Corrigendum: Person-based design and evaluation of MIA, a digital medical interview assistant for radiology.

[This corrects the article DOI: 10.3389/frai.2024.

Mapping the landscape of Hospital at home (HaH) care: a validated taxonomy for HaH care model classification.

Background: Hospital at home (HaH) care models have gained significant attention due to their potential to reduce healthcare costs, improve patient satisfaction, and lower readmission rates. However, the lack of a standardized classification system has hindered systematic evaluation and comparison of these models. Taxonomies serve as classification systems that simplify complexity and enhance understanding within a specific domain.

A timed epigenetic switch balances T and ILC lineage proportions in the thymus.

How multipotent progenitors give rise to multiple cell types in defined numbers is a central question in developmental biology. Epigenetic switches, acting at single gene loci, can generate extended delays in the activation of lineage-specifying genes and impact lineage decisions and cell type output. Here, we analyzed a timed epigenetic switch controlling expression of mouse Bcl11b, a transcription factor that drives T-cell commitment, but only after a multi-day delay.

The ethical aspects of integrating sentiment and emotion analysis in chatbots for depression intervention.

Introduction: Digital health interventions specifically those realized as chatbots are increasingly available for mental health. They include technologies based on artificial intelligence that assess user's sentiment and emotions for the purpose of responding in an empathetic way, or for treatment purposes, e.g.

Preparing for Hospital at Home: A Review of the Current Landscape of Training Practices.

Hospital at Home (HaH) is a model of care that provides hospital-level care in the patient's home, requiring a unique set of competencies and skills from both multidisciplinary care teams and informal caregivers. These skills are often different from those required in traditional hospital settings. The aim of this paper is to consolidate the information of HaH-related education and training to support the development of standardized curricula to ensure safe hospitalization at home.

Person-based design and evaluation of MIA, a digital medical interview assistant for radiology.

Introduction: Radiologists frequently lack direct patient contact due to time constraints. Digital medical interview assistants aim to facilitate the collection of health information. In this paper, we propose leveraging conversational agents to realize a medical interview assistant to facilitate medical history taking, while at the same time offering patients the opportunity to ask questions on the examination.

A scoping review of large language model based approaches for information extraction from radiology reports.

Radiological imaging is a globally prevalent diagnostic method, yet the free text contained in radiology reports is not frequently used for secondary purposes. Natural Language Processing can provide structured data retrieved from these reports. This paper provides a summary of the current state of research on Large Language Model (LLM) based approaches for information extraction (IE) from radiology reports.

Security and Privacy in Physical Activity Chatbots on Social Media: A Scoping Review.

Background And Objective: Social media physical activity chatbots use both chatbots and social media platforms for physical activity promotion and, thus, could face privacy and security challenges inherent in both technologies. This study aims to provide an overview of physical activity chatbot interventions delivered via social media platforms, specifically focusing on security and privacy measures.

Methods: We conducted a scoping review on this topic across 4 databases: PubMed, PsycINFO, ACM Digital Library, and IEEE Xplore.

Understanding Emotional Dynamics in Autism Social Media Communities.

Unlabelled: Searches for autism on social media have soared, making it a top topic. Social media posts convey not only plain text, but also sentiments and emotions that provide insight into the experiences of the autism community. While sentiment analysis categorizes overall sentiment, emotion analysis provides nuanced insights into specific emotional states.

Success Factors for Usability Testing of Digital Health Solutions.

Background: The design and development of patient-centered digital health solutions requires user involvement, for example through usability testing. Although there are guidelines for conducting usability tests, there is a lack of knowledge about the technical, human, and organizational factors that influence the success of the tests.

Objective: To summarize the success factors of usability testing in the context of patient-centered digital health solutions.

Identifying Reusable Core Assets of Digital Health Apps.

Unlabelled: Despite their variability, Digital Health Apps (DHAs) typically share functionalities (i.e. core assets) and can thus be considered as a family of similar products with unique features adapted to specific use cases.

Towards a Reporting Guideline for Studies on Information Extraction from Clinical Texts.

Background: The rapid technical progress in the domain of clinical Natural Language Processing and information extraction (IE) has resulted in challenges concerning the comparability and replicability of studies.

Aim: This paper proposes a reporting guideline to standardize the description of methodologies and outcomes for studies involving IE from clinical texts.

Methods: The guideline is developed based on the experiences gained from data extraction for a previously conducted scoping review on IE from free-text radiology reports including 34 studies.

Longitudinal Coadaptation of Older Adults With Wearables and Voice-Activated Virtual Assistants: Scoping Review.

