Background
Real-world data from electronic health records (EHR) have increasingly become a critical resource, offering a platform to advance pharmacometrics modeling that can inform real-time clinical decision making. The EHR contain vast information including demographics, dosing and outcomes which can catalyze a shift from population to individualized dosing based on the patient-specific characteristics. We conducted this systematic review to assess the current application of EHR data in pharmacometrics-based clinical decision-making in a clinical setting.
Methods
We systematically searched PubMed, Scopus, Google Scholar and Web of Science for original research articles published from January 2010 to August 2025. The review included studies that deployed EHR in pharmacometrics modeling to support clinical decision making. Study characteristics extracted included study population, EHR System, pharmacometrics methods used, clinical application and key outcomes. Data extraction and reporting followed PRISMA guidelines.
Results
Eight studies met the inclusion criteria. The common pharmacometrics approach used was Population PK/PD modeling with clinical decision support system (n=5) followed by model-informed precision dosing with Bayesian forecasting (n=3). Pediatrics (n=4) and neonates (2) were dominant target populations where these EHR data supported, Pharmacometrics-based, clinical decision strategies were applied. The strategies predominantly improved optimization and adjustment for antibiotics (n=4) and analgesics(n=2). Key challenges observed included missing data, variability of target population necessitating varying models and limited generalizability arising from poor interoperability of the EHR databases with partner health facilities. Additionally, all the studies included in this review were from high-income settings where EHR systems are relatively advanced compared to low-middle-income settings.
Conclusion
EHR-integrated pharmacometrics promises to revolutionize precision medicine. More clinical benefits can be realized by improving the EHR data quality and interoperability.
References
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