The Experts below are selected from a list of 103860 Experts worldwide ranked by ideXlab platform
Tobias Kowatsch - One of the best experts on this subject based on the ideXlab platform.
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personalization of conversational agent Patient Interaction styles for chronic disease management two consecutive cross sectional questionnaire studies
Journal of Medical Internet Research, 2021Co-Authors: Christoph Gross, Theresa Schachner, Andrea Hasl, Dario Kohlbrenner, Christian F Clarenbach, Florian Von Wangenheim, Tobias KowatschAbstract:Background: Conversational agents (CAs) for chronic disease management are receiving increasing attention in academia and the industry. However, long-term adherence to CAs is still a challenge and needs to be explored. Personalization of CAs has the potential to improve long-term adherence and, with it, user satisfaction, task efficiency, perceived benefits, and intended behavior change. Research on personalized CAs has already addressed different aspects, such as personalized recommendations and anthropomorphic cues. However, detailed information on Interaction styles between Patients and CAs in the role of medical health care professionals is scant. Such Interaction styles play essential roles for Patient satisfaction, treatment adherence, and outcome, as has been shown for physician-Patient Interactions. Currently, it is not clear (1) whether chronically ill Patients prefer a CA with a paternalistic, informative, interpretive, or deliberative Interaction style, and (2) which factors influence these preferences. Objective: We aimed to investigate the preferences of chronically ill Patients for CA-delivered Interaction styles. Methods: We conducted two studies. The first study included a paper-based approach and explored the preferences of chronic obstructive pulmonary disease (COPD) Patients for paternalistic, informative, interpretive, and deliberative CA-delivered Interaction styles. Based on these results, a second study assessed the effects of the paternalistic and deliberative Interaction styles on the relationship quality between the CA and Patients via hierarchical multiple linear regression analyses in an online experiment with COPD Patients. Patients’ sociodemographic and disease-specific characteristics served as moderator variables. Results: Study 1 with 117 COPD Patients revealed a preference for the deliberative (50/117) and informative (34/117) Interaction styles across demographic characteristics. All Patients who preferred the paternalistic style over the other Interaction styles had more severe COPD (three Patients, Global Initiative for Chronic Obstructive Lung Disease class 3 or 4). In Study 2 with 123 newly recruited COPD Patients, younger participants and participants with a less recent COPD diagnosis scored higher on Interaction-related outcomes when interacting with a CA that delivered the deliberative Interaction style (Interaction between age and CA type: relationship quality: b=−0.77, 95% CI −1.37 to −0.18; intention to continue Interaction: b=−0.49, 95% CI −0.97 to −0.01; working alliance attachment bond: b=−0.65, 95% CI −1.26 to −0.04; working alliance goal agreement: b=−0.59, 95% CI −1.18 to −0.01; Interaction between recency of COPD diagnosis and CA type: working alliance goal agreement: b=0.57, 95% CI 0.01 to 1.13). Conclusions: Our results indicate that age and a Patient’s personal disease experience inform which CA Interaction style the Patient should be paired with to achieve increased Interaction-related outcomes with the CA. These results allow the design of personalized health care CAs with the goal to increase long-term adherence to health-promoting behavior.
Katherine A Heller - One of the best experts on this subject based on the ideXlab platform.
