Introduction: Access to health data for patients is hindered by a fragmented healthcare system and the absence of unified, patient-centric solutions. Additionally, there are no mechanics for easy sharing of medical records with healthcare providers, risking incomplete diagnoses. To further intensify the problem, when patients seek care abroad, language barriers may prevent foreign doctors from understanding their health data, further complicating treatment.
Methods: Our study presents the development and evaluation of a mobile application designed to enable users to access and share their health records directly from their device, in multiple languages, ensuring ease of use and convenience. The solution utilizes OpenNCP for translating patient summaries into multiple languages and the FHIR Smart Health Links Protocol for secure sharing. We conducted a user acceptance study with 45 participants to evaluate our mobile app's interface and functionality.
Results: The feedback was positive, highlighting the app's user-friendliness and usefulness. The participants felt it would enhance communication between physicians and patients and the features of sharing and translating are going to give more control of their medical data to the patients.
Discussion: Based on the results and participants feedback, our mobile solution significantly enhances healthcare accessibility and efficiency by enabling easy access and sharing of health records in multiple languages, using relevant protocols and standards, reducing medical errors and ensuring personalized care.
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http://dx.doi.org/10.3389/fdgth.2025.1542485 | DOI Listing |
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Open Res Eur
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J Chem Inf Model
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The application of large language models in materials science has opened new avenues for accelerating materials development. Building on this advancement, we propose a novel framework leveraging large language models to optimize experimental procedures for synthesizing quantum dot materials with multiple desired properties. Our framework integrates the synthesis protocol generation model and the property prediction model, both fine-tuned on open-source large language models using parameter-efficient training techniques with in-house synthesis protocol data.
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