International expert consensus on hospital intelligent pharmacy

Cao Li , Wenshuo Jiang , Aizong Shen , Yilei Li , Junyan Wu , Hua Tao , Yongqiang Tang , Xiaolin Yue , Alice Hao , Zhigang Zhao

Intelligent Pharmacy ›› 2025, Vol. 3 ›› Issue (6) : 378 -386.

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Intelligent Pharmacy ›› 2025, Vol. 3 ›› Issue (6) :378 -386. DOI: 10.1016/j.ipha.2025.06.001
Consensus

International expert consensus on hospital intelligent pharmacy

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Abstract

As the rapid advancements in medical technology and increasing demands for personalized medication, Hospital Intelligent Pharmacy (HIP) integrates artificial intelligence, large-scale health data analytics, the Internet of Things (IoT), and other cutting-edge technologies to optimize end-to-end pharmaceutical supply chain processes, management, and clinical processes. In recent years, regulatory agencies such as the European Medicines Agency (EMA), the Medicines and Healthcare products Regulatory Agency (MHRA), China's National Medical Products Administration (NMPA), and the U.S. Food and Drug Administration (USFDA) have issued policies to promote intelligent pharmacy development. However, HIP still faces challenges including ambiguous definitions, absence of standardized technical protocols, and incomplete evaluation frameworks. To address these issues, international and domestic academic organizations collaboratively developed the International Expert Consensus on Hospital Intelligent Pharmacy. This consensus clarifies HIP's definition, core components, and systematic framework, providing scientific guidance for standardized implementation and clinical application of intelligent pharmacy in hospitals. Utilizing a Delphi method process, expert opinions will be collected, analyzed, and refined. The current consensus defines HIP's scope and principles, outlining a framework with 10 components:intelligent drug supply chain management, drug dispensing, prescription review, pharmacovigilance, medication therapy management, therapeutic drug monitoring, telepharmacy services, pharmacy administration, science popularization, and clinical trials. Future directions focus on 5 key areas:AI-augmented pharmacist competency development, advancing pharmaceutical scientific research, fostering intelligent pharmaceutical publications and journals, addressing ethical and legal challenges, and promoting international harmonization in pharmacy. The consensus offers critical references and exploratory pathways for HIP's global advancement.

Keywords

Intelligent pharmacy / Hospital pharmacy / Clinical pharmacist / Rational drug use / Consensus

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Cao Li, Wenshuo Jiang, Aizong Shen, Yilei Li, Junyan Wu, Hua Tao, Yongqiang Tang, Xiaolin Yue, Alice Hao, Zhigang Zhao. International expert consensus on hospital intelligent pharmacy. Intelligent Pharmacy, 2025, 3(6): 378-386 DOI:10.1016/j.ipha.2025.06.001

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References

[1]

Bu F , Sun H , Li L , et al. Artificial intelligence-based internet hospital pharmacy services in China:perspective based on a case study. Front Pharmacol. 2022; 13: 1027808.

[2]

Jaber D , Hasan HE , Abutaima R , Sawan HM , Al TS . The impact of artificial intelligence on the knowledge, attitude, and practice of pharmacists across diverse settings: a cross-sectional study. Int J Med Inform. 2024; 192: 105656.

[3]

Aungst TD . Beyond the fill: navigating pharmacy's technological future in 2050. J Am Pharmaceut Assoc:J Am Pharm Assoc JAPhA. 2024: 102285.

[4]

Smoke S . The two dimensions of pharmacy artificial intelligence tools. Am J Health Syst Pharm. 2024; 82 (3): e113- e116.

[5]

Schutz N , Olsen CA , Mclaughlin AJ , et al. ASHP statement on the use of artificial intelligence in pharmacy. Am J Health Syst Pharm. 2020; 77 (23): 2015- 2018.

[6]

Jarab AS , Abu HS , Al MA . Artificial intelligence (AI) in pharmacy: an overview of innovations. J Med Econ. 2023; 26 (1): 1261- 1265.

[7]

Dipiro JT , Hoffman JM , Schweitzer P , et al. ASHP and ASHP foundation pharmacy forecast 2024:strategic planning guidance for pharmacy departments in hospitals and health systems. Am J Health Syst Pharm. 2024; 81 (2): 5- 36.

