JMIR Medical Informatics

Emergency medicine can benefit from AI due to its unique challenges, such as high patient volume and the need for urgent interventions. However, it remains difficult to assess the applicability of AI systems to real-world emergency medicine practice, which requires not only medical knowledge but also adaptable problem-solving and effective communication skills.

Wearable devices are increasingly used for monitoring health and detecting digital biomarkers related to chronic diseases such as metabolic syndrome (MetS). Although circadian rhythm disturbances are known to contribute to MetS, few studies have explored wearable-derived circadian biomarkers for MetS identification.

Syndromes differentiation in traditional Chinese medicine (TCM) is an ancient principle guiding disease diagnosis and treatment. Among which, the cold and hot syndrome plays a crucial role in identifying the nature and guiding the treatment of viral pneumonia. However, the cold and hot syndrome differentiation is often considered esoteric. Machine learning (ML) offers a promising avenue for clinicians to identify the cold and hot syndrome, thereby supporting more informed clinical decision-making in the treatment.

Between 2020 and 2022, the Philippine Primary Care Studies program, a government-funded initiative supporting universal health care implementation, piloted two electronic health records (EHR) systems across urban, rural, and remote primary care sites.

Standardized registries, such as the International Classification of Diseases (ICD) codes, are commonly built using administrative codes assigned to patient encounters. However, fall patients are often coded using subsequent injury codes, such as hip fractures. This necessitates manual screening to ensure the accuracy of data registries.

Clinical practice guidelines (CPGs) serve as essential tools for guiding clinicians in providing appropriate patient care. However, clinical practice does not always reflect CPGs. This is particularly critical in acute diseases requiring immediate treatment such as acute ischemic stroke, one of the leading causes of morbidity and mortality worldwide. Adherence to CPGs improves patient outcomes, yet guidelines may not address all patient scenarios, resulting in variability in treatment decisions. Identifying such gaps would augment CPGs but is challenging when using traditional methods.

Artificial Intelligence/Machine Learning (AI/ML) has revolutionized the healthcare industry, particularly in the development and use of medical devices. The FDA has authorized over 878 AI/ML-enabled medical devices, reflecting a growing trend in both quantity and application scope. Understanding the distinct challenges, they present in terms of FDA regulation violations is crucial for avoiding recalls effectively. This is particularly pertinent for proactive measures regarding medical devices.

Electronic health records (EHRs) provide valuable insights to address clinical and epidemiological research concerning HIV, including the disproportionate impact of the COVID-19 pandemic on people living with HIV. To identify this population, most studies using EHR or claims databases start with diagnostic codes, which can result in misclassification without further refinement using drug or laboratory data. Furthermore, given that antiretrovirals now have indications for both HIV and COVID-19 (ie, ritonavir in nirmatrelvir/ritonavir), new phenotyping methods are needed to better capture people living with HIV. Therefore, we created a generalizable and innovative method to robustly identify people living with HIV, preexposure prophylaxis (PrEP) users, postexposure prophylaxis (PEP) users, and people not living with HIV using granular clinical data after the emergence of COVID-19.

Communication among healthcare professionals is essential for effective clinical care. Asynchronous text-based clinician communication—secure messaging— is rapidly becoming the preferred mode of communication. The use of secure messaging platforms across healthcare institutions creates large-scale communication networks that can be used to characterize how interaction structures affect the behaviors and outcomes of network members. However, the understanding of the structure and interactions within these networks is relatively limited.

Adverse drug reactions (ADR) pose serious risks to patient health, and effectively predicting and managing them is an important public health challenge. Given the complexity and specificity of biomedical text data, the traditional context-independent word embedding model, Word2Vec, has limitations in fully reflecting the domain specificity of such data. Although Bidirectional Encoder Representations from Transformers (BERT)-based models pre-trained on biomedical corpora have demonstrated high performance in ADR-related studies, research utilizing these models to predict previously unknown drug-side effect relationships remains insufficient.

Understanding and improving patient care is pivotal for healthcare providers. With increasing volumes of the Friends and Family Test (FFT) data in England, manual analysis of this patient feedback poses challenges for many healthcare organisations. This underscores the importance of automated text analysis, particularly in predicting sentiments and themes in real-time.

Operative notes are frequently mined for surgical concepts in clinical care, research, quality improvement, and billing, often requiring hours of manual extraction. These notes are typically analyzed at the document level to determine the presence or absence of specific procedures or findings (e.g., whether a hand-sewn anastomosis was performed or contamination occurred). Extracting several binary classification labels simultaneously is a multi-label classification problem. Traditional natural language processing (NLP) approaches––bag-of-words (BoW) and term frequency-inverse document frequency (tf-idf) with linear classifiers––have been used previously for this task but are now being augmented or replaced by Large Language Models (LLMs). However, few studies have examined their utility in surgery.