Doctor-patient Dialogue Research Articles

Conversational Implicatures in Medical Discourse: An Analysis of Doctor-Patient Dialogues in Amman, Jordan

Effective communication is crucial in medical practice, and adherence to Grice's conversational maxims (quantity, quality, relevance, and manner) can improve communication between doctors and patients. In medical practice, doctors must provide clear and concise information while being honest about medical conditions and treatment options. It is also essential for doctors to communicate using simple language to ensure that the patients understand. This study analyses conversational implicature considering medical discourse in Amman. Twenty-six observations (tape-recorded doctor-patient dialogues) were randomly selected from private and government clinics in Amman, Jordan. The data was sorted and analyzed using the qualitative method of a six-phase framework. This study identifies the cooperative principles, their functions, and conversational implicature in doctor-patient discourse. Our results indicate the significance of all four conversational maxims in medical discourse, with manner being the most prominent aspect, followed by quality, relevance, and quantity. The function of cooperative principles observed in presenting the concepts of expression, direction, assertion, and declaration enhances the quality of medical discourse and promotes clarification in conversation. We observed that non-cooperative conversational implicatures are more frequently used in the doctor-patient dialogue, flouting Grice’s maxims. This flaw could lead to ineffective communication and negative health outcomes. In medical discourse, 66.7% of instances violated the Grice maxim, indicating non-cooperative conversation, while 33.3% involved cooperative implicature. The effective observance of the conversational maxims can positively impact doctors' communication with patients and promote better understanding of medical condition and treatment options that may lead to improved health outcomes.

Preserving Patient Stories: Bioethical and Legal Implications Related to the Shift from Traditional to Digital Anamnesis.

It is since the beginning of the so-called 'digital revolution' in the 1950s that technological tools have been developed to simplify and optimise traditional, time-consuming, and laborious anamnestic collection for many physicians. In recent years, more and more sophisticated 'automated' anamnestic collection systems have been developed, to the extent that they can actually enter daily clinical practice. This article not only provides a historical overview of the evolution of such tools, but also explores the ethical and medico-legal implications of the transition from traditional to digital anamnesis, including the protection of data confidentiality, the preservation of the communicative effectiveness of the doctor-patient dialogue and the safety of care in patients with poor digital and health literacy.

The DIKWP (Data, Information, Knowledge, Wisdom, Purpose) Revolution: A New Horizon in Medical Dispute Resolution

The doctor–patient relationship has received widespread attention as a significant global issue affecting people’s livelihoods. In clinical practice within the medical field, applying existing artificial intelligence (AI) technology presents issues such as uncontrollability, inconsistency, and lack of self-explanation capabilities, even raising concerns about ethics and morality. To address the problem of doctor–patient interaction differences arising from the doctor–patient diagnosis and treatment, we collected the textual content of doctor–patient dialogues in outpatient clinics of local first-class hospitals. We utilized case scenario analysis, starting from two specific cases: multi-patient visits with the same doctor and multi-doctor interaction differences with the same patient. By capturing the external interactions and the internal thought processes, we unify the external expressions and internal subjective cognition in doctor–patient interactions into interactions between data, information, knowledge, wisdom, and purpose (DIKWP) models. We propose a DIKWP semantic model for the doctor–patient interactions on both sides, including a DIKWP content model and a DIKWP cognitive model, to achieve transparency throughout the entire doctor–patient interaction process. We semantically–bidirectionally map the diagnostic discrepancy space to DIKWP uncertainty and utilize a purpose-driven DIKWP semantic fusion transformation technique to disambiguate the uncertainty problem. Finally, we select four traditional methods for qualitative and quantitative comparison with our proposed method. The results show that our method performs better in content and uncertainty handling. Overall, our proposed DIKWP semantic model for doctor–patient interaction processing breaks through the uncertainty limitations of natural language semantics in terms of interpretability, enhancing the transparency and interpretability of the medical process. It will help bridge the cognitive gap between doctors and patients, easing medical disputes.

