Patient Safety and Medication Management

Patient safety and medication management encompass the processes by which medicines are selected, prescribed, dispensed and monitored to prevent harm and optimise therapeutic outcomes. This field addresses errors at every stage—from prescribing and transcription through dispensing and administration—recognising that adverse drug events remain a leading cause of avoidable harm worldwide. Strategies to enhance safety include the integration of digital tools, structured risk assessment, interdisciplinary collaboration and patient‐centred education. Emerging approaches leverage artificial intelligence, real-time data analytics and clinical decision support to reduce errors and near-misses. Equally important are robust transition‐of‐care protocols and active involvement of pharmacists in clinical teams, which have been shown to curb prescribing errors and improve adherence. Taken together, these interventions form a multifaceted framework for safeguarding patients and strengthening health-system resilience.

Research from Nature Portfolio

Recent studies have demonstrated how large language models, when fine-tuned with domain expertise and safety guardrails, can markedly reduce errors in pharmacy directions. A system trained on expert-annotated prescriptions was shown to lower near-miss events by a third compared with existing benchmarks, by accurately extracting and assembling core prescription components such as dosage and frequency. Deployment in an online pharmacy environment confirmed improvements in both accuracy and operational efficiency, highlighting the potential of AI-driven tools to support pharmacists and enhance communication of critical instructions to patients.

Patient Safety and Medication Management publication trend

The graph below shows the total number of articles in patient safety and medication management across all publications each year (not limited to Nature Index journals).

Technical terms

Large Language Model (LLM): A deep-learning system trained on vast text corpora to generate or interpret natural language, here adapted for medication instructions.

Near-miss event: An error caught and corrected before reaching the patient, indicating a vulnerability in safety processes.

Computerised Provider Order Entry (CPOE): An electronic system for entry and transmission of medication orders, designed to reduce errors associated with handwriting and transcription.

Clinical Decision Support System (CDSS): A digital tool integrated with health records that provides alerts and guidance to clinicians to improve decision-making.

Alert fatigue: A reduction in clinician responsiveness to system warnings due to high frequency or low relevance of alerts.

References

1. Large language models for preventing medication direction errors in online pharmacies. Nature Medicine (2024).
2. The impact of transition to a digital hospital on medication errors (TIME study). npj Digital Medicine (2023).
3. Effects of workload, work complexity, and repeated alerts on alert fatigue in a clinical decision support system. BMC Medical Informatics and Decision Making (2017).
4. The effectiveness of computerized order entry at reducing preventable adverse drug events and medication errors in hospital settings: a systematic review and meta-analysis. Systematic Reviews (2014).
5. On-ward participation of a hospital pharmacist in a Dutch intensive care unit reduces prescribing errors and related patient harm: an intervention study. Critical Care (2010).