Digital Transformation of Antimicrobial Stewardship: Mobile Applications, Clinical Decision Support Systems, Artificial Intelligence, and the Future of Antimicrobial Governance

Antimicrobial resistance (AMR) is a critical public health problem worldwide, affecting the effectiveness of available antimicrobial therapies. Antimicrobial stewardship programs (ASPs) have been successful in reducing the use of antimicrobials; however, classic stewardship strategies can be constrained by resources, time delays, and the complexity of clinical decision making. New digital health technologies have emerged over the last few years that offer the potential for improving stewardship by providing real-time decision support, monitoring and forecasting capabilities. To review the impact of digital technologies on antimicrobial stewardship, particularly mobile health apps, clinical decision support systems (CDSSs), artificial intelligence (AI), rapid diagnostics and digital antimicrobial governance. Methods: A narrative review was performed on the current literature to assess the applications, benefits, challenges and future directions of digital innovations for antimicrobial stewardship and AMR containment. Digital technologies are revolutionizing antimicrobial stewardship by enhancing the timeliness, accuracy, and scale of interventions. AI and machine learning aid in resistance prediction, optimizing empirical therapy, sepsis detection, surveillance, and antimicrobial management. Rapid diagnostics (RDT) can be integrated to achieve earlier identification of the pathogen, targeted treatment, and antimicrobial de-escalation. Data-driven decision-making and real-time monitoring are enhanced with digital surveillance platforms, supporting antimicrobial governance. However, implementation is still hindered by user adoption, cybersecurity, cost, data quality, interoperability, and infrastructure issues. An era of closed-loop stewardship models is giving way to a new era of predictive, proactive, and precision antimicrobial governance as digital health technologies, advanced diagnostics, and AI combine. These technologies can be strategically integrated to enhance antimicrobial optimization and contribute to the global fight against AMR.

Antimicrobial stewardship; Antimicrobial resistance; Digital health; Mobile health; Clinical decision support systems; Artificial intelligence; Antimicrobial governance

1. E, Johnson SC. Global burden of bacterial antimicrobial resistance in 2019: a systematic analysis. The lancet. 2022 Feb 12;399(10325):629-55. doi:10.1016/S0140-6736(21)02724-0 [Google Scholar] [Crossref]

2. Lewnard JA, Charani E, Gleason A, Hsu LY, Khan WA, Karkey A, Chandler CI, Mashe T, Khan EA, Bulabula AN, Donado-Godoy P. Burden of bacterial antimicrobial resistance in low-income and middle-income countries avertible by existing interventions: an evidence review and modelling analysis. The Lancet. 2024 Jun 1;403(10442):2439-54. doi:10.1016/S0140-6736(24)00862-6 [Google Scholar] [Crossref]

3. Barlam TF, Cosgrove SE, Abbo LM, MacDougall C, Schuetz AN, Septimus EJ, Srinivasan A, Dellit TH, Falck-Ytter YT, Fishman NO, Hamilton CW. Implementing an antibiotic stewardship program: guidelines by the Infectious Diseases Society of America and the Society for Healthcare Epidemiology of America. Clinical infectious diseases. 2016 May 15;62(10):e51-77. doi:10.1093/cid/ciw118 [Google Scholar] [Crossref]

4. Fishman N. Antimicrobial stewardship. American journal of infection control. 2006 Jun 1;34(5):S55-63. doi:10.1016/j.ajic.2006.05.237 [Google Scholar] [Crossref]

5. Rawson TM, Zhu N, Galiwango R, Cocker D, Islam MS, Myall A, Vasikasin V, Wilson R, Shafiq N, Das S, Holmes AH. Using digital health technologies to optimise antimicrobial use globally. The Lancet Digital Health. 2024 Dec 1;6(12):e914-25. doi:10.1016/S2589-7500(24)00198-5 [Google Scholar] [Crossref]

6. Charani E, Holmes AH. Antimicrobial stewardship programmes: the need for wider engagement. BMJ quality & safety. 2013 Nov 1;22(11):885-7. doi:10.1136/bmjqs-2013-002444 [Google Scholar] [Crossref]

