JMIR Medical Informatics

Major above-ankle amputation is a devastating outcome of diabetes-related foot disease. The Wagner classification is routinely available but places many inpatients in the same grade; few prediction models for major amputation have undergone temporal validation or reported calibration.

Electroencephalography (EEG) is widely used in medicine as a technique for the assessment of several brain-related pathologies. However, EEG interpretation requires specialized expertise and remains a labor-intensive task, particularly for prolonged recordings in intensive care, where timely expert review is often challenging.

Breast cancer is the most prevalent cancer in women, and postoperative disease-free survival (DFS) is an important indicator of long-term prognosis. However, tumor, node, metastasis staging has limited ability to integrate multidimensional clinical, pathological, and treatment-related factors for individualized DFS prediction. Therefore, machine learning (ML) may provide a more comprehensive approach to prognostic risk assessment.

The Sequential Organ Failure Assessment (SOFA) score is central to risk stratification in intensive care units (ICUs), but its thresholds and 0- to 4-point increments were set by expert consensus in 1996 and never reestimated from outcome data. Machine learning models discriminate better while discarding the additive, point-based structure that makes SOFA auditable at the bedside, and methods for updating a legacy score rather than replacing it remain scarce.

This article provides a critical analysis of system-system interoperability (SSI) in the health data domain. By SSI, we mean the idea of connecting technical systems with each other to exchange operational data, directly or via intermediate platforms or infrastructures. We start by giving a brief history of some uses of the word “interoperability,” from the Institute of Electrical and Electronics Engineers (IEEE) original definition in 1991 to the Interoperable Europe Act of 2024. We thereby identify ambiguity and hidden assumptions in the definitions themselves. We then establish precise terminology to make a stringent analysis possible. Aided by a simple case of SSI from our own clinical practice and using a method of logical analysis, we look closely at (1) the assumption that the semantics in SSI can be handled by referring to external semantic standards, (2) the assumption that SSI is a feasible route to the safe exchange of operational data between systems in primary use, and (3) the challenges encountered in scaling SSI solutions to involve multidirectional data exchange, multiple data types, and multiple systems. Our first conclusion is that semantic interoperability, as defined by the Healthcare Information and Management Systems Society (HIMSS) in 2019, may not be a feasible route to stabilizing the real-world meaning of data exchanged in SSI solutions. The main reason is that referring to external standards cannot capture enough contextual information to correctly interpret health data remote from the primary context in which they were first registered. Our second conclusion is that SSI may not be a feasible route to the exchange of operational data between systems in primary use. Multiple unresolved problems are identified—problems that cannot be overcome by adding new components to existing systems, as proposed in, for example, the European Health Data Space (EHDS) legislation. Instead, a thorough redesign of each system’s internal architecture, data model, and user interface to align them with each other would be required. It is far from clear how such redesign would be practically achievable. Our third conclusion is that SSI may not be a scalable solution. In part, this is a consequence of the difficulties in using external standards to handle the semantics. Instead, careful negotiations and agreements about the exact meaning of each data type to be exchanged would be needed to guard against misinterpretation and, hence, risks in both primary and secondary use. We conclude that interoperability should be repositioned from being a taken-for-granted pathway to “better use of health data” to being an explicit object of critical inquiry. Further research should establish a commonly agreed analytical framework for evaluating suggested solutions. This framework must include stringent definitions of concepts and terminology, theoretical requirements, and practical aspects to be addressed in a concrete implementation.

The Clinical Data Interchange Standards Consortium (CDISC) has become the cornerstone of regulatory data submission frameworks for pharmaceutical and biotechnology organizations worldwide. Although the US Food and Drug Administration and Japan’s Pharmaceuticals and Medical Devices Agency require standardized study data, clinical data teams continue to face persistent operational barriers, including controlled terminology misuse, ambiguity in mapping protocol-specific data to domains, insufficient validation automation, and conformance findings that add review cycles. This Viewpoint argues that sustainable improvement in CDISC submission quality requires organizations to move beyond isolated technical fixes toward a staged, systematically assessable model of implementation maturity and proposes the CDISC Implementation Maturity Model (CIMM) as a framework for that assessment. Technical fixes—domain selection discipline, controlled terminology governance, template-based and metadata-driven automation, and multilayered validation—matter, but organizations that treat them as independent problems tend to underinvest in the institutional structures that make any one fix durable. Adapted from staged capability maturity modeling and established maturity model design procedures, the CIMM characterizes organizational CDISC capability across 5 dimensions—process standardization, automation coverage, terminology governance, validation integration, and institutional knowledge distribution—and 5 maturity levels, from Ad Hoc to Optimizing. Each of the 25 level-dimension cells carries an observable indicator, and an organization’s overall maturity is set by its weakest dimension. The framework is offered as a conceptual synthesis; its level definitions and the submission outcomes associated with each level are hypotheses to be tested, not validated findings. Submissions that are efficient to produce and more likely to clear regulatory review on the first attempt are more plausibly reached through the deliberate, staged development of the 5 capability dimensions the CIMM describes than through any single tool or technique. Testing the reliability of CIMM level assignment and its association with observed submission outcomes is the necessary next step.

Discharge instructions are an important component of the transition from hospital to home for patients with diabetic kidney disease (DKD), whose care often involves multiple comorbidities, medications, and stage-specific management needs. However, producing complete, accurate, clear, and personalized instructions can be challenging for junior physicians.

One of the barriers to the wider adoption of clinical information modeling standards is the high cost associated with the time and effort required to model and map data from legacy systems, as well as the need for specialized modeling experts. Meanwhile, large language models (LLMs) are becoming powerful tools for data transformation and semantic processing.

Cirrhosis is a chronic liver disease, manifested by fibrosis and regenerative nodules. It can be classified clinically as compensated or decompensated. Accurate predictions of patients’ transition from compensated to decompensated cirrhosis are important but difficult because of the multifactorial nature of disease progression, comorbid conditions, and patient heterogeneity.

The European Health Data Space (EHDS) regulation aims to empower citizens and facilitate the seamless exchange of health data across European Union (EU) Member States. A critical enabler of this vision is the European Electronic Health Record Exchange Format (EEHRxF), which establishes a common structure and vocabulary for core health data domains. Despite its strategic importance, the practical implementation of this format faces significant challenges due to the fragmentation of systems, standards, and governance models across Europe.

Peripheral blood smear (PBS) examination provides morphological information on immune cells that reflects systemic pathophysiology, yet whether it can be used to predict systemic states has not been systematically established.

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.