Zhongjing: Enhancing the Chinese Medical Capabilities of Large Language Model through Expert Feedback and Real-World Multi-Turn Dialogue

Recent advances in Large Language Models (LLMs) have achieved remarkable breakthroughs in understanding and responding to user intents. However, their performance lag behind general use cases in some expertise domains, such as Chinese medicine. Existing efforts to incorporate Chinese medicine into LLMs rely on Supervised Fine-Tuning (SFT) with single-turn and distilled dialogue data. These models lack the ability for doctor-like proactive inquiry and multi-turn comprehension and cannot align responses with experts' intentions. In this work, we introduce Zhongjing, the first Chinese medical LLaMA-based LLM that implements an entire training pipeline from continuous pre-training, SFT, to Reinforcement Learning from Human Feedback (RLHF). Additionally, we construct a Chinese multi-turn medical dialogue dataset of 70,000 authentic doctor-patient dialogues, CMtMedQA, which significantly enhances the model's capability for complex dialogue and proactive inquiry initiation. We also define a refined annotation rule and evaluation criteria given the unique characteristics of the biomedical domain. Extensive experimental results show that Zhongjing outperforms baselines in various capacities and matches the performance of ChatGPT in some abilities, despite the 100x parameters. Ablation studies also demonstrate the contributions of each component: pre-training enhances medical knowledge, and RLHF further improves instruction-following ability and safety. Our code, datasets, and models are available at https://github.com/SupritYoung/Zhongjing.

Adapted large language models can outperform medical experts in clinical text summarization.

Analyzing vast textual data and summarizing key information from electronic health records imposes a substantial burden on how clinicians allocate their time. Although large language models (LLMs) have shown promise in natural language processing (NLP) tasks, their effectiveness on a diverse range of clinical summarization tasks remains unproven. Here we applied adaptation methods to eight LLMs, spanning four distinct clinical summarization tasks: radiology reports, patient questions, progress notes and doctor-patient dialogue. Quantitative assessments with syntactic, semantic and conceptual NLP metrics reveal trade-offs between models and adaptation methods. A clinical reader study with 10 physicians evaluated summary completeness, correctness and conciseness; in most cases, summaries from our best-adapted LLMs were deemed either equivalent (45%) or superior (36%) compared with summaries from medical experts. The ensuing safety analysis highlights challenges faced by both LLMs and medical experts, as we connect errors to potential medical harm and categorize types of fabricated information. Our research provides evidence of LLMs outperforming medical experts in clinical text summarization across multiple tasks. This suggests that integrating LLMs into clinical workflows could alleviate documentation burden, allowing clinicians to focus more on patient care.

Analysis on The Good Doctor Season 1 from the Perspective of Cooperative Principle

In 1967, the American language philosopher Grice proposed the cooperative principle, which provided a new theoretical framework for conversation analysis. In the following decades, the cooperative principle has been a trending topic of discussion in linguistics and continuously developed. Leech’s politeness principle and Wilson and Sperber’s relative theory have improved the cooperation principle from different aspects. The cooperative principle has been widely applied in the analysis of conversations in films, dramas, and literature, which contributes to a holistic understanding of characterization and dramatic conflicts. In this paper, doctor-patient dialogues in The Good Doctor Season 1 are analyzed from the theoretical perspective of the cooperative principle. It can be discerned that healthcare workers should comply with the maxim of quality and quantity, to no little extent, when communicating with patients. However, in different contexts, healthcare workers sometimes choose to deliberately violate the maxim of relation and manner in order to achieve better communication effects, such as helping patients understand their conditions or relieving patients’ tension. This paper intends to provide effective suggestions for real-life doctor-patient communication by applying the cooperative principle to the corpus analysis in The Good Doctor Season 1, so as to improve the communication efficiency of both parties and create a harmonious doctor-patient relationship.

A Clinical Information Extractor Integrated with Global Semantic Features via Dynamic Attention Mechanism

Electronic medical records have been rolled out in the past decades to facilitate the medical exports daily routine. However, the number of electronic medical records increases dramatically, which also causes huge workloads for the front-line clinical workers when they face the writing-up work. In this sense, researchers in the artificial intelligence domain wish to automate this process by constructing a natural language processing system, and medical information extraction is one of the key steps amongst the entire work. In this paper, we focus on medical information extraction from doctor-patient dialogues, and propose a novel encoder-decoder model which incorporates global information into the dialogue windows. The experiment on the MIE dataset suggests our model outperforms the compared baseline models, and achieves the state-of-the-art results, which proves our models effectiveness.

