ReviewEuropean journal of clinical pharmacology2026
Clinical decision support systems for polypharmacy optimization in older patients: a narrative review.
Review in European journal of clinical pharmacology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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.
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.
Who cites it
1 citing paper in PubMed.
- Comment on: "Adverse Oral Mucosal Reaction to Sublingual Captopril: A Case Report With Exploratory Insights Into AI-Assisted Clinical Reasoning".Special care in dentistry : official publication of the American Association of Hospital Dentists, the Academy of Dentistry for the Handicapped, and the American Society for Geriatric DentistryArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeMultimorbidity and polypharmacy are increasingly prevalent in the older population and are associated with a higher risk of potentially inappropriate medications (PIM), drug-drug interactions (DDI), and adverse drug reactions (ADR). Although medication review (MR) and deprescribing are effective strategies, their manual implementation can be complex, time-consuming, and prone to clinical variability. Clinical Decision Support Systems (CDSS) offer advanced digital solutions to optimize polypharmacy by analyzing multidimensional clinical data and generating personalized recommendations.
methodsA narrative review was conducted to identify and compare the main CDSS developed for the polypharmacy management, MR and deprescribing in older adults and patients with multimorbidity. Systems were classified as manual, hybrid, or automated according to their data acquisition modalities. Operational characteristics, integration into clinical workflows, decision-support functions, generated outputs, and available validation evidence across different healthcare settings were assessed. Owing to the narrative nature of the review and the heterogeneity of the included evidence, no formal risk-of-bias assessment or certainty-of-evidence evaluation was performed.
resultsManual CDSS require direct data entry by clinicians and are associated with a high operational burden. Hybrid systems combine automatic data acquisition with manual integration, balancing efficiency and clinical oversight. Automated systems, integrated into electronic health records (EHR), provide real-time decision support with minimal human intervention. Considerable heterogeneity was observed across identified platforms in terms of automation, implementation characteristics, and stage of validation, with evidence ranging from development and feasibility studies to observational analyses and randomized controlled trials (RCT).
conclusionCDSS represent promising tools for safer and more effective management of polypharmacy in complex patients. Advanced integration into clinical workflows and systematic use of multidimensional data may enhance their impact. However, the heterogeneity of available systems and the variability in their level of clinical validation highlight the need for comparative studies, pragmatic trials, and real-world implementation evaluations. Such studies are necessary to clarify the impact of different CDSS models on prescribing appropriateness, medication-related risks, patient outcomes, and long-term sustainability within routine healthcare settings.
Indexed as
Identifiers
42472763What OpenQuestion holds
Registered trials
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.