JMIR Medical Informatics

A large language model (LLM) is a type of artificial intelligence (AI) model that opens up great possibilities for healthcare practice, research, and education, although scholars have emphasized the need to proactively address the issue of unvalidated and inaccurate information regarding its use. One of the best-known LLMs is ChatGPT. ChatGPT is believed to be of great help to medical research, as it facilitates more efficient dataset analysis, code generation, and literature review, allowing researchers to focus on experimental design as well as drug discovery and development.

Ventilator-associated pneumonia (VAP) is a serious complication of mechanical ventilation therapy that affects patient treatment and prognosis. Owing to its excellent data mining capabilities, artificial intelligence (AI) has been increasingly used to predict VAP.

Artificial intelligence (AI), more specifically large language models (LLMs), holds significant potential in revolutionizing emergency care delivery by optimizing clinical workflows and enhancing the quality of decision-making. Although enthusiasm for integrating LLMs into emergency medicine (EM) is growing, the existing literature is characterized by a disparate collection of individual studies, conceptual analyses, and preliminary implementations. Given these complexities and gaps in understanding, a cohesive framework is needed to comprehend the existing body of knowledge on the application of LLMs in EM.

The Problem List (PL) is a repository of diagnoses for patients’ medical conditions and health-related issues. Unfortunately, over time, our problem lists have become overloaded with duplications, conflicting, and no-longer-valid diagnoses. Adding to the challenge for clinical use is the lack of a standardized structure for review. Previously, our default Electronic Health Record (EHR) organized the PL primarily via alphabetization with other options by clinical systems or by priority setting. The system’s PL were built with limited groupers that resulted in many diagnoses that were not consistent with the expected clinical system or not associated with any clinical system at all. As a consequence of these limited EHR configuration options, our problem list organization has supported clinical use poorly over time, particularly as the number of diagnoses on the PL grew.

Numerous pressure injury prediction models have been developed using electronic health record data. Yet, hospital-acquired pressure injuries (HAPIs) are increasing, which demonstrates the critical challenge of implementing these models in routine care.

Tolvaptan is the only FDA-approved drug to slow the progression of autosomal dominant polycystic kidney disease (ADPKD) but requires strict clinical monitoring due to potential serious adverse events.

Semantic interoperability facilitates the exchange of and access to health data that are being documented in Electronic Health Records (EHRs) with various semantic features. The main goals of semantic interoperability development entails patient data availability and use in diverse EHRs without loss of meaning. Internationally, there are current initiatives that aim to enhance semantic development of EHR data, and consequently, availability of patient data. Interoperability between health information systems is among the core goals of proposal for a regulation on the European Health Data Space and the WHO Global strategy on digital health.

The SARS-CoV-2 pandemic has demonstrated once again that rapid collaborative research is essential for the future of biomedicine. Large research networks are needed to collect, share, and reuse data and biosamples to generate collaborative evidence. However, setting up such networks is often complex and time-consuming, as common tools and policies are needed to ensure interoperability and the required flows of data and samples, especially in the context of handling personal data and the associated data protection issues. In biomedical research, pseudonymization detaches directly identifying details from biomedical data as well as biosamples and connects them using secure identifiers, the so-called pseudonyms. This protects privacy by design but allows necessary linkage and re-identification.

Lock To Live (L2L) is a novel web-based decision aid to help people at risk of suicide reduce access to firearms. Researchers have demonstrated that L2L is feasible to use and acceptable to patients, but little is known about how to implement L2L during virtual and in-person contact with healthcare providers.

Evidence-based medicine, rooted in randomized controlled trials, offers treatment estimates for the average patient but struggles to guide individualized care. This challenge is amplified in complex conditions like congenital heart disease due to disease variability and limited trial applicability. To address this, medicine-based evidence was proposed to synthesize information for personalized care. In their recent article, Li et al. introduced the patient similarity network “CHDmap”, which represents a promising technical rendition of the medicine-based evidence concept. Leveraging comprehensive clinical and echocardiographic data, CHDmap creates an interactive patient map, representing individuals with similar attributes. Using a k-nearest neighbor algorithm, CHDmap interactively identifies closely resembling patient groups based on specific characteristics. These approximate matches form the foundation for predictive analyses, including outcomes like hospital length of stay and complications. A key finding is the tool's dual capacity: not only did it corroborate clinical intuition in many scenarios, but in specific instances, it prompted a reevaluation of cases, culminating in an enhancement of overall performance across various classification tasks. While an important first step, future versions of CHDmap may aim to expand mapping complexity, increase data granularity, consider long-term outcomes, allow for treatment comparisons, and implement artificial intelligence-driven weighting of various input variables. Successful implementation of CHDmap and similar tools will require training for practitioners, robust data infrastructure, and interdisciplinary collaboration. Patient similarity networks may become valuable in multidisciplinary discussions, complementing clinicians' expertise. The symbiotic approach bridges evidence, experience, and real-life care, enabling iterative learning for future physicians.

Pseudonymization has become a best practice to securely manage the identities of patients and study participants in medical research projects and data sharing initiatives. This method offers the advantage of not requiring to directly identify data to support various research processes, while still allowing for advanced processing activities such as data linkage. Often, pseudonymization and related functionalities are bundled in specific technical and organization units, so-called Trusted Third Parties (TTPs). However, pseudonymization can significantly increase the complexity of data management and research workflows, necessitating adequate tool support. Common tasks of TTPs include support for the secure registration and pseudonymization of patient and sample identities as well as consent management.

Inhaled corticosteroid (ICS) is a mainstay treatment for controlling asthma and preventing exacerbations in patients with persistent asthma. Many types of ICS drugs are used, either alone or in combination with other controller medications. Despite the widespread use of ICSs, asthma control remains suboptimal in many people with asthma. Suboptimal control leads to recurrent exacerbations, causes frequent ER visits and inpatient stays, and is due to multiple factors. One such factor is the inappropriate ICS choice for the patient. While many interventions targeting other factors exist, less attention is given to inappropriate ICS choice. Asthma is a heterogeneous disease with variable underlying inflammations and biomarkers. Up to 50% of people with asthma exhibit some degree of resistance or insensitivity to certain ICSs due to genetic variations in ICS metabolizing enzymes, leading to variable responses to ICSs. Yet, ICS choice, especially in the primary care setting, is often not tailored to the patient’s characteristics. Instead, ICS choice is largely by trial and error and often dictated by insurance reimbursement, organizational prescribing policies, or cost, leading to a one-size-fits-all approach with many patients not achieving optimal control. There is a pressing need for a decision support tool that can predict an effective ICS at the point of care and guide providers to select the ICS that will most likely and quickly ease patient symptoms and improve asthma control. To date, no such tool exists. Predicting which patient will respond well to which ICS is the first step toward developing such a tool. However, no study has predicted ICS response, forming a gap. While the biologic heterogeneity of asthma is vast, few, if any, biomarkers and genotypes can be used to systematically profile all patients with asthma and predict ICS response. As endotyping or genotyping all patients is infeasible, readily available electronic health record data collected during clinical care offer a low-cost, reliable, and more holistic way to profile all patients. In this paper, we point out the need for developing a decision support tool to guide ICS selection and the gap in fulfilling the need. Then we outline an approach to close this gap via creating a machine learning model and applying causal inference to predict a patient’s ICS response in the next year based on the patient’s characteristics. The model uses electronic health record data to characterize all patients and extract patterns that could mirror endotype or genotype. This paper supplies a roadmap for future research, with the eventual goal of shifting asthma care from one-size-fits-all to personalized care, improve outcomes, and save health care resources.