JMIR Medical Informatics

Decentralized clinical trials (DCTs) are reshaping clinical research through digital technologies such as telemedicine, mobile apps, wearable and sensor-based tools, and digital platforms. These technologies can support participation, safety, accessibility, data collection, monitoring, and trial operations. Although DCT adoption accelerated during the COVID-19 pandemic, the commercial technology landscape remains fragmented and continues to evolve rapidly. There is limited understanding of how commercial technologies align with operational activities across the DCT life cycle. The Basic Building Block (BBB) framework used in this review divides the DCT life cycle into 7 functional areas: setup and design; recruitment and enrollment; data acquisition and processing; operation and coordination; patient engagement; intervention and follow-up; and close-out and reporting.

Therapeutic drug monitoring (TDM) supports individualized pharmacotherapy, yet its implementation in routine care remains limited by operational constraints, data quality issues, and the need for expert interpretation. Model-informed precision dosing (MIPD) addresses several of these limitations through population pharmacokinetic modeling and Bayesian forecasting, but its effective deployment in clinical practice still requires human TDM experts.

Large language models (LLMs) can extract information from unstructured clinical text, but it remains unclear whether their ability to assess current status translates into reliable predictions of longitudinal outcomes.

Forecasts of population-level cardiovascular risk factors can support surveillance and planning, but annual country-level health series are often short. With only 30 or 39 annual values per target, model flexibility can outpace the information available for training, making leakage control, repeated temporal evaluation, and comparison with simple baselines essential.

The need for national-level medical data management is recognized across various areas, such as health care management, drug development, epidemiology, and policymaking. Conventional integrated care electronic health record (EHR) projects in Japan that rely on subsidies are unsustainable and will be restricted over the next 5 years because of the limited duration of the general subsidy period. Therefore, the sustainability of EHR arrangements needs to be discussed not only from the perspective of system implementation but also from that of the legal framework. In response to the demand for the secondary use of medical data, a national-level project called the Sennen Karte Project was launched in Japan in 2015.

Emergency departments (EDs) are vital access points for urgent care, yet persistent overcrowding in health care systems worldwide compromises service quality and efficiency. Urban tertiary care centers are increasingly strained by surging patient numbers and scarce inpatient beds. Prior admission prediction studies mostly rely on limited triage features and discrimination metrics, neglecting operational factors, calibration, and explainability. Identifying likely admissions early could enable proactive resource allocation and shorter wait times.

Clinical documentation is a major contributor to physician workload and burnout, particularly in high-acuity settings such as emergency departments. Ambient AI scribes have emerged as a response, but commercial products cost US $99 to more than US $600 per physician per month, operate as opaque systems, and often process data on vendor-controlled infrastructure. These characteristics limit organizational control over data sovereignty, quality improvement, and clinical workflows and create barriers to adoption in resource-constrained settings.

Demand for health data to support research, innovation, and evidence-based policymaking is increasing, but access to patient-level data is constrained by privacy and data protection requirements. To address this challenge, the Czech Republic developed a national synthetic data infrastructure within the National Health Information System (NHIS). The infrastructure aims to support privacy-preserving secondary use of health data by enabling the development and validation of analytical workflows without providing direct access to operational patient-level databases.

Heart failure develops through complex longitudinal interactions among multiple chronic diseases, making longitudinal disease trajectories an important source of clinical information. Longitudinal disease trajectories contain rich structural information that reflects disease evolution and comorbidity interactions. However, this information is often lost when electronic health record (EHR) data are summarized using conventional pooling strategies or sparse International Classification of Diseases (ICD) representations.

Large language models (LLMs) are increasingly used for health information, but chronic obstructive pulmonary disease (COPD)–related questions often involve safety-sensitive decisions, including treatment escalation, oxygen therapy, and exacerbation management. Because generative outputs may vary across repeated prompts, response stability is also relevant to comparative evaluation.

Pharmacoepidemiologic studies using real-world data often lack clinical context embedded in free-text electronic health records (EHRs). However, sharing EHR narratives is restricted by privacy requirements, and conventional deidentification approaches may reduce analytic utility and limit auditability.

The integration of AI-based Clinical Decision Support Systems (AI-CDSS) into clinical practice has expanded rapidly, particularly in medical imaging. Beyond algorithmic performance, growing evidence suggests that clinicians’ engagement with AI-CDSS is shaped by how transparently systems communicate their outputs, and how trust is formed through human-AI interaction. However, a limited understanding of how these concepts are perceived by end users and developers continues to hinder real-world implementation.