JMIR Medical Informatics

Delirium is common in hospitalized patients and is correlated with increased morbidity and mortality. Despite this, delirium is underdiagnosed, and many institutions do not have sufficient resources to consistently apply effective screening and prevention.

Artificial intelligence (AI)-based clinical decision support systems are increasingly used in healthcare. Uncertainty-aware AI presents the model’s confidence in its decision alongside its prediction whereas black-box AI only provides a prediction. Little is known about how this type of AI affects healthcare providers’ work performance and reaction time.

Digital communication between nurses and Medicine interns can be a useful tool in providing patient care, yet it may also contribute to alert fatigue. This study quantitatively characterizes messaging patterns between nurses and interns at an academic medical center using transaction metadata, timestamps, and unique message tokens. Our analysis reveals that interns exchanged 2.5 times more messages per day with nurses than each nurse exchanged with interns. Additionally, messaging volume exhibited diurnal variation, suggesting periods of increased communication burden. These findings highlight the need for further research and new in-person and digital interventions to optimize communication workflows and reduce alert fatigue.

The European Union's General Data Protection Regulation (GDPR) has profoundly influenced health data management, with significant implications for Clinical Data Warehouses (CDWs). In 2021, France pioneered a national framework for GDPR-compliant CDW implementation, established by its Data Protection Authority (CNIL). This framework provides detailed guidelines for healthcare institutions, offering a unique opportunity to assess practical GDPR implementation in health data management.

In Spain, the prevalence of heart failure is twice the European average, partly due to inadequate patient management. To address this issue, a standardized care model, the Care Model for Patients with Heart Failure (Modelos Asistenciales de Atención al Paciente con Insuficiencia Cardíaca), was developed. This model emphasizes the importance of sequential visits from hospital discharge until the patient transitions to chronic care to prevent rehospitalization. The standardized care pathway has been implemented in certain areas of the Andalusia Health Service. However, there is uncertainty about whether the region’s electronic health record system, Diraya, can effectively support this model. If not properly integrated, it could lead to data inaccuracies and noncompliance with the standardized care pathway.

Social media is acknowledged by regulatory bodies (eg, the Food and Drug Administration) as an important source of patient experience data to learn about patients’ unmet needs, priorities, and preferences. However, current methods rely either on manual analysis and do not scale, or on automatic processing, yielding mainly quantitative insights. Methods that can automatically summarize texts and yield qualitative insights at scale are missing.

Improved processes around the management of Electronic Health Record (EHR) requests for chronic antihypertensive medication renewals may represent an opportunity to improve blood pressure management at the individual and population level.

Congestive heart failure (CHF) is a common cause of hospital admissions. Medical records contain valuable information about CHF, but manual chart review is time-consuming. Claims databases (using International Classification of Diseases [ICD] codes) provide a scalable alternative but are less accurate. Automated analysis of medical records through natural language processing (NLP) enables more efficient adjudication but has not yet been validated across multiple sites.

Valuable insights gathered by clinicians during their inquiries and documented in textual reports are often unavailable in the structured data recorded in electronic health records (EHRs).

Pulmonary embolism (PE) is a critical condition requiring rapid diagnosis to reduce mortality. Extracting PE diagnoses from radiology reports manually is time-consuming, highlighting the need for automated solutions. Advances in natural language processing, especially transformer models like GPT-4o, offer promising tools to improve diagnostic accuracy and workflow efficiency in clinical settings.

The traditional management method can no longer meet the demand for efficient and accurate management,an information-based and intelligent management system is necessary.