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Cited by 44Artificial Intelligence in Healthcare and EducationPharmaceutical Practices and Patient OutcomesPatient Safety and Medication Errors

Potentiality of algorithms and artificial intelligence adoption to improve medication management in primary care: a systematic review

Open access
Damiani, Gianfranco, Altamura, Gerardo, Zedda, Massimo, Nurchis, Mario Cesare, Aulino, Giovanni, Heidar Alizadeh, Aurora, Cazzato, Francesca, Della Morte, Gabriele +3 more·2023·BMJ Open
DOI 10.1136/bmjopen-2022-065301PMC PMC10040015
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Abstract

OBJECTIVES: The aim of this study is to investigate the effect of artificial intelligence (AI) and/or algorithms on drug management in primary care settings comparing AI and/or algorithms with standard clinical practice. Second, we evaluated what is the most frequently reported type of medication error and the most used AI machine type. METHODS: A systematic review of literature was conducted querying PubMed, Cochrane and ISI Web of Science until November 2021. The search strategy and the study selection were conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses and the Population, Intervention, Comparator, Outcome framework. Specifically, the Population chosen was general population of all ages (ie, including paediatric patients) in primary care settings (ie, home setting, ambulatory and nursery homes); the Intervention considered was the analysis AI and/or algorithms (ie, intelligent programs or software) application in primary care for reducing medications errors, the Comparator was the general practice and, lastly, the Outcome was the reduction of preventable medication errors (eg, overprescribing, inappropriate medication, drug interaction, risk of injury, dosing errors or in an increase in adherence to therapy). The methodological quality of included studies was appraised adopting the Quality Assessment of Controlled Intervention Studies of the National Institute of Health for randomised controlled trials. RESULTS: Studies reported in different ways the effective reduction of medication error. Ten out of 14 included studies, corresponding to 71% of articles, reported a reduction of medication errors, supporting the hypothesis that AI is an important tool for patient safety. CONCLUSION: This study highlights how a proper application of AI in primary care is possible, since it provides an important tool to support the physician with drug management in non-hospital environments.

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  • Ethical concerns, privacy issues, professional liability, data quality, and integration difficulties have been highlighted as major obstacles to AI in PHC.

    “Ethical concerns, privacy issues, professional liability, data quality, and integration difficulties have been highlighted as major obstacles [32, 37–39].”

    From Opportunities, challenges, and requirements for Artificial Intelligence (AI) implementation in Primary Health Care (PHC): a systematic review (2025) · cites this paper as [38]

    The Discussion (page not available) identifies lack of personal-data protection, opaque algorithmic decisions, and the quality of training data as potential AI issues. It also reports difficulty interfacing with software, which supports an integration concern. However, its discussion of medicolegal consequences concerns medication errors in primary care, not professional liability as a major obstacle to adopting AI; the paper does not characterize the full list as major obstacles. (LLM, conf=0.85)

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  • AI and machine-learning clinical decision support can help manage medications by checking doses and possible drug interactions, improving medication safety.

    “Medication management: AI/ML-driven CDS can aid management of medications by checking dosages and potential drug interactions to enhance medication safety [17].”

    From Machine learning-based clinical decision support using laboratory data (2024) · cites this paper as [17]

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Research works printed but not linked: 3

  1. [1]Aspden P, Corrigan JM, Wolcott J, et al. Patient safety: achieving a new standard for care. Washington (DC), 2004. 25009854
  2. [11]Institute of Medicine. Crossing the quality chasm: A new health system for the 21st century. Washington, DC: The National Academies Press, 2001. Available: https://www.nap.edu/catalog/10027/crossing-the-quality-chasm-a-new-health-system-for-the 25057539
  3. [23]Shapiro SC. Artificial intelligence. In: Shapiro SC, ed. Encyclopedia of Artificial Intelligence, 1. 2nd edn. New York: Wiley, 1992.

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Authors
Damiani, G., Altamura, G., Zedda, M., Nurchis, M. C., Aulino, G., Heidar Alizadeh, A., Cazzato, F., Della Morte, G., Caputo, M., Grassi, S., & Oliva, A.
Affiliations
  • Università Cattolica del Sacro Cuore (IT)
  • Agostino Gemelli University Polyclinic (IT)
  • Department of Life Sciences and Public Health, Università Cattolica del Sacro Cuore, Rome, Italy
  • Faculty of Law, Università Cattolica del Sacro Cuore, Milano, Italy
  • Section of Criminal Law, Department of Juridical Science, Università Cattolica del Sacro Cuore, Milano, Italy
Corresponding author
Nurchis, Mario Cesare
ORCID
Venue
BMJ Open
Publisher
BMJ
Published
2023-03
Volume
13
Issue
3
Pages
e065301
ISSN
2044-6055 (ISSN-L)
DOI
10.1136/bmjopen-2022-065301
PMC
PMC10040015
OpenAlex
W4360620584
Cited by
44
References
59
Citations by year
2026: 12 · 2025: 14 · 2024: 12 · 2023: 5 · 2013: 1
FWCI
0.51
Type
journal article
Language
en
Open access
Licence
Locations
  • BMJ Open (publishedVersion, cc-by-nc)
  • PubMed Central (publishedVersion, cc-by-nc)
  • PubMed (publishedVersion)
  • Florence Research (University of Florence) (submittedVersion, cc-by-nc)
  • PubliCatt (Università Cattolica del Sacro Cuore) (submittedVersion, cc-by-nc)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • HighWire Press Open Archive (submittedVersion)
Funders
Università Cattolica del Sacro Cuore (Linea D.3.2-Project R1024500180)
Topics
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Health Sciences › Medicine › Geriatrics and Gerontology › Pharmaceutical Practices and Patient Outcomes
  • Health Sciences › Health Professions › Emergency Medical Services › Patient Safety and Medication Errors
Keywords
artificial intelligence, clinical decision support algorithms, medication errors, medication management, primary care, patient safety, overprescribing, drug interactions, medication adherence, dosing errors, inappropriate medication use
MeSH terms
Artificial Intelligence, Child, Humans, Medication Errors, Primary Health Care, Medication Therapy Management, Patient Safety

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