Technology-assisted methods to assess the quality of the therapeutic alliance between health care providers and patients: a scoping review.

OBJECTIVE: The goal of this review was to identify and summarize technology-assisted methods that assess nonverbal behaviors known to influence the quality of the therapeutic alliance between health care providers and patients in clinical, research, or educational settings. INTRODUCTION: Strong therapeutic alliances in health care provider-patient relationships contribute to positive patient outcomes. Previous studies have shown that nonverbal behaviors from providers, such as tone of voice, facial expression, eye contact, and physical positioning, help build strong therapeutic relationships. Technological advances have created opportunities to automate detection and feedback about nonverbal behaviors in clinical interactions without the intervention of highly trained instructors. Automated detection and feedback will increase opportunities to develop better nonverbal communication, which is expected to improve the quality of the therapeutic alliance. ELIGIBILITY CRITERIA: Participants were current or aspiring health care providers who routinely would be expected to have patient encounters in clinical, educational, or research settings. Interactions could include actual patient interactions, model patients, virtual patients, or simulated patients. Included studies involved technology-assisted methods to collect or analyze at least 1 nonverbal behavior. This review included qualitative and quantitative studies and review articles. METHODS: PubMed, Embase, CINAHL, ERIC, Scopus, and Google Scholar were searched for published and unpublished literature in English between 2010 and 2023. Two reviewers independently completed title and abstract screening, full-text review, and data extraction. Any conflicts that arose were resolved by a third reviewer. Extraction was completed by 2 reviewers. RESULTS: Twenty-five sources were included. The most frequently measured behaviors included gaze frequency or duration on the patient's face (52% of studies) and facial expression (48%), with 24% of studies measuring social touch or speech patterns, 20% measuring posture and gestures, 12% measuring proximity to patient, 8% measuring synchrony, and 4% measuring voice. Technologies for data collection included audio or video recordings and other technologies such as eye-tracking glasses or multi-functional headsets, used alone or integrated into augmented reality or virtual reality systems. Challenges associated with analyzing data included recognition of patients' and providers' voices, facial features, and body parts, and classifying body posture and movement as well as facial expression and movements. Studies used commercial, open source, or customized software to meet each challenge. Many incorporated machine learning or algorithms associated with artificial intelligence (AI), which required significant training data. Technology-assisted methods for analyzing voice and social touch were limited, whereas there were several available analysis techniques for analyzing synchrony, gestures, posture, and proximity to patients. CONCLUSIONS: The use of technology in collection and analysis of provider-patient interactions to enhance nonverbal behaviors and communication skills is rapidly developing, with multiple options available. Future efforts should focus on technology-assisted methods to analyze voice and social touch, and should transparently report contributing factors and training data for machine learning models or AI-associated algorithms. Development of systems will require significant testing to ensure accessibility to the intended audience and broadly accurate and meaningful nonverbal behaviors. REVIEW REGISTRATION: OSF ( https://osf.io/kedmf ).