7. Hoff BM, Ford DC, Ince D, Ernst EJ, Livorsi DJ, Heinitz BH, Masse V, Brownlee MJ, Ford BA. Implementation of a mobile clinical decision support application to augment local antimicrobial stewardship. Journal of pathology informatics. 2018 Jan 1;9(1):10. doi:10.4103/jpi.jpi_77_17 [Google Scholar] [Crossref]

8. Rittmann B, Stevens MP. Clinical decision support systems and their role in antibiotic stewardship: a systematic review. Current infectious disease reports. 2019 Aug;21(8):29. doi:10.1007/s11908-019-0683-8 [Google Scholar] [Crossref]

9. Catho G, Sauser J, Coray V, Da Silva S, Elzi L, Harbarth S, Kaiser L, Marti C, Meyer R, Pagnamenta F, Portela J. Impact of interactive computerised decision support for hospital antibiotic use (COMPASS): an open-label, cluster-randomised trial in three Swiss hospitals. The Lancet Infectious Diseases. 2022 Oct 1;22(10):1493-502. doi:10.1016/S1473-3099(22)00308-5 [Google Scholar] [Crossref]

10. Howard A, Reza N, Green PL, Yin M, Duffy E, Mwandumba HC, Gerada A, Hope W. Artificial intelligence and infectious diseases: tackling antimicrobial resistance, from personalised care to antibiotic discovery. The Lancet Infectious Diseases. 2026 Mar 1;26(3):e181-92. doi:10.1016/S1473-3099(25)00313-5 [Google Scholar] [Crossref]

11. Pennisi F, Pinto A, Ricciardi GE, Signorelli C, Gianfredi V. The role of artificial intelligence and machine learning models in antimicrobial stewardship in public health: a narrative review. Antibiotics. 2025 Jan 30;14(2):134. doi:10.3390/antibiotics14020134 [Google Scholar] [Crossref]

12. Al Mazrouei N, Ahmed Elnour A, Badi S, Alsulami FT, Awadallah Mohamed Saeed A, Awad Al-Kubaisi K, Menon V, Yousif Khidir I, Ismail M, Osman Mahagoub MM, Ramisetti AK. The impact of artificial intelligence on the prescribing, selection, resistance, and stewardship of antimicrobials: a scoping review. BMC Infectious Diseases. 2025 Dec 30. doi:10.1186/s12879-025-12320-4 [Google Scholar] [Crossref]

13. Donà D, Barbieri E, Brigadoi G, Liberati C, Bosis S, Castagnola E, Colomba C, Galli L, Lancella L, Lo Vecchio A, Meschiari M. State of the art of antimicrobial and diagnostic stewardship in pediatric setting. Antibiotics. 2025 Jan 27;14(2):132. doi:10.3390/antibiotics14020132 [Google Scholar] [Crossref]

14. Fishman N. Antimicrobial stewardship. American journal of infection control. 2006 Jun 1;34(5):S55-63. doi:10.1016/j.ajic.2006.05.237 [Google Scholar] [Crossref]

15. Dellit TH, Owens RC, McGowan JE, Gerding DN, Weinstein RA, Burke JP, Huskins WC, Paterson DL, Fishman NO, Carpenter CF, Brennan PJ. Infectious Diseases Society of America and the Society for Healthcare Epidemiology of America guidelines for developing an institutional program to enhance antimicrobial stewardship. Clinical infectious diseases. 2007 Jan 15;44(2):159-77. doi:10.1086/510393 [Google Scholar] [Crossref]

16. Dyar OJ, Huttner B, Schouten J, Pulcini C. What is antimicrobial stewardship?. Clinical microbiology and infection. 2017 Nov 1;23(11):793-8. doi:10.1016/j.cmi.2017.08.026 [Google Scholar] [Crossref]

17. Tamma PD, Miller MA, Cosgrove SE. Rethinking how antibiotics are prescribed: incorporating the 4 moments of antibiotic decision making into clinical practice. Jama. 2019 Jan 15;321(2):139-40. doi:10.1001/jama.2018.19509 [Google Scholar] [Crossref]

18. Cox JA, Vlieghe E, Mendelson M, Wertheim H, Ndegwa L, Villegas MV, Gould I, Hara GL. Antibiotic stewardship in low-and middle-income countries: the same but different?. Clinical microbiology and infection. 2017 Nov 1;23(11):812-8. doi:10.1016/j.cmi.2017.07.010 [Google Scholar] [Crossref]

