JMIR Medical Informatics

Missing data in electronic health records (EHRs) is highly prevalent and results in analytical concerns such as heterogeneous sources of bias and loss of statistical power. One simple analytic method for addressing missing or unknown covariate values is to treat missing-ness for a particular variable as a category onto itself, which we refer to as the missing indicator method. For cross-sectional analyses, recent work suggested that there was minimal benefit to the missing indicator method; however, it is unclear how this approach performs in the setting of longitudinal data, in which correlation among clustered repeated measures may be leveraged for potentially improved model performance.

Publicly accessible critical care-related databases contain enormous clinical data, but their utilization often requires advanced programming skills. However, the growing complexity of large databases and unstructured data presents challenges for clinicians who need programming or data analysis expertise to utilize these systems directly.

This study compared the accuracy and efficiency of GPT-3.5 Turbo and GPT-4 Turbo in citation screening for systematic reviews in critical care. We used the data from the Japanese Clinical Practice Guidelines for Management of Sepsis and Septic Shock 2024. GPT-4 Turbo demonstrated superior specificity (0.98) compared to GPT-3.5 Turbo (0.51), with comparable sensitivity (0.85 vs. 0.83). GPT-3.5 Turbo processed 100 studies slightly faster than GPT-4 Turbo (0.9 vs. 1.6 min). GPT-4 Turbo may be more suitable in screening citations due to its higher specificity. The limitations of this study include the focus on sepsis, selection bias, reliance on variable accuracy metrics, the lack of other LLMs and prompts for comparison, and continually evolving GPT models. This study highlights the potential of large language models in optimizing literature selection processes.

Processing data from electronic health records (EHRs) to build research-grade databases is a lengthy and expensive process. Modern arthroplasty practice commonly employs multiple sites of care, including clinics and ambulatory care centers. However, most private data systems prevent obtaining usable insights for clinical practice.

Artificial intelligence (AI) has been deemed revolutionary in medicine; however, no AI tools have been implemented or validated in Danish general practice. General practice in Denmark has an excellent digitization system for developing and utilizing AI. Nevertheless, there is a lack of involvement of general practitioners (GPs) in developing AI. The perspectives of GPs as end users are essential for facilitating the next stage of AI development in general practice.

Patient portal use has been associated with improved patient health and improved adherence to medication including statins. However, there is limited research on the association between patient portal registration and outcomes such as statin prescription refill adherence in the context of the National Health Service of England where patient portals have been widely offered since 2015.  

Data standards are not only key to making data processing efficient but also fundamental to ensuring data interoperability. When clinical trial data are structured according to international standards, they become significantly easier to analyze, reducing the efforts required for data cleaning, preprocessing, and secondary use. A common language and a shared set of expectations facilitate interoperability between systems and devices.

Electronic medical records (EMRs) have undergone significant changes due to advancements in technology, including artificial intelligence, the Internet of Things, and cloud services. The increasing complexity within health care systems necessitates enhanced process reengineering and system monitoring approaches. Robotic process automation (RPA) provides a user-centric approach to monitoring system complexity by mimicking end user interactions, thus presenting potential improvements in system performance and monitoring.

Artificial Intelligence (AI) has shown exponential growth and advancements, revolutionizing various fields, including healthcare. However, domain adaptation remains a significant challenge, as machine learning (ML) models often need to be applied across different healthcare settings with varying patient demographics and practices. This issue is critical for ensuring effective and equitable AI deployment. Cardiovascular diseases (CVD), the leading cause of global mortality with 17.9 million annual deaths, encompass conditions like coronary heart disease and hypertension. The increasing availability of medical data, coupled with AI advancements, offers new opportunities for early detection and intervention in cardiovascular events, leveraging AI's capacity to analyze complex datasets and uncover critical patterns.

Unplanned readmissions increase unnecessary healthcare costs and reduce the quality of care. It is essential to plan the discharge care from the beginning of hospitalization to reduce the risk of readmission. Machine learning-based readmission prediction models can support patients’ preemptive discharge care services with improved predictive power.

Patients’ oral expressions serve as valuable sources of clinical information to improve pharmacotherapy. Natural language processing (NLP) is a useful approach for analyzing unstructured text data, such as patient narratives. However, few studies have focused on using NLP for narratives in the Japanese language.

Diagnosis-related group (DRG) payment has become the main way of medical expenses settlement, and its application is more and more extensive.