JMIR Medical Informatics

Large language models (LLMs) provide new opportunities to advance the intelligent development of Traditional Chinese Medicine (TCM). Syndrome differentiation thinking is an essential part of TCM, and equipping LLMs with this capability represents a crucial step toward more effective clinical applications of TCM. However, given the complexity of TCM syndrome differentiation thinking, acquiring this ability is a considerable challenge for the model.

Machine learning (ML) and big data analytics are rapidly transforming health care, particularly disease prediction, management, and personalized care. With the increasing availability of real-world data (RWD) from diverse sources, such as electronic health records (EHRs), patient registries, and wearable devices, ML techniques present substantial potential to enhance clinical outcomes. Despite this promise, challenges such as data quality, model transparency, generalizability, and integration into clinical practice persist.

The global aging crisis has precipitated significant public health challenges, including rising chronic diseases, economic burdens, and labor shortages, particularly in China. Activities of Daily Living (ADL) dysfunction, affecting over 40 million Chinese elderly (16% of the aging population), severely compromises independence and quality of life while increasing healthcare costs and mortality. ADL dysfunction encompasses both Basic ADL (BADL) and Instrumental ADL (IADL), which assess fundamental self-care and complex environmental interactions, respectively. With projections indicating 65 million cases by 2030, there is an urgent need for tools to predict ADL impairment and enable early interventions.

Automated, self-reported medical history taking has the potential to provide comprehensive patient-reported data across a wide range of clinical issues. In the Clinical Expert Operating System ‐Chest Pain Danderyd Study (CLEOS-CPDS), medical history data were entered by patients using tablets in an emergency department (ED). Since successful implementation of this technology depends on understanding patients’ views and willingness to use it, we have studied these factors following patients’ use of the CLEOS program.

Telemonitoring can enhance the efficiency of healthcare delivery by enabling risk stratification, thereby allowing healthcare professionals to focus on high-risk patients. Additionally, it reduces the need for physical care. In contrast, telemonitoring programs require a significant time investment for implementation and alert processing. A structured method for telemonitoring process optimization is lacking.

Insulin resistance (IR), a precursor to type 2 diabetes and a major risk factor for various chronic diseases, is becoming increasingly prevalent in China due to population aging and unhealthy lifestyles. Current methods like the gold standard hyperinsulinemic-euglycemic clamp has limitations in practical application. The development of more convenient and efficient methods to predict and manage IR in non-diabetic populations will have prevention and control value.

Breast ultrasound is essential for evaluating breast nodules, with BI-RADS providing standardized classification. However, interobserver variability among radiologists can affect diagnostic accuracy. Large language models(LLMs) like ChatGPT-4 have shown potential in medical imaging interpretation. This study explores its feasibility in improving BI-RADS classification consistency and malignancy prediction compared to radiologists.

As the importance of Person-Generated Health Data (PGHD) in healthcare and research has increased, efforts to standardize survey-based PGHD to improve its usability and interoperability have been made. Standardization efforts, such as the Patient-Reported Outcomes Measurement Information System (PROMIS) and the NIH Common Data Elements (CDE) repository, provided effective tools for managing and unifying health survey questions. However, Previous methods using ontology-mediated annotation are not only labor-intensive and difficult to scale, but also face challenges in identifying semantic redundancies in survey questions, especially across multiple languages.

Patients are increasingly being offered online record access (ORA) through patient-accessible electronic health records (PAEHRs), but implementation is often met with resistance from health care professionals (HCPs). Experiences from previous implementations may provide important insights into potential barriers and facilitators. 

The growing availability of electronic health records (EHRs) presents an opportunity to enhance patient care by uncovering hidden health risks and improving informed decisions through advanced deep learning methods. However, modeling EHR sequential data, that is, patient trajectories, is challenging due to the evolving relationships between diagnoses and treatments over time. Significant progress has been achieved using transformers and self-supervised learning. While BERT-inspired models using masked language modeling (MLM) capture EHR context, they often struggle with the complex temporal dynamics of disease progression and interventions.

There have been suggestions that electronic health records (EHRs) should be expanded beyond clinical mental health care services to a broader array of care services that support mental health service users, which we call an integrated electronic care record (IECR). Previous research has considered service users’ general views on information being stored and shared via an EHR. However, little consideration has been given to service users’ attitudes toward how EHRs should be used in the provision of care or the concept of an IECR.

Diabetes affects millions worldwide. Primary care physicians provide a significant portion of care, and they often struggle with selecting appropriate medications.