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| http://dx.doi.org/10.1016/j.medcle.2020.06.020 | DOI Listing |
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Developing a Machine Learning-Based Automated Patient Engagement Estimator for Telehealth: Algorithm Development and Validation Study.
JMIR Form Res
January 2025
Department of Computer Science, Purdue University, West Lafayett, IN, United States.
Background: Patient engagement is a critical but challenging public health priority in behavioral health care. During telehealth sessions, health care providers need to rely predominantly on verbal strategies rather than typical nonverbal cues to effectively engage patients. Hence, the typical patient engagement behaviors are now different, and health care provider training on telehealth patient engagement is unavailable or quite limited.
View Article and Find Full Text PDFTele-Dermatology during crisis, comparison of patients' perspective between tele and onsite clinics.
J Pak Med Assoc
January 2025
Department of Medicine, Aga Khan University Hospital, Karachi, Pakistan.
Objective: To assess patients' satisfaction with tele-dermatology versus face-to-face visits, and to identify their attitude toward the new modality.
Methods: The cross-sectional study was conducted from January to December 2021 at the Department of Dermatology, Aga Khan University Hospital, Karachi, and comprised patients of either gender. Those who opted to personally visit the outpatient department were placed in group A, while those opting for tele-dermatology were placed in group B.
Telehealth for Goals of Care Conversations in Advanced CKD: A Mixed-Methods Pilot Study of US Veterans and Their Clinicians.
Kidney Med
November 2024
Division of Geriatrics and Extended Care, Edward Hines, Jr. VA Hospital, Hines, IL.
Rationale & Objective: Engaging patients with advanced chronic kidney disease (CKD) in goals of care (GOC) conversations is essential to align life-sustaining treatments with patient preferences. This pilot study described the feasibility of engaging older Veterans with advanced CKD in GOC conversations via telehealth by (1) comparing patient characteristics, including life-sustaining treatment note completion rates and preferences by visit modality, and (2) exploring Veteran and clinician perspectives surrounding telehealth GOC conversations.
Study Design: Mixed-method convergent design including a prospective, quantitative observational cohort analysis (n = 40) and qualitative, semi-structured interviews with 4 clinicians and 11 Veterans.
Mapping the landscape of Hospital at home (HaH) care: a validated taxonomy for HaH care model classification.
BMC Health Serv Res
January 2025
Institute Patient-Centered Digital Health, Bern University of Applied Sciences, Quellgasse 21, Biel, 2502, Switzerland.
Background: Hospital at home (HaH) care models have gained significant attention due to their potential to reduce healthcare costs, improve patient satisfaction, and lower readmission rates. However, the lack of a standardized classification system has hindered systematic evaluation and comparison of these models. Taxonomies serve as classification systems that simplify complexity and enhance understanding within a specific domain.
View Article and Find Full Text PDFDevelopment and Beta Validation of an mHealth-Based Hearing Screener (SRESHT) for Young Children in Resource-Limited Countries: Pilot Validation Study.
JMIR Form Res
January 2025
Faculty of Audiology and Speech Language Pathology, Sri Ramachandra Institute of Higher Education and Research, Chennai, India.
Background: The prevalence of hearing loss in infants in India varies between 4 and 5 per 1000. Objective-based otoacoustic emissions and auditory brainstem response have been used in high-income countries for establishing early hearing screening and intervention programs. Nevertheless, the use of objective screening tests in low- and middle-income countries (LMICs) such as India is not feasible.
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