2026-09-19T03:01:08Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1479042023-02-07T03:25:28Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Picard, Rosalind W.
Harris, Caleb M.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2023-02-06T18:30:54Z
2023-02-06T18:30:54Z
2022-09
2022-08-08T15:11:41.835Z
https://hdl.handle.net/1721.1/147904
Opioid use is on the rise, with overdose deaths quadrupling since 1999. Consequently, so is substance use disorder (SUD), an illness caused by repeated use of substances such as alcohol or drugs that result in clinically significant impairment. Although physician interactions with patients with SUDs are dramatically increasing in frequency, the majority of medical training still fails to address the importance of building empathy and minimizing stigma in such clinical interactions. Furthermore, physicians receive only minimal instruction regarding the expression of empathy and its role in building rapport and eliciting positive responses from patients with SUDs. Such strategies not only improve the immediate clinical interaction by contributing to a warm, stigma-free environment, but also improve the long-term outcomes of the patient by driving them toward care instead of away from it. This thesis both identifies the affective features and expressions most attributed to positive clinical perception, from the perspective of actual patients with SUDs, and introduces Medship, a web-application-based tool embedded with affective computing models to provide real-time affective training for medical school students and physicians in simulated clinical interactions with patients with SUDs.
M.Eng.
Massachusetts Institute of Technology
In Copyright - Educational Use Permitted
Copyright MIT
http://rightsstatements.org/page/InC-EDU/1.0/
Medship: Affective Computing for Building Empathetic Behaviors Toward Patients with Substance Use Disorders
Thesis
application/pdf
Master
Master of Engineering in Computation and Cognition
Publication
unknown
unknown
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<Abstract>Opioid use is on the rise, with overdose deaths quadrupling since 1999. Consequently, so is substance use disorder (SUD), an illness caused by repeated use of substances such as alcohol or drugs that result in clinically significant impairment. Although physician interactions with patients with SUDs are dramatically increasing in frequency, the majority of medical training still fails to address the importance of building empathy and minimizing stigma in such clinical interactions. Furthermore, physicians receive only minimal instruction regarding the expression of empathy and its role in building rapport and eliciting positive responses from patients with SUDs. Such strategies not only improve the immediate clinical interaction by contributing to a warm, stigma-free environment, but also improve the long-term outcomes of the patient by driving them toward care instead of away from it. This thesis both identifies the affective features and expressions most attributed to positive clinical perception, from the perspective of actual patients with SUDs, and introduces Medship, a web-application-based tool embedded with affective computing models to provide real-time affective training for medical school students and physicians in simulated clinical interactions with patients with SUDs.</Abstract>
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