Background: The integration of smart technologies, including wearables and voice-activated devices, is increasingly recognized for enhancing the independence and well-being of older adults. However, the long-term dynamics of their use and the coadaptation process with older adults remain poorly understood. This scoping review explores how interactions between older adults and smart technologies evolve over time to improve both user experience and technology utility.

Human Factors in AI-Driven Digital Solutions for Increasing Physical Activity: Scoping Review.

Background: Artificial intelligence (AI) has the potential to enhance physical activity (PA) interventions. However, human factors (HFs) play a pivotal role in the successful integration of AI into mobile health (mHealth) solutions for promoting PA. Understanding and optimizing the interaction between individuals and AI-driven mHealth apps is essential for achieving the desired outcomes.

Integrating Laboratory Testing Results at Point-of-Care in Hospital@Home Care Settings: A FHIR-Based Approach.

Unlabelled: The care model Hospital@Home offers hospital-level treatment at home, aiming to alleviate hospital strain and enhance patient comfort. Despite its potential, integrating digital health solutions into this care model still remains limited. This paper proposes a concept for integrating laboratory testing at the Point of Care (POC) into Hospital@Home models to improve efficiency and interoperability.

A Comprehensive Framework for Hospital@Home Care Models.

Unlabelled: Hospital@home is a healthcare approach, where patients receive active treatment from health professionals in their own home for conditions that would normally necessitate a hospital stay.

Objective: To develop a framework of relevant features for describing hospital@home care models.

Methods: The framework was developed based on a literature review and thematic analysis.

Potential of Large Language Models in Health Care: Delphi Study.

Background: A large language model (LLM) is a machine learning model inferred from text data that captures subtle patterns of language use in context. Modern LLMs are based on neural network architectures that incorporate transformer methods. They allow the model to relate words together through attention to multiple words in a text sequence.

Large Language Model-Based Evaluation of Medical Question Answering Systems: Algorithm Development and Case Study.

Background: Healthcare systems are increasingly resource constrained, leaving less time for important patient-provider interactions. Conversational agents (CAs) could be used to support the provision of information and to answer patients' questions. However, information must be accessible to a variety of patient populations, which requires understanding questions expressed at different language levels.

Analysis of Critical Incident Reports Using Natural Language Processing.

Unlabelled: A Critical Incident Reporting System (CIRS) collects anecdotal reports from employees, which serve as a vital source of information about incidents that could potentially harm patients.

Objectives: To demonstrate how natural language processing (NLP) methods can help in retrieving valuable information from such incident data.

Methods: We analyzed frequently occurring terms and sentiments as well as topics in data from the Swiss National CIRRNET database from 2006 to 2023 using NLP and BERTopic modelling.

Transformer Models in Healthcare: A Survey and Thematic Analysis of Potentials, Shortcomings and Risks.

Large Language Models (LLMs) such as General Pretrained Transformer (GPT) and Bidirectional Encoder Representations from Transformers (BERT), which use transformer model architectures, have significantly advanced artificial intelligence and natural language processing. Recognized for their ability to capture associative relationships between words based on shared context, these models are poised to transform healthcare by improving diagnostic accuracy, tailoring treatment plans, and predicting patient outcomes. However, there are multiple risks and potentially unintended consequences associated with their use in healthcare applications.

Digital Interventions and Their Unexpected Outcomes - Time for Digitalovigilance?

The application of digital interventions in healthcare beyond research has been translated in the development of software as a medical device. Along with corresponding regulations for medical devices, there is a need for assessing adverse events to conduct post-market surveillance and to appropriately label digital health interventions to ensure proper use and patient safety. To date unexpected consequences of digital health interventions are neglected or ignored, or at least remain undescribed in literature.

Exploring the Evolution of Social Media in Mental Health Interventions: A Mapping Review.

Background: With the rise of social media, social media use for delivering mental health interventions has become increasingly popular. However, there is no comprehensive overview available on how this field developed over time.

Objectives: The objective of this paper is to provide an overview over time of the use of social media for delivering mental health interventions.

How Participatory Health Informatics Catalyzes One Digital Health.

Objective: To identify links between Participatory Health Informatics (PHI) and the One Digital Health framework (ODH) and to show how PHI could be used as a catalyst or contributor to ODH.

Methods: We have analyzed the addressed topics within the ODH framework in previous IMIA Yearbook contributions from our working group during the last 10 years. We have matched main themes with the ODH's framework three perspectives (individual health and wellbeing, population and society, and ecosystem).

Large language model-based information extraction from free-text radiology reports: a scoping review protocol.

Introduction: Radiological imaging is one of the most frequently performed diagnostic tests worldwide. The free-text contained in radiology reports is currently only rarely used for secondary use purposes, including research and predictive analysis. However, this data might be made available by means of information extraction (IE), based on natural language processing (NLP).