Orthodontic treatment is a common dental treatment. A complete treatment often takes several years and is costly. In order to increase the degree of satisfaction and reduce the gap between the expectations of the patient and the limitations of orthodontics, orthodontists and patients should have sufficient communication. A simulation system can provide a good channel for communication between orthodontists and patients. This paper proposes a 3D dentist–patient communication system for the simulation of orthodontics in advance. The system collects the clinical paradigms of orthodontic cases, which must contain photos from before, during, and after maxillofacial treatment. This research simulates treatment processes by tuning a 3D virtual model of the oral and maxillofacial areas, including the face, mouth, and teeth, to demonstrate the processes of clinical paradigms. These 3D models could be edited and synthesized to generate new 3D models for simulation with the 3DS Max tool. In addition, the proposed system provides a function for the orthodontist to manually operate the 3D virtual model, such as tooth arrangement, morphing of the gums with movement of the teeth, the placement (attachment) of arch-wires and brackets, and changes of facial appearance. The orthodontist can demonstrate the treatment processes, show movements of the teeth, and answer possible questions from the patient about the treatment processes by using the 3D model. To show the effectiveness of the proposed system, a questionnaire about the system was also used to demonstrate its acceptance, usability, and validity. Qualitative interviews with dentists and questionnaires for patients about the system are both performed. The results showed that the proposed system is an effective vehicle for communication between patients and orthodontists.

Background/Objectives: With the rapid development of smart healthcare, medical records have shifted from a disease-centered to a patient-centered approach. However, traditional formats, such as narratives and tables, often make it challenging for physicians to quickly grasp a patient’s condition within a limited timeframe, potentially leading to diagnostic errors and a decline in the quality of care. Recently, advances in information visualization and 3D technology have led many medical institutions to employ charts and graphs or use 3D simulations of organs to support clinical practice and education. However, few have integrated 3D models into medical records for use during physician consultations. Methods: This study presents the development and evaluation of a novel web-based 3D EMR system that integrates real-time ICD-10 diagnostic code mapping with interactive 3D human body models, enabling physicians to visualize patient-specific anatomical and diagnostic information in a dynamic and context-aware manner. Results: We employed the System Usability Scale (SUS) to evaluate the system’s usability, conducting a satisfaction survey. Results from the survey indicate that participants rated the system highly in terms of ease of use, satisfaction, and efficiency, with an average SUS score of 70.42, reflecting usability between moderate and good. Comparative evaluations and future expansion plans are also discussed. Conclusions: These findings demonstrate that integrating a 3D human model into the medical record retrieval process significantly improves visualization and interactivity, meeting the needs of healthcare professionals and enhancing both their efficiency and patient satisfaction.

Due to the increasing cost of health insurance, for decades, many countries have endeavored to constrain the cost of insurance by utilizing a DRG payment system. In most cases, under the DRG payment system, hospitals cannot exactly know which DRG code inpatients are until they are discharged. This paper focuses on the prediction of what DRG code appendectomy patients will be classified with when they are admitted to hospital. We utilize two models (or classifiers) constructed using the C4.5 algorithm and back-propagation neural network (BPN). We conducted experiments with the data collected from two hospitals. The results show that the accuracies of these two classification models can be up to 97.84% and 98.70%, respectively. According to the predicted DRG code, hospitals can effectively arrange medical resources with certainty, then, in turn, improve the quality of the medical care patients receive.