A novel information extraction model integrating multi-granularity global information for medical dialogue

Electronic medical records (EMRs) are one of the ways to help doctors effectively manage and analyze patient medical records. These EMRs can not only help doctors save a lot of time in analyzing medical records, but also reduce the hospital's demand for doctors and reduce the hospital's expenditure cost. Therefore, we propose a novel information extraction model integrating multi-granularity global information to efficiently extract information about a patient's physical condition in a doctor-patient dialogue. Experimental results show that our model achieves better results compared to the baseline model, indicating the effectiveness of the model.

A benchmark for automatic medical consultation system: frameworks, tasks and datasets.

In recent years, interest has arisen in using machine learning to improve the efficiency of automatic medical consultation and enhance patient experience. In this article, we propose two frameworks to support automatic medical consultation, namely doctor-patient dialogue understanding and task-oriented interaction. We create a new large medical dialogue dataset with multi-level fine-grained annotations and establish five independent tasks, including named entity recognition, dialogue act classification, symptom label inference, medical report generation and diagnosis-oriented dialogue policy. We report a set of benchmark results for each task, which shows the usability of the dataset and sets a baseline for future studies. Both code and data are available from https://github.com/lemuria-wchen/imcs21. Supplementary data are available at Bioinformatics online.

Способы выражения категории вежливости в медицинском дискурсе (на материале речевого общения врача с пациентом)

The article considers the communicative category of politeness and defines the role of this category in the process of professional doctor-patient communication. The category of politeness functions in the form of speech etiquette and is obligatory in doctor-patient conversations. The authors have analyzed the means of realization of speech etiquette and politeness in situations of professional medical communication, and determined those which are most frequently used in strategies of medical discourse. The hypothesis of the investigation is as follows: the efficiency of the dialogue created and planned by the doctor largely depends on following ethical norms of communication, which certainly is one of manifestations of politeness. The research is based on recorded materials of medical discourse in professional settings of healthcare institutions in the form of spontaneous conversations of doctors with patients. The authors prove that a polite behavior is manifested not only in the usage of speech etiquette patterns, but also in the means of expressing the doctor’s attitude and his speech address. The usage of euphemisms is associated with the doctor’s constant internal concern and care for the interlocutor, which is estimated as a manifestation of respect for the patient. Regarding the fact that speech taboos are socially conditioned, it is important to understand that the choice of an appropriate word depends not only on the level of the doctor’s speech culture, but also on the patient’s social and professional status, his or her religious beliefs, individual (personal) stability. Using other conversational elements (and extra-content components when initiating the conversation) in doctorpatient dialogues helps to create trusting relationships, the atmosphere of relaxed and unaffected communication, to ensure easy and natural speech. Speech etiquette and politeness contribute to achieving efficient professional communication. The most important role belongs to contact establishing means, the choice of which depends on the doctor’s communicative habits and peculiarities of the situation of communication.

An Intelligent Tongue Diagnosis System via Deep Learning on the Android Platform.

To quickly and accurately identify the pathological features of the tongue, we developed an intelligent tongue diagnosis system that uses deep learning on a mobile terminal. We also propose an efficient and accurate tongue image processing algorithm framework to infer the category of the tongue. First, a software system integrating registration, login, account management, tongue image recognition, and doctor–patient dialogue was developed based on the Android platform. Then, the deep learning models, based on the official benchmark models, were trained by using the tongue image datasets. The tongue diagnosis algorithm framework includes the YOLOv5s6, U-Net, and MobileNetV3 networks, which are employed for tongue recognition, tongue region segmentation, and tongue feature classification (tooth marks, spots, and fissures), respectively. The experimental results demonstrate that the performance of the tongue diagnosis model was satisfying, and the accuracy of the final classification of tooth marks, spots, and fissures was 93.33%, 89.60%, and 97.67%, respectively. The construction of this system has a certain reference value for the objectification and intelligence of tongue diagnosis.

