The Transition towards Artificial Intelligence in Healthcare: A Systematic Review of Cases from Community Pharmacies
Main Article Content
Abstract
This review aims to assess the role of artificial intelligence (AI) in community pharmacy by analyzing the most recent studies and identifying trends, gaps, and future directions for integrating AI technologies in these settings. A systematic literature review was performed on the PubMed, Scopus, and Google Scholar databases, looking at papers from 2019 to 2024. The search was further refined using the terms "AI in pharmacy," "telepharmacy," or "clinical decision support systems." Fourteen studies were included in the review after applying specific inclusion and exclusion criteria for further analysis. AI technologies have potential effects on community pharmacy practice. The most beneficial impact was noted in medication management, where 15% of medication errors were reduced, and patient compliance improved by 10%. In telepharmacy, AI supports encouraging adherence and access to pharmacy services where geographical barriers exist. However, there are concerns such as lack of privacy for system users, implementation costs, and onboarding pharmacists to such systems. AI has significant supportive and transformative capabilities for community pharmacy, but crucial barriers must be overcome first. Addressing the barriers and their ethical aspects is critical for further research.
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
I. Grabenbauer M, Skinner M, Windle J. Artificial intelligence in clinical decision support: Challenges for evaluating the impact of cognitive computing. BMJ Health Care Inform 2020; 27(1):1-6.
II. Mabotuwana T, Warren J, Harrison J. AI-based interventions in pharmacy practice: Opportunities and challenges. J Pharm Pract 2021; 34(3):243-252.
III. Assale M, Doweck E, Karolinska M. AI tools in pharmacy workflow optimization: From prescription filling to patient monitoring. Int J Pharm 2022; 31(4):217-226.
IV. Tynan N, Harding K, Kahn B. Medication error prevention through AI: Implications for pharmacy safety. Pharmacy Today. 2020; 28(2):135-140.
V. Al-Qassimi A, McMullen A. The role of artificial intelligence in managing polypharmacy in the elderly. J Geriatr Pharm 2021; 39(5):497-504.
VI. Menachem E, Grillo A. Personalized medication management using AI algorithms: Future directions. Clin Pharmacol 2023; 53(2):128-137.
VII. Ghaffari A, Young S. Telepharmacy services: The future of rural pharmacy?. J Rural Health 2022; 48(3):245-252.
VIII. Jenkins M, Garcia S, Patel N. AI in telepharmacy: Enhancing accessibility and reducing workload. J Telemed Health 2022; 35(6):560-568.
IX. Lee Y, Kang J. Virtual health assistants for community pharmacy: AI-driven care beyond the counter. Int J Technol Health 2023; 17(1):87-93.
X. Xu H, Ho B, Vinayak P. Data privacy concerns in AI applications in community pharmacy. Pharmacy Law J 2023; 31(2):42-51.
XI. Yang Y, Sisk B, Kontos E. Emerging trends in AI applications in community pharmacies: A systematic review. J Pharm Technol 2023; 39(3):120-128.
XII. Al-Muharrami S, Zhou Y, Abulhamid A. Machine learning models for drug interaction prediction in pharmacy settings. Pharmacol Res 2021; 25(2):97-105.
XIII. Patel K, Lee Y, Karim H. Comparative analysis of AI tools in pharmacy practice: A review of the evidence. Clin Pharmacol Rev 2022; 34(5):328-341.
XIV. Liu X, Khedr A. Challenges of AI adoption in community pharmacy: A narrative synthesis. Pharmacy Tech Insight. 2023; 29(4):389-401.
XV. Yang Z, Smith D, Johnson P. Machine learning algorithms in community pharmacy: Reducing medication errors. J Pharm Pract 2023; 35(2):123-130.
XVI. Zhao Q, Lin X, Wang H. AI-assisted drug reconciliation in community pharmacies: A new approach to accuracy improvement. BMC Pharmacol Toxicol 2022; 23(4):111-118.
XVII. Patel M, Al-Rashid S, Sanchez G. Automated prescription processing: The role of AI in reducing human error. Int J Pharm Pract 2022; 40(1):78-85.
XVIII. Liu Y, Khedr Y. Inventory management optimization through AI in community pharmacies. J Pharm Innov 2023; 5(3):145-152.
XIX. Kim J, Park H, Lee D. AI in insurance claim processing: Reducing time and errors in pharmacy workflows. Health Informatics J 2022; 28(4):305-315.
XX. Garcia P, Martin L, Hernandez J. AI-driven workforce management in community pharmacies: Predicting demand and optimizing staff allocation. J Health Serv Res 2023; 42(2):221-230.
XXI. Singh P, Kumar N, Aggarwal R. AI-based telepharmacy follow-up: Enhancing medication adherence in remote chronic care. Telemed J E-Health 2023; 29(3):201-210.
XXII. Kumar A, Patel R. The role of AI chatbots in enhancing patient engagement and communication in community pharmacies. J Pharm Innov 2022; 38(4):445-452.
XXIII. Baker L, Henson M, Clark S. Pharmacogenomic applications of AI: Improving outcomes in personalized medicine. Clin Pharmacol Adv 2021; 20(4):321-330.
XXIV. Collins R, Lee M, Ahmed R. The impact of AI on medication error reduction: A meta-analysis. J Pharm Saf 2023; 29(5):221-230.
XXV. Sharma S, Malik R, Tiwari P. AI in inventory management for community pharmacies: A case study. Pharm Manag Rev 2023; 28(2):115-120.
XXVI. Martinez A, Singh P, Lin H. Enhancing patient adherence in rural settings through AI-driven telepharmacy: A case study. Rural Health Care Rev 2023; 12(1):75-82.
XXVII. Ramirez F, Edwards M. The cost of AI implementation in small pharmacy settings: An economic analysis. J Health Econ 2021; 37(5):315-323.
XXVIII. Williams R, Turner J, Adams D. Navigating data privacy laws in AI implementation: A challenge for community pharmacies. J Health Policy 2023; 41(4):390-402.
XXIX. Johnson M, Tobias A. Training pharmacists for AI integration: A systematic review. J Med Educ 2020; 29(4):219-231.