Influence of doctor-patient trust on the adoption of mobile medical applications during the epidemic: a UTAUT-based analysis.

This study examines the factors influencing users' intention to continue using mobile medical apps within the framework of the Unified Theory of Acceptance and Use of Technology (UTAUT) model. Through a combination of questionnaire surveys and interviews, the research finds that doctor-patient trust, Performance Expectancy (PE), social influence, and facilitating conditions significantly impact users' intention to utilize mobile medical apps. Furthermore, the study reveals the moderating effect of doctor-patient trust on social influence, indicating an increased trust level during the epidemic, attributed to positive media coverage, complimentary medical services, and risk-sharing initiatives. These results provide valuable insights for the field of internet healthcare, COVID-19 response strategies, health information management, and the advancement of digital health technologies, spotlighting the pivotal roles of trust, PE, and social influence in fostering sustained engagement with mobile health apps.

Investigating the role of source and source trust in prebunks and debunks of misinformation in online experiments across four EU countries.

Misinformation surrounding crises poses a significant challenge for public institutions. Understanding the relative effectiveness of different types of interventions to counter misinformation, and which segments of the population are most and least receptive to them, is crucial. We conducted a preregistered online experiment involving 5228 participants from Germany, Greece, Ireland, and Poland. Participants were exposed to misinformation on climate change or COVID-19. In addition, they were pre-emptively exposed to a prebunk, warning them of commonly used misleading strategies, before encountering the misinformation, or were exposed to a debunking intervention afterwards. The source of the intervention (i.e. the European Commission) was either revealed or not. The findings show that both interventions change four variables reflecting vulnerability to misinformation in the expected direction in almost all cases, with debunks being slightly more effective than prebunks. Revealing the source of the interventions did not significantly impact their overall effectiveness. One case of undesirable effect heterogeneity was observed: debunks with revealed sources were less effective in decreasing the credibility of misinformation for people with low levels of trust in the European Union (as elicited in a post-experimental questionnaire). While our results mostly suggest that the European Commission, and possibly other public institutions, can confidently debunk and prebunk misinformation regardless of the trust level of the recipients, further evidence on this is needed.

In human-machine trust, humans rely on a simple averaging strategy.

With the growing role of artificial intelligence (AI) in our lives, attention is increasingly turning to the way that humans and AI work together. A key aspect of human-AI collaboration is how people integrate judgements or recommendations from machine agents, when they differ from their own judgements. We investigated trust in human-machine teaming using a perceptual judgement task based on the judge-advisor system. Participants ( n = 89 ) estimated a perceptual quantity, then received a recommendation from a machine agent. The participants then made a second response which combined their first estimate and the machine's recommendation. The degree to which participants shifted their second response in the direction of the recommendations provided a measure of their trust in the machine agent. We analysed the role of advice distance in people's willingness to change their judgements. When a recommendation falls a long way from their initial judgement, do people come to doubt their own judgement, trusting the recommendation more, or do they doubt the machine agent, trusting the recommendation less? We found that although some participants exhibited these behaviours, the most common response was neither of these tendencies, and a simple model based on averaging accounted best for participants' trust behaviour. We discuss implications for theories of trust, and human-machine teaming.

Effective community entry: reflections on community engagement in culturally sensitive research in southwestern Nigeria.

Effective community entry processes influence community participation and acceptance of public health interventions. Though there is a growing body of literature on the importance of community partnerships, there is a lack of pragmatic and practical documentation of the experiences involved in the community entry process as it relates to culturally sensitive topics such as child marriage which can help to support researchers working in this field. This article highlights key themes related to knowledge of the community, effective communication, cultural sensitivity, coproduction and giving feedback which help to build trust between the community members and the research team. Institutional representation, not managing expectations, and lack of clarity, along with personal opinions of community gatekeepers can create challenges for the fostering of trustworthy relationships with the community. These realities must be actively addressed right at the onset of the process between the research team and community stakeholders. Researchers can develop trust, form connections and engage different communities by working with local groups and leaders, using culturally appropriate methods, and addressing community concerns. Future projects working with communities on child marriage in Nigeria and other countries would benefit from the reflections presented in this paper.

Trusting AI made decisions in healthcare by making them explainable.

