable safety boundary. The architecture is conceptual and does not establish regulatory conformity, clinical effectiveness, universal applicability, or deployment readiness. Its original contribution is an integrated account of how physical containment, computational control, professional authority, and organizational governance may be connected without treating any individual component as a substitute for system-level safety assurance.


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Vancouver
Wright E, Bennett C, Turner J, Harris O. Safety Boundaries for Autonomous Sterile Compounding Cells. Arch Pharm Pract. 2026;17(2):84-92. https://doi.org/10.51847/WzmggKdwbq
APA
Wright, E., Bennett, C., Turner, J., & Harris, O. (2026). Safety Boundaries for Autonomous Sterile Compounding Cells. Archives of Pharmacy Practice, 17(2), 84-92. https://doi.org/10.51847/WzmggKdwbq

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References
  1. Yaniv AW, Orsborn A, Bonkowski JJ, Chew L, Krämer I, Calabrese S, et al. Robotic i.v. medication compounding: Recommendations from the international community of APOTECAchemo users. Am J Health Syst Pharm. 2017;74(1). doi:10.2146/ajhp151027
  2. Bhakta SB, Colavecchia AC, Coffey W, Curlee DR, Garey KW. Implementation and evaluation of a sterile compounding robot in a satellite oncology pharmacy. Am J Health Syst Pharm. 2018;75(11 Suppl 2). doi:10.2146/ajhp170461
  3. Fan M, Yang D, Ng B, Jackson J, Bouris K, Eng S, et al. Impact of technology-assisted versus manual sterile compounding on safety and efficiency in a Canadian community hospital. Am J Health Syst Pharm. 2022;79(19):1685-96. doi:10.1093/ajhp/zxac167
  4. 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
  5. 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
  6. Roberts PA, Willoughby IR, Barnes N, Eckel SF, Paruscio A, Valgus JM, et al. Evaluation of a gravimetric-based technology-assisted workflow system on hazardous sterile product preparation. Am J Health Syst Pharm. 2018;75(17):1286-92. doi:10.2146/ajhp170564
  7. Bucci TG, Hedrick TL, Roberts PA, Amerine LB. Evaluation of gravimetric-based technology-assisted workflow for nonhazardous sterile product preparation. Am J Health Syst Pharm. 2019;76(14):1071-7. doi:10.1093/ajhp/zxz097
  8. Geersing TH, Franssen EJF, Pilesi F, Crul M. Microbiological performance of a robotic system for aseptic compounding of cytostatic drugs. Eur J Pharm Sci. 2019;130:181-5. doi:10.1016/j.ejps.2019.01.034
  9. Sottani C, Grignani E, Cornacchia M, Negri S, della Cuna FSR, Cottica D, et al. Occupational exposure assessment to antineoplastic drugs in nine Italian hospital centers over a 5-year survey program. Int J Environ Res Public Health. 2022;19(14):8601. doi:10.3390/ijerph19148601
  10. Salch SA, Zamboni WC, Zamboni BA, Eckel SF. Patterns and characteristics associated with surface contamination of hazardous drugs in hospital pharmacies. Am J Health Syst Pharm. 2019;76(9):591-8. doi:10.1093/ajhp/zxz033
  11. Geersing TH, Klous MG, Franssen EJF, van den Heuvel JJG, Crul M. Robotic compounding versus manual compounding of chemotherapy: Comparing dosing accuracy and precision. Eur J Pharm Sci. 2020;155:105536. doi:10.1016/j.ejps.2020.105536
  12. Eckel SF, Higgins JP, Hess E, Cerbone T, Civiello JB, Conley C, et al. Multicenter study to evaluate the benefits of technology-assisted workflow on i.v. room efficiency, costs, and safety. Am J Health Syst Pharm. 2019;76(12):895-901. doi:10.1093/ajhp/zxz067
  13. Jean SJ, Francart SJ, Eckel SF, Schenkat D, Eberwein S, Lamm M, et al. Evaluation of telepharmacy and the use of a gravimetric technology-assisted workflow system for remote sterile product pharmacist checks. Am J Health Syst Pharm. 2020;77(7):560-7. doi:10.1093/ajhp/zxaa015
  14. Pang B, Earl M, Knoer S, Yaniv AW, Boyd AM. Comparison of IV oncology infusions compounded via robotics and gravimetrics-assisted workflow processes. Am J Health Syst Pharm. 2021;78(2):122-34. doi:10.1093/ajhp/zxaa366
  15. Carayon P, Wooldridge A, Hoonakker P, Schoofs Hundt A, 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
