CURRENT ISSUE

ABSTRACT: Japan's healthcare system recognized for its achievements in universal healthcare coverage, for one of the highest life expectancies, and for low infant mortality rate. These impressive results achieved after decades of thoughtful policy making process and financial investments into the equitable access. In addition to that Japan has a long history of strong public health traditions and sophisticated insurance model. On the other hand, despite these achievements, the healthcare system recently faces growing pressure which may impact its sustainability and fairness.  One of the challenges is rapid aging of population.  In combination with persistently low fertility, these challenges are reshaping the demand for healthcare and long-term care services. Moreover, financial pressure in increasing with health-related and social security expenditures consume a growing share of the national budget. A shrinking number of workforces, unequal healthcare provider distribution, and fragmentation across nearly 3,000 health private insurers create additional inefficiencies and threaten equitable access of the population to healthcare services, especially in the rural areas. In addition, the healthcare system is under strain from rising number of multimorbidity and increasing mental health issues among young population. Advance in technological progress creates opportunities but at the same time requires substantial adaptation.

ABSTRACT: Adverse drug reactions (ADRs) remain a major global challenge, contributing substantially to patient morbidity, mortality, and healthcare costs. Traditional pharmacovigilance approaches—spontaneous reporting and post-marketing surveillance—are hampered by underreporting, delays, and limited contextual data. The growing availability of electronic health records (EHRs), which capture longitudinal structured and unstructured patient information, presents an unprecedented opportunity to advance ADR prediction. This narrative review synthesizes recent progress in developing and validating predictive models that leverage EHRs, highlighting methodological approaches, challenges, and future directions. Predictive strategies range from traditional regression models to advanced machine learning and deep learning architectures, with multimodal frameworks increasingly integrating structured fields (demographics, labs, prescriptions) and unstructured clinical text through natural language processing. While ensemble and deep learning methods demonstrate superior performance, issues of data quality, missingness, bias, and interpretability persist. Robust validation frameworks—spanning internal cross-validation to multi-center external testing—are critical to ensure generalizability and clinical trustworthiness. Ethical considerations, including fairness, privacy, and transparency, remain central to safe deployment. Looking forward, promising avenues include federated learning across institutions, integration of multi-omics and pharmacogenomic data, explainable AI tailored for clinical use, and real-time monitoring through digital twin frameworks. These trajectories, combined with robust governance and clinician–data scientist collaboration, have the potential to transform ADR detection from a reactive process to proactive, personalized prevention. By synthesizing the existing evidence, this review provides insights into the development of more effective predictive models for ADRs and informs strategies for improving pharmacovigilance. This study will contribute to the ongoing efforts to leverage EHRs and predictive models for improving patient outcomes and reducing the burden of ADRs.

ABSTRACT: Background. There is growing interest in the association between antiepileptic drugs (AEDs) exposure and subsequent Parkinson’s disease (PD).
Methods. We conducted a literature search in the PubMed, SCOPUS, and Web of Science databases. We identified studies using an observational design and performed a meta-analysis to evaluate the association between AEDs exposure and incident PD. We assessed the quality of the studies and identified the pooled odds ratio (OR) for those exposed to AEDs compared to those who were not.
Results. Of the 1,775 unique studies identified, 55 were selected for full-text review. Five studies (n = 127,324) were included. Quality assessment revealed moderate-to-high methodological quality in the studies included. The overall OR for a PD was 1.82 (95% CI: 1.35-2.45) in AEDs recipients. When considering each drug individually, the magnitude of association was highest for valproate (OR 3.94, 95% CI: 3.15-4.92) and lowest for carbamazepine (OR 1.32, 95% CI: 1.16-1.49). Further interaction tests revealed higher odds for lamotrigine than for carbamazepine and valproate than for carbamazepine and lamotrigine.
Conclusion. This study revealed potential associations between AEDs and incident PD. However, existing evidence remains insufficient, making it premature to draw inferences on this matter.