Assessing the potentiality of algorithms and artificial intelligence adoption to disrupt patient primary care with a safer and faster medication management: a systematic review protocol
Open accessAbstract
Introduction: In primary care, almost 75% of outpatient visits by family doctors and general practitioners involve continuation or initiation of drug therapy. Due to the enormous amount of drugs used by outpatients in unmonitored situations, the potential risk of adverse events due to an error in the use or prescription of drugs is much higher than in a hospital setting. Artificial intelligence (AI) application can help healthcare professionals to take charge of patient safety by improving error detection, patient stratification and drug management. The aim is to investigate the impact of AI algorithms on drug management in primary care settings and to compare AI or algorithms with standard clinical practice to define the medication fields where a technological support could lead to better results. Methods and analysis: A systematic review and meta-analysis of literature will be conducted querying PubMed, Cochrane and ISI Web of Science from the inception to December 2021. The primary outcome will be the reduction of medication errors obtained by AI application. The search strategy and the study selection will be conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses and the population, intervention, comparator and outcome framework. Quality of included studies will be appraised adopting the quality assessment tool for observational cohort and cross-sectional studies for non-randomised controlled trials as well as the quality assessment of controlled intervention studies of National Institute of Health for randomised controlled trials. Ethics and dissemination: Formal ethical approval is not required since no human beings are involved. The results will be disseminated widely through peer-reviewed publications.
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Overcoming these challenges requires interdisciplinary collaboration, developing a concrete plan by policymakers and administrators, dedicated healthcare professional education, and ongoing research and development to support effective AI integration into PHC.
“Overcoming these challenges requires interdisciplinary collaboration, developing a concrete plan by policymakers and administrators, dedicated healthcare professional education, and ongoing research and development to support effective AI integration into PHC [30, 37, 39–42].”
From Opportunities, challenges, and requirements for Artificial Intelligence (AI) implementation in Primary Health Care (PHC): a systematic review (2025) · cites this paper as [41]
The Discussion (page not supplied) says the proposed review could inform guidelines and help political decision-makers allocate resources for AI-related infrastructure. It does not call for interdisciplinary collaboration, a concrete implementation plan by policymakers and administrators, dedicated professional education, or ongoing research and development as requirements for AI integration into primary care. (LLM, conf=0.84)
Experimental — accuracy not yet measuredAI can help patients with chronic diseases, such as diabetes, hypertension, or cancer, adhere to their medication regimens by providing reminders, alerts, education, and coaching using mobile phone applications, chatbots, or wearable devices.
“AI can help patients with chronic diseases, such as diabetes, hypertension, or cancer, to adhere to their medication regimens by providing reminders, alerts, education, and coaching using mobile phone applications, chatbots, or wearable devices.69,70”
From Artificial Intelligence and Decision-Making in Healthcare: A Thematic Analysis of a Systematic Review of Reviews (2024) · cites this paper as [70]
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References 17 · Cited by 11
Research works printed but not linked: 17
- [1]Weber RJ. Medication Safety Principles and Practices. In: DiPiro JT, Talbert RL, Yee GC, et al, eds. Pharmacotherapy: a pathophysiologic approach, 10e. McGraw-Hill education, 2017. http://accesspharmacy.mhmedical.com/content.aspx?aid=1145217026
- [3]Rowan-Robinson K. Professional, structural and organisational interventions in primary care for reducing medication errors. Br J Community Nurs 2019; 24: 77– 9. 10.12968/bjcn.2019.24.2.77 30698476 · DOI
- [4]Wittich CM, Burkle CM, Lanier WL. Medication errors: an overview for clinicians. Mayo Clin Proc 2014; 89: 1116– 25. 10.1016/j.mayocp.2014.05.007 24981217 · DOI
- [5]Dean B. Prescribing errors: what’s the story? Chronic*ill 2001; 5: 19– 22.
