Effective communication between patients and healthcare providers is pivotal for accurate diagnosis, appropriate treatment, and optimal patient care. However, individuals with hearing impairments often encounter significant communication barriers when accessing medical services, hindering their ability to convey health-related information effectively. A sign language detection mobile application tailored for Thai patients has been developed to address this challenge. The primary objectives of this initiative are to construct the most effective sign language detection model for Thai patients, to create a mobile application for Thai patients with a sign language detection model to support medical consultations, and to study satisfaction with the sign language detection mobile application. Leveraging cutting-edge technologies such as the Flutter and Dart framework, TensorFlow Lite, and Teachable Machine for model training, the application aims to bridge the communication gap between deaf and hard-of-hearing patients and healthcare professionals. The dataset utilized in this experiment comprised video recordings of 60 sign language vocabulary and phrases related to health and wellness.