e.g. mhealth
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However, evidence on digital health interventions (DHIs) to improve patient provider communication is limited. A recent systematic review identified only 13 DHIs that targeted improvement of patient-provider communication in cancer care, with just 4 focused on breast cancer. None of these interventions involve delivery through a m Health app [13].
JMIR Res Protoc 2025;14:e66050
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X (formerly known as Twitter) has been an important source of information for studying vaccine hesitancy, as social media platforms have been considered effective tools for communication between individuals and organizations, but they have also been used as tools to spread false information and conspiracy theories about vaccines [20-22].
Several studies have been published analyzing public opinions or sentiments about vaccines on X using artificial intelligence (AI).
JMIR Infodemiology 2025;5:e63223
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The system is designed to collect diabetes-related data, including participant-reported outcomes, sensor-based measures on physical activity, and participant-selected focus areas, aiming to support communication and shared decision-making between the HCP and the person with diabetes. The technical feasibility of using the Dia Focus app for T2 D management was assessed in a 6-week pilot study with 12 participants [13].
JMIR Diabetes 2025;10:e63894
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Due to the increasing number of breast cancer survivors and limited health care resources, there was a need to develop web-based systems for communication between patients and professionals. In 2015, a web-based patient report app Noona (Helsinki University Hospital; Noona Healthcare) was developed at the HUS CCC in collaboration with a Finnish start-up company. Noona app enabled patients to contact specialist breast cancer nurses between visits.
JMIR Cancer 2025;11:e58113
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Communicating Antimicrobial Resistance on Instagram: Content Analysis of #AntibioticResistance
The sender-message-channel-receiver (SMCR) communication model, developed by Berlo in 1960 [37,38], outlines 4 essential components in the communication process: the sender, the message, the channel, and the receiver. This model has been extensively used in communication research, including as a framework for understanding how SNS users disseminate and receive information [39-41].
JMIR Infodemiology 2025;5:e67825
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Specifically, we examined the impact of training with the Voice Adapt app on language and communication outcomes in individuals with chronic aphasia. Primary outcomes were naming ability, and secondary outcomes included measures of overall language, communication effectiveness, and quality of life.
JMIR Mhealth Uhealth 2025;13:e67711
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Minority patients have reported poor patient-centered communication (eg, respecting and responding to patients’ wants, needs, and preferences), which can influence assessments, recommendations, and outcomes [15].
JMIR Res Protoc 2025;14:e66086
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Frameworks for medical communication are widely accepted and taught, with a consensus on “best practice” for high-quality communication [18]. Ideal automation would ensure that standards for communication were consistently met, and such a tool would invariably be helpful to PCPs while maintaining or enhancing patient care. However, no prior studies have applied a medical communication framework to systematically evaluate Gen AI draft quality or the consistency of its quality.
JMIR Form Res 2025;9:e71966
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The purpose of this viewpoint paper is to share lessons learned from this novel application of web-monitoring software for health communication research. We aim to provide practical information to other researchers who are exploring technology-based methods to objectively measure the quality of cancer information individuals are exposed to online.
JMIR Cancer 2025;11:e65887
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Our study aims to address this gap by investigating patient and family perspectives on AI’s role in diagnosis and diagnostic communication across various theoretical and practical applications through a co-design approach. By engaging patients and families as partners in this research, we seek to uncover their expectations, concerns, and preferences for how AI should be used to enhance diagnostic safety, support clinician-patient communication, and promote equitable health care delivery.
J Particip Med 2025;17:e69564
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