Multi-Label Classification in Patient-Doctor Dialogues With the RoBERTa-WWM-ext + CNN (Robustly Optimized Bidirectional Encoder Representations From Transformers Pretraining Approach With Whole Word Masking Extended Combining a Convolutional Neural Network) Model: Named Entity Study.

BackgroundWith the prevalence of online consultation, many patient-doctor dialogues have accumulated, which, in an authentic language environment, are of significant value to the research and development of intelligent question answering and automated triage in recent natural language processing studies.ObjectiveThe purpose of this study was to design a front-end task module for the network inquiry of intelligent medical services. Through the study of automatic labeling of real doctor-patient dialogue text on the internet, a method of identifying the negative and positive entities of dialogues with higher accuracy has been explored.MethodsThe data set used for this study was from the Spring Rain Doctor internet online consultation, which was downloaded from the official data set of Alibaba Tianchi Lab. We proposed a composite abutting joint model, which was able to automatically classify the types of clinical finding entities into the following 4 attributes: positive, negative, other, and empty. We adapted a downstream architecture in Chinese Robustly Optimized Bidirectional Encoder Representations from Transformers Pretraining Approach (RoBERTa) with whole word masking (WWM) extended (RoBERTa-WWM-ext) combining a text convolutional neural network (CNN). We used RoBERTa-WWM-ext to express sentence semantics as a text vector and then extracted the local features of the sentence through the CNN, which was our new fusion model. To verify its knowledge learning ability, we chose Enhanced Representation through Knowledge Integration (ERNIE), original Bidirectional Encoder Representations from Transformers (BERT), and Chinese BERT with WWM to perform the same task, and then compared the results. Precision, recall, and macro-F1 were used to evaluate the performance of the methods.ResultsWe found that the ERNIE model, which was trained with a large Chinese corpus, had a total score (macro-F1) of 65.78290014, while BERT and BERT-WWM had scores of 53.18247117 and 69.2795315, respectively. Our composite abutting joint model (RoBERTa-WWM-ext + CNN) had a macro-F1 value of 70.55936311, showing that our model outperformed the other models in the task.ConclusionsThe accuracy of the original model can be greatly improved by giving priority to WWM and replacing the word-based mask with unit to classify and label medical entities. Better results can be obtained by effectively optimizing the downstream tasks of the model and the integration of multiple models later on. The study findings contribute to the translation of online consultation information into machine-readable information.

User Profiling Based on Nonlinguistic Audio Data

User profiling refers to inferring people’s attributes of interest ( AoIs ) like gender and occupation, which enables various applications ranging from personalized services to collective analyses. Massive nonlinguistic audio data brings a novel opportunity for user profiling due to the prevalence of studying spontaneous face-to-face communication. Nonlinguistic audio is coarse-grained audio data without linguistic content. It is collected due to privacy concerns in private situations like doctor-patient dialogues. The opportunity facilitates optimized organizational management and personalized healthcare, especially for chronic diseases. In this article, we are the first to build a user profiling system to infer gender and personality based on nonlinguistic audio. Instead of linguistic or acoustic features that are unable to extract, we focus on conversational features that could reflect AoIs. We firstly develop an adaptive voice activity detection algorithm that could address individual differences in voice and false-positive voice activities caused by people nearby. Secondly, we propose a gender-assisted multi-task learning method to combat dynamics in human behavior by integrating gender differences and the correlation of personality traits. According to the experimental evaluation of 100 people in 273 meetings, we achieved 0.759 and 0.652 in F1-score for gender identification and personality recognition, respectively.