OBJECTIVES: In solving the trust issues surrounding machine learning algorithms whose reasoning cannot be understood, advancements can be made toward the integration of machine learning algorithms into mHealth applications. The aim of this paper is to provide a transparency layer to black-box machine learning algorithms and empower mHealth applications to maximize their efficiency. METHODS: Using a machine learning testing framework, we present the process of knowledge transfer between a white-box model and a black-box model and the evaluation process to validate the success of the knowledge transfer. RESULTS: The presentation layer of the final output of the base white-box model and the knowledge-infused white-box model shows clear differences in reasoning. The correlation between the base black-box model and the new knowledge-infused model is very high, indicating that the knowledge transfer was successful. CONCLUSION: There is a clear need for transparency in digital health and health in general. Adding solutions to the toolbox of explainable artificial intelligence is one way to gradually decrease the obscurity of black-box models.

Overtrust in AI Recommendations About Whether or Not to Kill: Evidence from Two Human-Robot Interaction Studies.

This research explores prospective determinants of trust in the recommendations of artificial agents regarding decisions to kill, using a novel visual challenge paradigm simulating threat-identification (enemy combatants vs. civilians) under uncertainty. In Experiment 1, we compared trust in the advice of a physically embodied versus screen-mediated anthropomorphic robot, observing no effects of embodiment; in Experiment 2, we manipulated the relative anthropomorphism of virtual robots, observing modestly greater trust in the most anthropomorphic agent relative to the least. Across studies, when any version of the agent randomly disagreed, participants reversed their threat-identifications and decisions to kill in the majority of cases, substantially degrading their initial performance. Participants' subjective confidence in their decisions tracked whether the agent (dis)agreed, while both decision-reversals and confidence were moderated by appraisals of the agent's intelligence. The overall findings indicate a strong propensity to overtrust unreliable AI in life-or-death decisions made under uncertainty.

Trustworthy and ethical AI-enabled cardiovascular care: a rapid review.

BACKGROUND: Artificial intelligence (AI) is increasingly used for prevention, diagnosis, monitoring, and treatment of cardiovascular diseases. Despite the potential for AI to improve care, ethical concerns and mistrust in AI-enabled healthcare exist among the public and medical community. Given the rapid and transformative recent growth of AI in cardiovascular care, to inform practice guidelines and regulatory policies that facilitate ethical and trustworthy use of AI in medicine, we conducted a literature review to identify key ethical and trust barriers and facilitators from patients' and healthcare providers' perspectives when using AI in cardiovascular care. METHODS: In this rapid literature review, we searched six bibliographic databases to identify publications discussing transparency, trust, or ethical concerns (outcomes of interest) associated with AI-based medical devices (interventions of interest) in the context of cardiovascular care from patients', caregivers', or healthcare providers' perspectives. The search was completed on May 24, 2022 and was not limited by date or study design. RESULTS: After reviewing 7,925 papers from six databases and 3,603 papers identified through citation chasing, 145 articles were included. Key ethical concerns included privacy, security, or confidentiality issues (n = 59, 40.7%); risk of healthcare inequity or disparity (n = 36, 24.8%); risk of patient harm (n = 24, 16.6%); accountability and responsibility concerns (n = 19, 13.1%); problematic informed consent and potential loss of patient autonomy (n = 17, 11.7%); and issues related to data ownership (n = 11, 7.6%). Major trust barriers included data privacy and security concerns, potential risk of patient harm, perceived lack of transparency about AI-enabled medical devices, concerns about AI replacing human aspects of care, concerns about prioritizing profits over patients' interests, and lack of robust evidence related to the accuracy and limitations of AI-based medical devices. Ethical and trust facilitators included ensuring data privacy and data validation, conducting clinical trials in diverse cohorts, providing appropriate training and resources to patients and healthcare providers and improving their engagement in different phases of AI implementation, and establishing further regulatory oversights. CONCLUSION: This review revealed key ethical concerns and barriers and facilitators of trust in AI-enabled medical devices from patients' and healthcare providers' perspectives. Successful integration of AI into cardiovascular care necessitates implementation of mitigation strategies. These strategies should focus on enhanced regulatory oversight on the use of patient data and promoting transparency around the use of AI in patient care.

Encompassing trust in medical AI from the perspective of medical students: a quantitative comparative study.

