Implementation and Evaluation of an Artificial Intelligence Driven Simulation to Improve Resident Communication With Primary Care Providers

Merritt, Conor; Glisson, Michael; Dewan, Maya; Klein, Melissa; Zackoff, Matthew

doi:10.1016/j.acap.2021.12.013

Cited by 4 publications

(3 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Only live simulated encounters where the PCP was role-played in person were ranked as superior to the AIsimulated experience [74].…”

Section: Ai and Simulated Pediatric Referralsmentioning

confidence: 99%

See 1 more Smart Citation

Artificial Intelligence and Healthcare Simulation: The Shifting Landscape of Medical Education

Hamilton

2024

Cureus

View full text Add to dashboard Cite

The impact of artificial intelligence (AI) will be felt not only in the arena of patient care and deliverable therapies but will also be uniquely disruptive in medical education and healthcare simulation (HCS), in particular. As HCS is intertwined with computer technology, it offers opportunities for rapid scalability with AI and, therefore, will be the most practical place to test new AI applications. This will ensure the acquisition of AI literacy for graduates from the country's various healthcare professional schools. Artificial intelligence has proven to be a useful adjunct in developing interprofessional education and team and leadership skills assessments. Outcome-driven medical simulation has been extensively used to train students in imagecentric disciplines such as radiology, ultrasound, echocardiography, and pathology. Allowing students and trainees in healthcare to first apply diagnostic decision support systems (DDSS) under simulated conditions leads to improved diagnostic accuracy, enhanced communication with patients, safer triage decisions, and improved outcomes from rapid response teams. However, the issue of bias, hallucinations, and the uncertainty of emergent properties may undermine the faith of healthcare professionals as they see AI systems deployed in the clinical setting and participating in diagnostic judgments. Also, the demands of ensuring AI literacy in our healthcare professional curricula will place burdens on simulation assets and faculty to adapt to a rapidly changing technological landscape. Nevertheless, the introduction of AI will place increased emphasis on virtual reality platforms, thereby improving the availability of self-directed learning and making it available 24/7, along with uniquely personalized evaluations and customized coaching. Yet, caution must be exercised concerning AI, especially as society's earlier, delayed, and muted responses to the inherent dangers of social media raise serious questions about whether the American government and its citizenry can anticipate the security and privacy guardrails that need to be in place to protect our healthcare practitioners, medical students, and patients.

show abstract

“…Only live simulated encounters where the PCP was role-played in person were ranked as superior to the AIsimulated experience [74].…”

Section: Ai and Simulated Pediatric Referralsmentioning

confidence: 99%

“…Simulated exchanges were considered equal to or more effective than reading instructional materials, online didactics, or real-time observation. Only live simulated encounters where the PCP was role-played in person were ranked as superior to the AI-simulated experience [ 74 ].…”

Section: Reviewmentioning

confidence: 99%

Artificial Intelligence and Healthcare Simulation: The Shifting Landscape of Medical Education

Hamilton

2024

Cureus

View full text Add to dashboard Cite

show abstract

“…Accordingly, any research that was conducted on postgraduate level of health care education was excluded from this work. However, the AI-and ML-driven interventions and applications used in these studies may potentially be adapted to undergraduate education to enhance the richness of feedback on communication skills and on the performance in workplace-based assessments [71,72]. The same applies to AIand ML-enabled text analysis systems that are currently being researched but have not been applied to the context of communication skills training yet [73][74][75].…”

Section: Limitationsmentioning

confidence: 99%

Artificial Intelligence Supporting the Training of Communication Skills in the Education of Health Care Professions: Scoping Review

Stamer¹,

Steinhäuser²,

Flägel³

2023

J Med Internet Res

View full text Add to dashboard Cite

Background Communication is a crucial element of every health care profession, rendering communication skills training in all health care professions as being of great importance. Technological advances such as artificial intelligence (AI) and particularly machine learning (ML) may support this cause: it may provide students with an opportunity for easily accessible and readily available communication training. Objective This scoping review aimed to summarize the status quo regarding the use of AI or ML in the acquisition of communication skills in academic health care professions. Methods We conducted a comprehensive literature search across the PubMed, Scopus, Cochrane Library, Web of Science Core Collection, and CINAHL databases to identify articles that covered the use of AI or ML in communication skills training of undergraduate students pursuing health care profession education. Using an inductive approach, the included studies were organized into distinct categories. The specific characteristics of the studies, methods and techniques used by AI or ML applications, and main outcomes of the studies were evaluated. Furthermore, supporting and hindering factors in the use of AI and ML for communication skills training of health care professionals were outlined. Results The titles and abstracts of 385 studies were identified, of which 29 (7.5%) underwent full-text review. Of the 29 studies, based on the inclusion and exclusion criteria, 12 (3.1%) were included. The studies were organized into 3 distinct categories: studies using AI and ML for text analysis and information extraction, studies using AI and ML and virtual reality, and studies using AI and ML and the simulation of virtual patients, each within the academic training of the communication skills of health care professionals. Within these thematic domains, AI was also used for the provision of feedback. The motivation of the involved agents played a major role in the implementation process. Reported barriers to the use of AI and ML in communication skills training revolved around the lack of authenticity and limited natural flow of language exhibited by the AI- and ML-based virtual patient systems. Furthermore, the use of educational AI- and ML-based systems in communication skills training for health care professionals is currently limited to only a few cases, topics, and clinical domains. Conclusions The use of AI and ML in communication skills training for health care professionals is clearly a growing and promising field with a potential to render training more cost-effective and less time-consuming. Furthermore, it may serve learners as an individualized and readily available exercise method. However, in most cases, the outlined applications and technical solutions are limited in terms of access, possible scenarios, the natural flow of a conversation, and authenticity. These issues still stand in the way of any widespread implementation ambitions.

show abstract

A scoping review of artificial intelligence in medical education: BEME Guide No. 84

Gordon,

Daniel,

Ajiboye

et al. 2024

Medical Teacher

View full text Add to dashboard Cite

Implementation and Evaluation of an Artificial Intelligence Driven Simulation to Improve Resident Communication With Primary Care Providers

Cited by 4 publications

References 8 publications

Artificial Intelligence and Healthcare Simulation: The Shifting Landscape of Medical Education

Artificial Intelligence and Healthcare Simulation: The Shifting Landscape of Medical Education

Artificial Intelligence Supporting the Training of Communication Skills in the Education of Health Care Professions: Scoping Review

A scoping review of artificial intelligence in medical education: BEME Guide No. 84

Contact Info

Product

Resources

About