Towards an AI Coach to Infer Team Mental Model Alignment in Healthcare

Seo, Sangwon; Kennedy-Metz, Lauren R.; Zenati, Marco A.; Shah, Julie A.; Dias, Roger D.; Unhelkar, Vaibhav V.

doi:10.1109/cogsima51574.2021.9475925

Cited by 15 publications

(13 citation statements)

References 17 publications

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“…KG helps identify concepts of similar and different relevance/priority between families and health professionals. Knowing that shared understanding (shared mental model) has been shown as a key factor in effective collaboration and quality communication in health care [ 44 ], we aimed at identifying potential concepts of similar and different relevance between forums and medical literature. For comparing concepts, we considered the top 25 concepts under selected UMLS semantic types, which were the most related to each condition (ASD and ADHD) based upon the relatedness scores, and visualized them using Gephi.…”

Section: Resultsmentioning

confidence: 99%

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

Kaur¹,

Costello²,

Willis³

et al. 2022

J Med Internet Res

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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.

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Section: Resultsmentioning

confidence: 99%

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

Kaur¹,

Costello²,

Willis³

et al. 2022

J Med Internet Res

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show abstract

“…Because the agents serves as assistants, a natural assumption is that the agent should only assist the human minimally or intervene only when necessary, while maintaining good performance. This consideration is especially important in domains such as assistive robotics Gopinath, Jain, and Argall 2017;de Winter and Dodou 2011) in reality and assistive agent in video games where users may want to feel as independent as possible while performing the task at hand (Verdonck, McCormack, and Chard 2011;Palmer, Thursfield, and Judge 2005), AI assisted employee training (Seo et al 2021) where the AI agent is expected to gradually reduce assistance to ensure the user is learning the objective. Our work focuses on addressing this gap by incentivizing the agent to minimize interventions, while making sure that the humanagent collaboration achieves near-optimal performance.…”

Section: Introductionmentioning

confidence: 99%

On Optimizing Interventions in Shared Autonomy

Tan

Koleczek

Pradhan

et al. 2022

AAAI

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Shared autonomy refers to approaches for enabling an autonomous agent to collaborate with a human with the aim of improving human performance. However, besides improving performance, it may often also be beneficial that the agent concurrently accounts for preserving the user’s experience or satisfaction of collaboration. In order to address this additional goal, we examine approaches for improving the user experience by constraining the number of interventions by the autonomous agent. We propose two model-free reinforcement learning methods that can account for both hard and soft constraints on the number of interventions. We show that not only does our method outperform the existing baseline, but also eliminates the need to manually tune a black-box hyperparameter for controlling the level of assistance. We also provide an in-depth analysis of intervention scenarios in order to further illuminate system understanding.

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“…The concept of a shared mental model (SMM) has recently received increased attention in medical team performance literature as well as in other domains. SMM's are often brought in relation to the quality of team performance and safety (Burthscher et al 2011;Burtscher and Manser 2012;DeChurch and Mesmer-Magnus 2010;Wilson 2019;Todd 2018;Higgs et al 2018;Nini 2019;Seo et al 2021). A team has a shared mental model when relevant knowledge structures concerning how reality works or should work are held by all team members and when there is sufficient alignment in the internal representations of these knowledge structures (Fisschoff and Johnson 1997;Jones and Roelofsma 2000;Mathieu et al 2000).…”

Section: Introductionmentioning

confidence: 99%

Are We on the Same Page: A Controlled Adaptive Network Model for Shared Mental Models in Hospital Teamwork

Ments¹,

Treur

Klein

et al. 2022

Studies in Systems, Decision and Control

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Towards an AI Coach to Infer Team Mental Model Alignment in Healthcare

Cited by 15 publications

References 17 publications

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

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

On Optimizing Interventions in Shared Autonomy

Are We on the Same Page: A Controlled Adaptive Network Model for Shared Mental Models in Hospital Teamwork

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