JMIR Medical Informatics

Despite restrictive opioid management guidelines, opioid use disorder (OUD) remains a major public health concern. Machine learning (ML) offers a promising avenue for identifying and alerting clinicians about OUD, thus supporting better clinical decision-making regarding treatment. The performance of a ML application to alert clinicians of a patient’s risk of OUD, was evaluated by comparing it to a structured chart review by clinicians.

Event analysis is a promising option to estimate the acceptance of medication alerts issued by computerized physician order entry systems with integrated clinical decision support systems (CPOE-CDSS), particularly when alerts cannot be interactively confirmed in the CPOE-CDSS due to its system architecture. Medication documentation is then reviewed for documented evidence of alert acceptance, a time-consuming process, especially when performed manually.

After strict COVID-19-related restrictions were lifted, health systems globally were overwhelmed. Much has been discussed about how health systems could better prepare for future pandemics; however, primary healthcare (PHC) has been largely ignored.

Predicting hypoglycemia while maintaining low false alarm rate is a challenge for wide adoption of continuous glucose monitoring (CGM) in diabetes management. One small study suggested the long short-term memory (LSTM) network deep learning model had better performance of hypoglycemia prediction than traditional machine learning algorithms in European patients with type 1 diabetes. However, given that many well-recognized deep learning models perform poorly outside the training consideration, whether the LSTM model could be generalized to different populations or patients with other diabetes subtypes are unknown.

Individuals from minoritized racial and ethnic backgrounds suffer from pernicious and pervasive health disparities that have emerged, in part, from clinician bias.

With the increasing availability of data, computing resources, and easier-to-use software libraries, machine learning (ML) is increasingly utilized in disease detection and prediction, including Parkinson’s disease (PD). Despite the large number of studies published every year, very few ML systems have been adopted for real-world subject use.

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.