Evidence mapPaperPMID 36958780Full record

SynthesisBMJ open2023

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

Gianfranco Damiani, Gerardo Altamura, Massimo Zedda, Mario Cesare Nurchis, Giovanni Aulino, Aurora Heidar Alizadeh, Francesca Cazzato, Gabriele Della Morte, Matteo Caputo, Simone Grassi and 2 more

Open access · goldAbstract readSystematic Review
In one paragraph

Synthesis in BMJ open, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed, 2 pooled it
11.9field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

14 citing papers in PubMed, 2 syntheses or guidelines pooled it, 34 citations in OpenAlex.

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  6. To err is no more (only) human: where does legal medicine stands on?International journal of legal medicine · 2026
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors at 2 institutions in 1 country.

Gianfranco DamianiDepartment of Life Sciences and Public Health, Università Cattolica del Sacro Cuore, Rome, Italy.
Gerardo AltamuraDepartment of Life Sciences and Public Health, Università Cattolica del Sacro Cuore, Rome, Italy.ORCID 0000-0002-6063-0544
Massimo ZeddaDepartment of Health Surveillance and Bioethics, Section of Legal Medicine, Fondazione Policlinico A. Gemelli IRCCS, Università Cattolica del Sacro Cuore, Rome, Italy.
Mario Cesare NurchisDepartment of Woman and Child Health and Public Health, Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Roma, Italy nurchismario@gmail.com.ORCID 0000-0002-9345-4292
Giovanni AulinoDepartment of Health Surveillance and Bioethics, Section of Legal Medicine, Fondazione Policlinico A. Gemelli IRCCS, Università Cattolica del Sacro Cuore, Rome, Italy.
Aurora Heidar AlizadehDepartment of Life Sciences and Public Health, Università Cattolica del Sacro Cuore, Rome, Italy.
Francesca CazzatoDepartment of Health Surveillance and Bioethics, Section of Legal Medicine, Fondazione Policlinico A. Gemelli IRCCS, Università Cattolica del Sacro Cuore, Rome, Italy.
Gabriele Della MorteFaculty of Law, Università Cattolica del Sacro Cuore, Milano, Italy.
Matteo CaputoSection of Criminal Law, Department of Juridical Science, Università Cattolica del Sacro Cuore, Milano, Italy.
Simone GrassiDepartment of Health Surveillance and Bioethics, Section of Legal Medicine, Fondazione Policlinico A. Gemelli IRCCS, Università Cattolica del Sacro Cuore, Rome, Italy.
Antonio OlivaDepartment of Health Surveillance and Bioethics, Section of Legal Medicine, Fondazione Policlinico A. Gemelli IRCCS, Università Cattolica del Sacro Cuore, Rome, Italy.
D.3.2 group
Università Cattolica del Sacro Cuore · ITAgostino Gemelli University Polyclinic · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesThe 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.

methodsA 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.

resultsStudies 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.

conclusionThis 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.

Indexed as

Artificial IntelligenceMedication Therapy ManagementChildHumansMedication ErrorsPatient SafetyPrimary Health CareFORENSIC MEDICINEPRIMARY CAREPUBLIC HEALTHRisk management

Identifiers

PMID36958780
PMCPMC10040015
OpenAlexW4360620584

What Socratic holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.