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using a simulation centre to evaluate preliminary acceptability and impact of an artificial intelligence powered clinical decision support system for depression treatment on the physician Patient Interaction
BJPsych open, 2021Co-Authors: David Benrimoh, Myriam Tanguaysela, Kelly Perlman, Sonia Israel, Joseph Mehltretter, Caitrin Armstrong, Robert Fratila, Sagar V Parikh, Jordan F Karp, Katherine A HellerAbstract:Background Recently, artificial intelligence-powered devices have been put forward as potentially powerful tools for the improvement of mental healthcare. An important question is how these devices impact the physician-Patient Interaction. Aims Aifred is an artificial intelligence-powered clinical decision support system (CDSS) for the treatment of major depression. Here, we explore the use of a simulation centre environment in evaluating the usability of Aifred, particularly its impact on the physician–Patient Interaction. Method Twenty psychiatry and family medicine attending staff and residents were recruited to complete a 2.5-h study at a clinical Interaction simulation centre with standardised Patients. Each physician had the option of using the CDSS to inform their treatment choice in three 10-min clinical scenarios with standardised Patients portraying mild, moderate and severe episodes of major depression. Feasibility and acceptability data were collected through self-report questionnaires, scenario observations, interviews and standardised Patient feedback. Results All 20 participants completed the study. Initial results indicate that the tool was acceptable to clinicians and feasible for use during clinical encounters. Clinicians indicated a willingness to use the tool in real clinical practice, a significant degree of trust in the system's predictions to assist with treatment selection, and reported that the tool helped increase Patient understanding of and trust in treatment. The simulation environment allowed for the evaluation of the tool's impact on the physician–Patient Interaction. Conclusions The simulation centre allowed for direct observations of clinician use and impact of the tool on the clinician–Patient Interaction before clinical studies. It may therefore offer a useful and important environment in the early testing of new technological tools. The present results will inform further tool development and clinician training materials.
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using a simulation centre to evaluate the effect of an artificial intelligence powered clinical decision support system for depression treatment on the physician Patient Interaction
medRxiv, 2020Co-Authors: David Benrimoh, Myriam Tanguaysela, Kelly Perlman, Sonia Israel, Joseph Mehltretter, Caitrin Armstrong, Robert Fratila, Sagar V Parikh, Jordan F Karp, Katherine A HellerAbstract:Objective: Aifred is an artificial intelligence (AI)-powered clinical decision support system (CDSS) for the treatment of major depression. Here, we explore use of a simulation centre environment in evaluating the usability of Aifred, particularly its impact on the physician-Patient Interaction. Methods: Twenty psychiatry and family medicine attending staff and residents were recruited to complete a 2.5-hour study at a clinical Interaction simulation centre with standardized Patients. Each physician had the option of using the CDSS to inform their treatment choice in three 10-minute clinical scenarios with standardized Patients portraying mild, moderate, and severe episodes of major depression. Feasibility and acceptability data were collected through self-report questionnaires, scenario observations, interviews, and standardized Patient feedback. Results: All twenty participants completed the study. Initial results indicate that the tool was acceptable to clinicians and feasible for use during clinical encounters. Clinicians indicated a willingness to use the tool in real clinical practice, a significant degree of trust in the AI9s predictions to assist with treatment selection, and reported that the tool helped increase Patient understanding of and trust in treatment. The simulation environment allowed for the evaluation of the tool9s impact on the physician-Patient Interaction. Conclusions: The simulation centre allowed for direct observations of clinician use and impact of the tool on the clinician-Patient Interaction prior to clinical studies. It may therefore offer a useful and important environment in the early testing of new technological tools. The present results will inform further tool development and clinician training materials.
Susan A. Flocke - One of the best experts on this subject based on the ideXlab platform.
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teachable moments for health behavior change and intermediate Patient outcomes
Patient Education and Counseling, 2014Co-Authors: Susan A. Flocke, Peter J Lawson, Elizabeth C Clark, Elizabeth Antognoli, Mary Jane Mason, Stevens S Smith, Deborah J CohenAbstract:Objective Teachable moments (TM) are opportunities created through physician–Patient Interaction and used to encourage Patients to change unhealthy behaviors. We examine the effectiveness of TMs to increase Patients’ recall of advice, motivation to modify behavior, and behavior change.
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teachable moments for health behavior change a concept analysis
Patient Education and Counseling, 2009Co-Authors: Peter J Lawson, Susan A. FlockeAbstract:Abstract Objective “Teachable moments” have been proposed as events or circumstances which can lead individuals to positive behavior change. However, the essential elements of teachable moments have not been elucidated. Therefore, we undertook a comprehensive review of the literature to uncover common definitions and key elements of this phenomenon. Methods Using databases spanning social science and medical disciplines, all records containing the search term “teachable moment * ” were collected. Identified literature was then systematically reviewed and patterns were derived. Results Across disciplines, ‘teachable moment’ has been poorly developed both conceptually and operationally. Usage of the term falls into three categories: (1) “teachable moment” is synonymous with “opportunity” (81%); (2) a context that leads to a higher than expected behavior change is retrospectively labeled a ‘teachable moment’ (17%); (3) a phenomenon that involves a cueing event that prompts specific cognitive and emotional responses (2%). Conclusion The findings suggest that the teachable moment is not necessarily unpredictable or simply a convergence of situational factors that prompt behavior change but suggest the possible creation of a teachable moment through clinician–Patient Interaction. Practice implications Clinician–Patient Interaction may be central to the creation of teachable moments for health behavior change.
