The relationship of perceived empathy with levels of pain intensity and incapability among patients visiting a musculoskeletal specialist

Fatehi, Amirreza; Brown, Laura E.; Versluijs, Yvonne; Maren, Koen van; Ring, David; Gonzalez, Amanda; Ramtin, Sina

doi:10.1016/j.pec.2023.107900

Patient Education and Counseling

2023

DOI: 10.1016/j.pec.2023.107900

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The relationship of perceived empathy with levels of pain intensity and incapability among patients visiting a musculoskeletal specialist

Amirreza Fatehi¹,

Laura E. Brown²,

Yvonne Versluijs³

et al.

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2024

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Exploring factors affecting patient satisfaction in online healthcare: A machine learning approach grounded in empathy theory

Chen,

Wu,

Zhang

et al. 2024

DIGITAL HEALTH

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Objective Empathy between doctors and patients is crucial in enhancing patient satisfaction with medical consultations. This study, grounded in empathy theory, employs natural language processing and machine learning algorithms to explore the factors influencing patient satisfaction in online healthcare services, particularly the impact of doctor–patient empathy. Methods Utilizing the three dimensions of the Jefferson Scale of Physician Empathy, seven variables were extracted from patient–doctor dialogs as independent variables, with patient satisfaction as the dependent variable. Employing machine learning algorithms, a classification model was constructed to identify the best-fitting model for exploring the pivotal factors influencing patient satisfaction in online medical services. The optimal model was then chosen to investigate the essential factors impacting patients’ satisfaction with online healthcare. Results A total of 7586 data points were collected, with 5447 consultation dialogs (71.8%) receiving a satisfactory rating from patients. LightGBM emerged as the best-performing model, achieving an F1 score of 0.78 and an area under the curve value of 0.81. Factors within the Standing in Patient's Shoes and Perspective Taking dimensions were identified as key determinants of patient satisfaction in online healthcare services. Conclusion This study broadens the conventional scope of applying empathy theory, signifying its crucial role in cultivating doctor–patient empathy within the realm of online healthcare and elevating the overall quality of medical services. The findings indicate that two pivotal factors influencing patients’ satisfaction with online healthcare are doctors’ perceived competence and ability to empathize, understanding patients’ perspectives, and offering assistance.

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Exploring factors affecting patient satisfaction in online healthcare: A machine learning approach grounded in empathy theory

Chen,

Wu,

Zhang

et al. 2024

DIGITAL HEALTH

View full text Add to dashboard Cite

show abstract

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The relationship of perceived empathy with levels of pain intensity and incapability among patients visiting a musculoskeletal specialist

Cited by 1 publication

References 28 publications

Exploring factors affecting patient satisfaction in online healthcare: A machine learning approach grounded in empathy theory

Exploring factors affecting patient satisfaction in online healthcare: A machine learning approach grounded in empathy theory

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