[8]

Schneider PJ , Pedersen CA , Ganio MC , Scheckelhoff DJ . ASHP national survey of pharmacy practice in hospital settings:operations and technology-2023. Am J Health Syst Pharm. 2024; 81 (16): 684- 705.

[9]

Fernández DGE , Tortajada-Goitia B , Corte-García JJ , et al. Hospital pharmacy towards 2030. Farm Hosp. 2024; 48 (Suppl 1): S52- S58.

[10]

Jessica H , Britney R , Sarira ED , Parisa A , Joe Z , Betty BC . Applications of artificial intelligence in current pharmacy practice: a scoping review. Res Social Adm Pharm. 2024: S1551- S7411.

[11]

HMA-EMA Big Data Steering Group . Multi-annual AI workplan 2023-2028[cited 2025-3-31]. 2023

[12]

Medicines and Healthcare products Regulatory Agency . Impact of AI on the regulation of medical products[cited 2025-3-31]. 2024

[13]

Medicines and Healthcare products Regulatory Agency . Impact of AI on the regulation of medical products[cited 2025-3-31]. 2025

[14]

General Office of the National Health Commission . Comprehensive department of the national administration of traditional Chinese medicine, comprehensive department of the national disease control and prevention administration. Notice of the general office of the national health commission on issuing the reference guidelines for artificial intelligence application scenarios in the health industry.

[15]

Nelson SD , Walsh CG , Olsen CA , et al. Demystifying artificial intelligence in pharmacy. Am J Health Syst Pharm. 2020; 77 (19): 1556- 1570.

[16]

Dentzer S . Creating the future of artificial intelligence in health-system pharmacy. Am J Health Syst Pharm. 2019; 76 (24): 1995- 1996.

[17]

Abu-Shraie N . Could human pharmacy and therapeutics committees be replaced with artificial intelligence systems? Am J Health Syst Pharm; 2025. zxae413. Advance online publication.

[18]

Smoke S . Artificial intelligence in pharmacy: a guide for clinicians. Am J Health Syst Pharm. 2024; 81 (14): 641- 646.

[19]

Nelson SD . Artificial intelligence and the future of pharmacy. Am J Health Syst Pharm. 2024; 81 (4): 83- 84.

[20]

Senerth E , Whaley P , Akl E , et al. Development of a framework to structure decisionmaking in environmental and occupational health: a systematic review and delphi study. Environ Int. 2025; 195: 109209.

[21]

Rodgers M , Thomas S , Harden M , Parker G , Street A , Eastwood A . Developing a Methodological Framework for Organisational Case Studies: A Rapid Review and Consensus Development Process. Southampton (UK): NIHR Journals Library; 2016.

[22]

International Pharmaceutical Federation . A global competency framework for services provided by pharmacy workforce[cited 2025-5-5]. 2012.

[23]

Executive summary of the 2019. ASHP commission on goals:impact of artificial intelligence on healthcare and pharmacy practice. Am J Health Syst Pharm. 2019; 76 (24): 2087- 2092.

[24]

Cobaugh DJ , Thompson KK . Embracing the role of artificial intelligence in the medication-use process. Am J Health Syst Pharm. 2020; 77 (23): 1915- 1916.

[25]

Worrall C , Shirley D , Bullard J , Dao A , Morrisette T . Impact of a clinical pharmacistled, artificial intelligence-supported medication adherence program on medication adherence performance, chronic disease control measures, and cost savings, 2025. J Am Pharm Assoc. 2003; 65 (1): 102271.

[26]

Worrall C , Shirley D , Bullard J , Dao A , Morrisette T . Impact of a clinical pharmacistled, artificial intelligence-supported medication adherence program on medication adherence performance, chronic disease control measures, and cost savings. J Am Pharmaceut Assoc:J Am Pharm Assoc JAPhA. 2024: 102271.

[27]

Chaichulee S , Promchai C , Kaewkomon T , Kongkamol C , Ingviya T , Sangsupawanich P . Multi-label classification of symptom terms from free-text bilingual adverse drug reaction reports using natural language processing. PLoS One. 2022; 17 (8): e0270595.

[28]

Alqahtani T , Badreldin HA , Alrashed M , et al. The emergent role of artificial intelligence, natural learning processing, and large language models in higher education and research. Res Social Adm Pharm. 2023; 19 (8): 1236- 1242.

[29]

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.