19. Charani E, Holmes A. Antibiotic stewardship—twenty years in the making. Antibiotics. 2019 Jan 24;8(1):7. doi:10.3390/antibiotics8010007 [Google Scholar] [Crossref]

20. Baysari MT, Lehnbom EC, Li L, Hargreaves A, Day RO, Westbrook JI. The effectiveness of information technology to improve antimicrobial prescribing in hospitals: a systematic review and meta-analysis. International journal of medical informatics. 2016 Aug 1;92:15-34. [Google Scholar] [Crossref]

21. doi:10.1016/j.ijmedinf.2016.04.008 [Google Scholar] [Crossref]

22. Catho G, Sauser J, Coray V, Da Silva S, Elzi L, Harbarth S, Kaiser L, Marti C, Meyer R, Pagnamenta F, Portela J. Impact of interactive computerised decision support for hospital antibiotic use (COMPASS): an open-label, cluster-randomised trial in three Swiss hospitals. The Lancet Infectious Diseases. 2022 Oct 1;22(10):1493-502. doi:10.1016/S1473-3099(22)00308-5 [Google Scholar] [Crossref]

23. Timbrook TT, Morton JB, McConeghy KW, Caffrey AR, Mylonakis E, LaPlante KL. The effect of molecular rapid diagnostic testing on clinical outcomes in bloodstream infections: a systematic review and meta-analysis. Clinical Infectious Diseases. 2016 Sep 27:ciw649. doi:10.1093/cid/ciw649 [Google Scholar] [Crossref]

24. Rawson TM, Castro-Sánchez E, Charani E, et al. Socio-cultural and behavioural factors affecting hospital-based antibiotic prescribing: a systematic review and meta-analysis. Clin Microbiol Infect. 2019 Apr;25(4):471-482. doi:10.1016/j.cmi.2018.12.014. [Google Scholar] [Crossref]

25. Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nature medicine. 2019 Jan;25(1):44-56. doi:10.1038/s41591-018-0300-7 [Google Scholar] [Crossref]

26. World Health Organization. Global strategy on digital health 2020-2027. World Health Organization; 2025 Dec 1. Available from: https://www/who.int/publications/i/item/9789240116870 [Google Scholar] [Crossref]

27. Cresswell K, Mozaffar H, Shah S, Sheikh A. Approaches to promoting the appropriate use of antibiotics through hospital electronic prescribing systems: a scoping review. International Journal of Pharmacy Practice. 2017 Feb;25(1):5-17. doi:10.1111/ijpp.12274 [Google Scholar] [Crossref]

28. Pulcini C, Binda F, Lamkang AS, Trett A, Charani E, Goff DA, Harbarth S, Hinrichsen SL, Levy-Hara G, Mendelson M, Nathwani D. Developing core elements and checklist items for global hospital antimicrobial stewardship programmes: a consensus approach. Clinical Microbiology and Infection. 2019 Jan 1;25(1):20-5. doi:10.1016/j.cmi.2018.03.033 [Google Scholar] [Crossref]

29. Antimicrobial Resistance Collaborators. Global burden of bacterial antimicrobial resistance in 2019: a systematic analysis. Lancet (London, England). 2022 Jan 19;399(10325):629. doi:10.1016/S0140-6736(21)02724-0 [Google Scholar] [Crossref]

30. Cox JA, Vlieghe E, Mendelson M, Wertheim H, Ndegwa L, Villegas MV, Gould I, Hara GL. Antibiotic stewardship in low-and middle-income countries: the same but different?. Clinical microbiology and infection. 2017 Nov 1;23(11):812-8. doi:10.1016/j.cmi.2017.07.010 [Google Scholar] [Crossref]

31. Free C, Phillips G, Watson L, Galli L, Felix L, Edwards P, Patel V, Haines A. The effectiveness of mobile-health technologies to improve health care service delivery processes: a systematic review and meta-analysis. PLoS medicine. 2013 Jan 15;10(1):e1001363. doi:10.1371/journal.pmed.1001363 [Google Scholar] [Crossref]

32. Charani E, Castro-Sánchez E, Sevdalis N, Kyratsis Y, Drumright L, Shah N, Holmes A. Understanding the determinants of antimicrobial prescribing within hospitals: the role of “prescribing etiquette”. Clinical Infectious Diseases. 2013 Jul 15;57(2):188-96. doi:10.1093/cid/cit212 [Google Scholar] [Crossref]