Diabetes self-management in online health communities: an information exchange perspective

BackgroundOnline health communities (OHCs), with a wealth of multi-source information exchange, have provided a convenient way for people with diabetes to actively participate in their self-management and have been widely used. Information exchange assists people with diabetes with health-related decisions to actively engage in their care, and reduce the occurrence of potential complications of diabetes. However, there has been relatively little research on the information exchange behaviors and their effect on health in professional online medical platforms—OHCs.ObjectiveUsing a social exchange theory, this study focuses on two sources of information (doctors and people with diabetes) to investigate information exchange behaviors and consequences. Moreover, we also examine moderating effects of information price as patients need to pay prices for consulting with doctors to obtain medical information on OHCs.MethodsBy using the Python program, a rich dataset contained 22,746 doctor-patient dialogues from December 2017 to December 2018 is collected from the biggest OHC in China. Then the logistic and ordinal regression models are used to get empirical results.ResultsWe found that first information sharing from doctors and other people with diabetes can promote their information sharing behavior. Second, the moderating effects of information price are heterogeneous and change with the exchange participants. Third, rich information exchange supports self-management of people with diabetes and improves their health status.ConclusionThis study is among the first that tests the information exchange behavior and consequence for diabetes in OHCs and examines the moderating effects of the information price. The present study produces several insights, which have implications for social exchange, patient behavior, online health communities, and information technology in diabetes self-management literature.

Pay attention to doctor–patient dialogues: Multi-modal knowledge graph attention image-text embedding for COVID-19 diagnosis

The sudden increase in coronavirus disease 2019 (COVID-19) cases puts high pressure on healthcare services worldwide. At this stage, fast, accurate, and early clinical assessment of the disease severity is vital. In general, there are two issues to overcome: (1) Current deep learning-based works suffer from multimodal data adequacy issues; (2) In this scenario, multimodal (e.g., text, image) information should be taken into account together to make accurate inferences. To address these challenges, we propose a multi-modal knowledge graph attention embedding for COVID-19 diagnosis. Our method not only learns the relational embedding from nodes in a constituted knowledge graph but also has access to medical knowledge, aiming at improving the performance of the classifier through the mechanism of medical knowledge attention. The experimental results show that our approach significantly improves classification performance compared to other state-of-the-art techniques and possesses robustness for each modality from multi-modal data. Moreover, we construct a new COVID-19 multi-modal dataset based on text mining, consisting of 1393 doctor–patient dialogues and their 3706 images (347 X-ray + 2598 CT + 761 ultrasound) about COVID-19 patients and 607 non-COVID-19 patient dialogues and their 10754 images (9658 X-ray + 494 CT + 761 ultrasound), and the fine-grained labels of all. We hope this work can provide insights to the researchers working in this area to shift the attention from only medical images to the doctor–patient dialogue and its corresponding medical images.

Medical Spanish Musculoskeletal and Dermatologic Educational Module.

IntroductionWhile many medical schools provide opportunities in medical Spanish for medical students, schools often struggle with identifying a structured curriculum. The purpose of this module was to provide a flexible, organ system-based approach to teaching and learning musculoskeletal and dermatologic Spanish terminology, patient-centered communication skills, and sociocultural health contexts.MethodsAn 8-hour educational module for medical students was created to teach musculoskeletal and dermatologic medical communication skills in Spanish within the Hispanic/Latinx cultural context. Participants included 47 fourth-year medical students at an urban medical school with a starting minimum Spanish proficiency at the intermediate level. Faculty provided individualized feedback on speaking, listening, and writing performance of medical Spanish skills, and learners completed a written pre- and postassessment testing skills pertaining to communication domains of vocabulary, grammar, and comprehension as well as self-reported confidence levels.ResultsStudents demonstrated improvement in vocabulary, grammar, comprehension, and self-confidence of musculoskeletal and dermatologic medical Spanish topics. While students with overall lower starting proficiency levels (intermediate) scored lower on the premodule assessment compared to higher proficiency students (advanced/native), the postmodule assessment did not show significant differences in skills performance among these groups.DiscussionAn intermediate Spanish level prerequisite for this musculoskeletal and dermatologic module can result in skills improvement for all learners despite starting proficiency variability. Future study should evaluate learner clinical performance and integration of this module into other educational settings such as graduate medical education (e.g., orthopedic, rehabilitation, and dermatology residency programs) and other health professions (e.g., physical therapy and nursing).

