Clinical decision support systems for polypharmacy optimization in older patients: a narrative review

Clinical Snapshot

Key Findings

• P Value: Not reported

• Effect Size: Not applicable — no quantitative pooled effect size reported; narrative synthesis only

• Primary Outcome: Descriptive classification and comparison of CDSS for polypharmacy management in older adults, categorised as manual, hybrid, or automated systems, with assessment of operational characteristics, workflow integration, and validation evidence

• Nnt Or Sensitivity: Not reported; evidence ranged qualitatively from development and feasibility studies to observational analyses and RCTs, with no synthesis of diagnostic accuracy, NNT, or hazard ratios across included studies

• Confidence Interval: Not reported

Clinical Application

Feasibility of CDSS implementation varies substantially by system type. Automated CDSS integrated into EHR platforms offer the greatest efficiency but require significant infrastructure investment and interoperability. Manual CDSS impose high clinician burden. Hybrid systems may represent the most pragmatic near-term option for settings with partial EHR capability. Alert fatigue, workflow disruption, and clinician acceptance remain recognised implementation barriers across all system types. Polypharmacy is a significant and growing concern in Australian aged care and primary care settings. The RACGP supports medication review as a core component of chronic disease management, and Home Medicines Reviews (HMR) and Residential Medication Management Reviews (RMMR) are funded under the MBS. The Australian Commission on Safety and Quality in Health Care (ACSQHC) has identified medication safety in older people as a national priority. TGA-approved CDSS tools integrated into Australian EHR systems (e.g., Best Practice, Medical Director) exist but vary in sophistication. PBS-listed medications for older Australians frequently include high-risk drug classes (e.g., anticholinergics, benzodiazepines, NSAIDs) flagged by tools such as the Beers Criteria and STOPP/START. This review's taxonomy and findings are directly relevant to Australian health informaticians, pharmacists, and geriatricians evaluating or implementing CDSS, though the absence of Australian-specific validation data is a limitation. Older adults (≥65 years) with multimorbidity receiving five or more concurrent medications, particularly those managed in primary care, aged care facilities, hospital inpatient settings, and outpatient specialist clinics

Abstract

PURPOSE: Multimorbidity 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. METHODS: A 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. RESULTS: Manual 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). CONCLUSION: CDSS 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.

References

1. 1.Castellano, J., Caporlingua, M., & Ciurleo, R. (2026). Clinical decision support systems for polypharmacy optimization in older patients: a narrative review. European Journal of Clinical Pharmacology. https://doi.org/10.1186/s12911-020-01376-8