Video tools in pediatric goals of care communication: A systematic review

Medical advancesmean a growing array of interventions, therapies, and technologies are available to support care for children with chronic and serious conditions. Some of these approaches are supported by robust data drawn from populations that perfectly reflect an individual patient\u27s physiologic, psychologic, and social situation. But much more often, clinicians and families face decisions in the context of some—or much—uncertainty about whether the intervention will do a child more harm than good. This is particularly true for seriously ill children with a limited lifespan— whether the child is a neonate born with a life-threatening brain anomaly or an adolescent with cancer. Because there is no clear right decision in these circumstances, clinical teams review potential benefits and burdens and prioritize the care goals and expectations held by the child\u27s family [1]. Communication about these aspects of care are often referred to as Goals of Care”. This patient- and family-centered approach identifies valued care, avoids unwanted interventions, and fosters holistic family support [2]. In their 2019 paper, Secunda, et al. offer an operational definition of Goals of Care: “…the overarching aims of medical care for a patient that are informed by patients’ underlying values and priorities, established within the existing clinical context, and used to guide decisions about the use of or limitation on specificmedical interventions” [2]. It is fundamental to shared decision-making and relies on bidirectional communication, particularly since care goals are usually discussed in the setting of clinical conflict, poor prognosis, or treatment limitations [3]. Yet while the Goals of Care terminology is often a vernacular phrase for clinical teams, it is, in fact, jargon that can be ambiguous in conversations with families [4]. A clinician\u27s request to discuss Goals of Care may signal unfamiliar, confusing, intimidating, or emotionally laden conversational domains to families. Additional ambiguity arises from the fact that Goals of Care conversations often occur in the context of a changing prognosis and a background of baseline uncertainty. This gap in understanding and communication undermines family support. Families processing their own hopes and goals for a child find a sense of solidarity and support from hearing about the experiences of other families facing similar situations [5-7]. Several studies have identified the value of video modality as a source for Goals of Care education, including readying viewers for discussion, enhancing emotional awareness, and processing information in a safe space [8-11]. Given the emotion-laden nature of Goals of Care conversations, videos have been recognized as helpful preparation for families [12]. Thoughtfully produced video content offers viewers access to insight, emotion, and knowledge in an almost experiential way. This may be particularly important to families of children with serious illness who often feel isolated from other families experiencing similar challenges [13]

Multi-Site Clinical Federated Learning using Recursive and Attentive Models and NVFlare

The prodigious growth of digital health data has precipitated a mounting interest in harnessing machine learning methodologies, such as natural language processing (NLP), to scrutinize medical records, clinical notes, and other text-based health information. Although NLP techniques have exhibited substantial potential in augmenting patient care and informing clinical decision-making, data privacy and adherence to regulations persist as critical concerns. Federated learning (FL) emerges as a viable solution, empowering multiple organizations to train machine learning models collaboratively without disseminating raw data. This paper proffers a pragmatic approach to medical NLP by amalgamating FL, NLP models, and the NVFlare framework, developed by NVIDIA. We introduce two exemplary NLP models, the Long-Short Term Memory (LSTM)-based model and Bidirectional Encoder Representations from Transformers (BERT), which have demonstrated exceptional performance in comprehending context and semantics within medical data. This paper encompasses the development of an integrated framework that addresses data privacy and regulatory compliance challenges while maintaining elevated accuracy and performance, incorporating BERT pretraining, and comprehensively substantiating the efficacy of the proposed approach

Patient triage by topic modelling of referral letters: Feasibility study

Background: Musculoskeletal conditions are managed within primary care but patients can be referred to secondary care if a specialist opinion is required. The ever increasing demand of healthcare resources emphasizes the need to streamline care pathways with the ultimate aim of ensuring that patients receive timely and optimal care. Information contained in referral letters underpins the referral decision-making process but is yet to be explored systematically for the purposes of treatment prioritization for musculoskeletal conditions. Objective: This study aims to explore the feasibility of using natural language processing and machine learning to automate triage of patients with musculoskeletal conditions by analyzing information from referral letters. Specifically, we aim to determine whether referral letters can be automatically assorted into latent topics that are clinically relevant, i.e. considered relevant when prescribing treatments. Here, clinical relevance is assessed by posing two research questions. Can latent topics be used to automatically predict the treatment? Can clinicians interpret latent topics as cohorts of patients who share common characteristics or experience such as medical history, demographics and possible treatments? Methods: We used latent Dirichlet allocation to model each referral letter as a finite mixture over an underlying set of topics and model each topic as an infinite mixture over an underlying set of topic probabilities. The topic model was evaluated in the context of automating patient triage. Given a set of treatment outcomes, a binary classifier was trained for each outcome using previously extracted topics as the input features of the machine learning algorithm. In addition, qualitative evaluation was performed to assess human interpretability of topics. Results: The prediction accuracy of binary classifiers outperformed the stratified random classifier by a large margin giving an indication that topic modelling could be used to predict the treatment thus effectively supporting patient triage. Qualitative evaluation confirmed high clinical interpretability of the topic model. Conclusions: The results established the feasibility of using natural language processing and machine learning to automate triage of patients with knee and/or hip pain by analyzing information from their referral letters