BACKGROUND: In the years to come, artificial intelligence will become an indispensable tool in medical practice. The digital transformation will undoubtedly affect today's medical students. This study focuses on trust from the perspective of three groups of medical students - students from Croatia, students from Slovakia, and international students studying in Slovakia. METHODS: A paper-pen survey was conducted using a non-probabilistic convenience sample. In the second half of 2022, 1715 students were surveyed at five faculties in Croatia and three in Slovakia. RESULTS: Specifically, 38.2% of students indicated familiarity with the concept of AI, while 44.8% believed they would use AI in the future. Patient readiness for the implementation of technologies was mostly assessed as being low. More than half of the students, 59.1%, believe that the implementation of digital technology (AI) will negatively impact the patient-physician relationship and 51,3% of students believe that patients will trust physicians less. The least agreement with the statement was observed among international students, while a higher agreement was expressed by Slovak and Croatian students 40.9% of Croatian students believe that users do not trust the healthcare system, 56.9% of Slovak students agree with this view, while only 17.3% of international students share this opinion. The ability to explain to patients how AI works if they were asked was statistically significantly different for the different student groups, international students expressed the lowest agreement, while the Slovak and Croatian students showed a higher agreement. CONCLUSION: This study provides insight into medical students' attitudes from Croatia, Slovakia, and international students regarding the role of artificial intelligence (AI) in the future healthcare system, with a particular emphasis on the concept of trust. A notable difference was observed between the three groups of students, with international students differing from their Croatian and Slovak colleagues. This study also highlights the importance of integrating AI topics into the medical curriculum, taking into account national social & cultural specificities that could negatively impact AI implementation if not carefully addressed.

The effect of technology-based service characteristics on relationship quality in metaverse exercise services: a questionnaire survey of bicycle simulator users in Korea.

BACKGROUND: As non-face-to-face contact has become a daily routine owing to the development of science and technology and impact of the coronavirus disease (COVID-19) pandemic, online technology-based services (TBSSs) have been expanding. Consequently, in virtual metaverse exercise spaces, the number of participants using TBSS is increasing. This study aimed to examine the effects of TBSS characteristics on the quality of the relationship between service providers and users of metaverse exercise services. METHODS: The participants were metaverse exercise service users in Korea, who were selected through purposive sampling (n = 254, 194 men, 61 women). A questionnaire survey was conducted to measure the participants' TBSS characteristics (enjoyment, stability, usefulness, ease of use, and reliability) and relationship quality (satisfaction, trust, and commitment). We analyzed the frequency, validity, reliability, and descriptive statistics of the collected data. Moreover, correlation and multivariate multiple regression analyses were conducted. RESULTS: Enjoyment, stability, usefulness, and reliability of TBSS had positive effects on satisfaction; however, ease of use did not have a significant effect. In addition, enjoyment, usefulness, ease of use, and reliability of TBSS had positive effects on trust; however, stability did not have a significant effect. Furthermore, enjoyment and reliability of TBSS had positive effects on commitment; however, usefulness, ease of use, and stability had no significant effects. CONCLUSIONS: This study provides metaverse exercise service providers with management strategies for attracting and retaining members. The results of this study could help develop effective TBSS and aid metaverse service companies facing a fiercely competitive environment.

Unleashing the role of e-word of mouth on purchase intention in select Facebook fan pages of smart phone users.

OBJECTIVE: This study aims to examine the impact of e-WOM on customer purchase intentions in Facebook fan pages using theories of trust, value co-creation and brand attitude. The present research has set out to explore this emerging domain of study and has thus developed & tested propositions which attempt to establish a relationship between e-WOM and customer's purchase intentions. A deeper understanding of this possible association is obtained by studying the mediating roles of Trust, Value Co-Creation, Brand Image and Brand Attitude. METHODOLOGY: The context for exploring this phenomenon is chosen to be the fan pages of smartphone brands on Facebook. The study involved conducting a sample survey of 490 respondents, comprising of both male and female, who belong to 5 smartphone brands Facebook fan pages-Samsung, Moto G, Lenovo, MI and ASUS are considered for the study. Out of which sample of 100 each has been targeted individually. FINDINGS: The findings suggested that e-WOM significantly predicts the purchase intentions of the customers of a specific product and considerable impacted on the purchase decision. The findings of the study also reveal that customer 's trust beliefs, perceived value co-creation, brand image and brand attitude partially mediate in between relationships of e-WOM and purchase intention. CONCLUSION: The actual presence of different types of consumer electronics brands on the social media, more prominently, the smartphones, which undoubtedly are the most ubiquitous product of this segment. In fact, this indicates that presence on social media is a well- thought organizational strategy developed by companies to gain partial control over the customer 's decision- making process by establishing a close connect with the customers for a long period. IMPLICATION: This consequence will significantly impact the decision-making process of marketers or practitioners in relation to their marketing tactics. This research also indicates that marketers could devise more effective methods for distributing marketing content through social networking sites, while corporations can cultivate favorable electronic word-of-mouth for their products or services. Through the implementation of social media marketing strategies, companies can increase their sales volume and generate higher revenue. The study examined the role of trust, virtual community participation, and desire to purchase as mediators on smartphone brand fan sites on Facebook. It was observed that these factors had a partial influence on customer purchase intention.