Twin pregnancy of hydatidiform mole and viable fetus: a late diagnosis - case report / Gestação gemelar de mola hidatiforme e feto viável: um diagnóstico tardio - relato de caso

Twin pregnancy with hydatidiform mole and viable fetus is a rare event, evolving, in most cases, to abortion or medical interruption due the associated risks. Prenatal diagnosis allows individualized, rigorous and specialized monitoring, favoring the early identification of complications. We report, based on information obtained through medical record review, interviews with patient and professionals involved and literature review, a case of twin pregnancy with hydatidiform mole and healthy newborn diagnosed at delivery. Although the uncomplicated outcome that was presented suggests the possibility of expectant conduct, it is important to recognize the rarity of the case and the associated risks, both during pregnancy, after uterine emptying and in future pregnancies, and a follow-up after molar pregnancy is essential, which is interrupted approximately half of the patients. Adherence to follow-up is favored by the correct guidance to pregnant women, and prenatal diagnosis is fundamental for ensuring better doctor-patient dialogue. Therefore, it is important to highlight that, in order to properly manage hydatidiform mole pregnancies, due attention should be paid to the early diagnosis.

Realizing better doctor-patient dialogue about choices in palliative care and early phase clinical trial participation: towards an online value clarification tool (OnVaCT)

BackgroundPatients with advanced cancer for whom standard systemic treatment is no longer available may be offered participation in early phase clinical trials. In the decision making process, both medical-technical information and patient values and preferences are important. Since patients report decisional conflict after deciding on participation in these trials, improving the decision making process is essential. We aim to develop and evaluate an Online Value Clarification Tool (OnVaCT) to assist patients in clarifying their values around this end-of-life decision. This improved sharing of values is hypothesized to support medical oncologists in tailoring their information to individual patients’ needs and, consequently, to support patients in taking decisions in line with their values and reduce decisional conflict.MethodsIn the first part, patients’ values and preferences and medical oncologists’ views hereupon will be explored in interviews and focus groups to build a first prototype OnVaCT using digital communication (serious gaming). Next, we will test feasibility during think aloud sessions, to deliver a ready-to-implement OnVaCT. In the second part, the OnVaCT, with accompanied training module, will be evaluated in a pre-test (12–18 months before implementation) post-test (12–18 months after implementation) study in three major Dutch cancer centres. We will include 276 patients (> 18 years) with advanced cancer for whom standard systemic therapy is no longer available, and who are referred for participation in early phase clinical trials. The first consultation will be recorded to analyse patient-physician communication regarding the discussion of patients’ values and the decision making process. Three weeks afterwards, decisional conflict will be measured.DiscussionThis project aims to support the discussion of patient values when considering participation in early phase clinical trials. By including patients before their first appointment with the medical oncologist and recording that consultation, we are able to link decisional conflict to the decision making process, e.g. the communication during consultation. The study faces challenges such as timely including patients within the short period between referral and first consultation. Furthermore, with new treatments being developed rapidly, molecular stratification may affect the patient populations included in the pre-test and post-test periods.Trial registrationNetherlands Trial Registry number: NTR7551 (prospective; July 17, 2018).

Effect and underlying mechanism of refutation texts on the trust and moral judgment of patients