33. Helou RI, Foudraine DE, Catho G, Peyravi Latif A, Verkaik NJ, Verbon A. Use of stewardship smartphone applications by physicians and prescribing of antimicrobials in hospitals: a systematic review. PLoS One. 2020;15(9):e0239751. doi: 10.1371/journal.pone.0239751. [Google Scholar] [Crossref]

34. Pennisi F, Pinto A, Ricciardi GE, Signorelli C, Gianfredi V. The role of artificial intelligence and machine learning models in antimicrobial stewardship in public health: a narrative review. Antibiotics. 2025 Jan 30;14(2):134. doi:10.3390/antibiotics14020134 [Google Scholar] [Crossref]

35. Tamma PD, Cosgrove SE. Antimicrobial stewardship. Infectious Disease Clinics. 2011 Mar 1;25(1):245-60. doi:10.1016/j.idc.2010.11.011 [Google Scholar] [Crossref]

36. Yu KH, Beam AL, Kohane IS. Artificial intelligence in healthcare. Nature biomedical engineering. 2018 Oct;2(10):719-31. doi:10.1038/s41551-018-0305-z [Google Scholar] [Crossref]

37. Feretzakis G, Loupelis E, Sakagianni A, Gialeraki A, Kalles D, Martsoukou M, et al. Machine learning prediction of antimicrobial resistance. J Clin Med. 2020 Oct 14;9(10):3293. doi:10.3390/jcm9103293 [Google Scholar] [Crossref]

38. Yelin I, Snitser O, Novich G, Katz R, Tal O, Parizade M, Chodick G, Koren G, Shalev V, Kishony R. Personal clinical history predicts antibiotic resistance of urinary tract infections. Nature medicine. 2019 Jul;25(7):1143-52. doi:10.1038/s41591-019-0503-6 [Google Scholar] [Crossref]

39. Komorowski M, Celi LA, Badawi O, Gordon AC, Faisal AA. Artificial intelligence in intensive care medicine. Intensive Care Med. 2020 Jul;46(7):1407-1420. doi:10.1007/s00134-020-06096-3 [Google Scholar] [Crossref]

40. Sendak MP, Ratliff W, Sarro D, Alderton E, Futoma J, Gao M, Nichols M, Revoir M, Yashar F, Miller C, Kester K. Real-world integration of a sepsis deep learning technology into routine clinical care: implementation study. JMIR medical informatics. 2020 Jul 15;8(7):e15182. doi:10.2196/15182 [Google Scholar] [Crossref]

41. Ghassemi M, Oakden-Rayner L, Beam AL. The false hope of current approaches to explainable artificial intelligence in health care. The lancet digital health. 2021 Nov 1;3(11):e745-50. [Google Scholar] [Crossref]

42. doi:10.1016/S2589-7500(21)00208-9 [Google Scholar] [Crossref]

43. Wiens J, Saria S, Sendak M, Ghassemi M, Liu VX, Doshi-Velez F, Jung K, Heller K, Kale D, Saeed M, Ossorio PN. Do no harm: a roadmap for responsible machine learning for health care. Nature medicine. 2019 Sep;25(9):1337-40. doi:10.1038/s41591-019-0548-6 [Google Scholar] [Crossref]

44. Holzinger A, Biemann C, Pattichis CS, Kell DB. What do we need to build explainable AI systems for the medical domain?. arXiv preprint arXiv:1712.09923. 2017 Dec 28. Available from: [Google Scholar] [Crossref]

45. https://arxiv.org/abs/1712.09923. [Google Scholar] [Crossref]

46. Morgan DJ, Malani P, Diekema DJ. Diagnostic stewardship—leveraging the laboratory to improve antimicrobial use. Jama. 2017 Aug 15;318(7):607-8. doi:10.1001/jama.2017.8531 [Google Scholar] [Crossref]

47. Banerjee R, Humphries R. Clinical and laboratory considerations for rapid molecular diagnostics. Clin Infect Dis. 2017 Jan 15;64(2):223-228. doi:10.1093/cid/ciw720 [Google Scholar] [Crossref]