[30]

Imai S . Data-driven clinical pharmacy research:utilizing machine learning and medical big data. Biol Pharm Bull. 2024; 47 (10): 1594- 1599.

[31]

Frestel J , Teoh SWK , Broderick C , Dao A , Sajogo M . A health integrated platform for pharmacy clinical intervention data management and intelligent visual analytics and reporting. Explor Res Clin Soc Pharm. 2023; 12: 100332.

[32]

Van Antwerp GJ . Pharmacy 2050: a new clinical and patient experience. J Am Pharmaceut Assoc:J Am Pharm Assoc JAPhA. 2024: 102290.

[33]

Roosan D , Wu Y , Tatla V , et al. Framework to enable pharmacist access to health care data using blockchain technology and artificial intelligence. J Am Pharmaceut Assoc:J Am Pharm Assoc JAPhA. 2022; 62 (4): 1124- 1132.

[34]

Shen J , Bu F , Ye Z , et al. Management of drug supply chain information based on "artificial intelligence + vendor managed inventory" in China:perspective based on a case study. Front Pharmacol. 2024; 15: 1373642.

[35]

Pall R , Gauthier Y , Auer S , Mowaswes W . Predicting drug shortages using pharmacy data and machine learning. Health Care Manag Sci. 2023; 26 (3): 395- 411.

[36]

Ashraf AR , Somogyi-Végh A , Merczel S , Gyimesi N , Fittler A . Leveraging code-free deep learning for pill recognition in clinical settings: a multicenter, real-world study of performance across multiple platforms. Artif Intell Med. 2024; 150: 102844.

[37]

Corny J , Rajkumar A , Martin O , et al. A machine learning-based clinical decision support system to identify prescriptions with a high risk of medication error. J Am Med Inform Assoc. 2020; 27 (11): 1688- 1694.

[38]

Chen CY , Chen YL , Scholl J , Yang HC , Li YJ . 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.

[39]

Hogue SC , Chen F , Brassard G , et al. Pharmacists' perceptions of a machine learning model for the identification of atypical medication orders. J Am Med Inform Assoc. 2021; 28 (8): 1712- 1718.

[40]

Anderson AB , Grazal CF , Balazs GC , Potter BK , Dickens JF , Forsberg JA . Can predictive modeling tools identify patients at high risk of prolonged opioid use after ACL reconstruction? Clin Orthop Relat Res. 2020; 478 (7): 1618.

[41]

Longato E , Fadini GP , Sparacino G , Avogaro A , Tramontan L , Di Camillo B . A deep learning approach to predict diabetes' cardiovascular complications from administrative claims. IEEE J Biomed Health Inform. 2021; 25 (9): 3608- 3617.

[42]

Potier A , Dufay E , Dony A , et al. Pharmaceutical algorithms set in a real time clinical decision support targeting high-alert medications applied to pharmaceutical analysis. Int J Med Inform. 2022; 160: 104708.

[43]

Mcmaster C , Liew D , Keith C , Aminian P , Frauman A . A machine-learning algorithm to optimise automated adverse drug reaction detection from clinical coding. Drug Saf. 2019; 42 (6): 721- 725.

[44]

Fong A , Harriott N , Walters DM , Foley H , Morrissey R , Ratwani RR . Integrating natural language processing expertise with patient safety event review committees to improve the analysis of medication events. Int J Med Inform. 2017; 104: 120- 125.

[45]

Cho J , Ra LA , Koo D , et al. Development of machine-learning models using pharmacy inquiry database for predicting dose-related inquiries in a tertiary teaching hospital. Int J Med Inform. 2024; 185: 105398.

[46]

Beaudoin M , Kabanza F , Nault V , Valiquette L . Evaluation of a machine learning capability for a clinical decision support system to enhance antimicrobial stewardship programs. Artif Intell Med. 2016; 68: 29- 36.

[47]

Liu X , Barreto EF , Dong Y , et al. Discrepancy between perceptions and acceptance of clinical decision support systems:implementation of artificial intelligence for vancomycin dosing. BMC Med Inform Decis Mak. 2023; 23 (1): 157.

[48]

Herrin J , Abraham NS , Yao X , et al. Comparative effectiveness of machine learning approaches for predicting gastrointestinal bleeds in patients receiving antithrombotic treatment. JAMA Netw Open. 2021; 4 (5): e2110703.