Transparent ICD and DRG Coding Using Information Technology: Linking and Associating Information Sources with the eXtensible Markup Language

With the introduction of ICD-10 as the standard for diagnostics, it becomes necessary to develop an electronic representation of its complete content, inherent semantics, and coding rules. The authors' design relates to the current efforts by the CEN/TC 251 to establish a European standard for hierarchical classification systems in health care. The authors have developed an electronic representation of ICD-10 with the eXtensible Markup Language (XML) that facilitates integration into current information systems and coding software, taking different languages and versions into account. In this context, XML provides a complete processing framework of related technologies and standard tools that helps develop interoperable applications. XML provides semantic markup. It allows domain-specific definition of tags and hierarchical document structure. The idea of linking and thus combining information from different sources is a valuable feature of XML. In addition, XML topic maps are used to describe relationships between different sources, or "semantically associated” parts of these sources. The issue of achieving a standardized medical vocabulary becomes more and more important with the stepwise implementation of diagnostically related groups, for example. The aim of the authors' work is to provide a transparent and open infrastructure that can be used to support clinical coding and to develop further software applications. The authors are assuming that a comprehensive representation of the content, structure, inherent semantics, and layout of medical classification systems can be achieved through a document-oriented approac

A Review of Question Answering Systems: Approaches, Challenges, and Applications

Question answering (QA) systems are a type of natural language processing (NLP) technology that provide precise and concise answers to questions posed in natural language. These systems have the potential to revolutionize the way we access information and can be applied in a wide range of fields including education, customer service, and health care.There are several approaches to building QA systems, including rule-based, information retrieval, and machine learning-based approaches. Rule-based systems rely on predefined rules and patterns to extract answers from a given text, while information retrieval systems use search algorithms to retrieve relevant information from a large database. Machine learning-based systems, on the other hand, use training data to learn to extract answers from text.One of the main challenges faced by QA systems is the need to understand the context and intent behind a question. This requires the system to have a deep understanding of the language and the ability to make inferences based on the given information. Another challenge is the need to extract relevant information from a large and potentially unstructured dataset.Despite these challenges, QA systems have a wide range of applications, including education, customer service, and health care. In education, QA systems can be used to provide personalized learning experiences and help students learn more efficiently. In customer service, QA systems can be used to handle a high volume of queries and provide quick and accurate responses to customers. In health care, QA systems can be used to assist doctors and patients by providing timely and accurate information about medical conditions and treatments.Overall, this review aims to provide a comprehensive overview of QA systems, their approaches, challenges, and applications. By understanding the current state of development and the potential impact of QA systems, we can better utilize these technologies to improve various industries and enhance the way we access information

Protocol for an implementation and realist evaluation of a new organisational model for primary care practices in the canton of Vaud, Switzerland.

Continuity of care, especially for patients with complex needs, is a major challenge for healthcare systems in many high-income countries, including Switzerland. Since 2015, a collaborative project between Unisanté-Department of Family Medicine (DMF), some general practitioners (GPs) and canton of Vaud's public health authorities has sought to develop a new organisational model for the provision of primary care to ensure better care coordination and to provide adapted care deliveries to patients' healthcare needs. The model's main component is the addition of a primary care nurse to GPs practices. Three additional tools are individualised patient care plans, electronic medical records and patient empanelment. To assess this model, a 2-year pilot study has begun in nine GPs' practices in the canton. This paper presents the protocol for an evaluation of the implementation and effectiveness of the new organisational model. We will conduct a before-and-after study using a mixed-methods and a realist approach. First, we will use quantitative and qualitative data to assess the new organisational model's implementation (feasibility, fidelity, acceptability and costs) and effectiveness (healthcare services use, patient experience, staff experience and patient-level costs). Combining this data with focus group data will enable a realist evaluation of the pilot project, which will help understand the elements of context and mechanism that affect implementation. The evaluation will inform the canton of Vaud's health authorities about the limits of and perspectives for this organisational model. All results will also be made available to the practices and the patients involved. At the end of the project, we will propose organisational adaptations and a sustainable financial model for extending the model to other practices in the canton and potentially to the national level.The canton of Vaud's Human Research Ethics Committee approved the study, and Data Protection and Information Law Authority gave a favourable opinion concerning data processing procedures