48. Messacar K, Parker SK, Todd JK, Dominguez SR. Implementation of rapid molecular infectious disease diagnostics: the role of diagnostic and antimicrobial stewardship. Journal of clinical microbiology. 2017 Mar;55(3):715-23. doi:10.1128/JCM.02264-16 [Google Scholar] [Crossref]

49. Patel R. MALDI-TOF MS for the diagnosis of infectious diseases. Clinical chemistry. 2015 Jan 1;61(1):100-11. doi:10.1373/clinchem.2014.221770 [Google Scholar] [Crossref]

50. Clerc O, Prod'hom G, Vogne C, Meylan G, Albertini A, Greub G, et al. Impact of MALDI-TOF and stewardship interventions. Clin Infect Dis. 2013 Apr;56(8):1101-1107. doi:10.1093/cid/cit067 [Google Scholar] [Crossref]

51. Kumar A, Roberts D, Wood KE, Light B, Parrillo JE, Sharma S, Suppes R, Feinstein D, Zanotti S, Taiberg L, Gurka D. Duration of hypotension before initiation of effective antimicrobial therapy is the critical determinant of survival in human septic shock. Critical care medicine. 2006 Jun 1;34(6):1589-96. doi:10.1097/01.CCM.0000217961.75225.E9 [Google Scholar] [Crossref]

52. Chiu CY, Miller SA. Clinical metagenomics. Nature Reviews Genetics. 2019 Jun;20(6):341-55. doi:10.1038/s41576-019-0113-7 [Google Scholar] [Crossref]

53. Polk RE, Hohmann SF, Medvedev S, Ibrahim O. Benchmarking risk-adjusted adult antibacterial drug use in 70 US academic medical center hospitals. Clinical infectious diseases. 2011 Dec 1;53(11):1100-10. doi:10.1093/cid/cir672 [Google Scholar] [Crossref]

54. World Health Organization. Global antimicrobial resistance and use surveillance system (GLASS) report 2022. World Health Organization; 2022 Dec 8. [Google Scholar] [Crossref]

55. Available from: https://www.who.int/publications/i/item/9789240062702. [Google Scholar] [Crossref]

56. WHO W. Global action plan on antimicrobial resistance. World Health Organization. 2015 Apr 2. Available from: https://iris.who.int/items/b6a20419-f956-4c94-84ba-e0bf5f21e074. [Google Scholar] [Crossref]

57. World Health Organization. WHO consultation towards the development of guidance on ethics and governance of artificial intelligence for health: meeting report, Geneva, Switzerland, 2–4 October 2019. World Health Organization; 2021. [Google Scholar] [Crossref]

58. Available from: https://www.who.int/publications/i/item/9789240012752. [Google Scholar] [Crossref]

59. Corral-Acero J, Margara F, Marciniak M, Rodero C, Loncaric F, Feng Y, Gilbert A, Fernandes JF, Bukhari HA, Wajdan A, Martinez MV. The ‘Digital Twin’to enable the vision of precision cardiology. European heart journal. 2020 Dec 21;41(48):4556-64. doi:10.1093/eurheartj/ehaa159 [Google Scholar] [Crossref]

60. Schuetz P, Wirz Y, Sager R, Christ-Crain M, Stolz D, Tamm M, Bouadma L, Luyt CE, Wolff M, Chastre J, Tubach F. Effect of procalcitonin-guided antibiotic treatment on mortality in acute respiratory infections: a patient level meta-analysis. The Lancet infectious diseases. 2018 Jan 1;18(1):95-107. doi:10.1016/S1473-3099(17)30592-3 [Google Scholar] [Crossref]

61. Rieke N, Hancox J, Li W, Milletari F, Roth HR, Albarqouni S, Bakas S, Galtier MN, Landman BA, Maier-Hein K, Ourselin S. The future of digital health with federated learning. NPJ digital medicine. 2020 Sep 14;3(1):119. doi:10.1038/s41746-020-00323-1 [Google Scholar] [Crossref]

62. Murray CJ, Ikuta KS, Sharara F, Swetschinski L, Aguilar GR, Gray A, Han C, Bisignano C, Rao P, Wool E, Johnson SC. Global burden of bacterial antimicrobial resistance in 2019: a systematic analysis. The lancet. 2022 Feb 12;399(10325):629-55. doi:10.1016/S0140-6736(21)02724-0 [Google Scholar] [Crossref]