JMIR Medical Informatics

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.

In response to the intricate language, specialized terminology outside everyday life, and the frequent presence of abbreviations and acronyms inherent in health care text data, domain adaptation techniques have emerged as crucial to transformer-based models. This refinement in the knowledge of the language models (LMs) allows for a better understanding of the medical textual data, which results in an improvement in medical downstream tasks, such as information extraction (IE). We have identified a gap in the literature regarding health care LMs. Therefore, this study presents a scoping literature review investigating domain adaptation methods for transformers in health care, differentiating between English and non-English languages, focusing on Portuguese. Most specifically, we investigated the development of health care LMs, with the aim of comparing Portuguese with other more developed languages to guide the path of a non–English-language with fewer resources.

Reaching meaningful interoperability between proprietary healthcare systems is a ubiquitous task in medical informatics, where communication servers are traditionally used for referring and transforming data from source to target systems. The Mirth Connect Server, an open-source communication server, offers in addition to the exchange functionality, functions for the simultaneous manipulation of data. The standard Fast Healthcare Interoperability Resources (FHIR) is recently becoming more and more prevalent in national healthcare systems. FHIR specifies its own standardized mechanisms for transforming data structures using StructureMaps and the FHIR Mapping Language (FML).

Obtaining and describing the semiology efficiently and classifying seizures types correctly are crucial for the diagnosis and treatment of epilepsy. Nevertheless, there exists an inadequacy in related informatics resources and decision-support tools.

Computerized clinical decision support systems (CDSSs) enhance patient care through real-time, evidence-based guidance for health care professionals. Despite this, the effective implementation of these systems for health services presents multifaceted challenges, leading to inappropriate use and abandonment over the course of time. Using the Non-Adoption, Abandonment, Scale-Up, Spread, and Sustainability (NASSS) framework, this qualitative study examined CDSS adoption in a metropolitan health service, identifying determinants across implementation stages to optimize CDSS integration into health care practice.

Named entity recognition (NER) models are essential for extracting structured information from unstructured medical texts by identifying entities such as diseases, treatments, and conditions, enhancing clinical decision-making and research. Innovations in machine learning, particularly those involving Bidirectional Encoder Representations From Transformers (BERT)–based deep learning and large language models, have significantly advanced NER capabilities. However, their performance varies across medical datasets due to the complexity and diversity of medical terminology. Previous studies have often focused on overall performance, neglecting specific challenges in medical contexts and the impact of macrofactors like lexical composition on prediction accuracy. These gaps hinder the development of optimized NER models for medical applications.

Medical errors are becoming a major problem for health care providers and those who design health policies. These errors cause patients’ illnesses to worsen over time and can make recovery impossible. For the benefit of patients and the welfare of health care providers, a decrease in these errors is required to maintain safe, high-quality patient care.