Psychological Factors Determining Prognosis of Dental Treatments.

Many psychological factors may have a significant bearing on an individual's oral health and success of dental treatments. Overall, these factors may result in the avoidance of dental visits, emergency-based dental appointments, noncompliant dental behavior, the utilization of multiple oral health care providers, and poor oral health. These factors may affect the quality of life of individuals and may lead to patient dissatisfaction, poor prognosis, and failure of dental treatment. Multiple psychological factors may affect the dentist and the patient. Those factors may alter the prognosis for successful dental treatment. Physician empathy is fundamental in developing long-term physician-patient trust.

Exploring the influence of Chinese online patient trust on telemedicine behavior: insights into perceived risk and behavior intention.

As a supplement to medical services, telemedicine is of great significance to alleviate the shortage of health resources in China. Based on the traditional consumer behavior measurement model the Technology Acceptance Mode/Theory of Planned Behavior (TAM/TPB), this paper divides online patient trust into six dimensions: perceived risk, personal trust tendency, doctors' credibility, hospitals' credibility, websites' credibility, and system guarantee. On this basis, a structural equation model (SEM) was used to explore the influence of each dimension of online patient trust on online patient intention, behavior choice, and pre-factors. A total of 582 valid questionnaires were distributed to selected patients with experience in using mobile healthcare services in the vicinity of hospitals and communities, as well as to users who shared their experiences in the discussion forums of mobile healthcare websites. The results show that online patient trust has a significant positive impact on telemedicine behavior intention selection, with a standardized path coefficient being as high as 0.866. Doctors' credibility, system guarantee, and website credibility have significant positive effects on online patient trust, with standardized path coefficients of 0.401, 0.260, and 0.226, respectively. Hospital trustworthiness and personal trust propensity have no significant effect on online patient trust. Perceived risk has a significant negative effect on online patient trust, with a standardized path coefficient of -0.118. The research findings suggest that health departments and mobile healthcare providers can enhance mobile healthcare services by considering the patients' perspectives, elevate their online trust levels, and foster a deeper understanding, safety consciousness, and confidence in telehealth services. On this basis, it can be concluded that only the participation of government, medical subjects, and online patients can effectively reduce perceived risks, improve perceived characteristics of online patients, enhance online patient trust, and promote the real willingness and behavior choice for online medical services, effectively improving the positive role of telemedicine in increasing health benefits to people.

Sustainable Digital Transformation: Its Impact on Perceived Value and Adoption Intention of Industry 4.0 in Moderating Effects of Uncertainty Avoidance.

Background: Industry 4.0 is a significant technical revolution that combines big data analytics, the Internet of Things (IoT), and cyber-physical systems to improve manufacturing productivity. This study investigates the impact of digital trust and sustainable attitude on perceived value and the intention to adopt Industry 4.0 technologies. It also examines the moderating role of uncertainty avoidance in these relationships. Methods: Data were collected from 189 employees of leading manufacturing companies in Indonesia that are recognized for their Industry 4.0 practices. The data were analyzed using Partial Least Squares (PLS) methodology with SmartPLS software to test the proposed hypotheses and explore the moderating effects. Results: The findings reveal that both digital trust and sustainable attitude significantly influence perceived value. However, these factors do not directly affect the intention to adopt Industry 4.0 technologies. Uncertainty avoidance moderates the relationship between digital trust and adoption intention. Specifically, in environments with high uncertainty avoidance, digital trust becomes a critical factor influencing the decision to adopt Industry 4.0 technologies. Conclusions: The study provides valuable insights for organizations aiming to implement Industry 4.0 initiatives. It highlights the importance of fostering digital trust and considering cultural dimensions, such as uncertainty avoidance, in their technology adoption strategies.

Witness to Harm; Holding to Account: What Is the Importance of Information for Members of the Public Who Give Evidence and May Be Witness in a Regulatory Hearing of a Health or Care Professional?