<p id=C2>Mistrust between doctors and patients is a worldwide concern, especially in China. Among all possible driving factors, the knowledge gap between the two groups plays a key role in the formation of disparate cognitive styles between professional medical workers and laymen. If patients can be educated effectively and prompted to think like experts, they can become more compliant during medical treatment and more tolerant of unexpected diagnosis or treatment results, thus maintaining their trust in doctors. One possible effective way to make patients think like doctors is to conduct knowledge revision in health and medicine, a method that is counterintuitive to ordinary people but familiar to doctors. This process can be facilitated by the adoption of refutation texts, which state previously acquired but incorrect knowledge and then directly refute the wrong information while providing the correct knowledge. In the present set of experiments, we systematically examined the effect and underlying mechanism of refutation texts under medical situations. Refutation texts were constructed in the pilot study. The texts consisted of five items that were familiar to doctors but peculiar and interesting to laymen. After asking 103 college students to guess “True” or “False” on each question, correct answers were provided for the participants. Subsequently, they were asked to assess if these answers prompted them to reflect that their previous beliefs were incorrect. At least 97 participants (94.2%) made one error in the test, and 81 participants (78.6%) admitted that this process prompted a reflection on their previous beliefs, a result that proved the effectiveness of refutation texts. Study 1 was designed to test the effect of refutation texts constructed in the pilot study on patient’s trust and moral judgment on doctors using a 2 (intervention: with or without refutation texts) × 2 (compliance: following doctors’ instruction or not) × 2 (check result: common fever or leukemia) between-subject design. Participants were randomly assigned those eight conditions. First, participants were required to read a scenario of doctor-patient dialogue discussing whether bone marrow puncture (BMP) should be applied to a child. Then, they were required to guess the compliance of the child’s father before they were given the different combinations of the father’s decision and medical results. Participants were required to judge the intention of the doctor’s suggestion of BMP and assess how much trust they have in doctors. Results confirmed the significant effect of refutation texts on the participants’ trust and moral judgment. Participants who were provided refutation texts had higher trust scores and were more likely to judge the doctor’s intention positively and unselfishly compared with those who were not provided such texts. The medical inspection results influenced the participants’ judgment style and trust level. When the inspection showed a minor symptom, participants tended to show lower trust in the doctor and had negative attitude toward the doctor’s suggestion. Study 2 tested the chain mediating role of intolerance of uncertainty and tolerance on medical workers. The same procedure as in Study 1 was used, except that participants’ scores were collected using the Intolerance of Uncertainty Scale and Tolerance on Medical Worker’s Questionnaire. Data were analyzed by using Process Plugin. Results showed a chain mediation effect on moral judgment but failed to confirm the same effect on trust. Moreover, Study 3 confirmed the effectiveness of refutation texts under non-medical-related situations and the chain mediating role of intolerance of uncertainty and tolerance on medical workers’ trust. Our findings demonstrated the effectiveness of refutation texts in prompting laymen’s knowledge revision and reflection on their existing health beliefs. This result will improve the tolerance on unexpected treatment consequences and the doctors’ possible misdiagnosis, thus sustaining trust in the doctor. On the basis of our results, we recommend that odd but interesting knowledge of the human body and counter-intuitive medical facts can be employed as routine material in patient education to foster a reflexive attitude on possible unsatisfactory diagnosis or treatment results. Future research may construct more useful refutation text items and further explore the possible hindering effect of previous health beliefs and negative emotions of medical refutation texts.

Exploring emotions in dialog between health provider, parent and child. An observational study in pediatric primary care

IntroductionThis paper is part of the theoretical thread of health psychology and it presents a study aimed to recognize information useful to implement psychological interventions finalized to personalize medical treatments and engage users in pediatrics. ObjectiveIn this paper is presented an observational study aimed to explore doctor-patient dialog about worries in pediatric primary care; it has been carried out identifying dialogical interaction patterns, or typical dialog between health providers, parent and child. MethodWe documented conversations in 265 visits; we audio-recordered, transcribed and analyzed them with Verona Coding Definitions of Emotional Sequences. Dialogs between participants were analyzed one by one. Frequencies of emotions’ signals (cues/concerns) and responses were analyzed through Redundancy Analysis, aimed to establish a quantitative relationship between these pair of groups of variables considering the asymmetrical relationship between them. ResultsSix “Dialogical Interaction Patterns” were obtained by interpreting these relationships; they show dialogs mainly aimed at obtaining information useful for diagnosis and treatment, with a limited exploration of worries or issues related to the condition of the child. Pediatric conversations seem to be characterized by a very high attention to cognitive aspects of medical questions with a poor consideration of emotions as useful information to medical practice. ConclusionsThese dialogs seem to ensure the rapidity and the efficiency of medical visits. Nevertheless, it could be useful to implement psychological interventions to achieve an enrichment of the dialog between participants, helping them to recognize users’ emotions as useful to define shared medical strategies.