This article deals with the experience of the specific client of health services, that is, the patient. Satisfaction questionnaires are usually applied to assess patient experience. However, this tool provides only a cognitive evaluation; it does not afford an affective dimension of the experience. The objective of the present study is to verify the relationship between the cognitive dimension of patient experience, collected through questionnaires, and the affective dimension, derived from the analysis of neurophysiological data. We propose a novel methodology that integrates physiological data collected by facial expression analysis to identify patients’ emotions. A first, qualitative procedure was carried out to define the patient journey. This was recorded on video and later used in the experiment. The experiment collected information from the participants using two techniques. First, as they viewed the videos, facial expression analysis (FEA) was applied to assess their responses. Second, after they watched the videos, traditional questionnaires were presented. The results provided by the two techniques were then compared. The results show that there is no relationship between the emotional valence reported by questionnaires and the neurophysiological data. This reflects the two different dimensions of the experience, one cognitive and the other affective. Both facilitate the understanding of patient satisfaction.

Background In recent years, attempts have been made to incorporate patients' experiences into healthcare processes, to complement clinical indicators, with what are known as patient‐reported outcome measures (PROMs) and patient‐reported experience measures (PREMs). While the research into PROMs is more developed, the application of PREMs faces some difficulties. The incorporation of emotional indicators into assessments of the experience is an area that remains to be explored. Objectives This study proposes a new technique to analyse the emotions experienced by patients during the care process, examines how these emotions influence their satisfaction and propose that if healthcare services focus more on patients' emotions, they can improve the effectiveness of the sector. Methods The first, qualitative stage, gathered data from patients to design a patient journey (PJ). The PJ was then reproduced as a video. In a subsequent, quantitative stage, the video was shown to experimental participants, and their emotions were measured through facial expression analysis and a questionnaire. Results A new technique to gather emotional data showed that the emotions patients experience do not affect their satisfaction with their clinical care or the physical aspects of the process. However, their emotions did affect their satisfaction with people and organizations. Conclusions The importance of the emotional component of patients' experiences was underlined. Therefore, healthcare organizations should take account of this dimension, as well as the cognitive, to increase patient satisfaction and improve their care processes. Understanding the impact of the emotions identified at the subconscious level can help improve the patient experience. A new methodology was applied that may help health professionals to collect emotional data about patients' experiences and to develop PREMs. Patient/Public Contribution Patients were involved in all stages of this research. In the exploratory phase, some helped define the touchpoints of the PJ. The data from the subsequent experimental phase were collected from another group, and the emotions they experienced were identified through the analysis of their facial expressions. Based on the results of this study, a working group including patients has been established to work on improvements in the PJ.

This article presents the analysis of the main Spanish political candidates for the elections to be held on April 2019. The analysis focuses on the Facial Expression Analysis (FEA), a technique widely used in neuromarketing research. It allows to identify the micro-expressions that are very brief, involuntary. They are signals of hidden emotions that cannot be controlled voluntarily. The video with the final interventions of every candidate has been post-processed using the classification algorithms given by the iMotions's AFFDEX platform. We have then analyzed these data. Firstly, we have identified and compare the basic emotions showed by each politician. Second, we have associated the basic emotions with specific moments of the candidate's speech, identifying the topics they address and relating them directly to the expressed emotion. Third, we have analyzed whether the differences shown by each candidate in every emotion are statistically significant. In this sense, we have applied the non-parametric chi-squared goodness-of-fit test. We have also considered the ANOVA analysis in order to test whether, on average, there are differences between the candidates. Finally, we have checked if there is consistency between the results provided by different surveys from the main media in Spain regarding the evaluation of the debate and those obtained in our empirical analysis. A predominance of negative emotions has been observed. Some inconsistencies were found between the emotion expressed in the facial expression and the verbal content of the message. Also, evidences got from statistical analysis confirm that the differences observed between the various candidates with respect to the basic emotions, on average, are statistically significant. In this sense, this article provides a methodological contribution to the analysis of the public figures' communication, which could help politicians to improve the effectiveness of their messages identifying and evaluating the intensity of the expressed emotions.

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