BACKGROUND: Health and social care regulators are organisations that seek to maintain public trust in professionals and protect the public from harmful practitioners. For example, they ensure that practitioners have the correct qualifications to practice and investigate any concerns raised about them. Serious concerns can result in a fitness to practise (FtP) hearing where a member of the public may be required to give evidence as a witness. Being a witness and being cross-examined is known to often be traumatic, particularly for members of the public in criminal trials. There is some research evidence that registered professionals who are the subject of the proceedings may suffer mental ill health as result of the experience. But there is scant research that specifically explores the experiences of members of the public giving evidence in a FtP hearing. The regulator web pages are an important source of information for public witnesses to prepare themselves for a FtP hearing. AIM: This study aimed to examine the publicly available information for public witnesses from the 13 health and social care regulators in the United Kingdom to evaluate the content, amount, type and format of information available and make recommendations about how regulators can improve these. METHODS: Regulator websites were searched during November 2021-February 2022 for information for the public on what happens after raising a concern with a regulator. Resources were downloaded and qualitative content analysis conducted. Our findings were validated by interviews (n = 7) with the public including people with experience of FtP and a focus group of the public (n = 5). RESULTS: One hundred and forty-six resources (97 webpages and 25 public facing documents, 20 videos and 4 easy read documents) were found. Topics included screening and investigation, preparing for a hearing, during a hearing and after a hearing, and support for witnesses. DISCUSSION AND CONCLUSION: We conclude that there are many deficiencies in the information content and its presentation for the public and some exemplars, such as the use of flowcharts and short videos to explain the FtP processes. Recommendations for practice take the form of a framework with three themes, (i) co-production, (ii) preferred content and (iii) format. It may be used by regulators to enhance their support for members of the public as witnesses in FtP hearings. PUBLIC CONTRIBUTION: Our advisory group of people with lived experience of involvement as members of the public in FtP discussed the findings and contributed to the recommendations.

Initial Investigations into Physician Acceptance of Medical AI: Examining Trust, Resistance, Perceived Job Insecurity, and Usage Intentions.

This study evaluated physicians' attitudes towards medical AI across three Taiwanese hospitals, focusing on constructs of trust, resistance, job insecurity, and adoption willingness, with a survey based on the Dual-factor Model yielding 282 responses and a 94% response rate. Results showed positive trust in AI, low resistance and job insecurity concerns, and a high willingness to adopt AI, indicating a favorable view of AI as a supportive tool rather than a replacement. Key adoption factors were identified as regulatory standards, accuracy, workflow integration, and result clarity, providing valuable insights for future AI development in medicine.

Peer or tutor? The congruity effects of service robot role and service type on usage intention.

The invention of service robots has reduced the labor cost and improved enterprises' efficiency and service quality. However, it is still difficult to enhance consumers' intention to use robot-by-robot design efficiently. Based on social roles of anthropomorphic cues, service robots can be divided into peer (e.g., kind and amiable friends) or tutor (e.g., authoritative and professional experts) robots. From a matching perspective, this paper investigates (1) whether robot role and service type have an impact on consumers' intention to employ service robots in different ways, and (2) how cognitive trust and affective trust can play a mediating role during this process. In this paper, the authors conducted an online a scenario-based experiment and collected a valid sample of 332 consumers. The results show that the participants are more willing to apply the tutor robot in the scenario of utilitarian service, and the peer robot in the scenario of hedonic service. In addition, cognitive trust and affective trust have a matching mediation effect. Specifically, for the utilitarian service, cognitive trust mediates the effect of robot role on consumers' intention to adopt the robots, while the mediating effect of affective trust is not significant. As for the hedonic service, affective trust mediated the effect of robot role on the intention to use, whereas the mediating effect of cognitive trust is not significant.

Assessing Patient Trust in Automation in Health Care Systems: Within-Subjects Experimental Study.

BACKGROUND: Health care technology has the ability to change patient outcomes for the betterment when designed appropriately. Automation is becoming smarter and is increasingly being integrated into health care work systems. OBJECTIVE: This study focuses on investigating trust between patients and an automated cardiac risk assessment tool (CRAT) in a simulated emergency department setting. METHODS: A within-subjects experimental study was performed to investigate differences in automation modes for the CRAT: (1) no automation, (2) automation only, and (3) semiautomation. Participants were asked to enter their simulated symptoms for each scenario into the CRAT as instructed by the experimenter, and they would automatically be classified as high, medium, or low risk depending on the symptoms entered. Participants were asked to provide their trust ratings for each combination of risk classification and automation mode on a scale of 1 to 10 (1=absolutely no trust and 10=complete trust). RESULTS: Results from this study indicate that the participants significantly trusted the semiautomation condition more compared to the automation-only condition (P=.002), and they trusted the no automation condition significantly more than the automation-only condition (P=.03). Additionally, participants significantly trusted the CRAT more in the high-severity scenario compared to the medium-severity scenario (P=.004). CONCLUSIONS: The findings from this study emphasize the importance of the human component of automation when designing automated technology in health care systems. Automation and artificially intelligent systems are becoming more prevalent in health care systems, and this work emphasizes the need to consider the human element when designing automation into care delivery.