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Integrating Technology into Undergraduate Medical Education: Can Affective Computing Help Teach Empathy?

Academic Psychiatry Read the Article
ABSTRACT To the Editor:

Substance use disorders (SUDs) and overdose deaths continue at record levels in the USA. One major barrier to adequate treatment is the stigma attached to the condition. Evidence suggests that clinicians have more negative attitudes and less empathy toward patients with SUDs compared to other medical and mental health conditions, thereby affecting the overall quality of care these patients receive [1]. Stigma can become apparent during clinical interactions where providers may unintentionally convey negative emotions or judgments through their facial expressions.

Until recently, empathy toward this patient population was previously thought of as an inherent trait that could not be taught. However, studies in the medical literature have shown that medical trainees do have the capability to improve their empathy toward patients [2]. Given that a physician’s ability to communicate effectively is associated with better patient outcomes, it is imperative to educate future physicians about how stigma manifests in the clinical setting and the importance of empathetic communication.

A promising approach to achieving this goal is through a technology called affective computing, also called emotional artificial intelligence. Affective computing enables computers to recognize, interpret, process, and simulate human emotion. Researchers from the MIT Media Lab at the Massachusetts Institute of Technology and Weill Cornell Medical College have developed Medship, a computerized training module. Medship leverages affective computing to educate future medical providers about the stigma toward patients with SUDs. It offers interactions with virtual (i.e., computerized) patients who have a SUD, records the user in such interaction, and then simultaneously analyzes the user’s facial expressions to provide feedback on such expressions in real time. The software used was OpenFace, a lightweight, open source toolkit used for facial behavior analysis.

This project is being split into two studies. The initial study aimed to evaluate the usability and acceptability of Medship among medical students. Given the multitude of educational options currently available to medical students, their willingness to adopt the application is pivotal to its success. The second part of this project will be a randomized control trial to assess the module’s impact on decreasing negative attitudes to this patient population and will be critical in evaluating its efficacy.

The initial study of this project used a quantitative interventional design, including a cross-sectional survey following a single session of using Medship. The Institutional Review Board at Weill Cornell Medical College granted approval for the study protocol. The online link for Medship was emailed to medical students during the course of their regular education. All feedback as contained in the module. A total of 26 students at Weill Cornell Medical College participated, providing anonymous responses to demographic questions, a System Usability Scale [3] and a System Quality Scale [4]. Usability refers to the ease of using the module, while acceptability gauges students’ willingness to integrate the module into their medical curriculum.

The results from this pilot study demonstrated positive feedback. Regarding usability, all students found it easy to learn and navigate the module. Most students reported that the module was both enjoyable and user-friendly (n = 20; 77%) and found the graphics to be of high quality and resolution (n = 25; 96%). Participants assigned an average of 85 on the System Usability Scale, where a score of 73 or above indicates satisfactory usability. Regarding acceptability, each student believed that their medical institution should offer Medship as part of the educational curriculum, and a substantial portion felt that medical students would greatly benefit from using the module (n = 20; 77%). On the System Quality Scale, participants rated the module an average of 4, where a score of 3 or higher indicates satisfactory acceptability.

One limitation of Medship is its potential lack of cultural diversity in the inputs that it receives to use in its algorithms that analyze facial action units. Expression of empathy in Western culture often assumes a “one-size-fits-all” approach without taking into consideration intercultural contexts. The current version of Medship is limited from a diversity standpoint in terms of the number of inputs it has from users coming from different backgrounds, cultures, and ethnicities. Future iterations of Medship must address this to enhance external validity.

Previous research has revealed that patients with major depression perceive neutral faces as sad compared to healthy participants who interpret them as happy [5]. It raises the question of whether patients with SUDs might exhibit distinct perceptions of neutral faces, particularly in light of the common comorbidity of SUDs and mood disorders. While one approach could be controlling for these comorbidities, a more clinically valuable direction could be to develop a unique version of Medship addressing patients with SUDs and specific comorbidities.

SUDs are becoming increasingly prevalent, remain significantly undertreated, and are stigmatized by clinicians more so than other medical and psychiatric illnesses. Affective computing is gaining prominence across industries, and the field of medicine is now exploring both its safety and efficacy in enhancing patient care. Medship has the capability of improving empathetic communication between providers and their patients. The first iteration of this study has revealed positive results in terms of the technology’s usability and acceptability by medical students, and the next portion of this study will focus on assessing Medship’s efficacy as an application.

Contributors Michael Woods, Giselle Appel, Aidana Daulbayeva, Caleb Harris, Julia Iyasere, Jonathan Avery
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