The IP addresses a critical issue in healthcare: language barriers faced by patients and healthcare providers. As populations become increasingly diverse in language preferences, ensuring effective communication in healthcare settings is essential for delivering high-quality care. This IP proposes a system that combines machine learning with crowdsourcing to augment existing language services in healthcare. By leveraging automatic machine translation, which is then verified or corrected by certified medical translators and interpreters, this system aims to bridge the gap in communication between patients with limited English proficiency (LEP) and healthcare providers. It offers several advantages over traditional interpretation and translation methods. Unlike relying solely on internal interpreter services or third-party vendors, this system provides a more seamless and efficient approach to language services. Routine and short conversations, such as those between receptionists and patients or nurses and patients, can be facilitated without the need to engage certified services, improving the overall experience for both patients and healthcare staff. The establishment of a resource pool with certified translators and interpreters enhances accessibility and capacity, potentially reducing turnaround times for translation services. Additionally, the integration of a knowledge base with pre-translated FAQs in various languages further enhances the efficiency of communication in healthcare settings. The IP offers a unique solution to a widespread problem in healthcare. Its innovative approach utilizing machine learning and crowdsourcing appeals to healthcare organizations seeking to improve communication with LEP patients while optimizing resource utilization. While the IP is currently in the conceptual stage, its potential applications extend beyond immediate implementation. It could be integrated into various healthcare systems and services, to facilitate communication across diverse patient populations, thus improving overall healthcare outcomes.