[49]

Wang NN , Wang XG , Xiong GL , et al. Machine learning to predict metabolic drug interactions related to cytochrome p450 isozymes. J Cheminform. 2022; 14 (1): 23.

[50]

Chi CL , Wang J , Ying YP , et al. Producing personalized statin treatment plans to optimize clinical outcomes using big data and machine learning. J Biomed Inform. 2022; 128: 104029.

[51]

Bulaj G , Clark J , Ebrahimi M , Bald E . From precision metapharmacology to patient empowerment:delivery of self-care practices for epilepsy, pain, depression and cancer using digital health technologies. Front Pharmacol. 2021; 12: 612602.

[52]

Roosan D , Padua P , Khan R , Khan H , Verzosa C , Wu Y . Effectiveness of ChatGPT in clinical pharmacy and the role of artificial intelligence in medication therapy management. J Am Pharmaceut Assoc:J Am Pharm Assoc JAPhA. 2024; 64 (2): 422- 428.

[53]

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: e51635.

[54]

Chou W , Lin Z . Machine learning and artificial intelligence in physiologically based pharmacokinetic modeling. Toxicol Sci. 2023; 191 (1): 1- 14.

[55]

Swierczek A , Batko D , Wyska E . The role of pharmacometrics in advancing the therapies for autoimmune diseases. Pharmaceutics. 2024; 16 (12).

[56]

Haas K , Ben MZ , Mahoui M . Medication adherence prediction through online social forums: a case study of fibromyalgia. JMIR Med Inform. 2019; 7 (2): e12561.

[57]

Usui M , Aramaki E , Iwao T , Wakamiya S , Sakamoto T , Mochizuki M . Extraction and standardization of patient complaints from electronic medication histories for pharmacovigilance:natural language processing analysis in Japanese. JMIR Med Inform. 2018; 6 (3): e11021.

[58]

Bottacin WE , Luquetta A , Gomes-Jr L , de Souza TT , Reis W , Melchiors AC . Sentiment analysis in medication adherence:using ruled-based and artificial intelligencedriven algorithms to understand patient medication experiences. Int J Clin Pharm. 2024.

[59]

Thibault M , Tanguay C . Development and evaluation of a model to identify publications on the clinical impact of pharmacist interventions. Res Social Adm Pharm. 2024; 20: 1134- 1141.

[60]

Fu L , Jia G , Liu Z , Pang X , Cui Y . The applications and advances of artificial intelligence in drug regulation: a global perspective. Acta Pharm Sin B. 2025; 15 (1): 1- 14.

[61]

Del RC , Medrano IH , Yebes L , Poveda JL . Towards a symbiotic relationship between big data, artificial intelligence, and hospital pharmacy. J Pharm Policy Pract. 2020; 13 (1): 75.

[62]

Molokhia M , Majeed A . Current and future perspectives on the management of polypharmacy. BMC Fam Pract. 2017; 18 (1): 70.

[63]

Kufel WD , Hanrahan KD , Seabury RW , et al. Let's have a chat:how well does an artificial intelligence chatbot answer clinical infectious diseases pharmacotherapy questions? Open Forum Infect Dis. 2024; 11 (11): ofae641.

[64]

Grothen AE , Tennant B , Wang C , et al. Application of artificial intelligence methods to pharmacy data for cancer surveillance and epidemiology research: a systematic review. JCO Clin Cancer Inform. 2020; 4: 1051- 1058.

[65]

Aldughayfiq B , Sampalli S . Patients', pharmacists', and prescribers' attitude toward using blockchain and machine learning in a proposed eprescription system:online survey. JAMIA Open. 2022; 5 (1): ooab115.

[66]

Sirois C , Khoury R , Durand A , et al. Exploring polypharmacy with artificial intelligence:data analysis protocol. BMC Med Inform Decis Mak. 2021; 21 (1): 219.

[67]

Hasan HE , Jaber D , Khabour OF , Alzoubi KH . Ethical considerations and concerns in the implementation of AI in pharmacy practice: a cross-sectional study. BMC Med Ethics. 2024; 25 (1): 55.

[68]

Da SM , Horsley T , Singh D , et al. Legal concerns in health-related artificial intelligence: a scoping review protocol. Syst Rev. 2022; 11 (1): 123.

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