JMIR Medical Informatics

Artificial intelligence (AI) is rapidly being adopted to build products and aid in the decision-making process across industries. However, AI systems have been shown to exhibit and even amplify biases, causing a growing concern among people world-wide. Thus, investigating methods of measuring and mitigating bias within these AI-powered tools is necessary.

Accurate history-taking is essential for diagnosis, treatment, and patient care, yet miscommunications and time constraints often lead to incomplete information. Consequently, there has been a pressing need to establish a system whereby the questionnaire is duly completed before the medical appointment, entered into the Electronic Health Record (EHR), and stored in a structured format within a database.

Co-prescribing naloxone with opioid analgesics is a Centers for Disease Control and Prevention best practice to mitigate the risk of fatal opioid overdose (OD), yet co-prescription by emergency medicine clinicians is rare, occurring less than 5% of the time it is indicated. Clinical decision support (CDS) has been associated with increased naloxone prescribing; however, key CDS design characteristics and pragmatic outcome measures necessary to understand replicability and effectiveness have not been reported.

Electronic Health Records (EHRs) have an enormous potential to advance medical research and practice through easily accessible and interpretable EHR-derived databases. Attainability of this potential is limited by issues with data quality and performance assessment.

Social Determinants of Health (SDOH) are critical drivers of health disparities and patient outcomes. However, accessing and collecting patient level SDOH data can be operationally challenging in the emergency department clinical setting requiring innovative approaches. This scoping review examines the potential of artificial intelligence (AI) and data science for modeling, extraction, and incorporation of SDOH data specifically within emergency departments (ED), further identifying areas for advancement and investigation.

The bidirectional encoder representations from transformers (BERT) model has attracted considerable attention in clinical applications, such as patient classification and disease prediction. However, current studies have typically progressed to application development without a thorough assessment of the model's comprehension of clinical context. Furthermore, limited comparative studies have been conducted on BERT models using medical documents from non-English speaking countries. Therefore, the applicability of BERT models trained on English clinical notes to non-English contexts is yet to be confirmed. To address these gaps in literature, this study focused on identifying the most effective BERT model for non-English clinical notes.

Proper analysis and interpretation of health care data can significantly improve patient outcomes by enhancing services and revealing the impacts of new technologies and treatments. Understanding the substantial impact of temporal shifts in these data is crucial. For example, COVID-19 vaccination initially lowered the mean age of at-risk patients and later changed the characteristics of those who died. This highlights the importance of understanding these shifts for assessing factors that affect patient outcomes.

Assessing disease progression among patients with uncontrolled hypertension (HTN) is important for identifying opportunities for intervention.

Integrating decision support systems into telemedicine may optimize consultation efficiency and adherence to clinical guidelines; however, the extent of such effects remains underexplored.

Before deploying a clinical prediction model (CPM) in clinical practice, its performance needs to be demonstrated in the population of intended use. This is also called 'targeted validation'. Many CPMs developed in tertiary settings may be most useful in secondary care, where the patient case mix is broad and practitioners need to triage patients efficiently. However, since structured and/or rich datasets of sufficient quality from secondary to assess the performance of a CPM are scarce, a validation gap exists that hampers implementation of CPMs in secondary care settings. In this viewpoint, we highlight the importance of targeted validation and the use of clinical prediction models (CPMs) in secondary care settings and discuss the potential and challenges of using Electronic Health Record (EHR) data to overcome the existing validation gap. The introduction of software applications for text mining of EHRs allows the generation of structured 'big' datasets, but the imperfection of EHRs as a research database requires careful validation of data quality. When using EHR data for the development and validation of CPMs, in addition to widely accepted checklists, we propose considering three additional practical steps: 1) Involve a local EHR expert (clinician, nurse) in the data extraction process, 2) Perform validity checks on the generated datasets, and 3) Provide metadata on how variables were constructed from EHRs. These steps help to generated EHR datasets that are statistically powerful, of sufficient quality and replicable and enable targeted development and validation of CPMs in secondary care settings. This approach can fill a major gap in prediction modeling research and appropriately advance CPMs into clinical practice.

With the aging population on the rise, the demand for effective health care solutions to address adverse drug events is becoming increasingly urgent. Telemedicine has emerged as a promising solution for strengthening health care delivery in home care settings and mitigating drug errors. Due to the indispensable role of family caregivers in daily patient care, integrating digital health tools has the potential to streamline medication management processes and enhance the overall quality of patient care.

Systematic literature review (SLR), a robust method to identify and summarize evidence from published sources, is considered as a complex, time-consuming, labor-intensive and expensive task.