Gregor Petric - One of the best experts on this subject based on the ideXlab platform.
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the benefits and challenges of online professional Patient Interaction comparing views between users and health professional moderators in an online health community
Computers in Human Behavior, 2018Co-Authors: Sara Atanasova, Tanja Kamin, Gregor PetricAbstract:Abstract Online health communities (OHCs) have become new venues for online professional-Patient Interactions in which Patients, as OHC users, can undertake online consultations with health professional moderators. This Interaction has previously been investigated mainly from the user's perspective, whilst neglecting the insights of health professional moderators. The aim of this study is to explore and compare the benefits and challenges of online professional-Patient Interactions for users and health professional moderators and the effects on face-to-face medical encounters. The study employed a qualitative research design, with in-depth, semi-structured interviews conducted with users (n = 8) and health professional moderators (n = 7) from the largest OHC in Slovenia. Data analysis utilised inductive thematic analysis and principles of grounded theory. The results of this small study demonstrate that the OHC enabled users and health professional moderators to overcome weaknesses of face-to-face medical encounters. Both users and professionals view the primary benefits of online professional-Patient Interaction as delivering informational and emotional support for users' health-related needs. The main challenges for users and health professional moderators stem from the limitations of computer-mediated communication (CMC). Users and health professional moderators expressed different and ambivalent attitudes toward the OHC and its effect on face-to-face medical encounters.
Douglas W. Maynard - One of the best experts on this subject based on the ideXlab platform.
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problems and prospects in the study of physician Patient Interaction 30 years of research
2008Co-Authors: Douglas W. MaynardAbstract:Working within the functionalist perspective that he did so much to develop, Parsons (1951) conceptualized the physician-Patient relationship according to a normative framework defined by the pattern variable scheme. As Parsons clearly recognized, this normative conceptualization was one that empirical reality at best only approximates. In the 1970s, two major studies established doctor-Patient Interaction as a viable research domain. In the present review, we consider approaches to the medical interview developing from these initiatives and that have a primary focus on observable features of doctor-Patient Interaction. Within this orientation, we consider literature dealing with social, moral, and technical dilemmas that physicians and Patients face in primary care and the resources that they deploy in solving them. This literature embodies a steady evolution away from a doctor-centered emphasis toward a more balanced focus on the conduct of doctors and Patients together.
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conversation analysis doctor Patient Interaction and medical communication
Medical Education, 2005Co-Authors: Douglas W. MaynardAbstract:Introduction This paper introduces medical educators to the field of conversation analysis (CA) and its contributions to the understanding of the doctor−Patient relationship. The conversation analysis approach Conversation analysis attempts to build bridges both to the ethnographic and the coding and quantitative studies of medical interviews, but examines the medical interview as an arena of naturally occurring Interaction. This implies distinctive orientations and issues regarding the analysis of doctor−Patient Interaction. We discuss the CA approach by highlighting 5 basic features that are important to the enterprise, briefly illustrating each issue with a point from research on the medical interview. These features of conversation analytic theory and method imply a systematic approach to the organisation in Interaction that distinguishes it from studies that rely on anecdote, ethnographic inquiry or the systematic coding of utterances. Conversation analysis and the medical interview We then highlight recent CA studies of the ‘phases’ of the internal medicine clinic and the implications of these studies for medical education. We conclude with suggestions for how to incorporate CA into the medical curriculum. It fits with biopsychosocial, Patient-centred and relationship-centred approaches to teaching about medical communication.
