Clinician Facial Expression of Emotion Corresponds with Patient Mindset

Abstract

Background Mental health has a notable and perhaps underappreciated relationship with symptom intensity related to musculoskeletal pathophysiology. Tools for increasing awareness of mental health opportunities may help musculoskeletal specialists identify and address psychological distress and unhealthy misconceptions with greater confidence. One such type of technology—software that identifies emotions by analyzing facial expressions—could be developed as a clinician-awareness tool. A first step in this endeavor is to conduct a pilot study to assess the ability to measure patient mental health through specialist facial expressions. Questions/purposes (1) Does quantification of clinician emotion using facial recognition software correlate with patient psychological distress and unhealthy misconceptions? (2) Is there a correlation between clinician facial expressions of emotions and a validated measure of the quality of the patient-clinician relationship? Methods In a cross-sectional pilot study, between April 2019 and July 2019, we made video recordings of the clinician’s face during 34 initial musculoskeletal specialist outpatient evaluations. There were 16 men and 18 women, all fluent and literate in English, with a mean age of 43 ± 15 years. Enrollment was performed according to available personnel, equipment, and room availability. We did not track declines, but there were only a few. Video recordings were analyzed using facial-emotional recognition software, measuring the proportion of time spent by clinicians expressing measured emotions during a consultation. After the visit, patients completed a demographic questionnaire and measures of health anxiety (the Short Health Anxiety Inventory), fear of painful movement (the Tampa Scale for Kinesiophobia), catastrophic or worst-case thinking about pain (the Pain Catastrophizing Scale), symptoms of depression (the Patient Health Questionnaire), and the patient’s perception of the quality of their relationship with the clinician (Patient-Doctor Relationship Questionnaire). Results Clinician facial expressions consistent with happiness were associated with less patient health anxiety (r = -0.59; p < 0.001) and less catastrophic thinking (r = -0.37; p = 0.03). Lower levels of clinician expressions consistent with sadness were associated with less health anxiety (r = 0.36; p = 0.04), fewer symptoms of generalized anxiety (r = 0.36; p = 0.03), and less catastrophic thinking (r = 0.33; p = 0.05). Less time expressing anger was associated with greater health anxiety (r = -0.37; p = 0.03), greater symptoms of anxiety (r = -0.46; p < 0.01), more catastrophic thinking (r = -0.38; p = 0.03), and greater symptoms of depression (r = -0.42; p = 0.01). More time expressing surprise was associated with less health anxiety (r = -0.44; p < 0.01) and symptoms of depression (r = -0.52; p < 0.01). More time expressing fear was associated with less kinesiophobia (r = -0.35; p = 0.04). More time expressing disgust was associated with less catastrophic thinking (r = -0.37; p = 0.03) and less health anxiety (GAD-2; r = -0.42; p = 0.02) and symptoms of depression (r = -0.44; p < 0.01). There was no association between a clinicians’ facial expression of emotions and patient experience with patient-clinician interactions. Conclusion The ability to measure a patient’s mindset on the clinician’s face confirms that clinicians are registering the psychological aspects of illness, whether they are consciously aware of them or not. Future research involving larger cohorts of patients, mapping clinician-patient interactions during consultation, and more sophisticated capture of nonverbal and verbal cues, including a broader range of emotional expressions, may help translate this innovation from the research setting to clinical practice. Clinical Relevance Tools for measuring emotion through facial recognition could be used to train clinicians to become aware of the psychological aspects of health and to coach clinicians on effective communication strategies both for gentle reorientation of common misconceptions as well as for appropriate and timely diagnosis and treatment of psychological distress.

When Musculoskeletal Clinicians Respond to Empathetic Opportunities, do Patients Perceive Greater Empathy?

“…A study exploring the effects of communication skills training on care associated with patient emotional distress in the primary care setting randomized communication skills–trained clinicians and nontrained clinicians and found that trained clinicians reported more psychosocial problems, engaged in more strategies to manage emotional problems, and scored higher in clinical proficiency; additionally, their patients reported reductions in emotional distress up to 6 months after the clinical visit [62]. Although the current evidence reveals limited relationships between mindset and patient experience [7, 77], the high ceiling effect of patient-reported experience measures might hinder the ability to identify correlations with perception of clinician empathy [17, 49, 78].…”

The mind and the hand

“…Experienced doctors will usually identify unhelpful thoughts and symptoms of distress. Indeed, there is evidence that clinician facial expression of emotions correlates with the patient’s mindset, even if this is sometimes subconscious (Versluijs et al., 2021). The patient’s language in a consultation can signal unhelpful thoughts, including phrases such as ‘I can’t’ and ‘Something is wrong’, which are associated with unhelpful thinking (Bot et al., 2012).…”

There is Little or No Association Between Independently Assessed Communication Strategies and Patient Ratings of Clinician Empathy

“…For instance, current measures may not adequately assess nonverbal communication such as facial expressions [19,33], body language [35], and the linguistic and paralinguistic vocal cues in speech [17]. A study that documented correspondence between computer-rated emotional content of clinician facial expressions and patient thoughts and feelings regarding symptoms suggests computer-based tools can help identify aspects of patient-clinician interaction to improve patient experience [33]. Another study documented that natural language processing of verbatim text comments from patients about their care experience resulted in a more normal distribution of patient experience scores and no ceiling effect [27].…”

“…We may benefit from better ways for independent observers to discern the key elements of a patient-clinician interaction. For instance, current measures may not adequately assess nonverbal communication such as facial expressions [19,33], body language [35], and the linguistic and paralinguistic vocal cues in speech [17]. A study that documented correspondence between computer-rated emotional content of clinician facial expressions and patient thoughts and feelings regarding symptoms suggests computer-based tools can help identify aspects of patient-clinician interaction to improve patient experience [33].…”