Evidence map›Paper›PMID 41964348›Full record

ArticleHIV medicine2026

A multidisciplinary, AI-supported quality improvement intervention to manage polypharmacy in aging people with HIV.

Jovana Milic, Antonia Pugliese, Michela Belli, Gian Luca Lonardi, Caterina Ruffilli, Tommaso Albano, Marco Visicaro, Martina Ricciardetto, Pierluigi De Cosmo, Chiara Mussi and 4 more

Abstract read
In one paragraph

Article in HIV medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

14 authors.

Jovana MilicDepartment of Surgical, Medical, Dental and Morphological Sciences, University of Modena and Reggio Emilia, Modena, Italy.ORCID https://orcid.org/0000-0002-9806-8470
Antonia PuglieseDistribuzione Diretta AUSL Modena, Dipartimento Farmaceutico Interaziendale, Modena, Italy.
Michela BelliDepartment of Surgical, Medical, Dental and Morphological Sciences, University of Modena and Reggio Emilia, Modena, Italy.
Gian Luca LonardiEngineering Informatics, Modena, Italy.
Caterina RuffilliSchool of Medicine, University of Modena and Reggio Emilia, Modena, Italy.
Tommaso AlbanoInfectious Diseases Clinic, Azienda Ospedaliero-Universitaria, Policlinico of Modena, Modena, Italy.
Marco VisicaroInfectious Diseases Clinic, Azienda Ospedaliero-Universitaria, Policlinico of Modena, Modena, Italy.
Martina RicciardettoInfectious Diseases Clinic, Azienda Ospedaliero-Universitaria, Policlinico of Modena, Modena, Italy.
Pierluigi De CosmoInfologic Srl, Padova, Italy.
Chiara MussiDepartment of Biomedical, Metabolic and Neural Sciences and Center for Gerontological Evaluation and Research, University of Modena and Reggio Emilia, Modena, Italy.
Francesca GandolfiDistribuzione Diretta AUSL Modena, Dipartimento Farmaceutico Interaziendale, Modena, Italy.
Cristina MussiniDepartment of Surgical, Medical, Dental and Morphological Sciences, University of Modena and Reggio Emilia, Modena, Italy.
Costantino GranaEnzo Ferrari Department of Engineering, University of Modena and Reggio Emilia, Modena, Italy.
Giovanni GuaraldiDepartment of Surgical, Medical, Dental and Morphological Sciences, University of Modena and Reggio Emilia, Modena, Italy.ORCID https://orcid.org/0000-0002-5724-3914

Funding

Merck
6 · The paper itself

Abstract

objectivesAging people with HIV are increasingly affected by multimorbidity and polypharmacy, which heighten the risk of drug-drug interactions (DDIs) and potentially inappropriate medications (PIMs). This study evaluated a multidisciplinary, AI-supported quality improvement intervention designed to optimize polypharmacy management in older people with HIV.

methodsPeople with HIV aged ≥50 years attending the Modena HIV Metabolic Clinic (MHMC) were invited to submit photos of their medications via WhatsApp. Images were processed by AI for optical character recognition and automatically reconciled with the electronic patient chart (EPC). AI recognition accuracy was 94% when validated against manual review. Pharmacists reviewed AI-generated reports from the NavFarma® decision support system, generated alerts for PIM, defined according to Beers and the STOPP/START criteria, DDIs, anticholinergic burden (ACB), and risks of QTc prolongation and nephrotoxicity. Primary outcome was agreement between patient-reported and EPC-recorded medications. Secondary outcomes included pill burden, total prescribed drugs and actionable alerts.

resultsOf 181 participants (median age 63 years; 72% male), 111 (61.3%) showed complete agreement between EPC and patient lists, while 70 (38.7%) had discrepancies. Pharmacist evaluation identified major DDIs in 70.4% of cases, ACB in 26.5%, QTc-prolonging drugs in 81.6% and nephrotoxic agents in 95.9%. Participants with ≥10 total prescribed drugs had higher frailty, pill burden and PIM.

conclusionsAI-assisted medication reconciliation combined with pharmacist review improved the identification of PIM and medication-related risks, supporting safer prescribing in people with HIV. This model aligns with international calls to improve prescribing safety and offers a scalable framework for integrating digital tools into multidisciplinary HIV care.

Indexed as

Artificial IntelligenceHIV InfectionsPolypharmacyQuality ImprovementAgedDrug InteractionsElectronic Health RecordsFemaleHumansInappropriate PrescribingMaleMiddle Agedaginginappropriate prescriptionolder people with HIVpolypharmacyquality improvement

Identifiers

PMID41964348
PMCPMC13340986

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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 OpenQuestion graph.