Utilising semantic technologies for decision support in dementia care

The main objective of this work is to discuss our experience in utilising semantic technologies for building decision support in Dementia care systems that are based on the non-intrusive on the non-intrusive monitoring of the patient’s behaviour. Our approach adopts context-aware modelling of the patient’s condition to facilitate the analysis of the patient’s behaviour within the inhabited environment (movement and room occupancy patterns, use of equipment, etc.) with reference to the semantic knowledge about the patient’s condition (history of present of illness, dependable behaviour patterns, etc.). The reported work especially focuses on the critical role of the semantic reasoning engine in inferring medical advice, and by means of practical experimentation and critical analysis suggests important findings related to the methodology of deploying the appropriate semantic rules systems, and the dynamics of the efficient utilisation of complex event processing technology in order to the meet the requirements of decision support for remote healthcare systems

Semantic-based decision support for remote care of dementia patients

This paper investigates the challenges in developing a semantic-based Dementia Care Decision Support System based on the non-intrusive monitoring of the patient's behaviour. Semantic-based approaches are well suited for modelling context-aware scenarios similar to Dementia care systems, where the patient's dynamic behaviour observations (occupants movement, equipment use) need to be analysed against the semantic knowledge about the patient's condition (illness history, medical advice, known symptoms) in an integrated knowledgebase. However, our research findings establish that the ability of semantic technologies to reason upon the complex interrelated events emanating from the behaviour monitoring sensors to infer knowledge assisting medical advice represents a major challenge. We attempt to address this problem by introducing a new approach that relies on propositional calculus modelling to segregate complex events that are amenable for semantic reasoning from events that require pre-processing outside the semantic engine before they can be reasoned upon. The event pre-processing activity also controls the timing of triggering the reasoning process in order to further improve the efficiency of the inference process. Using regression analysis, we evaluate the response-time as the number of monitored patients increases and conclude that the incurred overhead on the response time of the prototype decision support systems remains tolerable

Consolidated List of Requirements

This document is a consolidated catalogue of requirements for the Electronic Health Care Record (EHCR) and Electronic Health Care Record Architecture (EHCRA), gleaned largely from work done in the EU Framework III and IV programmes and CEN, but also including input from other sources including world-wide standardisation initiatives. The document brings together the relevant work done into a classified inventory of requirements to inform the on-going standardisation process as well as act as a guide to future implementation of EHCRA-based systems. It is meant as a contribution both to understanding of the standard and to the work that is being considered to improve the standard. Major features include the classification into issues affecting the Health Care Record, the EHCR, EHCR processing, EHCR interchange and the sharing of health care information and EHCR systems. The principal information sources are described briefly. It is offered as documentation that is complementary to the four documents of the ENV 13606 Parts I-IV produced by CEN Pts 26,27,28,29. The requirements identified and classified in this deliverable are referenced in other deliverables

Medical data processing and analysis for remote health and activities monitoring

Recent developments in sensor technology, wearable computing, Internet of Things (IoT), and wireless communication have given rise to research in ubiquitous healthcare and remote monitoring of human\u2019s health and activities. Health monitoring systems involve processing and analysis of data retrieved from smartphones, smart watches, smart bracelets, as well as various sensors and wearable devices. Such systems enable continuous monitoring of patients psychological and health conditions by sensing and transmitting measurements such as heart rate, electrocardiogram, body temperature, respiratory rate, chest sounds, or blood pressure. Pervasive healthcare, as a relevant application domain in this context, aims at revolutionizing the delivery of medical services through a medical assistive environment and facilitates the independent living of patients. In this chapter, we discuss (1) data collection, fusion, ownership and privacy issues; (2) models, technologies and solutions for medical data processing and analysis; (3) big medical data analytics for remote health monitoring; (4) research challenges and opportunities in medical data analytics; (5) examples of case studies and practical solutions