- [6]Gandhi TK, Burstin HR, Cook EF, et al. Drug complications in outpatients. J Gen Intern Med 2000; 15: 149– 54. 10.1046/j.1525-1497.2000.04199.x 10718894 PMC1495358 · DOI
- [7]Thomsen LA, Winterstein AG, S⊘ndergaard B, et al. Systematic review of the incidence and characteristics of preventable adverse drug events in ambulatory care. Ann Pharmacother 2007; 41: 1411– 26. 10.1345/aph.1H658 17666582 · DOI
- [8]Jiang F, Jiang Y, Zhi H, et al. Artificial intelligence in healthcare: past, present and future. Stroke Vasc Neurol 2017; 2: 230– 43. 10.1136/svn-2017-000101 29507784 PMC5829945 · DOI · Paper page
- [9]Muehlematter UJ, Daniore P, Vokinger KN. Approval of artificial intelligence and machine learning-based medical devices in the USA and Europe (2015–20): a comparative analysis. Lancet Digit Health 2021; 3: e195– 203. 10.1016/S2589-7500(20)30292-2 33478929 · DOI · Paper page
- [10]De Silva K, Enticott J, Barton C, et al. Use and performance of machine learning models for type 2 diabetes prediction in clinical and community care settings: protocol for a systematic review and meta-analysis of predictive modeling studies. Digital Health 2021; 7: 205520762110473. 10.1177/20552076211047390 PMC8642048 34868616 · DOI
- [11]Roumeliotis N, Sniderman J, Adams-Webber T, et al. Effect of electronic prescribing strategies on medication error and harm in hospital: a systematic review and meta-analysis. J Gen Intern Med 2019; 34: 2210– 23. 10.1007/s11606-019-05236-8 31396810 PMC6816608 · DOI · Paper page
- [12]Choudhury A, Asan O. Role of artificial intelligence in patient safety outcomes: systematic literature review. JMIR Med Inform 2020; 8: e18599. 10.2196/18599 32706688 PMC7414411 · DOI · Paper page
- [14]Richardson WS, Wilson MC, Nishikawa J, et al. The well-built clinical question: a key to evidence-based decisions. ACP J Club 1995; 123: A12– 19. 10.7326/ACPJC-1995-123-3-A12 7582737 · DOI · Paper page
- [15]Institute of Medicine (US) Committee on Standards for Systematic Reviews of Comparative Effectiveness Research. Finding what works in health care: standards for systematic reviews. Eden J, Levit L, Berg a, Morton S, editors. Washington (DC): National Academies Press (US), 2011. 24983062
- [17]Deeks JJ, Higgins JPT, Altman DG. Chapter 10: analysing data and undertaking meta-analyses. In: Higgins JPT, Thomas J, Chandler J, et al, eds. Cochrane Handbook for systematic reviews of interventions version 6, 2019.
- [18]DerSimonian R, Laird N. Meta-Analysis in clinical trials. Control Clin Trials 1986; 7: 177– 88. 10.1016/0197-2456(86)90046-2 3802833 · DOI · Paper page
- [19]Higgins JPT, Thompson SG, Deeks JJ. Measuring inconsistency in meta-analyses. BMJ 2003; 327: 557– 60. 10.1136/bmj.327.7414.557 12958120 PMC192859 · DOI · Paper page
- [20]Walker E, Hernandez AV, Kattan MW. Meta-Analysis: its strengths and limitations. Cleve Clin J Med 2008; 75: 431– 9. 10.3949/ccjm.75.6.431 18595551 · DOI · Paper page
- Authors
- Oliva, A., Altamura, G., Nurchis, M. C., Zedda, M., Sessa, G., Cazzato, F., Aulino, G., Sapienza, M., Riccardi, M. T., Della Morte, G., Caputo, M., Grassi, S., & Damiani, G.
- Affiliations
- Università Cattolica del Sacro Cuore (IT)
- Agostino Gemelli University Polyclinic (IT)
- Faculty of Law, Università Cattolica del Sacro Cuore, Milan, Italy
- Department of Juridical Science, Section of Criminal Law, Università Cattolica del Sacro Cuore, Milan, Italy
- Corresponding author
- Nurchis, Mario Cesare
- ORCID
- Venue
- BMJ Open
- Publisher
- BMJ
- Published
- 2022-05
- Volume
- 12
- Issue
- 5
- Pages
- e057399
- ISSN
- 2044-6055 (ISSN-L)
- PMC
- PMC9114863
- OpenAlex
- W4280507168
- Cited by
- 11
- References
- 17
- Citations by year
- 2026: 2 · 2025: 3 · 2024: 4 · 2023: 2
- FWCI
- 0.15
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- BMJ Open (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (acceptedVersion)
- Florence Research (University of Florence) (submittedVersion, cc-by-nc)
- PubliCatt (Università Cattolica del Sacro Cuore) (submittedVersion, cc-by-nc)
- Funders
- Università Cattolica del Sacro Cuore (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, machine learning algorithms, medication errors, medication management, primary care, patient safety, adverse drug reactions, error detection, patient stratification, general practitioners
- MeSH terms
- Systematic Reviews as Topic, Artificial Intelligence, Cross-Sectional Studies, Patient Care, Health Personnel, Humans, Primary Health Care, Meta-Analysis as Topic
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