  16. Leso V, Sottani C, Santocono C, Russo F, Grignani E, Iavicoli I. Exposure to antineoplastic drugs in occupational settings: A systematic review of biological monitoring data. Int J Environ Res Public Health. 2022;19(6):3737. doi:10.3390/ijerph19063737
  17. Simon N, Guichard N, Odou P, Decaudin B, Bonnabry P, Fleury-Souverain S. Efficiency of four solutions in removing 23 conventional antineoplastic drugs from contaminated surfaces. PLoS One. 2020;15(6). doi:10.1371/journal.pone.0235131
  18. Wei R, Kelly TP, Dai X, Zhao S, Hawkins R. Model based system assurance using the Structured Assurance Case Metamodel. J Syst Softw. 2019;154:211-33. doi:10.1016/j.jss.2019.05.013
  19. Patalas-Maliszewska J, Łosyk H, Dudek A. Improving safety in human–robot collaboration towards sustainable production in Industry 5.0. J Intell Manuf. 2026;37(8):3069-104. doi:10.1007/s10845-025-02676-4
  20. Buysse L, Habli I, Vanoost D, Pissoort D. Safe autonomous systems in a changing world: Operationalising dynamic safety cases. Saf Sci. 2025;191:106965. doi:10.1016/j.ssci.2025.106965
  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. Amann J, Blasimme A, Vayena E, Frey D, Madai VI. 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
  23. Dvijotham KD, Winkens J, Barsbey M, Ghaisas S, Stanforth R, Pawlowski N, et al. Enhancing the reliability and accuracy of AI-enabled diagnosis via complementarity-driven deferral to clinicians. Nat Med. 2023;29(7):1814-20. doi:10.1038/s41591-023-02437-x
  24. Dratsch T, Chen X, Rezazade Mehrizi M, Kloeckner R, Mähringer-Kunz A, Püsken M, et al. Automation bias in mammography: The impact of artificial intelligence BI-RADS suggestions on reader performance. Radiology. 2023;307(4). doi:10.1148/radiol.222176
  25. Catchpole K, Cohen T, Alfred M, Lawton S, Kanji F, Shouhed D, et al. Human factors integration in robotic surgery. Hum Factors. 2024;66(3):683-700. doi:10.1177/00187208211068946
  26. Liu X, Cruz Rivera S, Moher D, Calvert MJ, Denniston AK; CONSORT-AI and SPIRIT-AI Working Group. Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: The CONSORT-AI extension. Nat Med. 2020;26(9):1364-74. doi:10.1038/s41591-020-1034-x
  27. Cruz Rivera S, Liu X, Chan AW, Denniston AK, Calvert MJ; SPIRIT-AI and CONSORT-AI Working Group. Guidelines for clinical trial protocols for interventions involving artificial intelligence: The SPIRIT-AI extension. Nat Med. 2020;26(9):1351-63. doi:10.1038/s41591-020-1037-7
  28. 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
  29. Norgeot B, Quer G, Beaulieu-Jones BK, Torkamani A, Dias R, Gianfrancesco M, et al. Minimum information about clinical artificial intelligence modeling: The MI-CLAIM checklist. Nat Med. 2020;26(9):1320-4. doi:10.1038/s41591-020-1041-y
  30. Collins GS, Moons KGM, Dhiman P, Riley RD, Beam AL, Van Calster B, et al. TRIPOD+AI statement: Updated guidance for reporting clinical prediction models that use regression or machine learning methods. BMJ. 2024;385. doi:10.1136/bmj-2023-078378
  31. Greenhalgh T, Wherton J, Papoutsi C, Lynch J, Hughes G, A'Court C, et al. Beyond adoption: A new framework for theorizing and evaluating nonadoption, abandonment, and challenges to the scale-up, spread, and sustainability of health and care technologies. J Med Internet Res. 2017;19(11). doi:10.2196/jmir.8775
  32. Damschroder LJ, Reardon CM, Widerquist MAO, Lowery J. The updated Consolidated Framework for Implementation Research based on user feedback. Implement Sci. 2022;17(1):75. doi:10.1186/s13012-022-01245-0
  33. Reddy S, Allan S, Coghlan S, Cooper P. A governance model for the application of AI in health care. J Am Med Inform Assoc. 2020;27(3):491-7. doi:10.1093/jamia/ocz192
  34. 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
  35. Challen R, Denny J, Pitt M, Gompels L, Edwards T, Tsaneva-Atanasova K. Artificial intelligence, bias and clinical safety. BMJ Qual Saf. 2019;28(3):231-7. doi:10.1136/bmjqs-2018-008370
  36. 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
  37. 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
  38. Roberts M, Driggs D, Thorpe M, Gilbey J, Yeung M, Ursprung S, et al. Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans. Nat Mach Intell. 2021;3(3):199-217. doi:10.1038/s42256-021-00307-0

 

 

 


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