Developing a Chatbot to Support Individuals With Neurodevelopmental Disorders: Tutorial.

Families of individuals with neurodevelopmental disabilities or differences (NDDs) often struggle to find reliable health information on the web. NDDs encompass various conditions affecting up to 14% of children in high-income countries, and most individuals present with complex phenotypes and related conditions. It is challenging for their families to develop literacy solely by searching information on the internet. While in-person coaching can enhance care, it is only available to a minority of those with NDDs. Chatbots, or computer programs that simulate conversation, have emerged in the commercial sector as useful tools for answering questions, but their use in health care remains limited. To address this challenge, the researchers developed a chatbot named CAMI (Coaching Assistant for Medical/Health Information) that can provide information about trusted resources covering core knowledge and services relevant to families of individuals with NDDs. The chatbot was developed, in collaboration with individuals with lived experience, to provide information about trusted resources covering core knowledge and services that may be of interest. The developers used the Django framework (Django Software Foundation) for the development and used a knowledge graph to depict the key entities in NDDs and their relationships to allow the chatbot to suggest web resources that may be related to the user queries. To identify NDD domain-specific entities from user input, a combination of standard sources (the Unified Medical Language System) and other entities were used which were identified by health professionals as well as collaborators. Although most entities were identified in the text, some were not captured in the system and therefore went undetected. Nonetheless, the chatbot was able to provide resources addressing most user queries related to NDDs. The researchers found that enriching the vocabulary with synonyms and lay language terms for specific subdomains enhanced entity detection. By using a data set of numerous individuals with NDDs, the researchers developed a knowledge graph that established meaningful connections between entities, allowing the chatbot to present related symptoms, diagnoses, and resources. To the researchers' knowledge, CAMI is the first chatbot to provide resources related to NDDs. Our work highlighted the importance of engaging end users to supplement standard generic ontologies to named entities for language recognition. It also demonstrates that complex medical and health-related information can be integrated using knowledge graphs and leveraging existing large datasets. This has multiple implications: generalizability to other health domains as well as reducing the need for experts and optimizing their input while keeping health care professionals in the loop. The researchers' work also shows how health and computer science domains need to collaborate to achieve the granularity needed to make chatbots truly useful and impactful.

Gastroscopy for dyspepsia: Understanding primary care and gastroenterologist mental models of practice: A cognitive task analysis approach.

Background: Gastroscopy to investigate dyspepsia without alarm symptoms rarely results in clinically actionable findings or sustained health-related quality-of-life improvements among patients aged 18-60 years and is, therefore, not recommended. Despite this, referrals for and performance of gastroscopy among this patient population remain high. The purpose of this study was to understand family physicians' and gastroenterologists' mental models of dyspepsia and the drivers behind referring or performing gastroscopy. Methods: Cognitive task analysis routine critical decision method interviews with family physicians (n = 8) and gastroenterologists (n = 4). Results: Family physicians and gastroenterologists hold rich mental models of dyspepsia that rely on sensemaking; however, gaps in information continuity affect their ability to plan and coordinate patient care. Drivers behind decisions to refer or perform gastroscopy were: eliminating risk for serious pathology, providing reassurance, perceived preference by patients to receive information and reassurance from gastroenterologists, maintaining relationships with patients, and saving costs to the health system. Conclusions: Family physicians refer for dyspepsia when they are seeking support from gastroenterologists, they believe that alternative factors may be impacting the patient's health or view it as a cost-saving measure. Likewise, gastroenterologists perform gastroscopy for dyspepsia when they perceive it as a cost-saving measure, they want to support their primary care colleagues and provide their colleagues and patients with reassurance. An improved degree of communication between speciality and primary care could allow for continuity in the transfer of information about patients and reduce referrals for dyspepsia.

Deciphering the Diversity of Mental Models in Neurodevelopmental Disorders: Knowledge Graph Representation of Public Data Using Natural Language Processing.

BACKGROUND: Understanding how individuals think about a topic, known as the mental model, can significantly improve communication, especially in the medical domain where emotions and implications are high. Neurodevelopmental disorders (NDDs) represent a group of diagnoses, affecting up to 18% of the global population, involving differences in the development of cognitive or social functions. In this study, we focus on 2 NDDs, attention deficit hyperactivity disorder (ADHD) and autism spectrum disorder (ASD), which involve multiple symptoms and interventions requiring interactions between 2 important stakeholders: parents and health professionals. There is a gap in our understanding of differences between mental models for each stakeholder, making communication between stakeholders more difficult than it could be. OBJECTIVE: We aim to build knowledge graphs (KGs) from web-based information relevant to each stakeholder as proxies of mental models. These KGs will accelerate the identification of shared and divergent concerns between stakeholders. The developed KGs can help improve knowledge mobilization, communication, and care for individuals with ADHD and ASD. METHODS: We created 2 data sets by collecting the posts from web-based forums and PubMed abstracts related to ADHD and ASD. We utilized the Unified Medical Language System (UMLS) to detect biomedical concepts and applied Positive Pointwise Mutual Information followed by truncated Singular Value Decomposition to obtain corpus-based concept embeddings for each data set. Each data set is represented as a KG using a property graph model. Semantic relatedness between concepts is calculated to rank the relation strength of concepts and stored in the KG as relation weights. UMLS disorder-relevant semantic types are used to provide additional categorical information about each concept's domain. RESULTS: The developed KGs contain concepts from both data sets, with node sizes representing the co-occurrence frequency of concepts and edge sizes representing relevance between concepts. ADHD- and ASD-related concepts from different semantic types shows diverse areas of concerns and complex needs of the conditions. KG identifies converging and diverging concepts between health professionals literature (PubMed) and parental concerns (web-based forums), which may correspond to the differences between mental models for each stakeholder. CONCLUSIONS: We show for the first time that generating KGs from web-based data can capture the complex needs of families dealing with ADHD or ASD. Moreover, we showed points of convergence between families and health professionals' KGs. Natural language processing-based KG provides access to a large sample size, which is often a limiting factor for traditional in-person mental model mapping. Our work offers a high throughput access to mental model maps, which could be used for further in-person validation, knowledge mobilization projects, and basis for communication about potential blind spots from stakeholders in interactions about NDDs. Future research will be needed to identify how concepts could interact together differently for each stakeholder.

How Knowledge Mapping Can Bridge the Communication Gap Between Caregivers and Health Professionals Supporting Individuals With Complex Medical Needs: A Study in Fragile X Syndrome.

The challenges of caring for children with complex health needs, such as intellectual disability (ID) and autism spectrum disorder (ASD), are multiple and experienced by both caregivers and health professionals. Fragile X syndrome (FXS) is the most common single gene cause of ID and ASD, and provides a pertinent model to understand these complexities of care, as well as the communication challenges experienced between caregivers and healthcare professionals. In recent years both caregivers and healthcare professionals have recognized the need for enhancing communication both in clinical and research settings. Knowledge mapping has emerged as a tool to support quality communication between team participants. Here we review how differences in mental models, as well as challenges related to health literacy and knowledge transfer can have an impact on communication. Next, we present different knowledge mapping approaches used in complex situations, with a focus on concept maps and care maps. Finally, we highlight the potential benefits and limitations of mapping to improve communication issues related to caring for individuals with FXS and potentially other neurodevelopmental disorders (NDDs).